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The US Defense Advanced Research Projects Agency ( ) is looking to improve traumatic memory consolidation by modulating REM sleep for stress and trauma adaption. DARPA This month, DARPA issued a for a potential research program called “Rapid Eye Movement Restoration and Enhancement for Sleep-deprived Trauma Adaptation” ( ), which “ .” Disruption Opportunity REM REST aims to identify neural mechanisms and demonstrate the ability to modulate REM sleep that improves stress adaptation and traumatic memory consolidation “Targeted Dream Incubation may have applications as a learning tool that can improve memory consolidation” Science Alert, 2020 Connecting post-traumatic disorder (PTSD) with memory consolidation and REM, DARPA says that: stress “Disrupted REM […] is associated with post-traumatic stress disorder (PTSD) and impaired emotional memory consolidation.” Of particular interest to DARPA are “ .” novel applications of existing neuromodulation technologies to target REM sleep and demonstrate the ability to measure and enhance neural mechanisms of stress and trauma adaption According to an MIT Media Lab , “ .” study on dream engineering nearly any sensory stimuli has potential for modulating experience in sleep “We present a novel protocol using a new wearable electronic device, Dormio, to automatically generate serial auditory dream incubations at sleep onset, wherein targeted information is repeatedly presented during the hypnagogic period, enabling direct incorporation of this information into dream content, a process we call targeted dream incubation (TDI)” MIT Media Lab, 2020 Although DARPA’s makes no mention of dreams, the idea of modulating and even artificially implanting dreams is not far off. REM REST After all, most dreams occur during REM. In fact, MIT came up with a hand-wearable electronic device called “to automatically generate serial auditory dream incubations at sleep onset, wherein targeted information is repeatedly presented during the hypnagogic period, enabling direct incorporation of this information into dream content, a process we call (TDI).” Dormio targeted dream incubation While targeted dream incubation may be beyond the scope of , says that there is “a similar experimental method called (TMR), which reactivates specific memories that are triggered by a cue delivered during sleep,” which may prove interesting. REM REST Science Alert targeted memory reactivation For , DARPA is looking for proposals that can “evaluate the mechanistic relationship with stress adaptation, traumatic , and/or other related emotional h resilience outcomes.” REM REST memory consolidation ealth and psychological says that “ .” Science Alert TDI may have applications as a learning tool that can improve memory consolidation With that in mind, could targeted dream incubation like in the movie be among the proposals? Inception “Novel solutions to restoring and enhancing the mechanisms by which REM sleep promotes stress and trauma adaptation could provide important breakthroughs for treating sleep disturbances, improving overall sleep quality, and preventing PTSD and other behavioral health outcomes, such as suicide, associated with disrupted REM sleep” DARPA REM REST, 2023 The US Department of Veteran Affairs says that will experience post-traumatic stress disorder at some point in their lives. seven percent of veterans PTSD, according to some researchers, may be looked upon as a disorder of basic memory function. As one states, “Approaching PTSD as a memory disorder, with a specific focus on (re)consolidation opens up exciting, new ways to think about treatment.” study from 2015 And from the University of Tsukuba and the University of Tokyo recently revealed that “adult-born neurons (ABNs) in the hippocampus, which is a brain region associated with memory, are responsible for memory consolidation during REM sleep.” researchers For , the Pentagon’s research funding arm is looking towards “ ” as this “could provide important breakthroughs for treating sleep disturbances, improving overall sleep quality, and preventing PTSD and other behavioral health outcomes, such as suicide, associated with disrupted REM sleep.” REM REST novel solutions to restoring and enhancing the mechanisms by which REM sleep promotes stress and trauma adaptation Last year, under the premise of identifying people at risk of depression and suicide, the Pentagon’s research funding arm launched the , which focused on “aggregating preconscious brain signals to determine what someone believes to be true.” Neural Evidence Aggregation Tool ( NEAT ) program While mental health among soldiers is a serious issue for the Department of Defense, applications coming out of the program have the very real potential to give governments and corporations the ability to hack human beings at the preconscious level. NEAT This article was originally published by on Tim Hinchliffe The Sociable.

DARPA Seeks to Modulate REM Sleep for Stress and Trauma Adaptation

DARPA স্ট্রেস এবং ট্রমা অ্যাডাপ্টেশনের জন্য REM ঘুমকে সংশোধন করতে চায়

DARPA versucht, den REM-Schlaf zur Anpassung an Stress und Traumata zu modulieren

DARPA busca modular el sueño REM para la adaptación al estrés y al trauma

La DARPA cherche à moduler le sommeil paradoxal pour l'adaptation au stress et aux traumatismes

DARPA तनाव और आघात अनुकूलन के लिए REM नींद को संशोधित करना चाहता है

DARPAはストレスとトラウマの適応のためにレム睡眠を調節することを目指す

DARPA는 스트레스와 트라우마 적응을 위해 REM 수면을 조절하려고 합니다.

