cssi/SciSciGPT-SciSciNet huggingface dataset:
On the Impact of Random Seeds on the Fairness of Clinical Classifiers

Recent work has shown that fine-tuning large networks is surprisingly sensitive to changes in random seed(s). We explore the implications of this phenomenon for model fairness across demographic groups in clinical prediction tasks over electronic health records (EHR) in MIMIC-III —— the standard dataset in clinical NLP research. Apparent subgroup performance varies substantially for seeds that yield similar overall performance, although there is no evidence of a trade-off between overall and subgroup performance. However, we also find that the small sample sizes inherent to looking at intersections of minority groups and somewhat rare conditions limit our ability to accurately estimate disparities. Further, we find that jointly optimizing for high overall performance and low disparities does not yield statistically significant improvements. Our results suggest that fairness work using MIMIC-III should carefully account for variations in apparent differences that may arise from stochasticity and small sample sizes.
Algorithmic Transparency with Strategic Users

Should firms that apply machine learning algorithms in their decision making make their algorithms transparent? Despite increasing calls for algorithmic transparency, most firms have kept their algorithms opaque citing potential gaming by users that may negatively affect the algorithm’s predictive power. We introduce an analytical model to investigate the issue of algorithmic transparency in the presence of strategic users from a firm’s perspective and present novel insights. Counter-intuitively, we show that the predictive power of ML algorithms may increase if the firm were to make them transparent. We identify a broad set of conditions under which making the algorithm transparent benefits the firm in terms of higher accuracy. The results hold even when the predictive power of the opaque algorithm comes mainly from correlational features and the cost for users to improve on them is close to zero.
Preparing for tenure at a research-intensive university

At research-intensive universities in the United States, eligible faculty must generally excel in research, teaching and service in order to receive tenure. To meet these high standards, junior faculty should begin planning for a strong tenure case from their first day on the job. Here, we provide practical information, commentary and advice on how biomedical faculty at research-intensive institutions can prepare strategically for a successful tenure review.
Comparison of Ares I-X Wind-Tunnel-Derived Buffet Environment with Flight Data

The Ares I-X Flight Test Vehicle (FTV), launched in October 2009, carried with it over 243 buffet verification pressure sensors and was one of the most heavily instrumented launch vehicle flight tests. This flight test represented a unique opportunity for NASA and its partners to compare the wind-tunnel derived buffet environment with that measured during the flight of Ares I-X. It is necessary to define the launch vehicle buffet loads to ensure that structural components and vehicle subsystems possess adequate strength, stress, and fatigue margins when the vehicle structural dynamic response to buffet forcing functions are considered. Ares I-X buffet forcing functions were obtained via wind-tunnel testing of a rigid buffet model (RBM) instrumented with hundreds of unsteady pressure transducers designed to measure the buffet environment across the desired frequency range. This paper discusses the comparison of RBM and FTV buffet environments, including fluctuating pressure coefficient and normalized sectional buffet forcing function root-mean-square magnitudes, frequency content of power-spectral density functions, and force magnitudes of an alternating flow phenomena. Comparison of wind-tunnel model and flight test vehicle buffet environments show very good agreement with root-mean-square magnitudes of buffet forcing functions at the majority of vehicle stations. Spectra proved a challenge to compare because of different wind-tunnel and flight test conditions and data acquisition rates. However, meaningful and promising comparisons of buffet spectra are presented. Lastly, the buffet loads resulting from the transition of subsonic separated flow to supersonic attached flow were significantly over-predicted by wind-tunnel results.
Tip vortex geometry of a hovering helicopter rotor in ground effect

The wide-field shadowgraph method has been used to photograph the tip vortices of a hovering helicopter rotor in ground effect. The shadowgraphs were used to obtain quantitative measurements of the rotor tip vortex geometry both in and out of ground effect. Many important phenomena are visible in the rotor wake using this method. These include the variation in descent and contraction rates of the tip vortices in ground effect, and the interaction between tip vortices in the far wake. The tip vortex geometry from the shadowgraphs is compared with the tip vortex geometry predicted using a free wake hover performance analysis. The free wake analysis accurately predicts the tip vortex geometry both in and out of ground effect. Performance data from the test is compared with the performance predicted using several methods, including the free wake analysis. All methods provided reasonable predictions for the helicopter performance in ground effect.
A Market Microstructure View of the Informational Efficiency of Security Prices

