AppEQ.ai provides one-click real-time customer insights to drive revenue retention in B2B companies

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AppEQ gives one in-app click customer ... B2B SaaS companies to get account information quickly &amp; easily on Inbox, CRM, CSP, Helpdesk etc ... Hello B2B SaaS providers! Are you ready to take your client value and revenue strategies to the next level? We&#x27;ve got an exciting webinar just for you! Secure your spot: https://lnkd.in/g_8_Gjsh ... Happy to share that I&#x27;ll be speaking with Sandy Yu on Optimizing B2B SaaS Revenue: Mastering Upselling &amp; Cross-Selling Strategies! Hope you can join us! AppEQ.ai ...

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appEQ - AI-based platform for enterprise collaboration.

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AppEQ.ai -a in-app overlay for enabling customer facing teams to have contextual data and collaboration on SaaS apps.

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Appeq Ai Private Limited, established in 2022, is engaged in the Information Technology sector in Bangalore. Explore their Corporate Structure, Financials, and More.

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Real-time Customer Insights @ Fingertips TO DRIVE REVENUE RETENTION At AppEQ, our passion lies in empowering customer-facing teams with efficient solutions that drive growth and profitability for B2B organizations. We believe that revenue retention is the key driver to profitability, and our ...

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AppEQ unifies customer data from ... revenue and growth without the complexities of traditional Customer Success Platforms. Fast-growing SaaS companies use AppEQ to retain and grow their accounts....

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Apple Inc. (formerly Apple Computer, Inc.) is an American multinational technology company headquartered in Cupertino, California, in Silicon Valley. It designs, develops, and sells consumer electronics, computer software, and online services. Devices include the iPhone, iPad, Mac, Apple Watch, ...

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appEQ.ai is an AI-powered collaboration productivity suite that is any overlay technology on top of your existing SaaS stack, giving users the ...

