Miracle-dz/UniFAR-FTUs huggingface dataset:
Existence of minimizers for the SDRI model

The SDRI model introduced in [Sh. Kholmatov, P. Piovano, Arch. Rational Mech. Anal. , in press] in the framework of the theory on Stress-Driven Rearrangement Instabilities for the morphology of crystalline material under stress is considered. The existence of solutions is established in dimension two in the absence of graph-like assumptions and of the restriction to a finite number $m$ of connected components for the free boundary of the region occupied by the crystalline material, thus extending previous available results for epitaxially-strained thin films and material cavities. Due to the lack of compactness and lower semicontinuity even for sequences of $m$-minimizers, i.e., energy minimizers among configurations with a fixed number $m$ of connected boundary-components, the minimizing candidate of the SDRI model is directly constructed. By means of uniform density estimates for the local decay of the energy at the $m$-minimizers' boundaries, such candidate is then shown to be a minimizer also in view of the convergence of the energy at $m$-minimizers to the energy infimum as $m\to\infty$. Finally, regularity properties for the morphology of any minimizer are deduced.
Matter over mind: causal inferences from neuroimaging data

Increased efforts in neuroscience try to understand mental disorders as brain disorders. In the present study we investigate how common a neuroreductionist inclination is in the popular mind. Identically graphed, simulated data of mind-brain correlations were shown in three contexts with presumably different presumptions about causality. 738 highly-educated laymen rated plausibility of causality attribution from brain to mind and from mind to brain for correlations between brain structural properties and mental phenomena in the context of commonly perceived brain-lesion induced behavior (aphasia), behavior-induced training effects (piano playing), and a newly described mental disorder (Body Integrity Dysphoria). The findings reveal the expected context-dependent modulation, but also a general tendency to more readily attribute causal inference from brain to mind than vice versa. We argue that this tendency has important consequences, especially in the context of Body Integrity Dysphoria, a condition where little is known about underlying causal mechanisms.
Large-Scale Adversarial Training for Vision-and-Language Representation Learning

We present VILLA, the first known effort on large-scale adversarial training for vision-and-language (V+L) representation learning. VILLA consists of two training stages: (i) task-agnostic adversarial pre-training; followed by (ii) task-specific adversarial finetuning. Instead of adding adversarial perturbations on image pixels and textual tokens, we propose to perform adversarial training in the embedding space of each modality. To enable large-scale training, we adopt the "free" adversarial training strategy, and combine it with KL-divergence-based regularization to promote higher invariance in the embedding space. We apply VILLA to current best-performing V+L models, and achieve new state of the art on a wide range of tasks, including Visual Question Answering, Visual Commonsense Reasoning, Image-Text Retrieval, Referring Expression Comprehension, Visual Entailment, and NLVR2.
Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward

It has long been recognized that multi-agent reinforcement learning (MARL) faces significant scalability issues due to the fact that the size of the state and action spaces are exponentially large in the number of agents. In this paper, we identify a rich class of networked MARL problems where the model exhibits a local dependence structure that allows it to be solved in a scalable manner. Specifically, we propose a Scalable Actor-Critic (SAC) method that can learn a near optimal localized policy for optimizing the average reward with complexity scaling with the state-action space size of local neighborhoods, as opposed to the entire network. Our result centers around identifying and exploiting an exponential decay property that ensures the effect of agents on each other decays exponentially fast in their graph distance.
VLSI Implementation of TDC Architectures Used in PET Imaging Systems

Positron emission tomography (PET) is a medical imaging method based on the measurement of concentrations of positron-emitting radionuclides in a living body. In the PET imaging system, glucose is labeled with a positron-emitting radionuclide and injected intravenously. Then, the positrons move through the tissue and collide with the electrons of the cells in which they interact. As a result of this interaction, two gamma rays are emitted in the opposite direction. Gama rays emitted from cancerous tissue that has retained radioactive glucose are detected through ring-shaped detectors. And the detected signals are converted into an electrical response. Subsequently, these responses are sampled with electronic circuits and recorded as histogram matrix to generate the image set. The gamma rays may not reach the detectors located in the opposite position in equal time. In PETs having TOF characteristics, it is aimed to obtain better positioning information by a method based on the principle of measuring the difference between the reach time of the two photons to detectors. The measurement of the flight time is carried out with TDC structures. The measurement of this time difference at the ps level is directly related to the spatial resolution of the PET system. In this study, 45 nm CMOS VLSI simulations of TDC structures that have various architectural approaches were performed for use in PET systems. With the designed TDC architectures, two gamma photons time reach to detectors have been simulated and the time difference has been successfully digitized. In addition, various performance metrics such as input and output voltages, time resolutions, measurement ranges, and power analysis of TDC architectures have been determined. Proposed Vernier oscillator-based TDC architecture has been reached 25 ps time resolution with a low power consumption of 1.62681 mW at 1V supply voltage.
Latent Transformations for Discrete-Data Normalising Flows

