cssi/SciSciGPT-SciSciNet huggingface dataset:
A system for 3D reconstruction of comminuted tibial plafond bone fractures

Abstract High energy impacts at joint locations often generate highly fragmented, or comminuted, bone fractures. Current approaches for treatment require physicians to decide how to classify the fracture within a hierarchy fracture severity categories. Each category then provides a best-practice treatment scenario to obtain the best possible prognosis for the patient. This article identifies shortcomings associated with qualitative-only evaluation of fracture severity and provides new quantitative metrics that serve to address these shortcomings. We propose a system to semi-automatically extract quantitative metrics that are major indicators of fracture severity. These include: (i) fracture surface area, i.e., how much surface area was generated when the bone broke apart, and (ii) dispersion, i.e., how far the fragments have rotated and translated from their original anatomic positions. This article describes new computational tools to extract these metrics by computationally reconstructing 3D bone anatomy from CT images with a focus on tibial plafond fracture cases where difficult qualitative fracture severity cases are more prevalent. Reconstruction is accomplished within a single system that integrates several novel algorithms that identify, extract and piece-together fractured fragments in a virtual environment. Doing so provides objective quantitative measures for these fracture severity indicators. The availability of such measures provides new tools for fracture severity assessment which may lead to improved fracture treatment. This paper describes the system, the underlying algorithms and the metrics of the reconstruction results by quantitatively analyzing six clinical tibial plafond fracture cases.
Mapping the functional landscape of the receptor binding domain of T7 bacteriophage by deep mutational scanning.

Bacteria can cause diseases, but they also battle their own microscopic enemies: a group of viruses known as bacteriophages. For instance, the T7 bacteriophage preys on various strains of Escherichia coli, a type of bacteria often found in the human gut. While many E. coli strains are inoffensive or even beneficial to human health, some can be deadly. Finding a way to kill harmful strains while sparing the helpful ones would be a helpful addition to the medicine toolkit. Bacteriophages identify and interact with their specific target through a structure known as the receptor binding protein, or RBP. However, it is still unclear exactly how RBP helps the viruses recognize which type of bacteria to infect. Here, Huss et al. set to map out and modify this structure in T7 bacteriophage so the virus is more efficient and specific about which strain of E. coli it kills. First, the role of each building block in the tip of RBP was meticulously dissected; this generated the knowledge required to genetically engineer a large number of different T7 bacteriophages, each with a slightly variation in their RBP. These viruses were then exposed to various strains of bacteria. Monitoring the bacteriophages that survived and multiplied the most after infecting different strains of E. coli revealed which RBP building blocks are important for efficiency and specificity. This was then confirmed by engineering highly active T7 bacteriophage variants against an E. coli strain that causes urinary tract infections. These findings demonstrate that even small changes to the bacteriophages can make a big difference to their ability to infect their preys. The approaches developed by Huss et al. help to understand exactly how the RBP allows a virus to infect a specific type of bacteria; this could one day pave the way for new therapies that harness those viruses to fight increasingly resistant bacterial infections.
Multipartite entanglement and topology in holography

Starting from the entanglement wedge of a multipartite mixed state we describe a purification procedure which involves the gluing of several copies. The resulting geometry has non-trivial topology and a single oriented boundary for each original boundary region. In the purified geometry the original multipartite entanglement wedge cross section is mapped to a minimal surface of a particular non-trivial homology class. In contrast, each original bipartite entanglement wedge cross section is mapped to the minimal wormhole throat around each boundary. Using the bit thread formalism we show how maximal flows for the bipartite and multipartite entanglement wedge cross section can be glued together to form maximal multiflows in the purified geometry. The defining feature differentiating the flows is given by the existence of threads which cross between different copies of the original entanglement wedge. Together these demonstrate a possible connection between multipartite entanglement and the topology of holographic spacetimes.
BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation

Recent advances in deep learning techniques have enabled machines to generate cohesive open-ended text when prompted with a sequence of words as context. While these models now empower many downstream applications from conversation bots to automatic storytelling, they have been shown to generate texts that exhibit social biases. To systematically study and benchmark social biases in open-ended language generation, we introduce the Bias in Open-Ended Language Generation Dataset (BOLD), a large-scale dataset that consists of 23,679 English text generation prompts for bias benchmarking across five domains: profession, gender, race, religion, and political ideology. We also propose new automated metrics for toxicity, psycholinguistic norms, and text gender polarity to measure social biases in open-ended text generation from multiple angles. An examination of text generated from three popular language models reveals that the majority of these models exhibit a larger social bias than human-written Wikipedia text across all domains. With these results we highlight the need to benchmark biases in open-ended language generation and caution users of language generation models on downstream tasks to be cognizant of these embedded prejudices.
Degree one Milnor K-invariants of groups of multiplicative type