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This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Rui Cao, Singapore Management University;
(2) Ming Shan Hee, Singapore University of Design and Technology;
(3) Adriel Kuek, DSO National Laboratories;
(4) Wen-Haw Chong, Singapore Management University;
(5) Roy Ka-Wei Lee, Singapore University of Design and Technology
(6) Jing Jiang, Singapore Management University. Table of Links Abstract and Introduction Related Work Preliminary Proposed Method Experiment Conclusion and References Appendix APPENDIX A DETAILS FOR IMPLEMENTATION We implement all models under the PyTorch Library with the CUDA11.2 version. We use the Tesla V 100 GPU, each with a dedicated memory of 32GB. For models specifically implemented for hateful meme detection, we take the codes published from the author for reimplementation [4]. For pre trained models which can be found under the Huggingface Library, we use the packages from Huggingface [5], specifically the BERT [4], VisualBERT [18] and the BLIP model. Gor ViLBERT [23], we take the released code from the authors [6]. For ALBEF [17] and BLIP-2 [15], we use the packages under the LAVIS Library [7] For each meme image, we constrain the total length of the meme text and the generic image caption (either from the captioning model or by asking about the content of the image) to be 65. For each additional questions, we restrict its length to be shorter than 20. If the concatenation of the sentence exceeds the limited length, the sentence will be truncated, otherwise, if the sentence is shorted than the limited length, it will be padded. We set the number of training epochs to be 10 for all models. The number of model parameters are summarized in Table 11. B FULL ABLATION STUDY RESULTS Due to the limitation of space, we only show results of accuracy in ablation studies in Table 6. The full results including both the AUC and the accuracy are provided in Table 12. C VISUALIZATION CASES In Section 5.5, we provide visualization of cases for comparing ProCapPromptHate with the basic PromptHate. Due to space constraints, we omit examples from the other two datasets. We provide more visualization cases in this part. The cases from the HarM dataset are illustrated in Table 9 and the cases from the MAMI dataset are shown in Table 10. D RESULTS WITH PRO-CAP ABOUT ONE TARGET In Section 5, we only report results when models use Pro-Cap from all probing questions. In this part, we report results (with entities) when using the answers from a single probing question in Table 13. According to the results, we observe models using answers to a single probing question are all powerful and some even surpass heuristically asking all probing questions (e.g., using the question asking about nationality on FHM is better than using all probing questions). It points out using all probing captions may not be the optimal solution and may generate irrelevant image descriptions. For instance, confronted with a hateful meme targeting at black people, it is meaningless to ask the religion of people in the image. Interestingly, on MAMI, when only using answers to the probing question about gender reaches teh best performance. It is because MAMI contains only hateful memes about woman. A promising direction would train the model to dynamically select probing questions essential for meme detection for different memes. [4] CLIP-BERT/MOMENTA: https://github.com/LCS2-IIITD/MOMENTA;DisMultiHate: https://gitlab.com/bottle_shop/safe/dismultihate; PromptHate: https://gitlab.com/bottle_shop/safe/prompthate [5] https://huggingface.co/ [6] https://github.com/facebookresearch/vilbert-multi-task [7] https://github.com/salesforce/LAVIS heuristically asking all probing questions (e.g., usin This paper is available on arxiv under CC 4.0 license. Authors: (1) Rui Cao, Singapore Management University; (2) Ming Shan Hee, Singapore University of Design and Technology; (3) Adriel Kuek, DSO National Laboratories; (4) Wen-Haw Chong, Singapore Management University; (5) Roy Ka-Wei Lee, Singapore University of Design and Technology (6) Jing Jiang, Singapore Management University. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Rui Cao, Singapore Management University; (2) Ming Shan Hee, Singapore University of Design and Technology; (3) Adriel Kuek, DSO National Laboratories; (4) Wen-Haw Chong, Singapore Management University; (5) Roy Ka-Wei Lee, Singapore University of Design and Technology (6) Jing Jiang, Singapore Management University. Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Preliminary Preliminary Proposed Method Proposed Method Experiment Experiment Conclusion and References Conclusion and References Appendix Appendix APPENDIX A DETAILS FOR IMPLEMENTATION We implement all models under the PyTorch Library with the CUDA11.2 version. We use the Tesla V 100 GPU, each with a dedicated memory of 32GB. For models specifically implemented for hateful meme detection, we take the codes published from the author for reimplementation [4]. For pre trained models which can be found under the Huggingface Library, we use the packages from Huggingface [5], specifically the BERT [4], VisualBERT [18] and the BLIP model. Gor ViLBERT [23], we take the released code from the authors [6]. For ALBEF [17] and BLIP-2 [15], we use the packages under the LAVIS Library [7] For each meme image, we constrain the total length of the meme text and the generic image caption (either from the captioning model or by asking about the content of the image) to be 65. For each additional questions, we restrict its length to be shorter than 20. If the concatenation of the sentence exceeds the limited length, the sentence will be truncated, otherwise, if the sentence is shorted than the limited length, it will be padded. We set the number of training epochs to be 10 for all models. The number of model parameters are summarized in Table 11. B FULL ABLATION STUDY RESULTS Due to the limitation of space, we only show results of accuracy in ablation studies in Table 6. The full results including both the AUC and the accuracy are provided in Table 12. C VISUALIZATION CASES In Section 5.5, we provide visualization of cases for comparing ProCapPromptHate with the basic PromptHate. Due to space constraints, we omit examples from the other two datasets. We provide more visualization cases in this part. The cases from the HarM dataset are illustrated in Table 9 and the cases from the MAMI dataset are shown in Table 10. D RESULTS WITH PRO-CAP ABOUT ONE TARGET In Section 5, we only report results when models use Pro-Cap from all probing questions. In this part, we report results (with entities) when using the answers from a single probing question in Table 13. According to the results, we observe models using answers to a single probing question are all powerful and some even surpass heuristically asking all probing questions (e.g., using the question asking about nationality on FHM is better than using all probing questions). It points out using all probing captions may not be the optimal solution and may generate irrelevant image descriptions. For instance, confronted with a hateful meme targeting at black people, it is meaningless to ask the religion of people in the image. Interestingly, on MAMI, when only using answers to the probing question about gender reaches teh best performance. It is because MAMI contains only hateful memes about woman. A promising direction would train the model to dynamically select probing questions essential for meme detection for different memes. [4] CLIP-BERT/MOMENTA: https://github.com/LCS2-IIITD/MOMENTA;DisMultiHate: https://gitlab.com/bottle_shop/safe/dismultihate; PromptHate: https://gitlab.com/bottle_shop/safe/prompthate [5] https://huggingface.co/ [6] https://github.com/facebookresearch/vilbert-multi-task [7] https://github.com/salesforce/LAVIS heuristically asking all probing questions (e.g., usin

Pro-Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme Detection: Appendix

প্রো-ক্যাপ: বিদ্বেষপূর্ণ মেম সনাক্তকরণের জন্য একটি হিমায়িত দৃষ্টি-ভাষা মডেল ব্যবহার করা: পরিশিষ্ট

Pro-Cap: Nutzung eines Frozen Vision-Language-Modells zur Erkennung hasserfüllter Memes: Anhang

Pro-Cap: Aprovechamiento de un modelo de visión y lenguaje congelado para la detección de memes de odio: Apéndice