DARPA procura modular o sono REM para adaptação ao estresse e trauma

DARPA, Stres ve Travmaya Uyum için REM Uykusunu Düzenlemeyi Amaçlıyor

DARPA tìm cách điều chỉnh giấc ngủ REM để thích ứng với căng thẳng và chấn thương

DARPA 寻求调节快速眼动睡眠以适应压力和创伤

Understanding the Shortcomings of Wehe on HTTPS Traffic Detection

HTTPS ট্র্যাফিক সনাক্তকরণে Wehe এর ত্রুটিগুলি বোঝা

Die Mängel von Wehe bei der Erkennung von HTTPS-Verkehr verstehen

Comprender las deficiencias de Wehe en la detección de tráfico HTTPS

Comprendre les lacunes de Wehe sur la détection du trafic HTTPS

HTTPS ट्रैफ़िक डिटेक्शन पर Wehe की कमियों को समझना

HTTPS トラフィック検出における Wehe の欠点を理解する

HTTPS 트래픽 감지에 대한 Wehe의 단점 이해

Compreendendo as deficiências do Wehe na detecção de tráfego HTTPS

Понимание недостатков Wehe при обнаружении HTTPS-трафика

Wehe'nin HTTPS Trafik Algılama Konusunda Eksikliklerini Anlamak

Tìm hiểu những thiếu sót của Wehe trong việc phát hiện lưu lượng truy cập HTTPS

了解 Wehe 在 HTTPS 流量检测方面的不足

Deep Learning: Edge Devices With mmWave FMCW Radars Part 1 - Signal Processing

ডিপ লার্নিং: mmWave FMCW রাডার সহ এজ ডিভাইস পার্ট 1 - সিগন্যাল প্রসেসিং

Deep Learning: Edge-Geräte mit mmWave-FMCW-Radaren, Teil 1 – Signalverarbeitung

Aprendizaje profundo: dispositivos perimetrales con radares mmWave FMCW Parte 1: Procesamiento de señales

Apprentissage profond : appareils Edge avec radars mmWave FMCW, partie 1 - Traitement du signal

डीप लर्निंग: एमएमवेव एफएमसीडब्लू रडार के साथ एज डिवाइस भाग 1 - सिग्नल प्रोसेसिंग

ディープラーニング: mmWave FMCW レーダーを搭載したエッジデバイス パート 1 - 信号処理

딥 러닝: mmWave FMCW 레이더를 사용한 엣지 장치 1부 - 신호 처리

Aprendizado profundo: dispositivos de borda com radares FMCW mmWave Parte 1 - Processamento de sinal

Глубокое обучение: периферийные устройства с радарами mmWave FMCW. Часть 1. Обработка сигналов