Abstract Let G be a commutative affine algebraic group over a field F, and let H : Fields F → AbGrps be a functor. A (homomorphic) H-invariant of G is a natural transformation Tors ( − , G ) → H , where Tors ( − , G ) is the functor Fields F → AbGrps taking a field extension L / F to the group of isomorphism classes of G L -torsors over Spec ( L ) . The goal of this paper is to compute the group Inv hom 1 ( G , H ) of H-invariants of G when G is a group of multiplicative type, and H is the functor taking a field extension L / F to L × ⊗ Z Q / Z .
KReC-MD: Knowledge Revision with Multiple Documents

The aim of this paper is two-fold. The first aim is to review the core representational and processing aspects of influential accounts of single-document and multiple-document comprehension with a particular emphasis on how readers negotiate conflicting information during reading. This review provides the groundwork for the second aim—to expand our current account of knowledge revision during reading of single documents to multiple documents. The product of this expansion is an initial conceptualization of the Knowledge Revision Components Framework–Multiple Documents (KReC-MD). This initial conceptualization presents the theoretical foundation necessary for future empirical work and further refinement.
Stateman: Using Metafunctions to Manage Large Terms Representing Machine States

When ACL2 is used to model the operational semantics of computing machines, machine states are typically represented by terms recording the contents of the state components. When models are realistic and are stepped through thousands of machine cycles, these terms can grow quite large and the cost of simplifying them on each step grows. In this paper we describe an ACL2 book that uses HIDE and metafunctions to facilitate the management of large terms representing such states. Because the metafunctions for each state component updater are solely responsible for creating state expressions (i.e., "writing") and the metafunctions for each state component accessor are solely responsible for extracting values (i.e., "reading") from such state expressions, they can maintain their own normal form, use HIDE to prevent other parts of ACL2 from inspecting them, and use honsing to uniquely represent state expressions. The last feature makes it possible to memoize the metafunctions, which can improve proof performance in some machine models. This paper describes a general-purpose ACL2 book modeling a byte-addressed memory supporting "mixed" reads and writes. By "mixed" we mean that reads need not correspond (in address or number of bytes) with writes. Verified metafunctions simplify such "read-over-write" expressions while hiding the potentially large state expression. A key utility is a function that determines an upper bound on the value of a symbolic arithmetic expression, which plays a role in resolving writes to addresses given by symbolic expressions. We also report on a preliminary experiment with the book, which involves the production of states containing several million function calls.
Improved Algorithms for Population Recovery from the Deletion Channel.

The population recovery problem asks one to recover an unknown distribution over $n$-bit strings given access to independent noisy samples of strings drawn from the distribution. Recently, Ban et al. [BCF+19] studied the problem where the noise is induced through the deletion channel. This problem generalizes the famous trace reconstruction problem, where one wishes to learn a single string under the deletion channel. Ban et al. showed how to learn $\ell$-sparse distributions over strings using $\exp\big(n^{1/2} \cdot (\log n)^{O(\ell)}\big)$ samples. In this work, we learn the distribution using only $\exp\big(\tilde{O}(n^{1/3}) \cdot \ell^2\big)$ samples, by developing a higher-moment analog of the algorithms of [DOS17, NP17], which solve trace reconstruction in $\exp\big(\tilde{O}(n^{1/3})\big)$ samples. We also give the first algorithm with a runtime subexponential in $n$, solving population recovery in $\exp\big(\tilde{O}(n^{1/3}) \cdot \ell^3\big)$ samples and time. Notably, our dependence on $n$ nearly matches the upper bound of [DOS17, NP17] when $\ell = O(1)$, and we reduce the dependence on $\ell$ from doubly to singly exponential. Therefore, we are able to learn large mixtures of strings: while Ban et al.'s algorithm can only learn a mixture of $O(\log n/\log \log n)$ strings with a subexponential number of samples, we are able to learn a mixture of $n^{o(1)}$ strings in $\exp\big(n^{1/3 + o(1)}\big)$ samples and time.
Reasonable Grounds Evidence Involving Sexual Violence in Darfur

Internal and international conflicts can often involve a level of impunity that allows sexual violence to persist unchecked by military and political leaders. The recent reversal by an appeals panel at the International Criminal Court of a pretrial decision not to charge President al-Bashir of Sudan with genocide in Darfur offers an important foundation for introducing new types of evidence that can increase the investigation and prosecution of sexual violence during conflicts. The reversal cited the incorrect use of the “beyond a reasonable doubt” standard when the lesser standard of “reasonable grounds” applied. Social science provides methods and measures that can be uniquely used to develop reasonable grounds evidence, for example, to demonstrate the roles of physical perpetrators acting together in horizontal relationships, as well as to establish the indirect participation through vertical relationships of higher-level defendants, in a chain of command of superior responsibility. We illustrate these points by presenting social science evidence of the responsibility of President al-Bashir and middle- and lower-level figures in genocidal violence in Darfur.
Movie-watching outperforms rest for functional connectivity-based prediction of behavior.

