Unifize

unifize

Authors:
(1) Qian Yang, Zhejiang University, Equal contribution. This work was conducted during Qian Yang’s internship at Alibaba Group;
(2) Jin Xu, Alibaba Group, Equal contribution;
(3) Wenrui Liu, Zhejiang University;
(4) Yunfei Chu, Alibaba Group;
(5) Xiaohuan Zhou, Alibaba Group;
(6) Yichong Leng, Alibaba Group;
(7) Yuanjun Lv, Alibaba Group;
(8) Zhou Zhao, Alibaba Group and Corresponding to Zhou Zhao (zhaozhou@zju.edu.cn);
(9) Yichong Leng, Zhejiang University
(10) Chang Zhou, Alibaba Group and Corresponding to Chang Zhou (ericzhou.zc@alibaba-inc.com);
(11) Jingren Zhou, Alibaba Group. Table of Links Abstract and 1. Introduction 2 Related Work 3 AIR-Bench and 3.1 Overview 3.2 Foundation Benchmark 3.3 Chat Benchmark 3.4 Evaluation Strategy 4 Experiments 4.1 Models 4.2 Main Results 4.3 Human Evaluation and 4.4 Ablation Study of Positional Bias 5 Conclusion and References A Detailed Results of Foundation Benchmark 3.4 Evaluation Strategy In this paper, we leverage a unified evaluation method, as shown in Fig. 3, by viewing both the single-choice question in the foundation benchmark, and the open-ended question in the chat benchmark, as the generation tasks for the purpose of better alignment with actual use case scenarios of LALMs. That is, given audio and questions, LALMs are required to directly generate the answers as hypotheses, rather than comparing the perplexity on the probability of different reference answers via teacher forcing. Automated and accurate evaluation of open-ended generation is a challenging problem. Traditional automatic metrics such as WER, ROUGE (Lin, 2004), METEOR (Banerjee and Lavie, 2005) have been shown low correlation with human judgements (Liu et al., 2023a). Recently, LLM-based evaluation, such as GPT-4, shows better human preference alignment (Zheng et al., 2023; Liu et al., 2023a). In this work, we adopt reference-based GPT-4 evaluators to judge the generation quality of LALMs in the audio domain. However, GPT-4 cannot be directly used as an evaluator since it cannot receive audio inputs. To address this limitation, we offer the GPT-4 model rich meta-information of audio to replace audio input. Subsequently, we present questions and employ GPT-4 to evaluate the hypotheses produced by LALMs. To ensure consistency and fairness for evaluation, each model’s answer is compared against the same reference answer for scoring. For the foundation benchmark, the reference answer is the golden choice, and we prompt the evaluator to determine whether the hypothesis is correct or not. For the chat benchmark, the reference answer is generated by GPT-4, and we prompt the evaluator to provide a score ranging from 1 to 10, based on the assessment of usefulness, relevance, accuracy, and comprehensiveness of the hypothesis. Note that for the chat benchmark, the role of the reference is not to serve as the ground truth answer, but rather as a reference for scoring by GPT-4, in order to stabilize its scoring. Additionally, to mitigate any potential position bias resulting from the order of hypothesis and reference, following Bai et al. (2023b), we perform a second scoring round by swapping their positions and then compute the average of the two scores. Unless otherwise specified, the GPT-4 evaluator is GPT-4 Turbo, the gpt-4- 0125-preview version [2]. This paper is available on arxiv under CC BY 4.0 DEED license. [2] https://platform.openai.com/docs/models/gpt-4-and-gpt4-turbo Authors: (1) Qian Yang, Zhejiang University, Equal contribution. This work was conducted during Qian Yang’s internship at Alibaba Group; (2) Jin Xu, Alibaba Group, Equal contribution; (3) Wenrui Liu, Zhejiang University; (4) Yunfei Chu, Alibaba Group; (5) Xiaohuan Zhou, Alibaba Group; (6) Yichong Leng, Alibaba Group; (7) Yuanjun Lv, Alibaba Group; (8) Zhou Zhao, Alibaba Group and Corresponding to Zhou Zhao (zhaozhou@zju.edu.cn); (9) Yichong Leng, Zhejiang University (10) Chang Zhou, Alibaba Group and Corresponding to Chang Zhou (ericzhou.zc@alibaba-inc.com); (11) Jingren Zhou, Alibaba Group. Authors: Authors: (1) Qian Yang, Zhejiang University, Equal contribution. This work was conducted during Qian Yang’s internship at Alibaba Group; (2) Jin Xu, Alibaba Group, Equal contribution; (3) Wenrui Liu, Zhejiang University; (4) Yunfei Chu, Alibaba Group; (5) Xiaohuan Zhou, Alibaba Group; (6) Yichong Leng, Alibaba Group; (7) Yuanjun Lv, Alibaba Group; (8) Zhou Zhao, Alibaba Group and Corresponding to Zhou Zhao (zhaozhou@zju.edu.cn); (9) Yichong Leng, Zhejiang University (10) Chang Zhou, Alibaba Group and Corresponding to Chang Zhou (ericzhou.zc@alibaba-inc.com); (11) Jingren Zhou, Alibaba Group. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related Work 2 Related Work 3 AIR-Bench and 3.1 Overview 3 AIR-Bench and 3.1 Overview 3.2 Foundation Benchmark 3.2 Foundation Benchmark 3.3 Chat Benchmark 3.3 Chat Benchmark 3.4 Evaluation Strategy 3.4 Evaluation Strategy 4 Experiments 4.1 Models 4.1 Models 4.2 Main Results 4.2 Main Results 4.3 Human Evaluation and 4.4 Ablation Study of Positional Bias 4.3 Human Evaluation and 4.4 Ablation Study of Positional Bias 5 Conclusion and References 5 Conclusion and References A Detailed Results of Foundation Benchmark A Detailed Results of Foundation Benchmark 3.4 Evaluation Strategy In this paper, we leverage a unified evaluation method, as shown in Fig. 3, by viewing both the single-choice question in the foundation benchmark, and the open-ended question in the chat benchmark, as the generation tasks for the purpose of better alignment with actual use case scenarios of LALMs. That is, given audio and questions, LALMs are required to directly generate the answers as hypotheses, rather than comparing the perplexity on the probability of different reference answers via teacher forcing. Automated and accurate evaluation of open-ended generation is a challenging problem. Traditional automatic metrics such as WER, ROUGE (Lin, 2004), METEOR (Banerjee and Lavie, 2005) have been shown low correlation with human judgements (Liu et al., 2023a). Recently, LLM-based evaluation, such as GPT-4, shows better human preference alignment (Zheng et al., 2023; Liu et al., 2023a). In this work, we adopt reference-based GPT-4 evaluators to judge the generation quality of LALMs in the audio domain. However, GPT-4 cannot be directly used as an evaluator since it cannot receive audio inputs. To address this limitation, we offer the GPT-4 model rich meta-information of audio to replace audio input. Subsequently, we present questions and employ GPT-4 to evaluate the hypotheses produced by LALMs. To ensure consistency and fairness for evaluation, each model’s answer is compared against the same reference answer for scoring. For the foundation benchmark, the reference answer is the golden choice, and we prompt the evaluator to determine whether the hypothesis is correct or not. For the chat benchmark, the reference answer is generated by GPT-4, and we prompt the evaluator to provide a score ranging from 1 to 10, based on the assessment of usefulness, relevance, accuracy, and comprehensiveness of the hypothesis. Note that for the chat benchmark, the role of the reference is not to serve as the ground truth answer, but rather as a reference for scoring by GPT-4, in order to stabilize its scoring. Additionally, to mitigate any potential position bias resulting from the order of hypothesis and reference, following Bai et al. (2023b), we perform a second scoring round by swapping their positions and then compute the average of the two scores. Unless otherwise specified, the GPT-4 evaluator is GPT-4 Turbo, the gpt-4- 0125-preview version [2]. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [2] https://platform.openai.com/docs/models/gpt-4-and-gpt4-turbo