Do equity prices efficiently reflect fundamental information, as the efficient markets hypothesis (EMH) suggests? The author challenges financial academicians’ widely held acceptance of the EMH. In a frictionless environment, information acquisition and trading would be costless, transaction prices would reflect information perfectly, prices would follow random walks, and the EMH would be validated. But markets are not frictionless. Academic research commonly invokes an assumption that informed investors have homogeneous expectations. But, given the enormity and complexity of information sets, investor expectations differ. Taking an intraday, microstructure focus, the author explains that price discovery in a nonfrictionless, divergent-expectations environment is a protracted, dynamic process that accentuates intraday price volatility and introduces return autocorrelations. This counters the EMH. He stresses the importance of instituting a market structure that further enhances the operational, and therefore informational, efficiency of a security market. TOPICS:Security analysis and valuation, portfolio theory, statistical methods, exchanges/markets/clearinghouses Key Findings ▪ Given the enormous size and complexity of information sets, equally informed investors form different expectations of stocks’ risks and returns. Consequently, shares do not have unique fundamental values that can be known by research analysts. Rather, prices must be discovered in a marketplace, and the efficiency with which this task is performed depends on how orders are handled and turned into trades (i.e., market structure matters). ▪ In a frictionless, zero-trading-cost market, security prices depend on two factors: risk and return. In a nonfrictionless market, three factors matter: risk, return, and liquidity. Illiquidity is closely associated with price discovery being a noisy, dynamic process. ▪ High-frequency, intraday data show that intraday price volatility is markedly accentuated and that stock returns are autocorrelated (i.e., share prices do not follow random walks). These properties of intraday price formation are manifestations of price discovery being a noisy process, and they explain why share prices can be informationally inefficient.
A Markov Chain Approach to Probabilistic Swarm Guidance

This paper introduces a probabilistic guidance approach for the coordination of swarms of autonomous agents. The main idea is to drive the swarm to a prescribed density distribution in a prescribed region of the configuration space. In its simplest form, the probabilistic approach is completely decentralized and does not require communication or collabo- ration between agents. Agents make statistically independent probabilistic decisions based solely on their own state, that ultimately guides the swarm to the desired density distribution in the configuration space. In addition to being completely decentralized, the probabilistic guidance approach has a novel autonomous self-repair property: Once the desired swarm density distribution is attained, the agents automatically repair any damage to the distribution without collaborating and without any knowledge about the damage.
Tracking Disease Outbreaks from Sparse Data with Bayesian Inference

The COVID-19 pandemic provides new motivation for a classic problem in epidemiology: estimating the empirical rate of transmission during an outbreak (formally, the time-varying reproduction number) from case counts. While standard methods exist, they work best at coarse-grained national or state scales with abundant data, and struggle to accommodate the partial observability and sparse data common at finer scales (e.g., individual schools or towns). For example, case counts may be sparse when only a small fraction of infections are caught by a testing program. Or, whether an infected individual tests positive may depend on the kind of test and the point in time when they are tested. We propose a Bayesian framework which accommodates partial observability in a principled manner. Our model places a Gaussian process prior over the unknown reproduction number at each time step and models observations sampled from the distribution of a specific testing program. For example, our framework can accommodate a variety of kinds of tests (viral RNA, antibody, antigen, etc.) and sampling schemes (e.g., longitudinal or cross-sectional screening). Inference in this framework is complicated by the presence of tens or hundreds of thousands of discrete latent variables. To address this challenge, we propose an efficient stochastic variational inference method which relies on a novel gradient estimator for the variational objective. Experimental results for an example motivated by COVID-19 show that our method produces an accurate and well-calibrated posterior, while standard methods for estimating the reproduction number can fail badly.
On the canonical bundle formula and log abundance in positive characteristic

We show that a weak version of the canonical bundle formula holds for fibrations of relative dimension one. We provide various applications thereof, for instance, using the recent result of Xu and Zhang, we prove the log non-vanishing conjecture for three-dimensional klt pairs over any algebraically closed field k of characteristic $$p>5$$ , which in turn implies the termination of any sequence of three-dimensional flips in the pseudo-effective case. We also show the log abundance conjecture for threefolds over k when the nef dimension is not maximal, and the base point free theorem for threefolds over $$\mathbb {{\overline{F}}}_p$$ when $$p>2$$ .
Distributed Sensor Networks Deployed Using Soft Growing Robots

Due to their ability to move without sliding relative to their environment, soft growing robots are attractive for deploying distributed sensor networks in confined spaces. Sensing of the state of such robots would add to their capabilities as human-safe, adaptable manipulators. However, incorporation of distributed sensors onto soft growing robots is challenging because it requires an interface between stiff and soft materials, and the sensor network needs to undergo significant strain. In this work, we present a method for adding sensors to soft growing robots that uses flexible printed circuit boards with self-contained units of microcontrollers and sensors encased in a laminate armor that protects them from unsafe curvatures. We demonstrate the ability of this system to relay directional temperature and humidity information in hard-to-access spaces. We also demonstrate and characterize a method for sensing the growing robot shape using inertial measurement units deployed along its length, and develop a mathematical model to predict its accuracy. This work advances the capabilities of soft growing robots, as well as the field of soft robot sensing.
The Parastagonospora nodorum necrotrophic effector SnTox5 targets the wheat gene Snn5 and facilitates entry into the leaf mesophyll.