Pro-Cap : Exploiter un modèle de langage de vision gelé pour la détection des mèmes haineux : Annexe

प्रो-कैप: घृणास्पद मीम का पता लगाने के लिए फ्रोज़न विज़न-लैंग्वेज मॉडल का लाभ उठाना: परिशिष्ट

Pro-Cap: ヘイトミーム検出のための凍結視覚言語モデルの活用: 付録

Pro-Cap: 혐오스러운 밈 탐지를 위해 고정된 시각-언어 모델 활용: 부록

Pro-Cap: Aproveitando um modelo de linguagem de visão congelada para detecção de memes odiosos: Apêndice

Pro-Cap: Использование модели Frozen Vision-Language для обнаружения ненавистных мемов: Приложение

Pro-Cap: Nefret Dolu Memlerin Tespiti için Dondurulmuş Görüş-Dil Modelinden Yararlanma: Ek

Pro-Cap: Tận dụng Mô hình ngôn ngữ tầm nhìn đông lạnh để phát hiện Meme đáng ghét: Phụ lục

Pro-Cap：利用冻结视觉语言模型进行仇恨模因检测：附录

Authors: (1) Maria Rigaki, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic and maria.rigaki@fel.cvut.cz; (2) Sebastian Garcia, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic and sebastian.garcia@agents.fel.cvut.cz. Table of Links Abstract & Introduction Threat Model Background and Related Work Methodology Experiments Setup Results Discussion Conclusion, Acknowledgments, and References Appendix Appendix A. Hyper-parameter Tuning The search space for the PPO hyper-parameters: – gamma: 0.01 - 0.75 – max grad norm: 0.3 - 5.0 – learning rate: 0.001 - 0.1 – activation function: ReLU or Tanh – neural network size: small or medium Selected parameters: gamma=0.854, learning rate=0.00138, max grad norm=0.4284, activation function=Tanh, small network size (2 layers with 64 units each). The search space for the LGB surrogate training hyper-parameters: – alpha: 1 - 1,000 – num boosting rounds: 100-2,000 – learning rate: 0.001 - 0.1 – num leaves: 128 - 2,048 – max depth: 5 - 16 – min child samples: 5 - 100 – feature fraction: 0.4 - 1.0 Hyper-parameter settings for the training of each LGB surrogate Table 4. This paper is under CC BY-NC-SA 4.0 DEED license. available on arxiv