Unified Evaluation Method for LALMs Using GPT-4 in Audio Tasks

Authors:
(1) Luyang Zhu, University of Washington and Google Research, and work done while the author was an intern at Google;
(2) Dawei Yang, Google Research;
(3) Tyler Zhu, Google Research;
(4) Fitsum Reda, Google Research;
(5) William Chan, Google Research;
(6) Chitwan Saharia, Google Research;
(7) Mohammad Norouzi, Google Research;
(8) Ira Kemelmacher-Shlizerman, University of Washington and Google Research. Table of Links Abstract and 1. Introduction 2. Related Work 3. Method 3.1. Cascaded Diffusion Models for Try-On 3.2. Parallel-UNet 4. Experiments 5. Summary and Future Work and References Appendix A. Implementation Details B. Additional Results 3.2. Parallel-UNet The 128×128 Parallel-UNet can be represented as Combining warp and blend in a single pass. Instead of warping the garment to the target body and then blending with the target person as done by prior works, we combine the two operations into a single pass. As shown in Fig. 2, we achieve it via two UNets that handle the garment and the person respectively. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Luyang Zhu, University of Washington and Google Research, and work done while the author was an intern at Google; (2) Dawei Yang, Google Research; (3) Tyler Zhu, Google Research; (4) Fitsum Reda, Google Research; (5) William Chan, Google Research; (6) Chitwan Saharia, Google Research; (7) Mohammad Norouzi, Google Research; (8) Ira Kemelmacher-Shlizerman, University of Washington and Google Research. Authors: Authors: (1) Luyang Zhu, University of Washington and Google Research, and work done while the author was an intern at Google; (2) Dawei Yang, Google Research; (3) Tyler Zhu, Google Research; (4) Fitsum Reda, Google Research; (5) William Chan, Google Research; (6) Chitwan Saharia, Google Research; (7) Mohammad Norouzi, Google Research; (8) Ira Kemelmacher-Shlizerman, University of Washington and Google Research. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2. Related Work 3. Method 3. Method 3.1. Cascaded Diffusion Models for Try-On 3.1. Cascaded Diffusion Models for Try-On 3.2. Parallel-UNet 3.2. Parallel-UNet 4. Experiments 4. Experiments 5. Summary and Future Work and References 5. Summary and Future Work and References Appendix Appendix A. Implementation Details A. Implementation Details B. Additional Results B. Additional Results 3.2. Parallel-UNet The 128×128 Parallel-UNet can be represented as Combining warp and blend in a single pass. Instead of warping the garment to the target body and then blending with the target person as done by prior works, we combine the two operations into a single pass. As shown in Fig. 2, we achieve it via two UNets that handle the garment and the person respectively. Combining warp and blend in a single pass. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

How Parallel-UNet Transforms Virtual Try-On with Implicit Warping and Unified Operations