Parastagonospora nodorum is an economically important necrotrophic fungal pathogen of wheat. Parastagonospora nodorum secretes necrotrophic effectors that target wheat susceptibility genes to induce programmed cell death (PCD). In this study, we cloned and functionally validated SnTox5 and characterized its role in pathogenesis. We used whole genome sequencing, genome-wide association study (GWAS) mapping, CRISPR-Cas9-based gene disruption, gain-of-function transformation, quantitative trait locus (QTL) analysis, haplotype and isoform analysis, protein modeling, quantitative PCR, and laser confocal microscopy to validate SnTox5 and functionally characterize SnTox5. SnTox5 is a mature 16.26 kDa protein with high structural similarity to SnTox3. Wild-type and mutant P. nodorum strains and wheat genotypes of SnTox5 and Snn5, respectively, were used to show that SnTox5 not only targets Snn5 to induce PCD but also facilitates the colonization of the mesophyll layer even in the absence of Snn5. Here we show that SnTox5 facilitates the efficient colonization of the mesophyll tissue and elicits PCD specific to host lines carrying Snn5. The homology to SnTox3 and the ability of SnTox5 to facilitate the colonizing of the mesophyll also suggest a role in the suppression of host defense before PCD induction.
Towards Safe Continuing Task Reinforcement Learning

Safety is a critical feature of controller design for physical systems. When designing control policies, several approaches to guarantee this aspect of autonomy have been proposed, such as robust controllers or control barrier functions. However, these solutions strongly rely on the model of the system being available to the designer. As a parallel development, reinforcement learning provides model-agnostic control solutions but in general, it lacks the theoretical guarantees required for safety. Recent advances show that under mild conditions, control policies can be learned via reinforcement learning, which can be guaranteed to be safe by imposing these requirements as constraints of an optimization problem. However, to transfer from learning safety to learning safely, there are two hurdles that need to be overcome: (i) it has to be possible to learn the policy without having to re-initialize the system; and (ii) the rollouts of the system need to be in themselves safe. In this paper, we tackle the first issue, proposing an algorithm capable of operating in the continuing task setting without the need of restarts. We evaluate our approach in a numerical example, which shows the capabilities of the proposed approach in learning safe policies via safe exploration.
Inference on the history of a randomly growing tree

The spread of infectious disease in a human community or the proliferation of fake news on social media can be modeled as a randomly growing tree-shaped graph. The history of the random growth process is often unobserved but contains important information such as the source of the infection. We consider the problem of statistical inference on aspects of the latent history using only a single snapshot of the final tree. Our approach is to apply random labels to the observed unlabeled tree and analyze the resulting distribution of the growth process, conditional on the final outcome. We show that this conditional distribution is tractable under a shape-exchangeability condition, which we introduce here, and that this condition is satisfied for many popular models for randomly growing trees such as uniform attachment, linear preferential attachment and uniform attachment on a $D$-regular tree. For inference of the root under shape-exchangeability, we propose O(n log n) time algorithms for constructing confidence sets with valid frequentist coverage as well as bounds on the expected size of the confidence sets. We also provide efficient sampling algorithms that extend our methods to a wide class of inference problems.
Accumulation time of stochastic processes with resetting

One of the characteristic features of a stochastic process under resetting is that the probability density converges to a non-equilibrium stationary state (NESS). In addition, the approach to the stationary state exhibits a dynamical phase transition, which can be interpreted as a traveling front separating spatial regions for which the probability density has relaxed to the NESS from those where transients persist. A very different mechanism for generating an NESS occurs within the context of diffusion-based morphogenesis, in which an extrinsic localized current source combined with degradation within the interior of the domain leads to the formation of a protein concentration gradient. A common method for characterizing the relaxation process is to calculate the so-called accumulation time. The latter is the analog of the mean first passage time of a search process, in which the survival probability density is replaced by an accumulation fraction density. In this paper, we extend the definition of the accumulation time to stochastic processes with resetting by showing how the probability density associated with trajectories that reset at least once evolves in an analogous fashion to protein concentration gradients. We consider a range of examples, including diffusion with instantaneous resetting, resetting with refractory periods and finite return times, and non-diffusive processes such as run-and-tumble particles. In each case we calculate the accumulation time as a function of the spatial separation from the reset point.
COMMONSENSE-FOCUSED DIALOGUES FOR RESPONSE GENERATION: AN EMPIRICAL STUDY

