Miracle-dz/UniFAR-FTUs huggingface dataset:
Salmonella enterica Serovar Typhimurium Interacts with CD209 Receptors To Promote Host Dissemination and Infection

Salmonella enterica serovar Typhimurium, a Gram-negative bacterium, can cause infectious diseases ranging from gastroenteritis to systemic dissemination and infection. However, the molecular mechanisms underlying this bacterial dissemination have yet to be elucidated. A study indicated that using the lipopolysaccharide (LPS) core as a ligand, S. ABSTRACT Salmonella enterica serovar Typhimurium, a Gram-negative bacterium, can cause infectious diseases ranging from gastroenteritis to systemic dissemination and infection. However, the molecular mechanisms underlying this bacterial dissemination have yet to be elucidated. A study indicated that using the lipopolysaccharide (LPS) core as a ligand, S. Typhimurium was able to bind human dendritic cell-specific intercellular adhesion molecule-3-grabbing nonintegrin (hCD209a), an HIV receptor that promotes viral dissemination by hijacking antigen-presenting cells (APCs). In this study, we showed that S. Typhimurium interacted with CD209s, leading to the invasion of APCs and potentially the dissemination to regional lymph nodes, spleen, and liver in mice. Shielding of the exposed LPS core through the expression of O-antigen reduces dissemination and infection. Thus, we propose that similar to HIV, S. Typhimurium may also utilize APCs via interactions with CD209s as a way to disseminate to the lymph nodes, spleen, and liver to initiate host infection.
Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing

Research on parsing language to SQL has largely ignored the structure of the database (DB) schema, either because the DB was very simple, or because it was observed at both training and test time. In spider, a recently-released text-to-SQL dataset, new and complex DBs are given at test time, and so the structure of the DB schema can inform the predicted SQL query. In this paper, we present an encoder-decoder semantic parser, where the structure of the DB schema is encoded with a graph neural network, and this representation is later used at both encoding and decoding time. Evaluation shows that encoding the schema structure improves our parser accuracy from 33.8% to 39.4%, dramatically above the current state of the art, which is at 19.7%.
Compact low-loss wideband-matched RF filters for class-S power amplifier systems

This paper describes the requirements for, and successful implementations of, a reconstruction filter suitable for a class-S power amplifier system. Beside tailoring the pass-band and the stop-band, mode and impedance matching to the push-pull switch-mode amplifier, driven by a delta-sigma modulator, over a wide bandwidth are crucial to sustain a high system efficiency. Two designs of balanced-input comb-line filters are presented for operating frequencies of 450 MHz and 900 MHz. Measurements revealed an insertion loss of less than 1 dB, fractional bandwidths of 6% and 500% for the pass-band and stop-band, respectively, as well as broadband impedance matching to the final amplifier stage.
TMA-93 for Diagnosing Amnestic Mild Cognitive Impairment: A Comparison With the Free and Cued Selective Reminding Test

Background: TMA-93 examines binding by images, an advantage for the less educated individuals. Aim: To compare the discriminative validity of TMA-93 against the picture version of Free and Cued Selective Reminding Test (FCSRT) to distinguish patients with amnestic mild cognitive impairment (aMCI) from normal controls (NCs) without excluding less educated individuals. Methods: Design: Phase I diagnostic evaluation study. Participants: A total of 30 patients with aMCI and 30 NCs matched for sociodemographics variables. Statistical Analysis: The diagnostic accuracy for each test was calculated by conducting receiver operating characteristic curve analysis. Hanley and McNeil method was used to compare diagnostic accuracy of different tests on the same sample. Results: Up to 41.7% of the sample had less than a first grade of education. Both tests showed excellent diagnostic accuracy. The comparisons did not show significant differences. Conclusions: TMA-93 is so accurate as FCSRT to differentiate aMCI from controls including less educated individuals. The test could be considered as a choice in this sociodemographic context.
Inner Hair Cell and Neuron Degeneration Contribute to Hearing Loss in a DFNA2-Like Mouse Model

HIGHLIGHTS KCNQ4 knock-out mouse shows hair cells and spiral ganglion neuron degeneration. Inner hair cells and spiral ganglion neuron loss begin 30 weeks later than outer hair cells in Kcnq4-/- mice. Inner hair cell loss kinetic is faster than that of outer hair cells in cochlear basal turn in Kcnq4-/-. Outer hair cells from Kcnq4-/- mice degenerate slower in apical than in basal turn. Kcnq4 knock-out allele expressed in C3H/HeJ strain reproduces the two phases of DFNA2 hearing loss. GRAPHICAL ABSTRACT
Neural decoding with visual attention using sequential Monte Carlo for leaky integrate-and-fire neurons