Derin Öğrenme: mmWave FMCW Radarlarına Sahip Uç Cihazlar Bölüm 1 - Sinyal İşleme

Học sâu: Thiết bị biên có radar mmWave FMCW Phần 1 - Xử lý tín hiệu

深度学习：配备毫米波 FMCW 雷达的边缘设备第 1 部分 - 信号处理

This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaoyu Ai, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia; (2) Robert Malaney, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia. Table of Links Abstract & Introduction System Overview Overcoming the limitation of key reconciliation Final Key Rate Experimental Results Discussion Conclusion & References Appendix II. SYSTEM OVERVIEW Although, as just stated, our analysis will apply to most CV-QKD protocols, for detailed quantitative discussion we will consider only one specific CV-QKD protocol - the “noswitching” protocol [27]–[29] based on heterodyne detection. In this protocol, the quantum signal is encoded using Gaussian-modulated coherent states [27]. The main advantage of the no-switching protocol is that Alice and Bob can utlise all measurement results [28] (in most other protocols some results are discarded due to a random quadrature selection). We also adopt a Slice Reconciliation (SR) variant named Multi-Stage Hard Decoding[4] (MSHD) [30], [33], [34] for the classical reconciliation step, where the number of bits derived from each measurement outcome is m. We refer to this variant simply as SR in the following. It is worth noting that the optimisation analysis to follow is to some extent independent of the details of the reconciliation scheme. However, SR [30], [36] can be compared with the other well-known reconciliation scheme for CV-QKD - multidimensional reconciliation [37]. It is known that SR achieves higher reconciliation efficiency when the Signal-to-Noise Ratio (SNR) is greater than 1 [36]. At low SNR the opposite is true. For focus, here we adopt SR (as multistage hard decoding [36]) since in many satellite scenarios post-selection is used to filter out the low SNR quantum signals [1]. Our adopted scheme will be more useful in such scenarios. We now briefly describe the steps of the protocol, a diagram of which shown in Fig. 1. In the following, we assume the quantum signalling rate is much larger than the reconciliation rate. • Alice selects a fixed modulation variance VA. For each quantum signal to be transmitted to Bob, Alice randomly selects a number from a Gaussian distribution, N(0, VA), and then prepares a signal by displacing one of the quadrature components of a vacuum state by this random number. The process is repeated on the signal for the other quadrature. The signal is then transmitted to Bob. Step 1: Signal Preparation. • Bob performs heterodyne detection to obtain the two quadrature values (real numbers) for each received signal. Bob compares each measured quantum signal with a given cut-off threshold and informs Alice to discard her corresponding quantum signal if his measured quantum signal is lower than the threshold[5] . A quantum signal that is lost in transit registers a null signal at Bob. Neglecting null signals, Bob holds 2No quadrature values at the end of this process. Step 2: Heterodyne Detection. • S Bob randomly selects a subset 2Ne from the 2No quadrature values and sends this estimation subset, along with the corresponding time information, to Alice via classical communications (we adopt Ne = 1 2No, unless otherwise stated). Alice uses the timing information to best pair the signals in this subset (and therefore the corresponding quadrature values) sent by her and then estimates the covariance matrix between the shared states. Based on the estimated covariance matrix, Alice determines the channel transmissivity, T , excess noise, ξ, Bob’s SNR, γ, the Holevo Information, χBE, between Bob and the eavesdropper (Eve), and the mutual information between Alice and Bob, IAB. Finally, for a given target reconciliation efficiency β, Alice compares χBE with βIAB. Alice aborts the protocol if χBE ≥ βIAB. Otherwise, Alice informs Bob of the estimation results, i.e. T , ξ, γ, χBE and IAB. tep 3: Parameter Estimation. • Using Gray Labelling, Alice and Bob represent each of the quadrature values embedded in each signal with m bits. Then, for quadrature values selected in the estimation subset, Alice forms a Ne-by-m bit matrix and Bob does the same. Next, Alice and Bob exchange their matrices and compare the j th column of the two matrices to estimate the Bit Error Ratio[6] (BER), pj , j ∈ {0, 1, · · · , m−1}, for all the digits in the j th column. The estimated pj will be used in SR. Finally, Alice and Bob discard all the quadrature values in the estimation subset. At the end of this step, Alice and Bob each hold a mN-bit string. Step 4: Bit Error Estimation for SR. • For each column, Alice and Bob agree on an LDPC code with block length NR that is closest to the capacity determined by pj. Bob forms a new NR-bit string (referred as a “slice” in SR) by selecting the j th digit (bit) of each of the NR quadrature values, encodes the new bit string (the slice) into syndrome bits, and sends those bits to Alice (see III.B for details). Alice then initiates SR to obtain her best estimate of Bob’s string. Alice repeats this process until all her mN bits are reconciled. Finally, Alice and Bob obtain two hashed strings by applying the same hash function to their reconciled strings and exchange the hash results to check whether SR is successful. If successful, Alice holds a mN-bit string identical to Bob’s mN-bit string. Otherwise, they abort the protocol and restart from Step 1. Step 5: Reverse Reconciliation. • Based on Eq. 19, Alice and Bob compute the length of the secret key that can be extracted and then apply a 2-universal hashing function on their reconciled string to obtain two identical and shorter secret key strings about which Eve has effectively no knowledge. Step 6: Privacy Amplification. [4] The slice reconciliation can be implemented with 2 other variants: Bit Interleaved Coded Modulation (BICM) [30] and Multi-Level Coding/MultiStage Decoding (MLC/MSD) [31], [32]. We note that the MLC/MSD takes advantage of the dependence between slices to select the optimal LDPC code rates [30], [32]. However, as a special case of MLC/MSD, MSHD assumes that the slices are independent [30], [33], [34]. Using MSHD leads to a tractable analysis at the expense of sub-optimal selection of LDPC code rates, but such expense is negligible if Gray Labelling [35] is adopted and the number of slices is at least 5 [30], [34], as is the case in this work. [5] The Gaussian post-selection technique at Bob’s side effectively improves the channel conditions between Alice and Bob [38] so that SR is preferred for reconciliation (rather than multidimensional reconciliation). [6] At this step, sources of bit errors include the channel transmission, heterodyne detection, and quantisation.

Optimised Multithreaded CV-QKD Reconciliation for Global Quantum Networks: System Overview