Smooth and effective communication requires the ability to perform latent or explicit commonsense inference. Prior commonsense reasoning benchmarks (such as SocialIQA and CommonsenseQA) mainly focus on the discriminative task of choosing the right answer from a set of candidates, and do not involve interactive language generation as in dialogue. Moreover, existing dialogue datasets do not explicitly focus on exhibiting commonsense as a facet. In this paper, we present an empirical study of commonsense in dialogue response generation. We first auto-extract commonsensical dialogues from existing dialogue datasets by leveraging ConceptNet, a commonsense knowledge graph. Furthermore, building on social contexts/situations in SocialIQA, we collect a new dialogue dataset with 25K dialogues aimed at exhibiting social commonsense in an interactive setting. We evaluate response generation models trained using these datasets and find that models trained on both extracted and our collected data produce responses that consistently exhibit more commonsense than baselines. Finally we propose an approach for automatic evaluation of commonsense that relies on features derived from ConceptNet and pre-trained language and dialog models, and show reasonable correlation with human evaluation of responses' commonsense quality. We are releasing a subset of our collected data, Commonsense-Dialogues, containing about 11K dialogs.
A surrogate virus neutralization test to quantify antibody-mediated inhibition of SARS-CoV-2 in finger stick dried blood spot samples.

The spike protein of SARS-CoV-2 engages the human angiotensin-converting enzyme 2 (ACE2) receptor to enter host cells, and neutralizing antibodies are effective at blocking this interaction to prevent infection. Widespread application of this important marker of protective immunity is limited by logistical and technical challenges associated with live virus methods and venous blood collection. To address this gap, we validated an immunoassay-based method for quantifying neutralization of the spike-ACE2 interaction in a single drop of capillary whole blood, collected on filter paper as a dried blood spot (DBS) sample. Samples are eluted overnight and incubated in the presence of spike antigen and ACE2 in a 96-well solid phase plate. Competitive immunoassay with electrochemiluminescent label is used to quantify neutralizing activity. The following measures of assay performance were evaluated: dilution series of confirmed positive and negative samples, agreement with results from matched DBS-serum samples, analysis of results from DBS samples with known COVID-19 status, and precision (intra-assay percent coefficient of variation; %CV) and reliability (inter-assay; %CV). Dilution series produced the expected pattern of dose-response. Agreement between results from serum and DBS samples was high, with concordance correlation = 0.991. Analysis of three control samples across the measurement range indicated acceptable levels of precision and reliability. Median % surrogate neutralization was 46.9 for PCR confirmed convalescent COVID-19 samples and 0.1 for negative samples. Large-scale testing is important for quantifying neutralizing antibodies that can provide protection against COVID-19 in order to estimate the level of immunity in the general population. DBS provides a minimally-invasive, low cost alternative to venous blood collection, and this scalable immunoassay-based method for quantifying inhibition of the spike-ACE2 interaction can be used as a surrogate for virus-based assays to expand testing across a wide range of settings and populations.
Confined vortex surface and irreversibility. 1. Properties of exact solution

We revise the steady vortex surface theory following the recent finding of asymmetric vortex sheets (Migdal, 2021). These surfaces avoid the Kelvin–Helmholtz instability by adjusting their disconti...
Wilson statistics: derivation, generalization and applications to electron cryomicroscopy

The power spectrum of proteins at high frequencies is remarkably well described by the flat Wilson statistics. Wilson statistics therefore plays a significant role in X-ray crystallography and more recently in electron cryomicroscopy (cryo-EM). Specifically, modern computational methods for three-dimensional map sharpening and atomic modelling of macromolecules by single-particle cryo-EM are based on Wilson statistics. Here the first rigorous mathematical derivation of Wilson statistics is provided. The derivation pinpoints the regime of validity of Wilson statistics in terms of the size of the macromolecule. Moreover, the analysis naturally leads to generalizations of the statistics to covariance and higher-order spectra. These in turn provide a theoretical foundation for assumptions underlying the widespread Bayesian inference framework for three-dimensional refinement and for explaining the limitations of autocorrelation-based methods in cryo-EM.
Deep One-Bit Compressive Autoencoding

Parameterized mathematical models play a central role in understanding and design of complex information systems. However, they often cannot take into account the intricate interactions innate to such systems. On the contrary, purely data-driven approaches do not need explicit mathematical models for data generation and have a wider applicability at the cost of interpretability. In this paper, we consider the design of a one-bit compressive autoencoder, and propose a novel hybrid model-based and data-driven methodology that allows us to not only design the sensing matrix for one-bit data acquisition, but also allows for learning the latent-parameters of an iterative optimization algorithm specifically designed for the problem of one-bit sparse signal recovery. Our results demonstrate a significant improvement compared to state-of-the-art model-based algorithms.
Extended Frontal Networks for Visual and Auditory Working Memory.