How the brain makes sense of a complicated environment is an important question, and a first step is to be able to reconstruct the stimulus that give rise to an observed brain response. Neural coding relates neurobiological observations to external stimuli using computational methods. Encoding refers to how a stimulus affects the neuronal output, and entails constructing a neural model and parameter estimation. Decoding refers to reconstruction of the stimulus that led to a given neuronal output. Existing decoding methods rarely explain neuronal responses to complicated stimuli in a principled way. Here we perform neural decoding for a mixture of multiple stimuli using the leaky integrate-and-fire model describing neural spike trains, under the visual attention hypothesis of probability mixing in which the neuron only attends to a single stimulus at any given time. We assume either a parallel or serial processing visual search mechanism when decoding multiple simultaneous neurons. We consider one or multiple stochastic stimuli following Ornstein-Uhlenbeck processes, and dynamic neuronal attention that switches following discrete Markov processes. To decode stimuli in such situations, we develop various sequential Monte Carlo particle methods in different settings. The likelihood of the observed spike trains is obtained through the first-passage time probabilities obtained by solving the Fokker-Planck equations. We show that the stochastic stimuli can be successfully decoded by sequential Monte Carlo, and different particle methods perform differently considering the number of observed spike trains, the number of stimuli, model complexity, etc. The proposed novel decoding methods, which analyze the neural data through psychological visual attention theories, provide new perspectives to understand the brain.
The Effect of Semantic Transparency on the Processing of Morphologically Derived Words: Evidence From Decision Latencies and Event-Related Potentials

Decomposition theories of morphological processing in visual word recognition posit an early morpho-orthographic parser that is blind to semantic information, whereas parallel distributed processing (PDP) theories assume that the transparency of orthographic-semantic relationships influences processing from the beginning. To test these alternatives, the performance of participants on transparent (foolish), quasi-transparent (bookish), opaque (vanish), and orthographic control words (bucket) was examined in a series of 5 experiments. In Experiments 1–3 variants of a masked priming lexical-decision task were used; Experiment 4 used a masked priming semantic decision task, and Experiment 5 used a single-word (nonpriming) semantic decision task with a color-boundary manipulation. In addition to the behavioral data, event-related potential (ERP) data were collected in Experiments 1, 2, 4, and 5. Across all experiments, we observed a graded effect of semantic transparency in behavioral and ERP data, with the largest effect for semantically transparent words, the next largest for quasi-transparent words, and the smallest for opaque words. The results are discussed in terms of decomposition versus PDP approaches to morphological processing.
Distribution Calibration for Regression

We are concerned with obtaining well-calibrated output distributions from regression models. Such distributions allow us to quantify the uncertainty that the model has regarding the predicted target value. We introduce the novel concept of distribution calibration, and demonstrate its advantages over the existing definition of quantile calibration. We further propose a post-hoc approach to improving the predictions from previously trained regression models, using multi-output Gaussian Processes with a novel Beta link function. The proposed method is experimentally verified on a set of common regression models and shows improvements for both distribution-level and quantile-level calibration.
Domain Adaptive Transfer Learning for Fault Diagnosis

Thanks to digitization of industrial assets in fleets, the ambitious goal of transferring fault diagnosis models from one machine to the other has raised great interest. Solving these domain adaptive transfer learning tasks has the potential to save large efforts on manually labeling data and modifying models for new machines in the same fleet. Although datadriven methods have shown great potential in fault diagnosis applications, their ability to generalize on new machines and new working conditions are limited because of their tendency to overfit to the training set in reality. One promising solution to this problem is to use domain adaptation techniques. It aims to improve model performance on the target new machine. Inspired by its successful implementation in computer vision, we introduced Domain-Adversarial Neural Networks (DANN) to our context, along with two other popular methods existing in previous fault diagnosis research. We then carefully justify the applicability of these methods in realistic fault diagnosis settings, and offer a unified experimental protocol for a fair comparison between domain adaptation methods for fault diagnosis problems.
Recipient Design in Communicative Pointing

Abstract A long‐standing debate in the study of human communication centers on the degree to which communicators tune their communicative signals (e.g., speech, gestures) for specific addressees, as opposed to taking a neutral or egocentric perspective. This tuning, called recipient design, is known to occur under special conditions (e.g., when errors in communication need to be corrected), but several researchers have argued that it is not an intrinsic feature of human communication, because that would be computationally too demanding. In this study, we contribute to this debate by studying a simple communicative behavior, communicative pointing, under conditions of successful (error‐free) communication. Using an information‐theoretic measure, called legibility, we present evidence of recipient design in communicative pointing. The legibility effect is present early in the movement, suggesting that it is an intrinsic part of the communicative plan. Moreover, it is reliable only from the viewpoint of the addressee, suggesting that the motor plan is tuned to the addressee. These findings suggest that recipient design is an intrinsic feature of human communication.
Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis

