cssi/SciSciGPT-SciSciNet huggingface dataset:
Sparsity, variance and curvature in multi-armed bandits

In (online) learning theory the concepts of sparsity, variance and curvature are well-understood and are routinely used to obtain refined regret and generalization bounds. In this paper we further our understanding of these concepts in the more challenging limited feedback scenario. We consider the adversarial multi-armed bandit and linear bandit settings and solve several open problems pertaining to the existence of algorithms with favorable regret bounds under the following assumptions: (i) sparsity of the individual losses, (ii) small variation of the loss sequence, and (iii) curvature of the action set. Specifically we show that (i) for $s$-sparse losses one can obtain $\tilde{O}(\sqrt{s T})$-regret (solving an open problem by Kwon and Perchet), (ii) for loss sequences with variation bounded by $Q$ one can obtain $\tilde{O}(\sqrt{Q})$-regret (solving an open problem by Kale and Hazan), and (iii) for linear bandit on an $\ell_p^n$ ball one can obtain $\tilde{O}(\sqrt{n T})$-regret for $p \in [1,2]$ and one has $\tilde{\Omega}(n \sqrt{T})$-regret for $p>2$ (solving an open problem by Bubeck, Cesa-Bianchi and Kakade). A key new insight to obtain these results is to use regularizers satisfying more refined conditions than general self-concordance
Adversarial feature matching for text generation

The Generative Adversarial Network (GAN) has achieved great success in generating realistic (real-valued) synthetic data. However, convergence issues and difficulties dealing with discrete data hinder the applicability of GAN to text. We propose a framework for generating realistic text via adversarial training. We employ a long short-term memory network as generator, and a con-volutional network as discriminator. Instead of using the standard objective of GAN, we propose matching the high-dimensional latent feature distributions of real and synthetic sentences, via a kernelized discrepancy metric. This eases adversarial training by alleviating the mode-collapsing problem. Our experiments show superior performance in quantitative evaluation, and demonstrate that our model can generate realistic-looking sentences.
Local refinement typing

We introduce the FUSION algorithm for local refinement type inference, yielding a new SMT-based method for verifying programs with polymorphic data types and higher-order functions. FUSION is concise as the programmer need only write signatures for (externally exported) top-level functions and places with cyclic (recursive) dependencies, after which FUSION can predictably synthesize the most precise refinement types for all intermediate terms (expressible in the decidable refinement logic), thereby checking the program without false alarms. We have implemented FUSION and evaluated it on the benchmarks from the LiquidHaskell suite totalling about 12KLOC. FUSION checks an existing safety benchmark suite using about half as many templates as previously required and nearly 2 × faster. In a new set of theorem proving benchmarks FUSION is both 10 — 50 × faster and, by synthesizing the most precise types, avoids false alarms to make verification possible.
Duality for toric Landau–Ginzburg models

We introduce a duality construction for toric Landau-Ginzburg models, applicable to complete intersections in toric varieties via the sigma model / Landau-Ginzburg model correspondence. This construction is shown to reconstruct those of Batyrev-Borisov, Berglund-H"ubsch, Givental, and Hori-Vafa. It can be done in more general situations, and provides partial resolutions when the above constructions give a singular mirror. An extended example is given: the Landau-Ginzburg models dual to elliptic curves in (P^1)^2 .
Symplectic Fillings, Contact Surgeries, and Lagrangian Disks

This paper completely answers the question of when contact (r)-surgery on a Legendrian knot in the standard contact structure on the 3-sphere yields a symplectically fillable contact manifold for r in (0,1]. We also give obstructions for other positive r and investigate Lagrangian fillings of Legendrian knots.
Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images

Automatic and accurate Gleason grading of histopathology tissue slides is crucial for prostate cancer diagnosis, treatment, and prognosis. Usually, histopathology tissue slides from different institutions show heterogeneous appearances because of different tissue preparation and staining procedures, thus the predictable model learned from one domain may not be applicable to a new domain directly. Here we propose to adopt unsupervised domain adaptation to transfer the discriminative knowledge obtained from the source domain to the target domain without requiring labeling of images at the target domain. The adaptation is achieved through adversarial training to find an invariant feature space along with the proposed Siamese architecture on the target domain to add a regularization that is appropriate for the whole-slide images. We validate the method on two prostate cancer datasets and obtain significant classification improvement of Gleason scores as compared with the baseline models.
On the infinitesimal space of UQR mappings