Working memory (WM) supports the persistent representation of transient sensory information. Visual and auditory stimuli place different demands on WM and recruit different brain networks. Separate auditory- and visual-biased WM networks extend into the frontal lobes, but several challenges confront attempts to parcellate human frontal cortex, including fine-grained organization and between-subject variability. Here, we use differential intrinsic functional connectivity from 2 visual-biased and 2 auditory-biased frontal structures to identify additional candidate sensory-biased regions in frontal cortex. We then examine direct contrasts of task functional magnetic resonance imaging during visual versus auditory 2-back WM to validate those candidate regions. Three visual-biased and 5 auditory-biased regions are robustly activated bilaterally in the frontal lobes of individual subjects (N = 14, 7 women). These regions exhibit a sensory preference during passive exposure to task stimuli, and that preference is stronger during WM. Hierarchical clustering analysis of intrinsic connectivity among novel and previously identified bilateral sensory-biased regions confirms that they functionally segregate into visual and auditory networks, even though the networks are anatomically interdigitated. We also observe that the frontotemporal auditory WM network is highly selective and exhibits strong functional connectivity to structures serving non-WM functions, while the frontoparietal visual WM network hierarchically merges into the multiple-demand cognitive system.
Representation of Task Structure in Human Hippocampus

Memories of previous experiences can be used to guide future decisions in similar situations. Recent evidence suggests that the hippocampus might support decision-making by forming representations that capture common elements across different events (e.g., “cognitive maps” or “schemas”). Here, we used functional magnetic resonance imaging (fMRI) to test how the human hippocampus represents decision relevant information extracted from previous experiences. Participants performed a task in which they learned to predict a customer preference for foods in four different store contexts. The task was structured such that we could examine the degree to which hippocampal representations reflected generalized information about the store contexts, food items, and also the kind of information that was relevant to decisions on a given trial. Results showed that hippocampal activity patterns carried information about the kind of information that was currently relevant to a decision. Across different store contexts, hippocampal representations differentiated between context-determined (deterministic) decisions and context-invariant (probabilistic) decisions. Results also showed that information about store contexts was represented by the hippocampus, but contrary to what might be expected, similar contexts were hyper-differentiated from one another. These results suggest that the hippocampus may support decision-making by systematically mapping relationships between task relevant information, decisions, and outcomes.
Manduca sexta experience high parasitoid pressures in the field but minor fitness costs of consuming plant secondary compounds.

Plant-herbivore coevolutionary interactions have led to a range of plant defenses that minimize insect damage and a suite of counter adaptations that allow herbivores to feed on defended plants. Consuming plant secondary compounds results in herbivore growth and developmental costs but can have beneficial effects such as deterrence or harm of parasitoid enemies. Therefore, the role of secondary compounds on herbivore fitness must be considered in the context of the abundance and level of harm from natural enemies and the costs herbivores incur feeding on plant secondary compounds.In this study, I combined field measurements of Cotesia congregata wasp parasitism pressure with detailed measurements of the costs of plant secondary compounds across developmental stages in the herbivore host, Manduca sexta.I show that C. congregata parasitoids exert large negative selective pressures, killing 31%-57% of M. sexta larvae in the field. Manduca sexta developed fastest during instars most at risk for parasitoid oviposition but growth was slowed by consumption of plant secondary compounds. The negative effects of consuming plant secondary compounds as larvae influenced adult size traits but there were no immune, survival, or fecundity costs.These results suggest that developmental costs experienced by M. sexta herbivores consuming defensive compounds are minor in comparison to the strong negative survival pressures from abundant parasitoid enemies.
Soil Power?: Can Microbial Fuel Cells Power Non-Trivial Sensors?

This paper explores the power delivery potential of soil-based microbial fuel cells. We build a prototype energy harvesting setup for a soil microbial fuel cell, measure the amount of power that we can harvest, and use that energy to drive an e-ink display as a representative example of a periodic energy-intensive load. Microbial fuel cells are highly sensitive to environmental conditions, especially soil moisture. In near-optimal, super moist conditions our cell provides approximately 100 μW of power at around 500 mV, which is ample power over time to power our system several times a day. We further explore how cell performance diminishes and recovers with varying moisture levels as well as how cell performance is affected by the load from the energy harvester itself. In sum, we find that the confluence of ever lower-power electronics and new understanding of microbial fuel cell design means that "soil-powered sensors" are now feasible. There remains, however, significant future work to make these systems reliable and maximally performant.
Model-Free Control of Dynamical Systems with Deep Reservoir Computing