Authors:
(1) Wanru Zhao, University of Cambridge, Shanghai AI Laboratory with Equal contribution;
(2) Yaxin Du, Shanghai Jiao Tong University with Equal contribution;
(3) Nicholas D. Lane, University of Cambridge and Flower Labs;
(4) Siheng Chen, Shanghai AI Laboratory and Shanghai Jiao Tong University;
(5) Yanfeng Wang, Shanghai AI Laboratory and Shanghai Jiao Tong University. Table of Links Abstract and Introduction
Motivation and Setup: How low-quality data affects the performance of Collaborative Training
Proposed Workflow for Data Quality Control
Experiments
Conclusion and Future Work, and References
A. Related Work
B. Heterogeneity Settings
C. Experimental Details
D. Ablation study of Unified Scoring with Anchor Data
E. Examples for low-and high- quality Data D ABLATION STUDY OF UNIFIED SCORING WITH ANCHOR DATA We perform a comparative analysis focusing on the number of selected data and the proportion of low-quality data. This comparison contrasts the approach of selection based on a unified scoring from anchor data, as depicted in the fourth bar, with methods that determine the threshold by acknowledging the overall proportion of low-quality data. In Figure 4, the method represented by the second bar involves each client selecting high-quality data by sorting each sample’s scores and eliminating the lowest-scored samples, informed by the proportion of low-quality data within the entire training set of FL. This approach, which does not need a score threshold and relies solely on the known proportion of low-quality data, proves to be impractical and less effective in settings with heterogeneous data quality due to varying data quality compositions among clients. The third bar illustrates the outcomes of implementing a global score threshold aligned with the exact proportion of global low-quality data. This method necessitates the server’s aggregation of all scores from clients within the FL framework and knowing the proportion of low-quality data, potentially leading to privacy concerns. The results indicate that selection by anchor score consistently yields a lower proportion of low-quality data compared to the other two methodologies. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Wanru Zhao, University of Cambridge, Shanghai AI Laboratory with Equal contribution; (2) Yaxin Du, Shanghai Jiao Tong University with Equal contribution; (3) Nicholas D. Lane, University of Cambridge and Flower Labs; (4) Siheng Chen, Shanghai AI Laboratory and Shanghai Jiao Tong University; (5) Yanfeng Wang, Shanghai AI Laboratory and Shanghai Jiao Tong University. Authors: Authors: (1) Wanru Zhao, University of Cambridge, Shanghai AI Laboratory with Equal contribution; (2) Yaxin Du, Shanghai Jiao Tong University with Equal contribution; (3) Nicholas D. Lane, University of Cambridge and Flower Labs; (4) Siheng Chen, Shanghai AI Laboratory and Shanghai Jiao Tong University; (5) Yanfeng Wang, Shanghai AI Laboratory and Shanghai Jiao Tong University. Table of Links Abstract and Introduction Motivation and Setup: How low-quality data affects the performance of Collaborative Training Proposed Workflow for Data Quality Control Experiments Conclusion and Future Work, and References A. Related Work B. Heterogeneity Settings C. Experimental Details D. Ablation study of Unified Scoring with Anchor Data E. Examples for low-and high- quality Data Abstract and Introduction Abstract and Introduction Motivation and Setup: How low-quality data affects the performance of Collaborative Training Motivation and Setup: How low-quality data affects the performance of Collaborative Training Proposed Workflow for Data Quality Control Proposed Workflow for Data Quality Control Experiments Experiments Conclusion and Future Work, and References Conclusion and Future Work, and References A. Related Work A. Related Work B. Heterogeneity Settings B. Heterogeneity Settings C. Experimental Details C. Experimental Details D. Ablation study of Unified Scoring with Anchor Data D. Ablation study of Unified Scoring with Anchor Data E. Examples for low-and high- quality Data E. Examples for low-and high- quality Data D ABLATION STUDY OF UNIFIED SCORING WITH ANCHOR DATA We perform a comparative analysis focusing on the number of selected data and the proportion of low-quality data. This comparison contrasts the approach of selection based on a unified scoring from anchor data, as depicted in the fourth bar, with methods that determine the threshold by acknowledging the overall proportion of low-quality data. In Figure 4, the method represented by the second bar involves each client selecting high-quality data by sorting each sample’s scores and eliminating the lowest-scored samples, informed by the proportion of low-quality data within the entire training set of FL. This approach, which does not need a score threshold and relies solely on the known proportion of low-quality data, proves to be impractical and less effective in settings with heterogeneous data quality due to varying data quality compositions among clients. The third bar illustrates the outcomes of implementing a global score threshold aligned with the exact proportion of global low-quality data. This method necessitates the server’s aggregation of all scores from clients within the FL framework and knowing the proportion of low-quality data, potentially leading to privacy concerns. The results indicate that selection by anchor score consistently yields a lower proportion of low-quality data compared to the other two methodologies. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Enhancing Data Quality: Ablation Study of Unified Scoring With Anchor Data