Abstract A major goal of human neuroscience is to relate differences in brain function to differences in behavior across people. Recent work has established that whole-brain functional connectivity patterns are relatively stable within individuals and unique across individuals, and that features of these patterns predict various traits. However, while functional connectivity is most often measured at rest, certain tasks may enhance individual signals and improve sensitivity to behavior differences. Here, we show that compared to the resting state, functional connectivity measured during naturalistic viewing—i.e., movie watching—yields more accurate predictions of trait-like phenotypes in the domains of both cognition and emotion. Traits could be predicted using less than three minutes of data from single video clips, and clips with highly social content gave the most accurate predictions. Results suggest that naturalistic stimuli amplify individual differences in behaviorally relevant brain networks.
Towards More Fine-grained and Reliable NLP Performance Prediction

Performance prediction, the task of estimating a system’s performance without performing experiments, allows us to reduce the experimental burden caused by the combinatorial explosion of different datasets, languages, tasks, and models. In this paper, we make two contributions to improving performance prediction for NLP tasks. First, we examine performance predictors not only for holistic measures of accuracy like F1 or BLEU, but also fine-grained performance measures such as accuracy over individual classes of examples. Second, we propose methods to understand the reliability of a performance prediction model from two angles: confidence intervals and calibration. We perform an analysis of four types of NLP tasks, and both demonstrate the feasibility of fine-grained performance prediction and the necessity to perform reliability analysis for performance prediction methods in the future.
A unified atlas of CD8 T cell dysfunctional states in cancer and infection

Summary CD8 T cells play an essential role in defense against viral and bacterial infections and in tumor immunity. Deciphering T cell loss of functionality is complicated by the conspicuous heterogeneity of CD8 T cell states described across experimental and clinical settings. By carrying out a unified analysis of over 300 assay for transposase-accessible chromatin sequencing (ATAC-seq) and RNA sequencing (RNA-seq) experiments from 12 studies of CD8 T cells in cancer and infection, we defined a shared differentiation trajectory toward dysfunction and its underlying transcriptional drivers and revealed a universal early bifurcation of functional and dysfunctional T cell states across models. Experimental dissection of acute and chronic viral infection using single-cell ATAC (scATAC)-seq and allele-specific single-cell RNA (scRNA)-seq identified state-specific drivers and captured the emergence of similar TCF1+ progenitor-like populations at an early branch point, at which functional and dysfunctional T cells diverge. Our atlas of CD8 T cell states will facilitate mechanistic studies of T cell immunity and translational efforts.
Local Clustering with Mean Teacher for Semi-supervised learning

The Mean Teacher (MT) model of Tarvainen and Valpola has shown good performance on several semi-supervised benchmark datasets. MT maintains a teacher model's weights as the exponential moving average of a student model's weights and minimizes the divergence between their probability predictions under diverse perturbations of the inputs. However, MT is known to suffer from confirmation bias, that is, reinforcing incorrect teacher model predictions. In this work, we propose a simple yet effective method called Local Clustering (LC) to mitigate the effect of confirmation bias. In MT, each data point is considered independent of other points during training; however, data points are likely to be close to each other in feature space if they share similar features. Motivated by this, we cluster data points locally by minimizing the pairwise distance between neighboring data points in feature space. Combined with a standard classification cross-entropy objective on labeled data points, the misclassified unlabeled data points are pulled towards high-density regions of their correct class with the help of their neighbors, thus improving model performance. We demonstrate on semi-supervised benchmark datasets SVHN and CIFAR-10 that adding our LC loss to MT yields significant improvements compared to MT and performance comparable to the state of the art in semi-supervised learning11The code is available at: https://github.com/jay1204/local_clustering_with_mt_for_ssl.
Quickest Detection over Sensor Networks with Unknown Post-Change Distribution

We propose a quickest change detection problem over sensor networks where both the subset of sensors undergoing a change and the local post-change distributions are unknown. Each sensor in the network observes a local discrete time random process over a finite alphabet. Initially, the observations are independent and identically distributed (i.i.d.) with known pre-change distributions independent from other sensors. At a fixed but unknown change point, a fixed but unknown subset of the sensors undergo a change and start observing samples from an unknown distribution. We assume the change can be quantified using concave (or convex) local statistics over the space of distributions. We propose an asymptotically optimal and computationally tractable stopping time for Lorden's criterion. Under this scenario, our proposed method uses a concave global cumulative sum (CUSUM) statistic at the fusion center and suppresses the most likely false alarms using information projection. Finally, we show some numerical results of the simulation of our algorithm for the problem described.
Bayesian tensorized neural networks with automatic rank selection