Related tasks often have inter-dependence on each other and perform better when solved in a joint framework. In this paper, we present a deep multi-task learning framework that jointly performs sentiment and emotion analysis both. The multi-modal inputs (i.e. text, acoustic and visual frames) of a video convey diverse and distinctive information, and usually do not have equal contribution in the decision making. We propose a context-level inter-modal attention framework for simultaneously predicting the sentiment and expressed emotions of an utterance. We evaluate our proposed approach on CMU-MOSEI dataset for multi-modal sentiment and emotion analysis. Evaluation results suggest that multi-task learning framework offers improvement over the single-task framework. The proposed approach reports new state-of-the-art performance for both sentiment analysis and emotion analysis.
Learning Active Spine Behaviors for Dynamic and Efficient Locomotion in Quadruped Robots

In this work, we provide a simulation framework to perform systematic studies on the effects of spinal joint compliance and actuation on bounding performance of a 16-DOF quadruped spined robot Stoch 2. Fast quadrupedal locomotion with active spine is an extremely hard problem, and involves a complex coordination between the various degrees of freedom. Therefore, past attempts at addressing this problem have not seen much success. Deep-Reinforcement Learning seems to be a promising approach, after its recent success in a variety of robot platforms, and the goal of this paper is to use this approach to realize the aforementioned behaviors. With this learning framework, the robot reached a bounding speed of 2.1m /s with a maximum Froude number of 2. Simulation results also show that use of active spine, indeed, increased the stride length, improved the cost of transport, and also reduced the natural frequency to more realistic values.
Human motion trajectory prediction: a survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand, and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots, and advanced surveillance systems. This article provides a survey of human motion trajectory prediction. We review, analyze, and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.
A Study of Magnetic Diverter Properties in the WISTOR-U Ultimate Torsatron

The WISTOR-U ultimate torsatron design was modeled to determine the effect of various vertical coil configurations particularly with respect to divertor properties. The separatrix and divertor behavior were studied using a modified version of a previously developed code. The divertor properties were studied for various vertical field coil configurations and strengths. The possibility of using structural members for vertical coils and the resultant divertor action around the structural members was investigated. A reduction in area inside the separatrix was observed with deviations from the pure ultimate torsatron base case.
The impact of postsynaptic density 95 blocking peptide (Tat‐NR2B9c) and an iNOS inhibitor (1400W) on proteomic profile of the hippocampus in C57BL/6J mouse model of kainate‐induced epileptogenesis

Antiepileptogenic agents that prevent the development of epilepsy following a brain insult remain the holy grail of epilepsy therapeutics. We have employed a label‐free proteomic approach that allows quantification of large numbers of brain‐expressed proteins in a single analysis in the mouse (male C57BL/6J) kainate (KA) model of epileptogenesis. In addition, we have incorporated two putative antiepileptogenic drugs, postsynaptic density protein‐95 blocking peptide (PSD95BP or Tat‐NR2B9c) and a highly selective inducible nitric oxide synthase inhibitor, 1400W, to give an insight into how such agents might ameliorate epileptogenesis. The test drugs were administered after the induction of status epilepticus (SE) and the animals were euthanized at 7 days, their hippocampi removed, and subjected to LC‐MS/MS analysis. A total of 2,579 proteins were identified; their normalized abundance was compared between treatment groups using ANOVA, with correction for multiple testing by false discovery rate. Significantly altered proteins were subjected to gene ontology and KEGG pathway enrichment analyses. KA‐induced SE was most robustly associated with an alteration in the abundance of proteins involved in neuroinflammation, including heat shock protein beta‐1 (HSP27), glial fibrillary acidic protein, and CD44 antigen. Treatment with PSD95BP or 1400W moderated the abundance of several of these proteins plus that of secretogranin and Src substrate cortactin. Pathway analysis identified the glutamatergic synapse as a key target for both drugs. Our observations require validation in a larger‐scale investigation, with candidate proteins explored in more detail. Nevertheless, this study has identified several mechanisms by which epilepsy might develop and several targets for novel drug development.
Exploring Interactions with Voice-Controlled TV

Intelligent agents such as Alexa, Siri, and Google Assistant are now built into streaming TV systems, allowing people to use voice input to navigate the increasingly complex set of apps available on a TV. However, these systems typically support a narrow range of control- and search-oriented commands, and do not support deeper recommendation or exploration queries. To learn about how people interact with a recommendation-oriented voice-controlled TV, we use research through design methods to explore an early prototype movie recommendation system where the only input modality is voice. We describe in-depth qualitative research sessions with 11 participants. We contribute implications for designers of voice-controlled TV: mitigating the drawbacks of voice-only interactions, navigating the tension between expressiveness and efficiency, and building voice-driven recommendation interfaces that facilitate exploration.
Diagonal Acceleration for Covariance Matrix Adaptation Evolution Strategies