Meet New & Improved BigQuery: Single, Unified AI-Ready Data Platform

The digital landscape is both a battleground and a fertile ground for innovation and recent announcement of its strategic consolidation marks a pivotal moment in the cybersecurity and enterprise tech sectors. By amalgamating the distinct capabilities of Cytek Security, Tek Experts, and Elev8, TeKnowledge is not merely expanding; it is redefining its trajectory towards cybersecurity excellence and comprehensive enterprise growth. TeKnowledge’s Enterprise technology continues to be characterized by rapid evolution and unprecedented challenges. Organizations across the globe are grappling with the dual pressures of digital transformation and the need to fortify their defenses against an increasingly sophisticated cyber threat landscape. By the end of 2024, on the global economy is predicted to top USD 10.5 trillion. the cost of cyberattacks The push towards adopting cloud computing, artificial intelligence, and automation technologies is accelerating, pushing enterprises to innovate while ensuring the security and integrity of their digital assets. Furthermore, the shift towards remote and hybrid work models has necessitated a reevaluation of IT infrastructure and security protocols, underscoring the importance of technical skilling and training programs to equip the workforce for the demands of this new digital era. Companies which aim to offer comprehensive solutions spanning cybersecurity, technical skilling, and digital transformation consultancy, reflects a broader industry trend towards integrated services designed to address the multifaceted challenges of enterprise tech in 2024. Amid economic uncertainty and a global tech talent shortage, a predicted that by 2030, there could be a global talent shortage of more than 85.2 million people in the tech sector, the need for such holistic approaches to enterprise technology solutions has never been more acute, setting the stage for transformative changes in how businesses secure and leverage their digital futures. report from Korn Ferry Cybersecurity is no longer a standalone concern but a critical foundation for digital enterprise growth: robust cybersecurity measures are a significant driver of innovation, trust, and competitive advantage. TeKnowledge’s approach integrates cybersecurity into every facet of its service offerings, ensuring that digital transformations are secure by design. This integrated approach is not only a response to the growing sophistication of cyber threats but also a proactive measure to embed security into the digital architecture of businesses and governments. "At TeKnowledge, we're not just responding to cybersecurity, technological innovation, and talent development challenges; we're anticipating them, crafting a unified strategy that leverages the strengths of Cytek Security, Tek Experts, and Elev8 Tek. The new TeKnowledge brand signifies more than an expansion—it's a commitment to transforming the digital ecosystem by directly addressing the tech talent gap while accelerating cybersecurity and AI readiness and implementation. Our mission is clear: to accelerate progress and secure futures by equipping our customers with the tools, talent, and strategies needed for success in this rapidly evolving digital landscape,” says Aileen Allkins, President and CEO of TeKnowledge. At the core of TeKnowledge’s bold move is a recognition of the intertwined nature of cybersecurity and digital transformation. In the face of a global tech talent shortage and the escalating pace of digital threats, the amalgamation is a strategic response designed to elevate the company’s service offerings. TeKnowledge is positioning itself as a one-stop shop for governments and enterprises worldwide, offering a cohesive suite of services ranging from advisory and consultancy to advanced cybersecurity protection and technical skilling.\ Leveraging Global Expertise for Local Impact With a leadership team boasting veterans from Microsoft, HP, Convergys, CA Technologies, and Concentrix, TeKnowledge is leveraging global expertise to foster local and global enterprise growth. The company’s strategy emphasizes the importance of technical skilling and capacity building to bridge the tech talent gap—a move that is as much about empowering its clientele as it is about securing the digital future. A Unified Strategy for the Digital Age TeKnowledge’s unified strategy is a testament to the power of integration in the digital age. By bringing together the strengths of Cytek Security, Tek Experts, and Elev8, the company is not just expanding its service offerings but is also creating a new paradigm for how cybersecurity and digital transformation are approached. This strategic consolidation aims to provide unparalleled expertise and comprehensive solutions that enable customers to achieve their strategic imperatives more quickly and effectively. As TeKnowledge launches under this unification and expansion strategy, the company is demonstrating a model for how enterprises can navigate the complexities of digital innovation securely and efficiently.  By harnessing the collective strengths of its component companies, TeKnowledge is setting a new standard for excellence, innovation, and comprehensive digital solutions in a world where technology is both the greatest asset and the biggest challenge.

TeKnowledge's Expansion: Tackling the Tech Talent Crisis with Unified Cybersecurity and Upskilling