Abstract Tensor decomposition is an effective approach to compress over-parameterized neural networks and to enable their deployment on resource-constrained hardware platforms. However, directly applying tensor compression in the training process is a challenging task due to the difficulty of choosing a proper tensor rank. In order to address this challenge, this paper proposes a low-rank Bayesian tensorized neural network. Our Bayesian method performs automatic model compression via an adaptive tensor rank determination. We also present approaches for posterior density calculation and maximum a posteriori (MAP) estimation for the end-to-end training of our tensorized neural network. We provide experimental validation on a two-layer fully connected neural network, a 6-layer CNN and a 110-layer residual neural network where our work produces 7.4 × to 137 × more compact neural networks directly from the training while achieving high prediction accuracy.
A Statistical Perspective on Coreset Density Estimation

Coresets have emerged as a powerful tool to summarize data by selecting a small subset of the original observations while retaining most of its information. This approach has led to significant computational speedups but the performance of statistical procedures run on coresets is largely unexplored. In this work, we develop a statistical framework to study coresets and focus on the canonical task of nonparameteric density estimation. Our contributions are twofold. First, we establish the minimax rate of estimation achievable by coreset-based estimators. Second, we show that the practical coreset kernel density estimators are near-minimax optimal over a large class of Holder-smooth densities.
Trading volume and time varying betas

Existing models of trading volume depend on shocks to agent heterogeneity to generate trade. I propose an alternative model where instead shocks to the riskiness of traded securities generate trade. The model makes three novel predictions about trading volume and securities' time-varying risk exposures: trading volume and changes in risk exposures are positively correlated; decreases in risk exposure are associated with more trading volume than increases in risk exposure; and the trading volume response to changing risk exposure decreases with the level of the risk exposure. Using data from 1962 to 2001 and measuring risk exposures as market betas, I find strong empirical support for these predictions. For example a one standard deviation decrease in beta leads to a 13% increase in turnover. This sensitivity has grown along with the secular increase in trading volume, peaking at 73% in the 2007 to 2011 subperiod. My model also provides alternative explanations for three well documented links between trading volume and (contemporaneous as well as future) price changes. This link between trading volume and conditional risk exposure provides a new channel to learn about asset prices.
A single cell gene expression atlas of 28 human livers.

ABSTRACT The liver is the largest solid organ in the human body and is responsible for a multitude of essential functions for survival. Chronic liver injury affects over 1 billion people worldwide and therapeutic options other than liver transplantation are a critical unmet medical need. Thus, advances in molecular hepatology are essential to facilitate the discovery of new therapeutic targets. Here we describe the aggregation and integration of single cell RNA-sequencing in more than 36,000 cells from 28 human livers reported in five independent studies. Noteworthy, the merged data shows a high degree of overlap, demonstrating the robustness of liver gene expression at single cell level independent of age, gender, liver collection, processing and sequencing methods. Hence, this data allowed us to develop a user-friendly web browser for quick and easy interrogation and comparison of gene expression across a variety of parenchymal and non-parenchymal liver cell populations. Collectively, this study provides the largest human liver transcriptomic single cell atlas accessible for interactive visualization via an open-access web portal to the research community worldwide.
Developing replacement heifers

Proper development of replacement heifers leads to:Higher pregnancy rate first breeding seasonLess calving difficultyHigher pregnancy rate second breeding seasonConsequently, higher returns
Predicting anomalous quantum confinement effect in van der Waals materials

Materials with van der Waals bonding are known to exhibit a quantum confinement effect, in which the electronic band gap of the three-dimensional realization of a material is lower than that of its two-dimensional (2D) counterpart. However, the possibility of an anomalous quantum confinement effect (AQCE) exists, where the band gap trend is reversed. In this work, we computationally identify materials for which such AQCE occurs. Using density-functional theory, we compute \ensuremath{\sim}1000 OptB88vdW (semilocal functional), \ensuremath{\sim}50 HSE06, and \ensuremath{\sim}50 PBE0 (hybrid functional) band gaps for bulk and their corresponding monolayers in the JARVIS-DFT database. OptB88vdW identifies 65 AQCE materials, but the hybrid functionals only confirm such findings in 14 cases. Some of the AQCE systems identified through HSE06 and PBE0 are hydroxides or oxide hydroxide compounds [$\mathrm{AlO}{\mathrm{H}}_{2}$, $\mathrm{Mg}{(\mathrm{OH})}_{2}$, ${\mathrm{Mg}}_{2}{\mathrm{H}}_{2}{\mathrm{O}}_{3}$, $\mathrm{Ni}{(\mathrm{OH})}_{2}$, and $\mathrm{Sr}{\mathrm{H}}_{2}{\mathrm{O}}_{3}$], as well as Sb-halogen-chalcogenide compounds (SbSBr and SbSeI) and alkali-chalcogenides (RbLiS and RbLiSe). A detailed electronic structure analysis, based on band structure and projected density of states, shows AQCE is often characterized by lowering of the conduction band in the monolayer and corresponding changes in the ${p}_{z}$ electronic orbital contribution, with $z$ being the nonperiodic direction in the 2D case. We investigated the possibility of these materials being topological through spin-orbit spillage calculations and determined that they are not. We believe our computational results would spur the effort to validate the results experimentally and will have an impact on band-gap engineering applications based on low-dimensional materials. Our systematic approach also shows that data-driven approaches can be used to discover a new phenomenon such as AQCE.
An Empirical Analysis of UI-based Flaky Tests