This paper is available on arxiv under CC 4.0 license. Authors: (1) Mattia Atzeni, EPFL, Switzerland and mattia.atzeni@outlook.it; (2) Mrinmaya Sachan, ETH Zurich, Switzerland; (3) Andreas Loukas, Prescient Design, Switzerland. Table of Links Abstract & Introduction Formalizing the Group-Action Learning Problem Attention Masks for Core Geometry Priors The LATFORMER Architecture Experiments Related Work Conclusion & References A. Additional Details on the Model B. Additional Experiments and Details on the Experimental Setup C. Limitations and Future Work D. Deferred Proofs 4. The LATFORMER Architecture While in principle the problem of inferring group actions from input-output pairs can be solved via search over finite groups, in practice the size of the group for lattice symmetry actions makes this approach unfeasible1 . Moreover, we are interested in learning unknown functions jointly with the transformation, which cannot be solved by searching on the space of group actions. Using a neural agent to search the space of possible actions would be a viable alternative, but this would make the problem non-differentiable and we would need to resort to reinforcement learning methods. In this work, we aim to solve the problem in a differentiable way. Inspired by the observations above, we introduce LATFORMER, which incorporates the insights of Section 3 into a neural architecture. We propose to use gated CNNs to parameterize the masks and we introduce an additional smoothing technique for easier optimization. 4.1. Lattice Mask Experts as Convolutional Networks Attention modules in neural networks usually include an attention mechanism with learnable linear transformations of the inputs2 followed by a feed-forward network (FFN), as in the Transformer encoder layer (Vaswani et al., 2017). To infuse core geometry priors in the attention module, we propose to modulate the attention weights with a mask generated by an additional layer, as shown in Figure 3a. We refer to this layer as Lattice mask expert, as it specializes towards specific transformations of the lattice. To understand the purpose of this layer, it is useful to remember that, by the analysis conducted in Section 3, even if the attention and FFN layers are initialized to the identity function, the mask expert can generate attention masks that produce precise geometric transformations of the input. By Corollary 3.3, we know that each group action on the lattice can be represented by a mask that is a convolution of the identity and we have an analytical expression to calculate the kernels of the convolution. We can leverage this notion to design CNNs that produce attention masks corresponding to specific group actions by following the general formulation: 4.2. Mask Smoothing for Easier Training The framework described so far parameterizes discrete transformations of a lattice in a differentiable manner. Nevertheless, to improve the training of LATFORMER, we found it beneficial to also apply a smoothing operation on the attention masks. Our approach entails defining an adjacency relation between group elements and applying graph convolution with a heat kernel on the corresponding graph. This encourages the optimizer to favor weight updates that change the masks in a smooth manner w.r.t. the geodesic distance implied by the graph. Concretely, we define the neighbors of each element gi on the lattice as those gj = e ◦ gi reachable by an application of a primitive action e, such as translation by a single pixel in one dimension, rotation by 90◦ , and vertical/horizontal reflection. The notion of neighborhood gives rise to a graph whose vertex set is the lattice group and that contains one edge for every pair of neighboring actions. As before, it helps to consider different kinds of transformations separately. For instance, as shown in Figure 4, for 2D rotations the underlying graph is a cycle with 4 elements due to the underlying point group for 4-fold rotations being the cyclic group C4. Performing heat diffusion can be achieved by repeated neighborhood averaging over the cycle and yields a smoothed rotation mask that performs all rotations at the same time (rightmost image in Figure 4). We can extend the same approach to all lattice transformations: for instance, in the case of translation, the underlying graph is a grid and the smoothing operation is akin to convolution with a Gaussian kernel. To train LATFORMER with smoothed masks, we compute two predictions: one with the non-smooth mask predicted by the model and one with a smoothed version of the same mask. The final loss is the sum of two cross-entropy losses calculated separately for the two predictions.