Authors: (1) JunJie Wee, Department of Mathematics, Michigan State University; (2) Jiahui Chen, Department of Mathematical Sciences, University of Arkansas; (3) Kelin Xia, Division of Mathematical Sciences, School of Physical and Mathematical Sciences Nanyang Technological University & xiakelin@ntu.edu.sg; (4)Guo-Wei Wei, 1Department of Mathematics, Michigan State University, Department of Biochemistry and Molecular Biology, Michigan State University, Department of Electrical and Computer Engineering, Michigan State University & xiakelin@ntu.edu.sg. Table of Links Abstract & Introduction Results Discussion Conclusion Materials and Methods Software and resources, Code and Data Availability Supporting Information, Acknowledgments & References 2 Results 2.1 Overview of TopLapGBT TopLapGBT integrates both structure-based and sequence-based features, derived from protein structures and sequences respectively, into a unified model. Our architecture comprises three distinct embedding modules: persistent Laplacian-based embeddings, sequence-based embeddings, and auxiliary feature embeddings, all of which feed into an ensemble classifier as depicted in Figure 1. In the persistent Laplacian-based feature embedding module, we employ persistent Laplacian techniques to generate features that encapsulate the structural attributes of proteins both pre- and post-mutation. This approach is particularly effective in capturing the structural alterations induced by mutations within the localized neighborhoods of the mutation sites. Mathematically, the persistent Laplacian builds a sequence of simplicial complexes through a filtration process, thereby characterizing atom-atom interactions across multiple scales (details in the Methods section). In the sequence-based feature embedding module, a pre-trained transformer model generates latent feature vectors extracted from protein sequences. Specifically, the transformer model used here is a 650M-parameter protein language model, trained on a corpus of 250M protein sequences spanning multiple organisms [39]. Finally, the auxiliary feature embedding module incorporates a variety of attributes such as surface area, partial charge, pKa shifts, solvation free energy, and secondary structural information, synthesized from both protein sequences and structures. These three distinct sets of feature embeddings are subsequently concatenated to produce a comprehensive feature vector. This vector is then fed into a gradient-boosting tree classifier to categorize the mutation-induced samples. 2.2 Performance of TopLapGBT on PON-Sol2 dataset In our study, we utilize the dataset employed by PON-Sol2 as detailed in [11]. The dataset is comprised of 6,328 mutation samples, originating from 77 distinct proteins. These samples are categorized into three labels: decrease in solubility, increase in solubility, and no change in solubility. Specifically, the dataset contains 3,136 samples demonstrating a decrease in solubility, 1,026 samples showing an increase, and 2,166 samples with no change. Notably, the dataset exhibits a class imbalance, with a ratio of 1 : 0.69 : 0.34, indicating a bias towards samples that exhibit a decrease in solubility. To assess the performance of our model, we initially carry out a random 10-fold cross-validation on the dataset. Subsequently, an independent blind test prediction is executed to provide further validation of the model’s efficacy. In Table 1, we present a comparative analysis of the performance of existing classifiers by PON-Sol [5] and PON-Sol2 [11] against our proposed model, TopLapGBT, using 10-fold cross-validation. It should be noted that PON-Sol2 incorporates feature selection techniques such as recursive feature elimination (RFE). To provide a robust assessment of TopLapGBT’s performance, we conduct 10 repeated runs, and the mean values of these runs are reported to account for any randomness in the model’s output. Performance evaluation of our model, TopLapGBT, is conducted using a range of metrics, including Positive Predictive Value (PPV), Negative Predictive Value (NPV), Sensitivity, Specificity, Correct Prediction Ratio (CPR), and Generalized Squared Correlation (GC2 ). PPV and NPV quantify the proportions of correct positive and negative predictions for each solubility class, respectively. Given that we are dealing with a K-class problem with three distinct solubility classes, CPR and GC2 are particularly relevant for providing a holistic view of the model’s performance [40]. Specifically, CPR measures the overall accuracy of the model, while GC2 quantifies the correlation coefficient of the classification, ranging from 0 to 1. Larger values for these metrics denote better performance. Importantly, due to the class imbalance in the number of mutation samples across the categories, all performance metrics are normalized to ensure a robust and reliable evaluation of the model’s efficacy (further details are elaborated in the Methods section). Table 1: Comparison of performance metrics between TopLapGBT and both single layer and double layer classifiers of PON-Sol2 in the 10-fold crossvalidation. The negative solubility samples are denoted as ”-” whereas the positive solubility change samples are denoted as ”+”. The samples with no solubility change are denoted as ”N”. Performance metrics include the positive predicted values (PPV), negative predicted values (NPV), sensitivity, specificity, correct prediction ratio (CPR) and generalised correlation (GC2 ). PPV refers to the proportions of positive predictions for each solubility class while NPV refers to the proportions of negative predictions for each solubility class. CPR calculates the percentage of correctly classified samples while GC2 measures the correlation coefficient of the classification. All normalized metrics are also reported. For each metric, the first value is without normalization while the second one is with normalization. The proposed model, TopLapGBT, demonstrates significant performance gains over existing featurization methods in PON-Sol2 across all evaluation metrics [11]. Specifically, normalized CPR and GC2 scores of TopLapGBT stand at 0.688 and 0.361, marking improvements of 4.88% and 15.71% over PON-Sol2, respectively. These gains underscore the merit of incorporating both structure-based and sequence-based features into the model. To elucidate the contribution of Persistent Laplacian (PL)-based features, we also present a comparative analysis with our TopGBT model in Table 1. The TopGBT model utilizes persistent homology-based embeddings alongside auxiliary and pre-trained transformer features. While TopGBT still outperforms all existing PON-Sol2 models, the incorporation of PL-based features in TopLapGBT leads to an incremental improvement of 1% and 2% in CPR and GC2 metrics, respectively. This validates our approach of leveraging Persistent Laplacian to comprehensively capture both the topological and homotopic nuances in the evolution of protein structures. 2.3 Performance of TopLapGBT on independent test set To robustly assess the performance of TopLapGBT, we subjected it to an independent test using the same dataset employed by PON-Sol2 [11]. In this validation, TopLapGBT consistently outperformed all five existing models, as evidenced in Table 2. Specifically, TopLapGBT registers a normalized CPR of 0.564 and a normalized GC2 of 0.185, surpassing PON-Sol2 by 3.49% and 17.83%, respectively. Relative to TopGBT, the inclusion of PL-based features in TopLapGBT yielded incremental gains in both CPR and GC2 metrics, thereby further substantiating the utility of Persistent Laplacian in capturing the homotopic shape evolution within protein structures. This paper is under CC 4.0 license. available on arxiv