Normalising flows (NFs) for discrete data are challenging because parameterising bijective transformations of discrete variables requires predicting discrete/integer parameters. Having a neural network architecture predict discrete parameters takes a non-differentiable activation function (eg, the step function) which precludes gradient-based learning. To circumvent this non-differentiability, previous work has employed biased proxy gradients, such as the straight-through estimator. We present an unbiased alternative where rather than deterministically parameterising one transformation, we predict a distribution over latent transformations. With stochastic transformations, the marginal likelihood of the data is differentiable and gradient-based learning is possible via score function estimation. To test the viability of discrete-data NFs we investigate performance on binary MNIST. We observe great challenges with both deterministic proxy gradients and unbiased score function estimation. Whereas the former often fails to learn even a shallow transformation, the variance of the latter could not be sufficiently controlled to admit deeper NFs.
Chow groups and $L$-derivatives of automorphic motives for unitary groups

In this article, we study the Chow group of the motive associated to a tempered global $L$-packet $\pi$ of unitary groups of even rank with respect to a CM extension, whose global root number is $-1$. We show that, under some restrictions on the ramification of $\pi$, if the central derivative $L'(1/2,\pi)$ is nonvanishing, then the $\pi$-nearly isotypic localization of the Chow group of a certain unitary Shimura variety over its reflex field does not vanish. This proves part of the Beilinson--Bloch conjecture for Chow groups and $L$-functions, which generalizes the Birch and Swinnerton-Dyer conjecture. Moreover, assuming the modularity of Kudla's generating functions of special cycles, we explicitly construct elements in a certain $\pi$-nearly isotypic subspace of the Chow group by arithmetic theta lifting, and compute their heights in terms of the central derivative $L'(1/2,\pi)$ and local doubling zeta integrals. This confirms the conjectural arithmetic inner product formula proposed by one of us, which generalizes the Gross--Zagier formula to higher dimensional motives.
GENETIC CHANGES IN MEAT-TYPE CHICKENS IN THE LAST TWENTY YEARS

Three experimental strains and two modern commercial stocks of meat-type chickens were compared in 1978. One experimental strain, K, an unselected control, represented broilers of 20 yr ago. A second experimental strain, A, selected for high broiler weight and a third, D, selected for high broiler and low adult weights had been derived from strain K and represented experimental strains genetically improved by artificial selection. All stocks were hatched and reared simultaneously using modern industrial practices. At 47 days, 70 birds of each sex and stock were slaughtered and the carcasses chemically analyzed. Also, 25 hens of each of strains A, D and K were slaughtered when 68 wk old and abdominal fat and carcass weight data were analyzed. The selection for higher broiler weight in strain A increased both broiler and adult body weights more and percent carcass fat less than the selection for high broiler and low adult weight in strain D. Relative to strain A, carcass weight of strain D was 10% lower in .
COVID-19-CT-CXR: A Freely Accessible and Weakly Labeled Chest X-Ray and CT Image Collection on COVID-19 From Biomedical Literature