We introduce an acceleration for covariance matrix adaptation evolution strategies (CMA-ES) by means of adaptive diagonal decoding (dd-CMA). This diagonal acceleration endows the default CMA-ES with the advantages of separable CMA-ES without inheriting its drawbacks. Technically, we introduce a diagonal matrix D that expresses coordinate-wise variances of the sampling distribution in DCD form. The diagonal matrix can learn a rescaling of the problem in the coordinates within a linear number of function evaluations. Diagonal decoding can also exploit separability of the problem, but, crucially, does not compromise the performance on nonseparable problems. The latter is accomplished by modulating the learning rate for the diagonal matrix based on the condition number of the underlying correlation matrix. dd-CMA-ES not only combines the advantages of default and separable CMA-ES, but may achieve overadditive speedup: it improves the performance, and even the scaling, of the better of default and separable CMA-ES on classes of nonseparable test functions that reflect, arguably, a landscape feature commonly observed in practice. The article makes two further secondary contributions: we introduce two different approaches to guarantee positive definiteness of the covariance matrix with active CMA, which is valuable in particular with large population size; we revise the default parameter setting in CMA-ES, proposing accelerated settings in particular for large dimension. All our contributions can be viewed as independent improvements of CMA-ES, yet they are also complementary and can be seamlessly combined. In numerical experiments with dd-CMA-ES up to dimension 5120, we observe remarkable improvements over the original covariance matrix adaptation on functions with coordinate-wise ill-conditioning. The improvement is observed also for large population sizes up to about dimension squared.
A Scalable Learned Index Scheme in Storage Systems

Index structures are important for efficient data access, which have been widely used to improve the performance in many in-memory systems. Due to high in-memory overheads, traditional index structures become difficult to process the explosive growth of data, let alone providing low latency and high throughput performance with limited system resources. The promising learned indexes leverage deep-learning models to complement existing index structures and obtain significant memory savings. However, the learned indexes fail to become scalable due to the heavy inter-model dependency and expensive retraining. To address these problems, we propose a scalable learned index scheme to construct different linear regression models according to the data distribution. Moreover, the used models are independent so as to reduce the complexity of retraining and become easy to partition and store the data into different pages, blocks or distributed systems. Our experimental results show that compared with state-of-the-art schemes, AIDEL improves the insertion performance by about 2$\times$ and provides comparable lookup performance, while efficiently supporting scalability.
Deep Neural Architecture Search with Deep Graph Bayesian Optimization

Image recognition aims to identify objects, places, people, or other targeted items in a given image, and has a wide range of social applications such as natural disasters recognition, plant disease detection, and traffic jam detection. Currently state-of-the-art methods of image recognition are based on deep learning and remain a common pattern in designing and using convolutional neural networks (CNNs). However, designing CNNs is extremely time intensive and requires an expert. Neural architecture search (NAS) can solve this problem by automatically identifying architectures of CNNs that are superior to hand-designed ones. Recently BO has been applied to neural architecture search and shows better performance than pure evolutionary strategies. All these methods adopt Gaussian processes (GPs) as surrogate function, with the handcraft similarity metrics as input. In this work, we propose a Bayesian graph neural network as a new surrogate, which can automatically extract features from deep neural architectures, and use such learned features to fit and characterize black-box objectives and their uncertainty. Based on the new surrogate, we then develop a graph Bayesian optimization framework to address the challenging task of deep neural architecture search. Experiment results show our method significantly outperforms the comparative methods on benchmark tasks. CCS CONCEPTS • Networks → Network performance evaluation; • Computing methodologies → Artificial intelligence.
Approximating Orthogonal Matrices with Effective Givens Factorization

We analyze effective approximation of unitary matrices. In our formulation, a unitary matrix is represented as a product of rotations in two-dimensional subspaces, so-called Givens rotations. Instead of the quadratic dimension dependence when applying a dense matrix, applying such an approximation scales with the number factors, each of which can be implemented efficiently. Consequently, in settings where an approximation is once computed and then applied many times, such a representation becomes advantageous. Although effective Givens factorization is not possible for generic unitary operators, we show that minimizing a sparsity-inducing objective with a coordinate descent algorithm on the unitary group yields good factorizations for structured matrices. Canonical applications of such a setup are orthogonal basis transforms. We demonstrate numerical results of approximating the graph Fourier transform, which is the matrix obtained when diagonalizing a graph Laplacian.