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Twin-field quantum key distribution (QKD) across an 830-km fibre

phys.org | 8 min read | listed 2 hours ago in reddit/singularity/Twin field quantum key distribution (QKD) across an 830-km fibre

By using quantum key distribution (QKD), quantum cryptographers can share information via theoretic secure keys between remote peers through physics-based protocols. The laws of quantum physics dictate that photons carrying signals cannot be amplified or relayed through classical optical methods to maintain quantum security. The resulting transmission loss of the channel can limit its achievable distance to form a huge barrier to build large-scale quantum secure networks. In a new report now published in Nature Photonics, Shuang Wang and a research team in quantum information, cryptology and quantum physics in China developed an experimental ...

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Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins | PNAS

www.pnas.org | 2 min read | listed 4 hours ago in European Media Monitor - Deep Learning

We present a deep learning-based predictor of protein tertiary structure that uses only a multiple sequence alignment (MSA) as input. To date, most emphasis has been on the accuracy of such deep learning methods, but here we show that accurate structure prediction is also possible in very short timeframes (a few hundred milliseconds). In our method, the backbone coordinates of the target protein are output directly from the neural network, which makes the predictor extremely fast. As a demonstration, we generated over 1.3 million models of uncharacterized proteins in the BFD, a large sequence database including many metagenomic sequences. Our...

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Prediction and recommendation by machine learning through repetitive internal validation for hepatic veno-occlusive disease/sinusoidal obstruction syndrome and early death after allogeneic hematopoietic cell transplantation | Bone Marrow Transplantation

www.nature.com | 6 min read | listed 5 hours ago in European Media Monitor - Machine Learning
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Worst-Case Topological Entropy and Minimal Data Rate for State Estimation of Switched Linear Systems | February 2022 | Communications of the ACM

cacm.acm.org | 31 min read | listed 7 hours ago in Communications of the ACM: Artificial Intelligence

In this paper, we study the problem of estimating the state of a switched linear system when the observation of the system is subject to communication constraints.

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[1810.09050] A Comparison of Five Multiple Instance Learning Pooling Functions for Sound Event Detection with Weak Labeling

arxiv.org | 1 min read | listed 9 hours ago in reddit/tensorflow/Attention pooling layer in Keras

Sound event detection (SED) entails two subtasks: recognizing what types of sound events are present in an audio stream (audio tagging), and pinpointing their onset and offset times (localization). In the popular multiple instance learning (MIL) framework for SED with weak labeling, an important component is the pooling function. This paper compares five types of pooling functions both theoretically and experimentally, with special focus on their performance of localization. Although the attention pooling function is currently receiving the most attention, we find the linear softmax pooling function to perform the best among the five. Using t...

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[2201.07425] WebUAV-3M: A Benchmark Unveiling the Power of Million-Scale Deep UAV Tracking

arxiv.org | 1 min read | listed 11 hours ago in Import AI 281: China does more surveillance research than US and Europe; Google reveals its text model LaMDA; Microsoft improves MoEs

In this work, we contribute a new million-scale Unmanned Aerial Vehicle (UAV) tracking benchmark, called WebUAV-3M. Firstly, we collect 4,485 videos with more than 3M frames from the Internet. Then, an efficient and scalable Semi-Automatic Target Annotation (SATA) pipeline is devised to label the tremendous WebUAV-3M in every frame. To the best of our knowledge, the densely bounding box annotated WebUAV-3M is by far the largest public UAV tracking benchmark. We expect to pave the way for the follow-up study in the UAV tracking by establishing a million-scale annotated benchmark covering a wide range of target categories. Moreover, considering...

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[2201.05596] DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale

arxiv.org | 1 min read | listed 11 hours ago in Import AI 281: China does more surveillance research than US and Europe; Google reveals its text model LaMDA; Microsoft improves MoEs

As the training of giant dense models hits the boundary on the availability and capability of the hardware resources today, Mixture-of-Experts (MoE) models become one of the most promising model architectures due to their significant training cost reduction compared to a quality-equivalent dense model. Its training cost saving is demonstrated from encoder-decoder models (prior works) to a 5x saving for auto-aggressive language models (this work along with parallel explorations). However, due to the much larger model size and unique architecture, how to provide fast MoE model inference remains challenging and unsolved, limiting its practical u...

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[2201.08239] LaMDA: Language Models for Dialog Applications

arxiv.org | 2 min read | listed 11 hours ago in Import AI 281: China does more surveillance research than US and Europe; Google reveals its text model LaMDA; Microsoft improves MoEs

We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters and are pre-trained on 1.56T words of public dialog data and web text. While model scaling alone can improve quality, it shows less improvements on safety and factual grounding. We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding. The first challenge, safety, involves ensuring that the model's responses are co...

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External validation of deep learning-based bone-age software: a preliminary study with real world data | Scientific Reports

www.nature.com | 4 min read | listed 15 hours ago in European Media Monitor - Artificial Intelligence
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Zipf's law - Wikipedia

en.wikipedia.org | 13 min read | listed 17 hours ago in reddit/LanguageTechnology/How to test statistical significance on text data?
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Connectivity and variability of related cognitive subregions lead to different stages of progression toward Alzheimer's disease: Heliyon

www.cell.com | 18 min read | listed 18 hours ago in European Media Monitor - Deep Learning

Multimodal Cerebral Cortical Measures; Multimodal Deep Learning; Multi-group Classification; Alzheimer`s Disease; Mild Cognitive Impairment

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Genome sequencing and RNA sequencing of urinary cells reveal an intronic FBN1 variant causing aberrant splicing | Journal of Human Genetics

www.nature.com | 2 min read | listed 22 hours ago in European Media Monitor - Deep Learning
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Worldline numerics applied to custom Casimir geometry generates unanticipated intersection with Alcubierre warp metric | SpringerLink

link.springer.com | 9 min read | listed 1 day ago in reddit/transhumanism/What is the next big progression after Transhumanism?