The Abstraction and Reasoning Corpus: The LATFORMER Architecture

This paper is available on arxiv under CC 4.0 license. Authors: (1) Mattia Atzeni, EPFL, Switzerland and mattia.atzeni@outlook.it; (2) Mrinmaya Sachan, ETH Zurich, Switzerland; (3) Andreas Loukas, Prescient Design, Switzerland. Table of Links Abstract & Introduction Formalizing the Group-Action Learning Problem Attention Masks for Core Geometry Priors The LATFORMER Architecture Experiments Related Work Conclusion & References A. Additional Details on the Model B. Additional Experiments and Details on the Experimental Setup C. Limitations and Future Work D. Deferred Proofs Abstract The Abstraction and Reasoning Corpus (ARC) (Chollet, 2019) and its most recent languagecomplete instantiation (LARC) has been postulated as an important step towards general AI. Yet, even state-of-the-art machine learning models struggle to achieve meaningful performance on these problems, falling behind non-learning based approaches. We argue that solving these tasks requires extreme generalization that can only be achieved by proper accounting for core knowledge priors. As a step towards this goal, we focus on geometry priors and introduce LATFORMER, a model that incorporates lattice symmetry priors in attention masks. We show that, for any transformation of the hypercubic lattice, there exists a binary attention mask that implements that group action. Hence, our study motivates a modification to the standard attention mechanism, where attention weights are scaled using soft masks generated by a convolutional network. Experiments on synthetic geometric reasoning show that LATFORMER requires 2 orders of magnitude fewer data than standard attention and transformers. Moreover, our results on ARC and LARC tasks that incorporate geometric priors provide preliminary evidence that these complex datasets do not lie out of the reach of deep learning models. 1. Introduction Infusing inductive biases and knowledge priors in neural networks is regarded as a critical step to improve their sample efficiency (Battaglia et al., 2018; Bengio, 2017; Lake et al., 2017; Lake & Baroni, 2018; Bahdanau et al., 2019). The Core Knowledge priors for human intelligence have been studied extensively in developmental science (Spelke & Kinzler, 2007), following the theory that humans are endowed with a small number of separable systems of core knowledge, so that new flexible skills and belief systems can build on these core foundations. Recent research in artificial intelligence (AI) has postulated the idea that the same priors should be incorporated in AI systems (Chollet, 2019), but it is an open question how to incorporate these priors in neural networks. Following this chain of thought, the Abstraction and Reasoning Corpus (ARC) (Chollet, 2019) was proposed as an AI benchmark built on top of the Core Knowledge priors from developmental science. Chollet (2019) posits that developing a domain-specific approach based on the Core Knowledge priors is a challenging first step and that “solving this specific subproblem is critical to general AI progress”. Further, he argues that ARC “cannot be meaningfully approached by current machine learning techniques, including Deep Learning”. An important category of Core Knowledge priors includes geometry and topology priors. Indeed, significant attention has been devoted to incorporating such priors in deep learning architectures by rendering neural networks invariant (or equivariant) to transformations represented through group actions (Bronstein et al., 2021). However, group-invariant learning helps to build models that systematically ignore specific transformations applied to the input (such as translations or rotations). We take a complementary perspective and aim to help neural networks to learn functions that incorporate geometric transformations of their input (rather than to be invariant to such transformations). In particular, we focus on group actions that belong to the symmetry group of a lattice. These transformations are pervasive in machine learning applications, as basic transformations of sequences, images, and other higher-dimensional regular grids fall in this category. While attention and transformers can in principle learn these kind of group actions, we show that they require a significant amount of training data to do so. To address this sample complexity issue, we introduce LATFORMER, a model that relies on attention masks in order to learn actions belonging to the symmetry group of a lattice, such as translation, rotation, reflection, and scaling, in a differentiable manner. We show that, for any such action, there exists an attention mask such that an untrained self-attention mechanism initialized to the identity function performs that action. We further prove that these attention masks can be expressed as convolutions of the identity, which motivates a modification to the standard attention module where the attention weights are modulated by a mask generated by a convolutional neural network (CNN). Our experiments focus on abstract geometric reasoning and, more specifically, on ARC and its variants, as they are widely regarded as challenging benchmarks for machine learning models (Acquaviva et al., 2021; Chollet, 2019). On these datasets, we aim to reduce the gap between neural networks and hand-engineered search algorithms. To probe the sample efficiency of our method, we compared its ability to learn synthetic geometric transformations against Transformers and attention modules. Then, we annotated ARC tasks based on the knowledge priors they require, and we evaluated LATFORMER on the ARC tasks requiring geometric knowledge priors. Finally, we performed experiments on the more recent Language-complete ARC (LARC) (Acquaviva et al., 2021), which enriches ARC tasks with natural-language descriptions, and we compared our model against strong baselines based on neural program synthesis. Our results provide evidence that LATFORMER can learn geometric transformations with 2 orders of magnitude fewer training data than transformers and attention. We also significantly reduce the gap between neural and classical approaches on ARC, providing the first neural network that reaches good performance on ARC tasks requiring geometric knowledge priors.