TopLapGBT: A Unified Approach for Enhanced Protein Solubility Prediction

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Ke Hong, Tsinghua University & Infinigence-AI; (2) Guohao Dai, Shanghai Jiao Tong University & Infinigence-AI; (3) Jiaming Xu, Shanghai Jiao Tong University & Infinigence-AI; (4) Qiuli Mao, Tsinghua University & Infinigence-AI; (5) Xiuhong Li, Peking University; (6) Jun Liu, Shanghai Jiao Tong University & Infinigence-AI; (7) Kangdi Chen, Infinigence-AI; (8) Yuhan Dong, Tsinghua University; (9) Yu Wang, Tsinghua University. Table of Links Abstract & Introduction Backgrounds Asynchronized Softmax with Unified Maximum Value Flat GEMM Optimization with Double Buffering Heuristic Dataflow with Hardware Resource Adaption Evaluation Related Works Conclusion & References 3 Asynchronized Softmax with Unified Maximum Value The partial softmax operation requires synchronization among different partial vectors, leading to ∼20% overheads of the attention operation. As is shown in Figure 2, the synchronization is required after the maximum value of the partial vector is calculated. The maximum value is used to update previous partial softmax (i.e., recompute previous attention) results. Thus, to reduce synchronization overheads, Motivation. the key problem to be solved is how to compute each partial softmax result without requiring results from other partial softmax computation. The reason that synchronization is required lies in that the maximum value of each partial vector is different. The maximum value is used to avoid overflow of the exponent operation (f(x) in Figure 4(a)), and exponents are summed (l(x) in Figure 4(a)) as the denominator of the softmax operation. Such a non-linear operation on each partial maximum value makes the synchronization among each partial softmax computation unavoidable. Challenge. According to the formula of softmax computation, the maximum value is used as the scaling factor for both the numerator and the denominator (i.e., f(x) and l(x) in Figure 4(a)). Our key insight is, the scaling factor can be an arbitrary number rather than using the maximum value mathematically, shown in Equation (3). When we set ϕ = 0, it becomes the original softmax computation [30]. Analysis and Insights. However, the scaling factor cannot be an arbitrary number considering the overflowing of the exponent computation. For the case where xi ≫ ϕ, e xi−ϕ overflows and cannot be represented using a fix-width floating point number (e.g., float32 for exponent results in current LLM engines). For another case where xi ≪ ϕ, e xi−ϕ → 0, leading to precision loss. Thus, a proper scaling factor ϕ should be carefully selected to avoid the two cases above. Figure 5 shows the statistical distribution of xi (elements in the input vectors of softmax) in typical LLMs with different inputs [31]. Our key insight is, > 99.99% xi Specifically, for Llama2-7B, we have −16.8 < xi < 6.5 for > 99.99% xi . Because e b−a and e a−b can be represented by a float32 format, we can set ϕ = a in Equation (3). For OPT-6.7B, we do not apply the technique in this section because of the large range in Figure 5. are within a certain range. Based on the insights above, each partial softmax computation shares a unified maximum value, ϕ. After the softmax operation, an inner product operation is executed between the softmax result and a column of V (i.e., v). Assume that the input vector x can be divided into p partial vectors, x = [x (1), ..., x(p) ] (v = [v (1), ..., v(p) ] correspondingly), we have: Approach: Asynchronization. The inner accumulation in both the numerator and the denominator only take the partial vectors x (j) and v (j) as input, thus they can be processed asynchronously and individually. The outer accumulation is only processed after all partial vectors are processed. As we can see in Figure 4(c), each f(x (j) ) is calculated individually, and sof tmax(x) is calculated after all x (j) is calculated. Without loss of generality, we assume a < xi − ϕ < b for each xi to ensure precision and avoid overflow. Then, the partial softmax operation is processed individually. However, when xi − ϕ ≤ a or xi − ϕ ≥ b, the asynchronized partial softmax computation is terminated for the vector x where xi belongs to. The softmax is then recomputed using the synchronized partial softmax scheme (used in FlashAttention [18, 19] and FlashDecoding [13]) shown in Figure 4(b). Such a recomputation scheme avoids overflow while introducing negligible overheads based on the statistical data shown in Figure 5. Approach: Recomputation.

FlashDecoding++: Faster Large Language Model Inference on GPUs: Asynchronized Softmax with Unified

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The TechBeat: Web3 Is Fragmented, Or Why We Need a Unified Base Layer (12/8/2023)