Adversarial Malware Creation with Model-Based Reinforcement Learning: Appendix

Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal: Appendix

It may not have been the cuts that some speculators had hoped for back in Q4, but the news that the US Federal Reserve were set to cut rates three times throughout 2024 helped to push all three major stock indexes towards new record highs following its March meeting. The prospect of rate cuts will be music to the ears of risk-taking investors, with the prospect of greater confidence levels and stronger liquidity helping to drive markets higher. Riding the wave of good news was the Dow Jones Industrial Average, S&P 500, and the Nasdaq Composite, all of which added posted of between 0.2% and 0.7% as the anticipation of upcoming cuts grew. healthy gains Although the Fed opted to keep rates unchanged in March, new forecasts confirmed plans for three 25-basis-point rate cuts in 2024 in a move that improved optimism throughout Wall Street. Federal Reserve Chair Jerome Powell also hinted that rate cuts will take place provided that the US economy continues to grow as expected, and downplayed the impact of the in inflation that took place in January and February. As a result, markets anticipate a of a rate cut arriving by June. unexpected rises 72% chance The largely optimistic note saw Wall Street’s chip stocks perform exceptionally well, with Nvidia (NASDAQ:NVDA) soaring to yet further highs while the positive sentiment dovetailed for Micron Technology’s (NASDAQ:MT) stock which has now shown Q1 growth of 45% following an report. impressive earnings Provided that the Fed’s anticipation of economic growth meets expectations, it’s likely that higher flows of investor optimism will manifest itself in Wall Street’s tech stocks and various riskier plays that would’ve seemed too hot to handle as recently as 2022. So, is it time for the good times to roll once more? Winning the Battle with Inflation Throughout 2022 and 2023, the economic landscape throughout the US was punctuated by a hawkish monetary policy from the Federal Reserve. Soaring inflation prompted by the post-pandemic recovery and supply chain squeezes throughout industries saw inflation peak at 9.1% in June 2022. By late 2023, optimism began to grow that rate hikes may finally be over, and that cuts may be on the way as soon as Q1 2024. However, January and February of the new year brought more concern. Inflation began to rise unexpectedly, causing more investors to fear that expectations over cuts may be short lived. Despite this, Powell highlighted that recent inflation worries were likely down in part to ‘seasonal effects,’ and although the bank would take the available data seriously, it shouldn’t impact its transition into a more dovish stance. "It certainly hasn't raised anyone's confidence; but I would say that the story is really essentially the same, and that is of inflation coming down gradually toward 2% on a sometimes bumpy path," . explained Powell "We've got nine months of 2.5% inflation now. We've had two months of bumpy inflation; it's going to be a bumpy ride." While the anticipation of cuts has seen investor sentiment become more positive on Wall Street, other investors have sought to move quickly in of advantageous interest rates while they’re still available. taking advantage According to fund tracker EPFR, $22.8 billion has flooded into corporate bond funds in 2024 before anticipated rate cuts impacted yields. The data represents the brightest start to a year since 2019. Big Tech Wins Investor Confidence Wall Street’s brightening outlook saw in the wake of the Fed’s March meeting. US stock futures rally Futures for the Dow Jones Industrial Average advanced 30 points, or 0.1%, the S&P 500 gained 0.2%, and the tech-oriented Nasdaq climbed 0.3% moving into the quieter Easter Weekend period for markets as investors began to bet on clearing skies over US stocks. Crucially, this growing interest in the performance of US markets falls in-line with more bullish revisions of where indexes like the S&P 500 is likely to be by the end of the year. Notably, John Stoltzfus, chief investment strategist at Oppenheimer, has raised expectations for the S&P 500 by 5% to a year ending benchmark of 5,500–representing a strong increase on the initial specified target of 5,200. "All of the above prompts us to increase our year-end price target acknowledging the possibility that we might need to raise the target price again later this year should this economic and market outlook prove us too conservative in our projections," . wrote Stoltzfus Even more bullish is the prediction of a team of Goldman Sachs strategists led by David Kostin who suggested that the S&P 500 could be driven to thanks to big tech rallies in one scenario where megacaps foster growth of 15%. 6,000 this year However, the team also highlighted one prospective scenario that could see the index crash to 4,500. The End of Risk? Although markets have become more optimistic off the back of the Federal Reserve’s March meeting, the performance of inflation between now and June will have a major impact on whether investors retain their confidence in Wall Street’s megacap tech stocks. Another confounding factor may be the continued escalation of geopolitical tensions and a prospective tumultuous presidential election in November, which could be intensified should it become a close-run event. There’s also the danger that markets have already long factored in the inevitability of rate cuts, meaning that there may be less distance to run when the hotly anticipated cuts finally occur. “It is important to realise that the market has changed the paradigm from ‘will there be a rate cut?’ to ‘when?’,” , head of investment research at Freedom Finance Europe. explained Maxim Manturov “In other words, the markets have built into expectations a rate cut soon, and even if it comes later, from a fundamental perspective it won't have much of an impact given the solid Q4 gains for much of the S&P 500 index. And even though expectations for a Fed rate cut have been pushed back to a later date, the threat of a rate hike is off the table for now.” While we’re likely to see the positive sentiment flow back into Wall Street as expectations for rate cuts continue to grow, the impact they’ll have on the risky stocks that have helped to facilitate such a strong end to 2023 may have already been priced in. This means that optimism should be balanced with caution in navigating the Fed’s new dovish stance.

Risk Appetite Floods Back Into Wall Street as Rate Cut Confidence Soars for Investors

ঝুঁকির ক্ষুধা বন্যা ওয়াল স্ট্রিটে ফিরে এসেছে কারণ বিনিয়োগকারীদের জন্য রেট কাটের আস্থা বেড়েছে

Die Risikobereitschaft kehrt an die Wall Street zurück, da das Vertrauen der Anleger in Zinssenkungen steigt

El apetito por el riesgo vuelve a inundar Wall Street a medida que aumenta la confianza de los inversores en el recorte de tipos

L’appétit pour le risque revient à Wall Street alors que la confiance des investisseurs en matière de réduction des taux monte en flèche

निवेशकों के लिए दर में कटौती का विश्वास बढ़ने से वॉल स्ट्रीट में जोखिम की भूख फिर से बढ़ गई है

投資家の利下げに対する信頼感が高まる中、リスク選好がウォール街に再び流入

투자자들의 금리 인하 신뢰도가 높아지면서 위험에 대한 욕구가 다시 월스트리트에 몰려들고 있습니다.