The Abstraction and Reasoning Corpus: Abstract & Introduction

This paper is available on arxiv under CC 4.0 license. Authors: (1) Terence Blésin, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (2) Wil Kao, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (3) Anat Siddharth, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (4) Alaina Attanasio, OxideMEMS lab, Purdue University; (5) Hao Tian, OxideMEMS lab, Purdue University; (6) Sunil A. Bhave, OxideMEMS lab, Purdue University; (7) Tobias J. Kippenberg, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL). Table of Links Introduction Results A. Physics and design B. Device characterization C. Bidirectional microwave-optical transduction D. Pulsed transduction Discussion Acknowledgments Appendix A: Theory Appendix B: Fabrication process flow Appendix C: Optical characterization Appendix D: Characterization of acoustic resonances Appendix E: Characterization of bidirectional transduction Appendix F: Optimization of optical extraction efficiency Appendix C: Optical characterization 1. Resonance linewidths Fitting the hybridized transmission spectrum in Fig. 2c to the coupled mode theory presented in Appendix A 1 and in Ref. [2] yields κr/(2π) = 154 MHz and κl/(2π) = 190 MHz, with κex,l/(2π) = κex,r/(2π) = 60 MHz. 2. Hybridization of optical resonances via integrated thermo-optic heaters For the purpose of rapid characterization of the photonic molecules at room temperature, the bottom electrode of the piezoelectric actuator also serves as an integrated heater to control the relative detuning between the microrings by thermo-optic effect. As seen in Fig. 2a, we pattern Mo to make three electrical connections to the bottom electrode. The top and bottom connections are connected to ground, whereas the central connection is biased to a constant voltage. The finite resistance of Mo at room temperature leads to Joule heating as current flows through the bottom electrode, modifying the refractive index of the Si3N4 waveguide buried directly underneath. 3. Thermal response characterization through cavity-enhanced photothermal spectroscopy. present transducer due to optical pump heating. Specifically, thermally induced refractive index change causes a shift in resonance frequency of the Si3N4 micro-ring, which is detected using cavity-enhanced pump-probe spectroscopy [12]. The intensity-modulated pump addresses a TE resonance, while the probe measures the resulting side-of-fringe modulation of another TE mode. To ascertain the physical mechanism of each cross phase modulation (XPM) process, we sweep the pump modulation frequency to leverage the separation of time scales. Seen in Supplementary Fig. 2, the response exhibits three plateaus. We associate the process at modulation frequencies ≳ 1 MHz with Kerr-induced XPM. The two slower processes at ∼ 100 kHz and ∼ 1 kHz are characteristic of photothermal XPM in quasi-freestanding microresonators such as spheres [13] and toroids [14]. Here, the former “local” time scale can be attributed to thermalization of the mode volume with the acoustic resonator, while the thermalization of the suspended structure with the rest of the chip constitutes the latter “global” time scale. Therefore, it is expected that for future cryogenic operations, employing a pulse-on time (τon) shorter than the local time scale to gate the optical pump will significantly reduce thermal occupancy in the cladding acoustic mode. The pulse repetition rate (frep) and hence pulse-off time can then be chosen based on the available cooling power.

Bidirectional Microwave-optical Transduction Based on Integration: Appendix C: Optical