Flaky tests have gained attention from the research community in recent years and with good reason. These tests lead to wasted time and resources, and they reduce the reliability of the test suites and build systems they affect. However, most of the existing work on flaky tests focus exclusively on traditional unit tests. This work ignores UI tests that have larger input spaces and more diverse running conditions than traditional unit tests. In addition, UI tests tend to be more complex and resource-heavy, making them unsuited for detection techniques involving rerunning test suites multiple times. In this paper, we perform a study on flaky UI tests. We analyze 235 flaky UI test samples found in 62 projects from both web and Android environments. We identify the common underlying root causes of flakiness in the UI tests, the strategies used to manifest the flaky behavior, and the fixing strategies used to remedy flaky UI tests. The findings made in this work can provide a foundation for the development of detection and prevention techniques for flakiness arising in UI tests.
Channel Estimation for Intelligent Reflecting Surface Assisted Wireless Communications

In this paper, the minimum mean square error (MMSE) channel estimation for intelligent reflecting surface (IRS) assisted wireless communication systems is investigated. In the considered setting, each row vector of the equivalent channel matrix from the base station (BS) to the users is shown to be Bessel K distributed, and all these row vectors are independent of each other. By introducing a Gaussian scale mixture model, we obtain a closed-form expression for the MMSE estimate of the equivalent channel, and determine analytical upper and lower bounds on the mean square error. Using the central limit theorem, we conduct an asymptotic analysis of the MMSE estimate, and show that the upper bound on the mean square error of the MMSE estimate is equal to the asymptotic mean square error of the MMSE estimation when the number of reflecting elements at the IRS tends to infinity. Numerical simulations show that the gap between the upper and lower bounds are very small, and they almost overlap with each other at medium signal-to-noise ratio (SNR) levels and moderate number of elements at the IRS.
The Trueness of Obturator Prosthesis Base Manufactured by Conventional and 3D Printing Techniques.

PURPOSE To compare the intaglio surface trueness of obturator prosthesis bases manufactured by traditional compression molding, injection molding, and 3D printing techniques. MATERIALS AND METHODS A complete edentulous master cast with Aramany Class I maxillary defect was selected for this in vitro study. Four study groups (n = 10/group) were included in this study, Group A: Compression Molding, Group B: Injection Molding, and Group C: Cara Print 3D DLP Printer, and Group D: Carbon 3D DLS Printer. All obturator prostheses' intaglio surfaces were scanned with a laboratory scanner (E4; 3Shape Inc, New Providence, NJ) and the dimensional differences between study samples and their corresponding casts were calculated as the root mean square (measured in mm, absolute value) using a surface matching software (Geomagic design X; 3D Systems, Rock Hill, SC). One-way Analysis of variance (ANOVA) and Fisher's least significant difference (LSD) test were used to compare groups differences in RMS (α = 0.05). RESULTS There was a significant effect of manufacturing technique on the RMS values for the 4 conditions [F(3,36) = 5.743, p = 0.003]. Injection Molding (0.070 mm) and Compression Molding groups (0.076 mm) had a lower interquartile range, and the Cara Print 3D-Printer group (0.427 mm) and Carbon 3D-Printer (0.149 mm) groups had a higher interquartile range. The Injection Molding group showed the best and uniform surface matching with the most area in green in the color maps. The Injection Molding group (0.139 ± 0.049 mm) had significantly lower RMS than all other groups (p < 0.001 for all comparisons). Compression Molding (0.269 ± 0.057 mm), Cara Print 3D-Printer (0.409 ± 0.270 mm), and Carbon 3D-Printer (0.291 ± 0.082 mm) groups were not significantly different from each other (Compression Molding versus Carbon 3D-Printer, p = 0.59; Compression Molding versus Cara Print 3D-Printer, p = 0.25; Cara Print 3D-Printer versus Carbon 3D-Printer, p = 0.40). CONCLUSION Obturator prosthesis bases manufactured with injection molding technique showed better intaglio surface trueness than ones made by the compression molding technique and 3D printers. Although obturator prosthesis bases manufactured from different 3D printers showed similar trueness, a DLP 3D printer produced less consistent outcome than a DLS 3D printer.
Gene Ontology Meta Annotator for Plants (GOMAP)