Web3, envisioned as the next phase of the internet, promises decentralization, enhanced security and greater user autonomy. However, a decade since its inception, the landscape of Web3 paints a complex picture, marked by fragmented development and slow adoption. The challenges include steep learning curves for developers, fragmented platform models and a disparity between speculative interest and genuine user adoption. The Ethereum Paradigm: The "iOS" Model in Web3 Ethereum emerged as a trailblazer in the Web3 space, introducing a novel platform that required developers to adopt Solidity, a new programming language, and to adapt to its specific development framework. This approach, reminiscent of Apple's iOS ecosystem, was revolutionary yet restrictive. It set a precedent, but also led to barriers. Learning a new language and adapting to a unique framework meant a steeper learning curve for developers. This exclusivity, while fostering a dedicated community, inadvertently narrowed the scope of developer engagement. The Rush of Liquidity and Its Consequences The early success of Ethereum led to a significant influx of liquidity into the Web3 space. This premature allocation of capital, while fueling growth, inadvertently encouraged a trend of replication rather than innovation. Many projects, lured by the immediate availability of funding, adopted Ethereum's architecture model, leading to a lot of similar platforms, slowing down progress. This rush of liquidity thus fueled a cycle of 'copy- paste (copy-pasta)' in architectural designs, stifling the evolution of more diverse and potentially more effective Web3 solutions. Rise of "Android-like" Platforms In response, "Android-like" platforms such as Polkadot, Cosmos and Avalanche with its parachain, appchain or subnet approaches surfaced, promoting interoperability and flexibility. These platforms proposed a model where individual blockchain projects, akin to apps in the Android ecosystem, could operate on their own but remain interconnected. While this approach aimed to address Ethereum's limitations, it too imposed its own set of constraints, mainly by requiring adherence to specific frameworks and languages. The result was a diversification in the Web3 space, but not necessarily a simplification or unification. Interoperability in Web3: A Band-Aid, Not a Cure In the Web3 world, the push for interoperability as a solution to its fragmentation is akin to applying a band-aid on a deeper wound. These efforts to bridge diverse blockchain ecosystems, while innovative, often complicate the user experience, placing undue burdens on both developers and users. Such bridges add layers of complexity that a polished interface can hardly mitigate, revealing a fundamental flaw: the focus on fixing surface-level issues rather than addressing the root causes of fragmentation. This approach hinders genuine user adoption, as it complicates rather than simplifies interaction within the Web3 space. Developer Perspectives and Project Quality The rise of many platforms in Web3 created a diverse landscape, with about 100,000 active developers—a fraction compared to traditional programming environments. Having many options is good, but it also splits the developer community. Moreover, the ease of funding in Web3, through grants and blockchains paying to build on them, led to many projects with varying quality. The emphasis on rapid funding and market speculation overshadowed the need for solid product-market fit, often resulting in projects with limited user engagement and retention. This has led to low on-chain transaction volumes, creating a cycle of hype and decline that is temporarily masked by speculative market interest during bull runs. The Illusion of Growth and Its Aftermath In the pursuit of growth, many projects resorted to tactics like artificially inflating social media engagement, creating a false sense of progress and interest. But, after getting money, it became clear that not many people were using their product.  Excuses ranged from the need for more public education on crypto and calls for clearer regulation, to blaming poor market conditions like bear markets. There was a prevalent hope that a future bull market would resolve these issues. However, this way of thinking ignores a big problem: if a project's token truly had utility, its adoption and value should not be so heavily correlated with market cycles. The overreliance on market trends over actual utility undermines the foundational principle of tokenomics, calling into question the purported 'utility' of these tokens. The Reality of Token Misuse and On-Chain Activity Token misuse, often employed for short-term gains rather than true utility, has undermined the credibility of numerous Web3 ventures. This is clear when you see the low transaction volumes on many projects, a clear sign of the disparity between the hype-driven speculative interest and actual, sustainable use. In response to this lack of genuine activity, some blockchains themselves have resorted to generating artificial transactions. This hidden inflation, designed to create an illusion of bustling activity, aims to appease network validators and investors. It fails to address the underlying issue: the absence of substantial user adoption and real-world utility in the projects built on these platforms. This cycle of unrealistic expectations and then feeling let down doesn't just hurt one project—it makes people doubt if the whole Web3 idea is trustworthy and will last. Enterprise Adoption Hurdles in Web3 The fragmented and inflexible development environment of Web3 has also been a significant barrier to enterprise adoption. For large corporations, navigating the Web3 landscape is not merely a technological shift but a comprehensive change management challenge. The necessity to either hire new talent proficient in niche programming languages or to upskill existing teams has proven to be a daunting task. Moreover, committing to a specific blockchain infrastructure carries the risk of betting on a potentially less capable or eventually obsolete technology. This uncertainty has led many enterprises to either explore private blockchain solutions, more tailored to their specific needs, or to adopt a 'wait-and-see' approach, hoping for the Web3 space to mature and become more accessible. As a result, the last decade has seen little to no significant enterprise engagement on public blockchains, reflecting a cautious stance towards the promises of increased transparency and decentralization offered by Web3. Comparing Web3’s Adoption with AI The adoption trajectory of Web3 starkly contrasts with that of AI technology. AI's rapid uptake can be attributed to developer freedom and minimal framework restrictions, highlighting the need for a more open and accessible environment in Web3 development. The success of AI and other technologies underscores the importance of a developer-centric approach, something that Web3 has struggled to balance. The Need for a Unified Base Layer The fragmented state of Web3 highlights a crucial requirement for a solid foundation, much like the role the internet played for Web2. This essential base layer should empower developers from various backgrounds, allowing them to innovate without being tied down to a specific framework. By adopting this unified approach, we can not only make innovation more feasible but also create a user-friendly environment, fostering wider adoption. The key to the future success of Web3 lies in addressing these challenges and moving towards a more cohesive, accessible and practical framework. By aligning with the needs of both developers and users, Web3 has the potential to fulfill its promise as a transformative force in the digital world.