We propose and demonstrate a nonlinear control method that can be applied to unknown, complex systems where the controller is based on a type of artificial neural network known as a reservoir computer. In contrast to many modern neural-network-based control techniques, which are robust to system uncertainties but require a model nonetheless, our technique requires no prior knowledge of the system and is thus model-free. Further, our approach does not require an initial system identification step, resulting in a relatively simple and efficient learning process. Reservoir computers are well-suited to the control problem because they require small training data sets and remarkably low training times. By iteratively training and adding layers of reservoir computers to the controller, a precise and efficient control law is identified quickly. With examples on both numerical and high-speed experimental systems, we demonstrate that our approach is capable of controlling highly complex dynamical systems that display deterministic chaos to nontrivial target trajectories.
Ensemble-variational assimilation of statistical data in large-eddy simulation

Data assimilation is employed to improve the accuracy of large-eddy simulation (LES) of wall-bounded flows. The methodology is adjoint-free and can assimilate any reference statistical quantities that are available from experiments or direct simulations. The outcome is enhanced fidelity LES that outperforms a variety of traditional subgrid models.
SPECTRE: defending against backdoor attacks using robust statistics

Modern machine learning increasingly requires training on a large collection of data from multiple sources, not all of which can be trusted. A particularly concerning scenario is when a small fraction of poisoned data changes the behavior of the trained model when triggered by an attacker-specified watermark. Such a compromised model will be deployed unnoticed as the model is accurate otherwise. There have been promising attempts to use the intermediate representations of such a model to separate corrupted examples from clean ones. However, these defenses work only when a certain spectral signature of the poisoned examples is large enough for detection. There is a wide range of attacks that cannot be protected against by the existing defenses. We propose a novel defense algorithm using robust covariance estimation to amplify the spectral signature of corrupted data. This defense provides a clean model, completely removing the backdoor, even in regimes where previous methods have no hope of detecting the poisoned examples. Code and pre-trained models are available at this https URL .
COMISR: Compression-Informed Video Super-Resolution

Most video super-resolution methods focus on restoring high-resolution video frames from low-resolution videos without taking into account compression. However, most videos on the web or mobile devices are compressed, and the compression can be severe when the bandwidth is limited. In this paper, we propose a new compression-informed video super-resolution model to restore high-resolution content without introducing artifacts caused by compression. The proposed model consists of three modules for video super-resolution: bi-directional recurrent warping, detail-preserving flow estimation, and Laplacian enhancement. All these three modules are used to deal with compression properties such as the location of the intra-frames in the input and smoothness in the output frames. For thorough performance evaluation, we conducted extensive experiments on standard datasets with a wide range of compression rates, covering many real video use cases. We showed that our method not only recovers high-resolution content on uncompressed frames from the widely-used benchmark datasets, but also achieves state-of-the-art performance in super-resolving compressed videos based on numerous quantitative metrics. We also evaluated the proposed method by simulating streaming from YouTube to demonstrate its effectiveness and robustness.
Neural-Network-Optimized Degree-Specific Weights for LDPC MinSum Decoding

Neural Normalized MinSum (N-NMS) decoding delivers better frame error rate (FER) performance on linear block codes than conventional Normalized MinSum (NMS) by assigning dynamic multiplicative weights to each check-to-variable node message in each iteration. Previous N-NMS efforts primarily investigated short block codes (N < 1000), because the number of N-NMS parameters required to be trained scales proportionately to the number of edges in the parity check matrix and the number of iterations. This imposes an impractical memory requirement for conventional tools such as Pytorch and Tensorflow to create the neural network and store gradients. This paper provides efficient methods of training the parameters of N-NMS decoders that support longer block lengths. Specifically, this paper introduces a family of Neural 2-dimensional Normalized (N-2D-NMS) decoders with various reduced parameter sets and shows how performance varies with the parameter set selected. The N-2D-NMS decoders share weights with respect to check node and/or variable node degree. Simulation results justify a reduced parameter set, showing that the trained weights of N- NMS have a smaller value for the neurons corresponding to larger check/variable node degree. Further simulation results on a (3096,1032) Protograph-Based Raptor-Like (PBRL) code show that the N-2D-NMS decoder can achieve the same FER as N- NMS while also providing at least a 99.7% parameter reduction. Furthermore, the N-2D-NMS decoder for the (16200,7200) DVBS- 2 standard LDPC code shows a lower error floor than belief propagation. Finally, this paper proposes a hybrid decoder training structure that utilizes a neural network which combines a feedforward module with a recurrent module. The decoding performance and parameter reduction of the hybrid training depends on the length of recurrent module of the neural network.
High-probability Bounds for Non-Convex Stochastic Optimization with Heavy Tails