Let's learn about via these 60 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Conversational Ai /Learn Repo The automated chatbot that most websites nowadays have as customer service; does basic answer and response tasks. 1. The Social Impact That Deepfake Porn Has On Its Victims Recently, Deeptrance revealed that 15,000 deepfake videos were uploaded daily. 96% were clear pornography of morphed celeb faces onto adult entertainers. 2. Take ChatGPT With You: Introducing Ariana, the ChatGPT Assistant That Lives in WhatsApp Meet Ariana, a ChatGPT AI assistant living in WhatsApp, providing instant answers, translations, and idea generation. 3. Welcome to the World of Artificial Intelligence Programming Discover everything you need to know about artificial intelligence programming 4. Artificial Intelligence That Doesn’t Annoy Customers How to make virtual assistant (conversational AI chatbot), that doesn't get users angry 5. Interview with CEO & Co-founder of Symbl.AI Surbhi Rathore is the cofounder of Symbl, a programmable platform enabling developers to build next-generation AI-powered communication experiences 6. How to Build a Conversational AI bot Using Blenderbot How to build a conversational Bot with the Blenderbot model, an Open Source Language Generation Model made by Facebook AI so that you can have your own Siri. 7. Why is the Education Industry Opting for Conversational AI? As we all know that education is very much important in ones life. Conversational AI can help to improve the  teacher and student interaction. 8. How the Conversational AI Analytics will transform the business? 9. 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A recent survey of 5,700 Harvard Business School alumni found that even in the elite group, 52% believe that the number of employees in general companies will decrease in the three years from now. 13. ChatGPT is a Huge Win for Conversational AI Companies (and Their Customers) — Here's Why Find out why we believe ChatGPT is a massive win for conversational AI companies and how this incredible language model can be harnessed to enhance NLU. 14. Improving AI-Assisted Conversation With Zero-Shot Learning Assimilating zero-shot learning with machine learning networks holds many advantages for developers across a wide range of fields. 15. [Infographic] The State of Conversational AI in 2020 Conversational AI was always poised to take off in 2020. In fact, Gartner predicted that 80% of businesses would implement some sort of conversational interface by the end of this year. With the emergence of COVID-19 came compounded growth for the category - and I wanted to capture just how far we’ve come. So for the conversationally curious out there, I created this infographic that offers a clear depiction of where conversational AI stands at this very moment in time. [16. MAKE CONVERSATIONAL AI WORK at JUNCTION 2020 HACKATHON](https://hackernoon.com/make-conversational-ai-work-at-junction-2020-hackathon-pts3egp) 17. The Power of AI in Recruiting: How The Business World is Changing for Good AI drastically improved the world around us in many different ways. However, there are still many notable concerns with regard to overall impact on employment. 18. The Conversational AI Arms Race Has Begun The grown-ups have entered the room. Google and Microsoft have officially entered the conversational AI arms race in search. 19. Voice is the Safest and Most Accurate for Emotion AI Analysis Voice is one of several unique, innate, and immutable biometric identifiers. Other identifiers include retinal scans, iris scans and facial geometry scans. As technology evolves, so does the public’s concern regarding issues of privacy. Consider, for example, the turmoil DNA evidence introduced to our legal system in the late 1990’s, or, the controversial introduction of fingerprint evidence in the 1910 murder trial of Clarence Hiller in Chicago, Illinois. 20. How To Build an AI-Powered Financial Assistant App Find answers to the most common questions related to the development of a financial management application in 2022. 21. Conversational AI in Healthcare: 2 Key Use Cases If there is a silver lining somewhere with the COVID-19 pandemic engulfing the globe, it’s that we have been forced to introspect on the healthcare sector, both public and private, and whether it has the tools it needs – not only to deal with such crises, but to ensure the best possible healthcare outcomes even under normal circumstances. 22. 7 AI-powered Chatbots If you’re a millennial, you’ll know SmarterChild, the first-ever instant messaging bot with natural language comprehension ability. It was developed in 2000 and demonstrated exceptional wit, which most of today’s bot cannot. SmarterChild used to chat with about 2,50,000 humans every day with funny, sad, and sarcastic emotions. Today, we’ve traveled a distance with technologies like AI, ML, NLP, etc. and bots like Xiaocle have passed Turing tests of 10 minutes (i.e. users couldn’t identify that they’re talking to a bot for about 10 minutes). 23. Discussing The Applications of AI Chatbots in Healthcare at the World Economic Forum The World Economic Forum is mostly known for its highly anticipated annual meeting in Davos, Switzerland — a scene of global intrigue and a convergence point for the planet’s most influential politicians, investors, activists, CEOs, and economists. 24. ChatGPT for Office Politics: When is it Ethical? Have you ever stopped to consider the power of language models like ChatGPT and how they can impact our beliefs and opinions? 25. Drive Success with Conversational Marketing: The Feedback-Oriented Approach Conversational marketing can be a solution to achieve faster response times. In this guide, we discuss how to develop effective chatbot applications. 26. What Patients Are Asking Our COVID-19 Virtual Assistant According to a recent Pew Research Center poll, in just one week (March 16–24), the number of Americans who view the coronavirus as a major threat to public health spiked by nearly 20%, from 47% to 66% — a figure that is growing exponentially. 27. An Intro to MedPaLM: ChatGPT's Healthcare-Focused "Cousin" ChatGPT for healthcare? Learn everything you need to know about MedPaLM, a new LLM developed by Google specifically for medical and clinical applications. 28. 7 Ways Banks Can Use Conversational AI to Stay Relevant In Today's FinTech Era Learn how banks can leverage conversational AI and stay relevant in the age of FinTech including the rise of contactless payment methods, omnichannel, and more. 29. How We Built a Voice-Activated Lovecraftian Adventure Game In this article, we will discuss why we decided to create a voice-controlled game, what was the hardest part, and why we decided to open up about the money we make in the emerging market of conversational AI. 30. [Reviewed] 6 Conversational Feedback Tools Chatbots for surveys, surveybots, conversational surveys, feedback chatbots, conversational survey tools, AI survey tools, chatbot questionnaires. The list of names goes on and on. But how do they compare and which one is the best performer? 31. The Quest for Better Music Recommendations Through ChatGPT Prompt Engineering Prompt Engineering leads to an Open Source Obsidian Plugin to help researchers, an imagination-powered music recommendation engine, and some solid playlists. 