The latest threat to global health is the COVID-19 outbreak. Although there exist large datasets of chest X-rays (CXR) and computed tomography (CT) scans, few COVID-19 image collections are currently available due to patient privacy. At the same time, there is a rapid growth of COVID-19-relevant articles in the biomedical literature, including those that report findings on radiographs. Here, we present COVID-19-CT-CXR, a public database of COVID-19 CXR and CT images, which are automatically extracted from COVID-19-relevant articles from the PubMed Central Open Access (PMC-OA) Subset. We extracted figures, associated captions, and relevant figure descriptions in the article and separated compound figures into subfigures. Because a large portion of figures in COVID-19 articles are not CXR or CT, we designed a deep-learning model to distinguish them from other figure types and to classify them accordingly. The final database includes 1,327 CT and 263 CXR images (as of May 9, 2020) with their relevant text. To demonstrate the utility of COVID-19-CT-CXR, we conducted four case studies. (1) We show that COVID-19-CT-CXR, when used as additional training data, is able to contribute to improved deep-learning (DL) performance for the classification of COVID-19 and non-COVID-19 CT. (2) We collected CT images of influenza, another common infectious respiratory illness that may present similarly to COVID-19, and fine-tuned a baseline deep neural network to distinguish a diagnosis of COVID-19, influenza, or normal or other types of diseases on CT. (3) We fine-tuned an unsupervised one-class classifier from non-COVID-19 CXR and performed anomaly detection to detect COVID-19 CXR. (4) From text-mined captions and figure descriptions, we compared 15 clinical symptoms and 20 clinical findings of COVID-19 versus those of influenza to demonstrate the disease differences in the scientific publications. Our database is unique, as the figures are retrieved along with relevant text with fine-grained descriptions, and it can be extended easily in the future. We believe that our work is complementary to existing resources and hope that it will contribute to medical image analysis of the COVID-19 pandemic. The dataset, code, and DL models are publicly available at https://github.com/ncbi-nlp/COVID-19-CT-CXR.
Multivalent actin-binding proteins augment the variety of morphologies in actomyosin networks

We study the role of multivalent actin-binding proteins in remodeling actomyosin networks, using a numerical model of semiflexible filaments. We find that the valency of actin-binding proteins greatly affects the organization of actomyosin networks. The rich variety of emergent architectures and morphologies inspired us to develop order parameters based on network theory, to quantitatively characterize the self-assembled organization in the simulated network. First, actin-binding proteins with a valency greater than two promote filament bundles and large filament clusters to a much greater extent than bivalent multilinkers. Second, active myosin-like motor proteins promote rich phases of actomyosin, as characterized by network theory. Lastly, we find that both high content of multilinkers and active motors are necessary to the reduction of node degree correlation in the network of filaments. Our work provides a framework to describe the dendritic structures of actomyosin networks mediated by ABP with the ability of binding multiple filaments, such as Ca2+/Calmodulin-dependent Kinase II (CaMKII). We expect this work to motivate future directions to embrace network theory as a tool to analyze experiments as well as to establish a quantitative understanding of the role of dendritic spine growth in synaptic plasticity.
Exploring Weaknesses of VQA Models through Attribution Driven Insights

Deep Neural Networks have been successfully used for the task of Visual Question Answering for the past few years owing to the availability of relevant large scale datasets. However these datasets are created in artificial settings and rarely reflect the real world scenario. Recent research effectively applies these VQA models for answering visual questions for the blind. Despite achieving high accuracy these models appear to be susceptible to variation in input questions.We analyze popular VQA models through the lens of attribution (input’s influence on predictions) to gain valuable insights. Further, We use these insights to craft adversarial attacks which inflict significant damage to these systems with negligible change in meaning of the input questions. We believe this will enhance development of systems more robust to the possible variations in inputs when deployed to assist the visually impaired.
Learning Continuous-Time Dynamics by Stochastic Differential Networks

Learning continuous-time stochastic dynamics from sparse or irregular observations is a fundamental and essential problem for many real-world applications. However, for a given system whose latent states and observed data are high-dimensional, it is generally impossible to derive a precise continuous-time stochastic process to describe the system behaviors. To solve the above problem, we apply Variational Bayesian method and propose a flexible continuous-time framework named Variational Stochastic Differential Networks (VSDN), which can model high-dimensional nonlinear stochastic dynamics by deep neural networks. VSDN introduces latent states to modulate the estimated distribution and defines two practical methods to model the stochastic dependency between observations and the states. The first variant, which is called VSDN-VAE, incorporates sequential Variational Auto-Encoder (VAE) to efficiently model the distribution of the latent states. The second variant, called VSDN-SDE, further extends the model capacity of VSDN-VAE by learning a set of Stochastic Differential Equations (SDEs) to fully describe the state transitions. Through comprehensive experiments on symbolic MIDI and speech datasets, we show that VSDNs can accurately model the continuous-time dynamics and achieve remarkable performance on challenging tasks, including online prediction and sequence interpolation.
The nuclear pore complex as an entropic gate: theory and simulation

Protein chains of the (FG)$_n$ ($n \simeq$ 300) type cap the cytoplasmatic side of the nucleopore complex, which connects the nucleus to the remainder of an eukaryotic cell. We study the properties of three fundamental polymer models that represent these filaments using Monte Carlo computer simulations. Random walks and the worm like chain model cannot account for the unusual size selectivity of the pore, while a two-dimensional arrangement of intrinsically disordered block copolymers with a high content of $\alpha$-helices is in agreement with the biochemical findings. We predict a linear increase of the free energy barrier of protein transport through the pore with increasing protein diameter, which can be probed experimentally using atomic force microscopy or optical tweezers.
See What I’m Saying? Comparing Intelligent Personal Assistant Use for Native and Non-Native Language Speakers