While conducting analysis related to a DARPA-funded project to evaluate possible structure of the energy density present in a Casimir cavity as predicted by the dynamic vacuum model, a micro/nano-scale structure has been discovered that predicts negative energy density distribution that closely matches requirements for the Alcubierre metric. The simplest notional geometry being analyzed as part of the DARPA-funded work consists of a standard parallel plate Casimir cavity equipped with pillars arrayed along the cavity mid-plane with the purpose of detecting a transient electric field arising from vacuum polarization conjectured to occur along ...

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Reliable multilevel memristive neuromorphic devices based on amorphous matrix via quasi-1D filament confinement and buffer layer

www.science.org | 11 min read | listed 1 day ago in reddit/singularity/The structural approach enables reliable multilevel memristor devices for neuromorphic computing system development

The structural approach enables reliable multilevel memristor devices for neuromorphic computing system development.

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Does BERT need domain adaptation for clinical negation detection? | Journal of the American Medical Informatics Association | Oxford Academic

academic.oup.com | 31 min read | listed 1 day ago in reddit/LanguageTechnology/First foray into NLP at work, need feedback on the workflow.

AbstractIntroduction. Classifying whether concepts in an unstructured clinical text are negated is an important unsolved task. New domain adaptation and transfe

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ESOU 2022: State-of-the-art Lecture: Molecular Advances in Risk Stratification of Localized Prostate Cancer

www.urotoday.com | 6 min read | listed 1 day ago in Bing News
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Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents

wenlong.page | 1 min read | listed 2 days ago in Synced Global AI Weekly — Synced Global AI Weekly 2022.1.22
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Long Short-Term Memory | Neural Computation

dl.acm.org | 1 min read | listed 2 days ago in reddit/datasets/A dataset of Reber Grammar Sequences
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[2110.00990] Hierarchical Kinematic Probability Distributions for 3D Human Shape and Pose Estimation from Images in the Wild

arxiv.org | 1 min read | listed 2 days ago in reddit/MachineLearning/[R] Hierarchical Kinematic Probability Distributions for 3D Human Shape and Pose Estimation from Images in the Wild

This paper addresses the problem of 3D human body shape and pose estimation from an RGB image. This is often an ill-posed problem, since multiple plausible 3D bodies may match the visual evidence present in the input - particularly when the subject is occluded. Thus, it is desirable to estimate a distribution over 3D body shape and pose conditioned on the input image instead of a single 3D reconstruction. We train a deep neural network to estimate a hierarchical matrix-Fisher distribution over relative 3D joint rotation matrices (i.e. body pose), which exploits the human body's kinematic tree structure, as well as a Gaussian distribution over...

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Reinforcement Learning in Games | SpringerLink

link.springer.com | 1 min read | listed 2 days ago in reddit/ArtificialInteligence/Help! Understanding AI using Games

Reinforcement learning and games have a long and mutually beneficial common history. From one side, games are rich and challenging domains for testing reinforcement learning algorithms. From the...

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Beyond LDA: State-of-the-art Topic Models With BigARTM - Topic Modeling for Text with BigARTM

databricks.com | 11 min read | listed 3 days ago in Databricks

Learn more about the open-source text modeling project BigARTM and how it surpasses other techniques, such as LDA, for NLP and other semantic use cases.

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Why Spectral Normalization Stabilizes GANs: Analysis and Improvements – Machine Learning Blog | ML@CMU | Carnegie Mellon University

blog.ml.cmu.edu | 7 min read | listed 3 days ago in Blog | Machine Learning | Carnegie Mellon University

Figure 1: Training instability is one of the biggest challenges in training GANs. Despite the existence of successful heuristics like Spectral Normalization (SN) for improving stability, it is poorly-understood why they work. In our research, we theoretically explain why SN stabilizes GAN training.

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[1606.05830] Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

arxiv.org | 1 min read | listed 3 days ago in reddit/robotics/Advances in SLAM since 2016

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for m...

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[2110.06399] Dynamic Inference with Neural Interpreters

arxiv.org | 1 min read | listed 3 days ago in reddit/MachineLearning/[D] First Author Interview - Dynamic Inference with Neural Interpreters (Video Walkthrough & Author Interview)

Modern neural network architectures can leverage large amounts of data to generalize well within the training distribution. However, they are less capable of systematic generalization to data drawn from unseen but related distributions, a feat that is hypothesized to require compositional reasoning and reuse of knowledge. In this work, we present Neural Interpreters, an architecture that factorizes inference in a self-attention network as a system of modules, which we call \emph{functions}. Inputs to the model are routed through a sequence of functions in a way that is end-to-end learned. The proposed architecture can flexibly compose computa...

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An energy-based perspective on learning observation models - ΑΙhub

aihub.org | 9 min read | listed 3 days ago in European Media Monitor - Machine Learning
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