Web3 Is Fragmented, Or Why We Need a Unified Base Layer

Key Takeaways : The flexibility of remote work comes with the challenge of asynchronous communication, which can slow down the code review process. Asynchronous Isn't always awesome : Constantly switching between coding and code reviews can kill your productivity faster than you can say "merge conflict." Context switching is a buzzkill : They can handle the repetitive tasks and even offer valuable insights, but they're not a replacement for human expertise. AI and automation are game changers : Bringing together humans, AI, and automation into one seamless conversation can make the code review process more efficient and enjoyable. Unified, conversation-driven workflows are the future Alright, let's cut to the chase. We're all coding in our pajamas now, and while that's awesome for work-life balance, it's a bit of a dumpster fire for code reviews. Remember the good ol' days when you could just roll your chair over to a colleague's desk and say, "Hey, can you take a quick look at this function?" Yeah, those days are as gone as the headphone jack on an iPhone. So, what's the deal? We're all remote, and that's messing with our code review mojo. It's like trying to play a co-op video game when your friend has a laggy internet connection—it's frustrating and less fun. But hey, we're engineers. We solve problems for a living, right? So let's dive into this mess and see if we can clean it up a bit. The good, the bad, and the ugly of remote code reviews Asynchronous woes: The waiting game Asynchronous communication is both the blessing and curse of remote work. On the one hand, it's fantastic. You can work when you're most productive, whether you're an early bird or a night owl. But here's the kicker: while you're sipping your morning coffee, your teammate on the other side of the world is probably halfway into their REM sleep. So, you're stuck playing the waiting game, hoping they'll review your code before the deadline looms. It's like waiting for a software update to complete—you know it's necessary, but oh boy, is it tedious. Context switching: The brain's worst enemy You're deep in the code, in the zone, and then—BAM!—Slack notification. It's a code review request. Normally, this would derail your focus, forcing you to switch gears and contexts. But what if that Slack notification was actually helpful? What if it allowed you to handle the review without leaving your coding environment? addresses the issue by offering bi-directional integration with your existing tools. It streamlines your workflow so that even a Slack notification becomes a seamless part of your coding process, rather than a disruptive element. Pullflow Tool overload: The Swiss Army nightmare GitHub, Jira, Slack, Jenkins, and don't even get me started on the bespoke internal tools that companies love to build that promise to "streamline your workflow." But let's be honest, it often feels like you're a circus performer spinning plates. Each tool has its own set of notifications, quirks, and "must-use" features that you didn't even ask for. It's like owning a Swiss Army knife where you only use the blade but have to carry around a corkscrew, a toothpick, and a mini-screwdriver "just in case." AI and automation: The future or just a fad? AI: The rubber duck that talks back AI in code reviews is no longer just a glorified spell-checker. Think of it as a code consultant with a Ph.D. in computer science. Leveraging machine learning algorithms such as Decision Trees, it optimizes your code and even suggests algorithmic improvements. Using Natural Language Processing (NLP), it can analyze the tone of your comments. It's like having an ever-vigilant code reviewer that also flags security risks in real time. Automation: The CI/CD symphony Continuous Integration and Continuous Deployment (CI/CD) are like the rhythm section of a band—often overlooked but crucial for keeping the tempo. Automated tests, builds, and deployments have become an integral part of modern code review workflows. When you submit a pull request, automated tests kick in like clockwork, providing immediate feedback on whether your changes are up to snuff. It's like having a personal proofreader who checks your work before anyone else sees it. But remember, CI/CD is only as good as the tests you write. Garbage in, garbage out. Chatbots: Digital interns we never knew we needed Chatbots in the code review process are like interns who are always eager to help but don't expect a paycheck. They can handle mundane tasks like updating ticket statuses, merging approved pull requests, and even notifying team members about pending reviews. It's like having an extra pair of hands that take care of the grunt work, allowing you to focus on more complex and creative tasks. But beware, chatbots are programmed to be literal-minded. A poorly configured bot can wreak havoc, like an intern who misunderstands "make me a coffee" and ends up ordering 100 espresso machines. Tackling code review complexity with Pullflow Let's take a moment to discuss Pullflow, a tool that's making waves in the developer community for its unique approach to code review challenges. Rather than being just another platform, Pullflow aims to be a solution to the specific issues we've been dissecting. How Pullflow addresses the issues: : Remember the mental toll of juggling between IDEs, chat apps, and code repositories? Pullflow integrates these platforms bi-directionally, reducing the need for constant tab-switching. Context switching : A single thread for all PR activities keeps everyone updated in real-time, mitigating the delays caused by time-zone differences and asynchronous communication. Asynchronous woes : Pullflow's contextual AI offers real-time answers and feedback in your Slack PR discussions, acting like an on-call code review guru. AI-Assisted Conversations : Allows immediate actions via Slack commands, reducing the need to switch contexts just to perform simple tasks like assigning a PR. : Offers real-time metrics to help you track and improve review cycles and response times Actionable commands Performance metrics Caveats to consider: : Currently, it only supports GitHub, Slack, and VS Code, which might be a constraint for some teams. Platform limitations Pullflow brings together all your PR activities, automations, and conversations into one place. It's like having a mission control center for your code reviews. The dream: A unified, conversation-driven workflow What we're all yearning for is a unified, conversation-driven workflow for code reviews. Imagine a seamless integration of human expertise, AI insights, and automation, all working in harmony. Imagine a single thread where code, comments, and automated feedback coexist, allowing for real-time collaboration and decision-making. The human element: the heart of code reviews At the core of any code review process are the human reviewers. We bring to the table our experience, intuition, and creativity. We catch the nuances that AI might miss, understand the context in which the code will run, and offer insights that go beyond mere syntax and logic. We're the jazz musicians who know when to stick to the sheet music and when to improvise. AI and automation: The support cast AI and automation are great, but they're the support cast, not the lead actors. They should enhance the human element, not replace it. It's like using a calculator to do math—you still need to know how to do the math; the calculator just makes it faster and more accurate. Instruments of the trade The tools: The tools we use—GitHub, Jira, CI/CD platforms, chatbots—are the instruments that produce the sound. They need to be finely tuned and well-maintained to produce a harmonious result. The choice of tools can make or break your code review process, so choose wisely. Case study: f rom the trenches: 's code review turnaround Runn.io Here's some food for thought. faced the same code review bottlenecks many of us know all too well. After integrating Pullflow, their review wait times plummeted from a dragging 14 hours to a brisk 4. That's less time waiting and more time coding, which is what we all want, right? Runn.io Conclusion: the future is what we make it, so let's get to work We're at the crossroads of human ingenuity and tech innovation, with a keyboard in one hand and a pull request in the other. The future of code reviews isn't about picking the perfect tool or methodology; it's about creating a holistic approach that combines the best of human skill and cutting-edge tech. Your Next Moves : Identify bottlenecks and areas ripe for improvement. Audit your current workflow : If AI isn't part of your toolkit yet, now's the time to start experimenting. Dabble in AI : Consider tools like Pullflow that centralize PR activities and reduce context switching. Optimize communication : Make sure your team is up-to-date with the latest best practices in code reviews, whether it's through internal training or external courses. Invest in training So let's be the architects of a future where code reviews are not just a checkbox but a collaborative, enriching process that elevates both our code and ourselves. And if we can do that while sipping artisanal coffee and rocking our favorite band's t-shirt, we've truly hit the jackpot.

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