We consider non-convex stochastic optimization using first-order algorithms for which the gradient estimates may have heavy tails. We show that a combination of gradient clipping, momentum, and normalized gradient descent yields convergence to critical points in high-probability with best-known rates for smooth losses when the gradients only have bounded $\mathfrak{p}$th moments for some $\mathfrak{p}\in(1,2]$. We then consider the case of second-order smooth losses, which to our knowledge have not been studied in this setting, and again obtain high-probability bounds for any $\mathfrak{p}$. Moreover, our results hold for arbitrary smooth norms, in contrast to the typical SGD analysis which requires a Hilbert space norm. Further, we show that after a suitable "burn-in" period, the objective value will monotonically decrease for every iteration until a critical point is identified, which provides intuition behind the popular practice of learning rate "warm-up" and also yields a last-iterate guarantee.
Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learning

In natural language processing, most models try to learn semantic representations merely from texts. The learned representations encode the distributional semantics but fail to connect to any knowledge about the physical world. In contrast, humans learn language by grounding concepts in perception and action and the brain encodes grounded semantics for cognition. Inspired by this notion and recent work in vision-language learning, we design a two-stream model for grounding language learning in vision. The model includes a VGG-based visual stream and a Bert-based language stream. The two streams merge into a joint representational space. Through cross-modal contrastive learning, the model first learns to align visual and language representations with the MS COCO dataset. The model further learns to retrieve visual objects with language queries through a cross-modal attention module and to infer the visual relations between the retrieved objects through a bilinear operator with the Visual Genome dataset. After training, the language stream of this model is a stand-alone language model capable of embedding concepts in a visually grounded semantic space. This semantic space manifests principal dimensions explainable with human intuition and neurobiological knowledge. Word embeddings in this semantic space are predictive of human-defined norms of semantic features and are segregated into perceptually distinctive clusters. Furthermore, the visually grounded language model also enables compositional language understanding based on visual knowledge and multimodal image search with queries based on images, texts, or their combinations.
Covariant color-kinematics duality

We show that color-kinematics duality is a manifest property of the equations of motion governing currents and field strengths. For the nonlinear sigma model (NLSM), this insight enables an implementation of the double copy at the level of fields, as well as an explicit construction of the kinematic algebra and associated kinematic current. As a byproduct, we also derive new formulations of the special Galileon (SG) and Born-Infeld (BI) theory. For Yang-Mills (YM) theory, this same approach reveals a novel structure — covariant color-kinematics duality — whose only difference from the conventional duality is that 1/□ is replaced with covariant 1/D2. Remarkably, this structure implies that YM theory is itself the covariant double copy of gauged biadjoint scalar (GBAS) theory and an F3 theory of field strengths encoding a corresponding kinematic algebra and current. Directly applying the double copy to equations of motion, we derive general relativity (GR) from the product of Einstein-YM and F3 theory. This exercise reveals a trivial variant of the classical double copy that recasts any solution of GR as a solution of YM theory in a curved background. Covariant color-kinematics duality also implies a new decomposition of tree-level amplitudes in YM theory into those of GBAS theory. Using this representation we derive a closed-form, analytic expression for all BCJ numerators in YM theory and the NLSM for any number of particles in any spacetime dimension. By virtue of the double copy, this constitutes an explicit formula for all tree-level scattering amplitudes in YM, GR, NLSM, SG, and BI.
On Path Integration of Grid Cells: Group Representation and Isotropic Scaling

Understanding how grid cells perform path integration calculations remains a fundamental problem. In this paper, we conduct theoretical analysis of a general representation model of path integration by grid cells, where the 2D self-position is encoded as a higher dimensional vector, and the 2D self-motion is represented by a general transformation of the vector. We identify two conditions on the transformation. One is a group representation condition that is necessary for path integration. The other is an isotropic scaling condition that ensures locally conformal embedding, so that the error in the vector representation translates proportionally to the error in the 2D self-position. Then we investigate the simplest transformation, i.e., the linear transformation, uncover its explicit algebraic and geometric structure as matrix Lie group of rotation, and establish the connection between the isotropic scaling condition and hexagon grid patterns of grid cells under the linear transformation. Finally, with our optimization-based approach, we manage to learn hexagon grid patterns that share similar properties of the grid cells in the rodent brain. The learned model is capable of accurate long distance path integration.
A Central Limit Theorem for Differentially Private Query Answering

Perhaps the single most important use case for differential privacy is to privately answer numerical queries, which is usually achieved by adding noise to the answer vector. The central question, therefore, is to understand which noise distribution optimizes the privacy-accuracy trade-off, especially when the dimension of the answer vector is high. Accordingly, extensive literature has been dedicated to the question and the upper and lower bounds have been matched up to constant factors [BUV18, SU17]. In this paper, we take a novel approach to address this important optimality question. We first demonstrate an intriguing central limit theorem phenomenon in the high-dimensional regime. More precisely, we prove that a mechanism is approximately Gaussian Differentially Private [DRS21] if the added noise satisfies certain conditions. In particular, densities proportional to $\mathrm{e}^{-\|x\|_p^\alpha}$, where $\|x\|_p$ is the standard $\ell_p$-norm, satisfies the conditions. Taking this perspective, we make use of the Cramer--Rao inequality and show an "uncertainty principle"-style result: the product of the privacy parameter and the $\ell_2$-loss of the mechanism is lower bounded by the dimension. Furthermore, the Gaussian mechanism achieves the constant-sharp optimal privacy-accuracy trade-off among all such noises. Our findings are corroborated by numerical experiments.
Multiscale modeling of materials: Computing, data science, uncertainty and goal-oriented optimization