Annotating gene structures and functions to genome assemblies is necessary to make assembly resources useful for biological inference. Gene Ontology (GO) term assignment is the most used functional annotation system, and new methods for GO assignment have improved the quality of GO-based function predictions. The Gene Ontology Meta Annotator for Plants (GOMAP) is an optimized, high-throughput, and reproducible pipeline for genome-scale GO annotation of plants. We containerized GOMAP to increase portability and reproducibility and also optimized its performance for HPC environments. Here we report on the pipeline’s availability and performance for annotating large, repetitive plant genomes and describe how GOMAP was used to annotate multiple maize genomes as a test case. Assessment shows that GOMAP expands and improves the number of genes annotated and annotations assigned per gene as well as the quality (based on $$F_{max}$$ ) of GO assignments in maize. GOMAP has been deployed to annotate other species including wheat, rice, barley, cotton, and soy. Instructions and access to the GOMAP Singularity container are freely available online at https://bioinformapping.com/gomap/ . A list of annotated genomes and links to data is maintained at https://dill-picl.org/projects/gomap/ .
Chromosomal characteristics of salt stress heritable gene expression in the rice genome

Background Gene expression is potentially an important heritable quantitative trait that mediates between genetic variation and higher-level complex phenotypes through time and condition-dependent regulatory interactions. Therefore, we sought to explore both the genomic and condition-specific characteristics of gene expression heritability within the context of chromosomal structure. Results Heritability was estimated for biological gene expression using a diverse, 84-line, Oryza sativa (rice) population under optimal and salt-stressed conditions. Overall, 5936 genes were found to have heritable expression regardless of condition and 1377 genes were found to have heritable expression only during salt stress. These genes with salt-specific heritable expression are enriched for functional terms associated with response to stimulus and transcription factor activity. Additionally, we discovered that highly and lowly expressed genes, and genes with heritable expression are distributed differently along the chromosomes in patterns that follow previously identified high-throughput chromosomal conformation capture (Hi-C) A/B chromatin compartments. Furthermore, multiple genomic hot-spots enriched for genes with salt-specific heritability were identified on chromosomes 1, 4, 6, and 8. These hotspots were found to contain genes functionally enriched for transcriptional regulation and overlaps with a previously identified major QTL for salt-tolerance in rice. Conclusions Investigating the heritability of traits, and in-particular gene expression traits, is important towards developing a basic understanding of how regulatory networks behave across a population. This work provides insights into spatial patterns of heritable gene expression at the chromosomal level.
Constructing Taxonomies from Pretrained Language Models.

We present a method for constructing taxonomic trees (e.g., WordNet) using pretrained language models. Our approach is composed of two modules, one that predicts parenthood relations and another that reconciles those pairwise predictions into trees. The parenthood prediction module produces likelihood scores for each potential parent-child pair, creating a graph of parent-child relation scores. The tree reconciliation module treats the task as a graph optimization problem and outputs the maximum spanning tree of this graph. We train our model on subtrees sampled from WordNet, and test on nonoverlapping WordNet subtrees. We show that incorporating web-retrieved glosses can further improve performance. On the task of constructing subtrees of English WordNet, the model achieves 66.7 ancestor F1, a 20.0% relative increase over the previous best published result on this task. In addition, we convert the original English dataset into nine other languages using Open Multilingual WordNet and extend our results across these languages.
A Frustratingly Easy Approach for Entity and Relation Extraction.

End-to-end relation extraction aims to identify named entities and extract relations between them. Most recent work models these two subtasks jointly, either by casting them in one structured prediction framework, or performing multi-task learning through shared representations. In this work, we present a simple pipelined approach for entity and relation extraction, and establish the new state-of-the-art on standard benchmarks (ACE04, ACE05 and SciERC), obtaining a 1.7%-2.8% absolute improvement in relation F1 over previous joint models with the same pre-trained encoders. Our approach essentially builds on two independent encoders and merely uses the entity model to construct the input for the relation model. Through a series of careful examinations, we validate the importance of learning distinct contextual representations for entities and relations, fusing entity information early in the relation model, and incorporating global context. Finally, we also present an efficient approximation to our approach which requires only one pass of both entity and relation encoders at inference time, achieving an 8-16× speedup with a slight reduction in accuracy.
Image restoration using a conjugate gradient-based adaptive filtering algorithm

A new two-dimensional (2D) sample-based conjugate gradient (SCG) algorithm is developed for adaptive filtering. This algorithm is based on the conjugate gradient method of optimization and therefore has a fast convergence characteristic. The SCG is computationally simpler than the recursive least squares (RLS) algorithm. The SCG algorithm with the equation-error and output-error methods is investigated for application in image restoration. Simulation results show that the new algorithm significantly outperforms existing algorithms in the restoration of noisy images.
Social Media and Misleading Information in a Democracy: A Mechanism Design Approach