Limited linguistic coverage for Intelligent Personal Assistants (IPAs) means that many interact in a non-native language. Yet we know little about how IPAs currently support or hinder these users. Through native (L1) and non-native (L2) English speakers interacting with Google Assistant on a smartphone and smart speaker, we aim to understand this more deeply. Interviews revealed that L2 speakers prioritised utterance planning around perceived linguistic limitations, as opposed to L1 speakers prioritising succinctness because of system limitations. L2 speakers see IPAs as insensitive to linguistic needs resulting in failed interaction. L2 speakers clearly preferred using smartphones, as visual feedback supported diagnoses of communication breakdowns whilst allowing time to process query results. Conversely, L1 speakers preferred smart speakers, with audio feedback being seen as sufficient. We discuss the need to tailor the IPA experience for L2 users, emphasising visual feedback whilst reducing the burden of language production.
Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction

Many practical graph problems, such as knowledge graph construction and drug-to-drug interaction, require to handle multi-relational graphs. However, handling real-world multi-label graphs with Graph Neural Networks (GNNs) is often challenging due to their evolving nature, where new entities (nodes) can emerge over time. Moreover, newly emerged entities often have few links, which makes the learning even more difficult. Motivated by this challenge, we introduce a realistic problem of few-shot out-of-graph link prediction, where we not only predict the links between the seen and unseen nodes as in a conventional out-of-knowledge link prediction but also between the unseen nodes, with only few edges per node. We tackle this problem with a novel transductive meta-learning framework which we refer to as Graph Extrapolation Networks (GEN). GEN meta-learns both the node embedding network for inductive inference (seen-to-unseen) and the link prediction network for transductive inference (unseen-to-unseen). For transductive link prediction, we further propose a stochastic embedding layer to model uncertainty in the link prediction between unseen entities. We validate our model on multiple benchmark datasets for knowledge graph link prediction and drug-to-drug interaction prediction. The results show that our model significantly outperforms relevant baselines for out-of-graph link prediction tasks.
Is Broadcasting Seed an Effective Winter Cover Crop Planting Method

Agricultural nutrient runoff to the Chesapeake Bay has been under intense scrutiny for more than a decade in Maryland. One method for capturing these nutrients, especially N, is the use of winter cover crops. This study compared various broadcast cover crop treatments with and without soil incorporation to planting winter cover crop seed with a no-till drill. Seedling emergence and N uptake were the dependent variables measured for two planting dates and seven planting methods. The effects of planting date and planting method for winter wheat (Triticum aestivum L.) and cereal rye (Secale cereale L.) following corn (Zea mays L.) harvest were investigated at two locations. The study was conducted over two winter cover crop growing seasons: 2007―2008 and 2008―2009. Treatments that incorporated the seed into the soil consistently established better stands of cover crops and took up more N regardless of fluctuations in temperature, rainfall, and planting date. Early planted cover crops consistently took up more N than those planted on the later planting date. Performance of the broadcast treatments was highly dependent on rainfall and mild temperatures for success, but did take up notable amounts of N when planted early under good growing conditions. The few differences that were found in the N uptake between wheat and rye within the same planting treatment always indicated that the rye achieved better N uptake than wheat.
XiaoiceSing: A High-Quality and Integrated Singing Voice Synthesis System

This paper presents XiaoiceSing, a high-quality singing voice synthesis system which employs an integrated network for spectrum, F0 and duration modeling. We follow the main architecture of FastSpeech while proposing some singing-specific design: 1) Besides phoneme ID and position encoding, features from musical score (e.g.note pitch and length) are also added. 2) To attenuate off-key issues, we add a residual connection in F0 prediction. 3) In addition to the duration loss of each phoneme, the duration of all the phonemes in a musical note is accumulated to calculate the syllable duration loss for rhythm enhancement. Experiment results show that XiaoiceSing outperforms the baseline system of convolutional neural networks by 1.44 MOS on sound quality, 1.18 on pronunciation accuracy and 1.38 on naturalness respectively. In two A/B tests, the proposed F0 and duration modeling methods achieve 97.3% and 84.3% preference rate over baseline respectively, which demonstrates the overwhelming advantages of XiaoiceSing.