O apetite pelo risco volta a Wall Street à medida que aumenta a confiança dos investidores no corte de taxas

Аппетит к риску возвращается на Уолл-стрит, поскольку уверенность инвесторов в снижении ставок растет

Yatırımcılara Faiz İndirimi Güveni Arttıkça Risk İştahı Wall Street'e Yeniden Taşındı

Sự thèm muốn rủi ro tràn trở lại Phố Wall khi niềm tin cắt giảm lãi suất tăng vọt đối với các nhà đầu tư

投资者对降息信心飙升，风险偏好重回华尔街

United States v. Apple INC Court Filing, retrieved on March 21, 2024 is part of . You can jump to any part in this filing . This part is 13 of 25. HackerNoon’s Legal PDF Series here A. Background 148. Mobile phones are portable devices that enable communications over radio frequencies instead of telephone landlines. These signals are transmitted by equipment covering distinct geographic areas, or “cells,” which is why mobile phones were called cell phones. The first commercial cell phones became available in the 1980s. Since then, improvements in both cell phone components and wireless technology have made it possible to transfer large volumes of data around the globe in a short period. As a result, mobile phones began to offer a wider array of features and the adoption of mobile phones dramatically increased. Today, nearly all American adults own a mobile phone. 149. Smartphones combine the functionality of a traditional mobile phone with advanced hardware and software components. Smartphones not only make phone calls, but allow users to listen to music, send text messages, take pictures, play games, access software for work, manage their finances, and browse the internet. Consumers choose between smartphones based, in part, on their functionality. Today, smartphone functionality is driven in large part, though not exclusively, by a combination of hardware and software components. Thus, in a competitive market, smartphone manufacturers would compete and innovate to provide the best functionality. 150. Although consumers could replace some smartphone functionality with separate devices such as by always carrying a camera and laptop, they generally prefer to access this combination of functionality as part of a single device. Thus, phones with some but not all of these features are not reasonable substitutes for smartphones. For example, a Canon or Nikon camera is not a substitute for an Apple or Samsung smartphone notwithstanding that both these products are capable of taking digital pictures. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on March 21, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. justice.gov

The All-in-One Appeal of Smartphones and Apple's Monopoly Framework 

This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) Tomáš Jakl, Czech Academy of Sciences and Czech Technical University; (2) Dan Marsden, School of Computer Science University of Nottingham; (3) Nihil Shah, Department of Computer Science University of Oxford. Table of Links Abstract & Introduction Prelimenaries FVM Theorems for Positive Existential Fragments FVM Theorems for Counting Logic FVM Theorems for The Full Logic Abstract FVM Theorems for Products Adding Equality and Other Enrichment Conclusions, Acknowledgments & References Appendix A FVM theorems for coproducts Appendix B Proofs Omitted from Section III Appendix C Proofs Omitted from Section IV Appendix D Proofs Omitted from Section V Appendix E Proofs Omitted from Section VI Appendix F Proofs Omitted from Section VII APPENDIX D PROOFS OMITTED FROM SECTION V

Appendix D Proofs Omitted from Section V

This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) Tomáš Jakl, Czech Academy of Sciences and Czech Technical University; (2) Dan Marsden, School of Computer Science University of Nottingham; (3) Nihil Shah, Department of Computer Science University of Oxford. Table of Links Abstract & Introduction Prelimenaries FVM Theorems for Positive Existential Fragments FVM Theorems for Counting Logic FVM Theorems for The Full Logic Abstract FVM Theorems for Products Adding Equality and Other Enrichment Conclusions, Acknowledgments & References Appendix A FVM theorems for coproducts Appendix B Proofs Omitted from Section III Appendix C Proofs Omitted from Section IV Appendix D Proofs Omitted from Section V Appendix E Proofs Omitted from Section VI Appendix F Proofs Omitted from Section VII APPENDIX E PROOFS OMITTED FROM SECTION VI Here we show FVM theorems for products of arbitrary collection of structures. Note that Section VI is stated in terms of binary products only but here we work with infinitary products, to support the claim preceding Example VI.6.

Appendix E Proofs Omitted from Section VI

This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) Tomáš Jakl, Czech Academy of Sciences and Czech Technical University; (2) Dan Marsden, School of Computer Science University of Nottingham; (3) Nihil Shah, Department of Computer Science University of Oxford. Table of Links Abstract & Introduction Prelimenaries FVM Theorems for Positive Existential Fragments FVM Theorems for Counting Logic FVM Theorems for The Full Logic Abstract FVM Theorems for Products Adding Equality and Other Enrichment Conclusions, Acknowledgments & References Appendix A FVM theorems for coproducts Appendix B Proofs Omitted from Section III Appendix C Proofs Omitted from Section IV Appendix D Proofs Omitted from Section V Appendix E Proofs Omitted from Section VI Appendix F Proofs Omitted from Section VII APPENDIX F PROOFS OMITTED FROM SECTION VII

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