32. Building A Chatbot On Your Own Might Not Make As Much Sense As You Think Over the past decade plus, chatbots have dominated the conversation (no pun intended) when it comes to digital engagement. You’ve undoubtedly had experiences interacting with them, some helpful while others underwhelming, and perhaps even fiddled around with building one on your own. 33. Amazon QuickSight Q Review Vs. NLSQL: A Comparison New Amazon QuickSight Q AI-powered product for Data Analytics Review and comparison with NLSQL competitors NLP to SQL API software (including pricing details) 34. How to Build Basic Chatbot Without Coding and Deploy to Websites Build best automated AI chat bot using Google Dialog flow 35. 7 Effective Ways to Improve Your Content Marketing with AI Tools Artificial Intelligence (AI) has numerous business use cases and can be applied to customer service, sales, lead generation, and marketing. 36. Understanding Conversational AI: As Chat Enabled Customer Service Technological innovations are necessary to cope up with the customer demands. Customers nowadays use multiple channels to access the services from a business. Thus, they expect multiple channel customer service from companies. 37. Math GPT: Can AI Help Solve Unified Theory ? What if we trained AI to complete equations instead of images of Cats? 38. "AI Can’t 'Think” Like Us Independently," - says Machine Learning Engineer Mani Sarkar In our new blog series, we’re interviewing data scientists and machine learning engineers about their career paths, areas of interest and thoughts on the future of AI. We kick off this week with a 20-year veteran and jack-of-all-trades when it comes to machine learning and data science: Mani Sarkar. Mani is a strategic machine learning engineer based in London, UK, who believes in getting beyond the theoretical and applying AI to real-world problems. [39. Generative AI is Coming for Publishing, But It's Not as Clear-Cut as It Looks](https://hackernoon.com/generative-ai-is-coming-for-publishing-but-its-not-as-clear-cut-as-it-looks) A look at the future of generative AI. 40. Can Artificial Intelligence Claim To Be the Future of Account-Based Marketing? To be honest, I hate phrases like “harness the power of AI” or “use AI to revolutionize your sales”. 41. How to Improve Privacy in Conversational AI Artificial intelligence is a great tool that assists you with fostering your business on the internet. 42. Introducing Conversation.ai This post has been authored by Aakrit Vaish, Co-Founder & CEO of Haptik 43. ChatGPT Writes a Story With Me I started with a story prompt to ChatGPT and then we kept going, I like it. 44. Where are the Chatbots and What is the Progress and Limitation of Virtual Assistants [2020 Edition] Cat's out of the bag: it may have been a long while since you’ve last conversed with a human customer service agent. More and more companies have turned to digital assistants to address the needs of customer inquiries. With messaging being the preferred method of communication, chatbots have taken over online facing services, due to their simplicity, affordability, and accuracy. 45. Data and Analytics Predictions for 2020 [A Top 5 List] It would be no exaggeration to say that the capacity of technology to advance itself is proceeding at a faster rate than our ability to process these changes all at the same time. This is both amazing and alarming in the same breath. 46. Conversational AI is Changing the Way We Interact With Data Large language models like ChatGPT are making it easier to manage data. Akkio has come up with an LLM-based tool to manage tabular data using conversational AI. 47. AWS Chatbot Magic: Building Conversational AI with AWS Services AWS offers a range of services that can be used for chatbot development and deployment. The steps outlined in this article show you how. 48. Why Chatbots Are The Future Of Customer Retention? Customer retention has become a genuinely challenging task for entrepreneurs. In the midst of all this, AI-assisted Chatbots are the real game changers. 49. Can Conversational AI Become The Face of Tomorrow's Business? In recent times, conversational AI has become a tech buzzword in the global market. However, the aggressively growing popularity of AI is leaving many individuals in a great dilemma of whether they should be fear of losing jobs in the future or get excited to leverage the latest edition of the AI-driven applications in businesses. 50. How to Streamline the Landlord-Tenant Relationship Through Conversational AI According to Mashivor, 50% of tenants move out because they are not happy with their landlord. The landlord-tenant relationship is more often than not a contentious one. It features two sides with similar intentions but entirely different priorities. Both parties are interested in peaceful, fluid, and uneventful correspondence and both are wary of being cheated, ill-treated, and misinformed. 51. Conversational AI for President: The Covid-19 Chatbot Use Case Our interactions with government agencies hardly ever find their way into our dinner-table conversations, yet they play a huge role in our everyday lives. 52. LSTM Based Word Detectors This article aims to provide the basics of LSTMs (Long Short Term Memory) and implements a word detector using the architecture. 53. Why Are We Training AI like Dogs Instead of Humans? The fundamental problem of the modern AI is that it tries to create a sophisticated trained dog. This approach is a dead end and needs to be drastically changed 54. Building a Conversational AI Chatbot With AWS Lambda Function and Amazon EFS Amazon announced the general availability of AWS Lambda support for Amazon Elastic File System. Amazon EFS is a fully managed, elastic, shared file system and designed to be consumed by other AWS services. 55. How Conversational AI Tool Can Help You Attract Real Estate Buyers And Residents Step One: Prospecting 56. A Comparison of GPT-3 and Existing Conversational AI Solutions Earlier this year, Elon Musk-backed artificial intelligence laboratory, OpenAI, released its latest, much anticipated autoregressive language model, the Generative Pre-trained Transformer 3 (GPT-3). Emerging to much fanfare and slated as the usherer of a new age of artificial intelligence, the number of articles, blog posts, and news pieces about this language model, perhaps match only the number of parameters the GPT-3 learned; 175 billion (Ok, this may be an exaggeration, but you get my point). 57. The Robots are Coming! - An Interview with Ilan Kasan, CEO at Exceed AI This article talks about automation and artificial intelligence and how it is impacting the sales industry and how the future will look in the age of AI. 58. What We Can Learn From Great Examples of Conversational User Interface Top-notch CUI’s offer a more human-like conversation. This helps in bridging the gap between physical and online conversations, and these brands do it right. 59. New Weapon in the Fight Against COVID-19: Digital Health Passports *Note: This article first appeared in the Los Angeles Tribune by Hyro's Head of Marketing Aaron Bours 60. What Is Conversational AI: Principles and Examples In this article, we will take the time to explain what conversational AI is: principles and examples to have a better idea of ​​how you can implement it. Thank you for checking out the 60 most read stories about Conversational Ai on HackerNoon. to find the most read stories about any technology. 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