Abstract The recent decades have seen various attempts at accelerating the process of developing materials targeted towards specific applications. The performance required for a particular application leads to the choice of a particular material system whose properties are optimized by manipulating its underlying microstructure through processing. The specific configuration of the structure is then designed by characterizing the material in detail, and using this characterization along with physical principles in system level simulations and optimization. These have been advanced by multiscale modeling of materials, high-throughput experimentations, materials data-bases, topology optimization and other ideas. Still, developing materials for extreme applications involving large deformation, high strain rates and high temperatures remains a challenge. This article reviews a number of recent methods that advance the goal of designing materials targeted by specific applications.
Academic Resources: Jewish Studies Programs, Holocaust and Genocide Studies Programs, Jewish Social Work Programs, Major Books, Scholarly Articles, Websites and Jewish Organizations, Judaic and Holocaust Research Libraries

This chapter provides lists with city locations, degrees offered, and websites for 257 Jewish Studies Programs, 26 Holocaust and Genocide Studies Programs, 19 Israel Studies Programs, 14 Israel Studies Professorships, and 10 Jewish Social Work Programs. The chapter also includes (1) bibliographic information on 76 books on North American Jewry; (2) names, descriptions, and websites of 42 academic journals covering North American Jewish communities; (3) 108 scholarly articles on the study of North American Jewish communities; (4) names, descriptions, and websites for 45 websites and organizations for North American Jewish community research; (5) names, descriptions, and websites for 46 major Judaic research and holocaust research libraries.
Hacia una relectura de la matriz cultural ignaciana desde nuestras necesidades civilizatorias actuales

No abstract available. Revista latinoamericana de teologia, Vol. 31, No. 93, 2014: 247-270.
The production of KIR-Fc fusion proteins and their use in a multiplex HLA class I binding assay.

Abstract Soluble recombinant proteins that comprise the extracellular part of a surface expressed receptor attached to the Fc region of an IgG antibody have facilitated the determination of ligand specificity for an array of immune system receptors. Among such receptors is the family of killer cell immunoglobulin-like receptors (KIR) that recognize HLA class I ligands. These receptors, expressed on natural killer (NK) cells and T cells, play important roles in both immune defense and placental development in early pregnancy. Here we describe a method for the production of two domain KIR–Fc fusion proteins using baculovirus infected insect cells. This method is more scalable than traditional mammalian cell expression systems and produces efficiently folded proteins that carry posttranslational modifications found in native KIR. We also describe a multiplex binding assay using the Luminex platform that determines the avidity and specificity of two domain KIR–Fc for a panel of microbeads, each coated with one of 97 HLA class I allotypes. This assay is simple to perform, and represents a major improvement over the assays used previously, which were limited in the number of KIR and HLA class I combinations that could be assayed at any one time. The results obtained from this assay can be used to predict the response of NK cell and T cells when their KIR recognize HLA class I.
Muscle-Bone Crosstalk in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS), also called Lou Gehrig’s disease, is a fatal neuromuscular disorder characterized by degeneration of motor neurons and by skeletal muscle atrophy. Although the death of motor neurons is a pathological hallmark of ALS, the potential role of other organs in disease progression remains to be elucidated. Skeletal muscle and bone are the two largest organs in the human body. They are responsible not only for locomotion but also for maintaining whole body normal metabolism and homeostasis. Patients with ALS display severe muscle atrophy, which may reflect intrinsic defects in mitochondrial respiratory function and calcium (Ca) signaling in muscle fibers, in addition to the role of axonal withdrawal associated with ALS progression. Incidence of fractures is high in ALS patients, indicating there are potential bone defects in individuals with this condition. There is a lifelong interaction between skeletal muscle and bone. The severe muscle degeneration that occurs during ALS progression may potentially have a significant impact on bone function, and the defective bone may also contribute significantly to neuromuscular degeneration in the course of the disease. Due to the nature of the rapid and severe neuromuscular symptoms, a majority of studies on ALS have focused on neurodegeneration. Just a few studies have explored the possible contribution of muscle defects, even fewer on bone defects, and fewer still on possible muscle-bone crosstalk in ALS. This review article discusses current studies on bone defects and potential defects in muscle-bone crosstalk in ALS.