In this paper, we present a resource allocation mechanism to incentivize misinformation filtering among strategic social media platforms, and thus, to indirectly prevent the spread of fake news. We consider the presence of a strategic government and private knowledge of how misinformation affects the users of the social media platforms. Our proposed mechanism strongly implements all generalized Nash equilibria for efficient filtering of misleading information in the induced game with a balanced budget. We also show that for quasi-concave utilities our mechanism implements a Pareto efficient solution.
Annealing Characteristics of Amorphous Silicon Alloy Solar Cells Irradiated with 1.00 MeV Protons

a-Si:H solar cells were irradiated with 1.00 MeV proton fluences in the range of 1.00E14 to 1.25E15 cm−2. Annealing of the short-circuit current density was studied at 0, 22, 50, 100 and 150 °C. Annealing times ranged from an hour to several days. The measurements confirmed that annealing occurs at 0 °C and the initial characteristics of the cells are restored by annealing at 200 °C. It is proposed that the degradation in the short-circuit current density with irradiation is due to carrier recombination through the fraction of D° states bounded by the quasi-Fermi energies. The time dependence of the rate of annealing in the short-circuit current density appears to be consistent with the interpretation that there is dispersive transport mechanism which leads to the annealing of the irradiation induced defects.
Fourier neural networks as function approximators and differential equation solvers

We present a Fourier neural network (FNN) that can be mapped directly to the Fourier decomposition. The choice of activation and loss function yields results that replicate a Fourier series expansion closely while preserving a straightforward architecture with a single hidden layer. The simplicity of this network architecture facilitates the integration with any other higher-complexity networks, at a data pre- or postprocessing stage. We validate this FNN on naturally periodic smooth functions and on piecewise continuous periodic functions. We showcase the use of this FNN for modeling or solving partial differential equations with periodic boundary conditions. The main advantages of the current approach are the validity of the solution outside the training region, interpretability of the trained model, and simplicity of use.
Data-Free Knowledge Distillation with Soft Targeted Transfer Set Synthesis.

Knowledge distillation (KD) has proved to be an effective approach for deep neural network compression, which learns a compact network (student) by transferring the knowledge from a pre-trained, over-parameterized network (teacher). In traditional KD, the transferred knowledge is usually obtained by feeding training samples to the teacher network to obtain the class probabilities. However, the original training dataset is not always available due to storage costs or privacy issues. In this study, we propose a novel data-free KD approach by modeling the intermediate feature space of the teacher with a multivariate normal distribution and leveraging the soft targeted labels generated by the distribution to synthesize pseudo samples as the transfer set. Several student networks trained with these synthesized transfer sets present competitive performance compared to the networks trained with the original training set and other data-free KD approaches.
COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences

Commonsense reasoning is intuitive for humans but has been a long-term challenge for artificial intelligence (AI). Recent advancements in pretrained language models have shown promising results on several commonsense benchmark datasets. However, the reliability and comprehensiveness of these benchmarks towards assessing model's commonsense reasoning ability remains unclear. To this end, we introduce a new commonsense reasoning benchmark dataset comprising natural language true/false statements, with each sample paired with its complementary counterpart, resulting in 4k sentence pairs. We propose a pairwise accuracy metric to reliably measure an agent's ability to perform commonsense reasoning over a given situation. The dataset is crowdsourced and enhanced with an adversarial model-in-the-loop setup to incentivize challenging samples. To facilitate a systematic analysis of commonsense capabilities, we design our dataset along the dimensions of knowledge domains, reasoning scenarios and numeracy. Experimental results demonstrate that our strongest baseline (UnifiedQA-3B), after fine-tuning, achieves ~71% standard accuracy and ~51% pairwise accuracy, well below human performance (~95% for both metrics). The dataset is available at this https URL.
HERALD: An Annotation Efficient Method to Detect User Disengagement in Social Conversations

Open-domain dialog systems have a user-centric goal: to provide humans with an engaging conversation experience. User engagement is one of the most important metrics for evaluating open-domain dialog systems, and could also be used as real-time feedback to benefit dialog policy learning. Existing work on detecting user disengagement typically requires hand-labeling many dialog samples. We propose HERALD, an efficient annotation framework that reframes the training data annotation process as a denoising problem. Specifically, instead of manually labeling training samples, we first use a set of labeling heuristics to label training samples automatically. We then denoise the weakly labeled data using the Shapley algorithm. Finally, we use the denoised data to train a user engagement detector. Our experiments show that HERALD improves annotation efficiency significantly and achieves 86% user disengagement detection accuracy in two dialog corpora.
Moving Beyond Narcan: A Police, Social Service, and Researcher Collaborative Response to the Opioid Crisis.

The opioid crisis is the most persistent, long-term public health emergency facing the United States, and available evidence suggests the crisis has worsened during the COVID-19 global pandemic. Naloxone is an effective overdose response that saves lives, but the drug does not address problematic drug use, addiction, or the underlying conditions that lead to overdoses. The opioid crisis is at its core a multidisciplinary, multisystem problem, and an effective response to the crisis requires collaboration across those various systems. This paper describes such a collaborative effort. The Tempe First-Responder Opioid Recovery Project is a multidisciplinary partnership that includes police officers, social workers, substance use peer counselors, public health professionals, police researchers, and drug policy/harm reduction researchers. The project, 10 months underway, trained and equipped Tempe (AZ) police officers to administer Narcan. In addition, a 24/7 in-person "Crisis Outreach Response Team" rapidly responds to any suspected overdose and offers follow-up support, referrals, and services to the individual (and loved ones) for up to 45 days after the overdose. We present preliminary project data including interviews with project managers, counselors, and police officers, descriptions of Narcan administrations in the field, and aggregate data on client service engagement. These data highlight the complexity of the opioid crisis, the collaborative nature of the Tempe project, and the importance of initiating a multidisciplinary, comprehensive response to effectively deal with the opioid problem.
A Distributed Model-Free Algorithm for Multi-Hop Ride-Sharing Using Deep Reinforcement Learning

The growth of autonomous vehicles, ridesharing systems, and self-driving technology will bring a shift in the way ride hailing platforms plan out their services. However, these advances in technology coupled with road congestion, environmental concerns, fuel usage, vehicles emissions, and the high cost of the vehicle usage have brought more attention to better utilize the use of vehicles and their capacities. In this paper, we propose a novel distributed multi-hop ride-sharing (MHRS) algorithm that uses deep reinforcement learning to learn optimal vehicle dispatch and matching decisions by interacting with the external environment. By allowing customers to transfer between vehicles, i.e., ride with one vehicle for some time and then transfer to another one, MHRS helps in attaining 30% lower cost and 20% more efficient utilization of fleets, as compared to the ride-sharing algorithms. This flexibility of multi-hop feature gives a seamless experience to customers and ride-sharing companies, and thus improves ride-sharing services.
Characteristic classes and stability conditions for projective Kleinian orbisurfaces

We construct Bridgeland stability conditions on the derived category of smooth quasi-projective Deligne–Mumford surfaces whose coarse moduli spaces have ADE singularities. This unifies the construction for smooth surfaces and Bridgeland’s work on Kleinian singularities. The construction hinges on an orbifold version of the Bogomolov–Gieseker inequality for slope semistable sheaves on the stack, and makes use of the Toen–Hirzebruch–Riemann–Roch theorem.
Recent Case: The Turkish Decision on Hagia Sophia

On July 2, 2020, a division of Turkey’s highest administrative appellate court annulled[reference_link 1] a 1934 presidential decision[reference_link 2] by Kemal Ataturk, founding president of Turkey, converting Hagia Sophia (tr. Aya Sofya) into a museum. Days later, on July 10, 2020, Recep Tayyip Erdogan issued a decision[reference_link 3] based on the court ruling, restoring its status as a mosque open to worship and transferring its maintenance to the country’s Presidency of Religious Affairs. Following a Turkish administrative court ruling that revoked an earlier administrative decision (1934) converting the mosque into a museum, President Erdogan of Turkey was expected[reference_link 4] to restore Hagia Sophia’s status as a mosque. Upon his decision to restore the site’s status as a mosque open to worship, Erdogan personally inspected[reference_link 5] the site and the preparations to have it ready for the Friday prayer on July 24, 2020. The government quickly named[reference_link 6] 3 imāms, one a professor of religious studies, for Hagia Sophia. On July 24, 2020, Erdogan, accompanied by top government officials and politicians, participated[reference_link 7] in the first Friday prayer at the site after a 86-year hiatus where he recited passages from the Qur’ān. 350,000 people are estimated[reference_link 8] to have been in attendance). For further context, see the Case Roundup on the Islamic Law Blog.[reference_link 9]
A Differential Game Approach for Beyond Visual Range Tactics

An operational relevant conflict between teams of autonomous vehicles in the Beyond Visual Range domain is addressed in this paper. Optimal strategies are designed in order for a team of air interceptors to protect a high value asset and block the attacking team at a safe distance from such asset. The attacking agents take specific roles of leader and wingman and also devise their own optimal strategies in order to launch an attack as close as possible from the asset. The problem is formulated as a zero-sum differential game between players with different speed over two stages: the attack and the retreat stages. For each stage, the state-feedback optimal strategies of each player are derived in analytical form.