Generalized derivatives and infinitesimal spaces generalize the idea of derivatives to mappings which need not be differentiable. It is particularly powerful in the context of quasiregular mappings, where normal family arguments imply generalized derivatives always exist. The main result of this paper is to show that if f is any uniformly quasiregular mapping with \(x_0\) a topologically attracting or repelling fixed point, at which f is locally injective, then f may be conjugated to a uniformly quasiregular mapping g with fixed point 0 and so that the infinitesimal space of g at 0 contains uncountably many elements. This should be contrasted with the fact that f (and also g) is conjugate to \(x\,\mapsto\, x/2\) or \(x\,\mapsto\, 2x\) in the attracting or repelling cases respectively.
The asymptotic equivalence of fixed heat flux and fixed temperature thermal boundary conditions for rapidly rotating convection

The influence of fixed temperature and fixed heat flux thermal boundary conditions on rapidly rotating convection in the plane layer geometry is investigated for the case of stress-free mechanical boundary conditions. It is shown that whereas the leading-order system satisfies fixed temperature boundary conditions implicitly, a double boundary layer structure is necessary to satisfy the fixed heat flux thermal boundary conditions. The boundary layers consist of a classical Ekman layer adjacent to the solid boundaries that adjust viscous stresses to zero, and a layer in thermal wind balance just outside the Ekman layers that adjusts the normal derivative of the temperature fluctuation to zero. The influence of these boundary layers on the interior geostrophically balanced convection is shown to be asymptotically weak, however. Upon defining a simple rescaling of the thermal variables, the leading-order reduced system of governing equations is therefore equivalent for both boundary conditions. These results imply that any horizontal thermal variation along the boundaries that varies on the scale of the convection has no leading-order influence on the interior convection, thus providing insight into geophysical and astrophysical flows where stress-free mechanical boundary conditions are often assumed.
Neural mechanisms underlying catastrophic failure in human-machine interaction during aerial navigation.

Objective. We investigated the neural correlates of workload buildup in a fine visuomotor task called the boundary avoidance task (BAT). The BAT has been known to induce naturally occurring failures of human–machine coupling in high performance aircraft that can potentially lead to a crash—these failures are termed pilot induced oscillations (PIOs). Approach. We recorded EEG and pupillometry data from human subjects engaged in a flight BAT simulated within a virtual 3D environment. Main results. We find that workload buildup in a BAT can be successfully decoded from oscillatory features in the electroencephalogram (EEG). Information in delta, theta, alpha, beta, and gamma spectral bands of the EEG all contribute to successful decoding, however gamma band activity with a lateralized somatosensory topography has the highest contribution, while theta band activity with a fronto-central topography has the most robust contribution in terms of real-world usability. We show that the output of the spectral decoder can be used to predict PIO susceptibility. We also find that workload buildup in the task induces pupil dilation, the magnitude of which is significantly correlated with the magnitude of the decoded EEG signals. These results suggest that PIOs may result from the dysregulation of cortical networks such as the locus coeruleus (LC)—anterior cingulate cortex (ACC) circuit. Significance. Our findings may generalize to similar control failures in other cases of tight man-machine coupling where gains and latencies in the control system must be inferred and compensated for by the human operators. A closed-loop intervention using neurophysiological decoding of workload buildup that targets the LC-ACC circuit may positively impact operator performance in such situations.
Altered Fingerprints: Detection and Localization

Fingerprint alteration, also referred to as obfuscation presentation attack, is to intentionally tamper or damage the real friction ridge patterns to avoid identification by an AFIS. This paper proposes a method for detection and localization of fingerprint alterations. Our main contributions are: (i) design and train CNN models on fingerprint images and minutiae-centered local patches in the image to detect and localize regions of fingerprint alterations, and (ii) train a Generative Adversarial Network (GAN) to synthesize altered fingerprints whose characteristics are similar to true altered fingerprints. A successfully trained GAN can alleviate the limited availability of altered fingerprint images for research. A database of 4,815 altered fingerprints from 270 subjects, and an equal number of rolled fingerprint images are used to train and test our models. The proposed approach achieves a True Detection Rate (TDR) of 99.24% at a False Detection Rate (FDR) of 2%, outperforming published results. The altered fingerprint detection and localization model and code, and the synthetically generated altered fingerprint dataset will be open-sourced.
Every finite subset of an abelian group is an asymptotic approximate group

Abstract If A is a nonempty subset of an additive abelian group G, then the h-fold sumset is h A = { x 1 + ⋯ + x h : x i ∈ A i for i = 1 , 2 , … , h } . We do not assume that A contains the identity, nor that A is symmetric, nor that A is finite. The set A is an ( r , l ) -approximate group in G if there exists a subset X of G such that | X | ≤ l and r A ⊆ X A . The set A is an asymptotic ( r , l ) -approximate group if the sumset hA is an ( r , l ) -approximate group for all sufficiently large h. It is proved that every polytope in a real vector space is an asymptotic ( r , l ) -approximate group, that every finite set of lattice points is an asymptotic ( r , l ) -approximate group, and that every finite subset of every abelian group is an asymptotic ( r , l ) -approximate group.
Identification, Interpretability, and Bayesian Word Embeddings

Social scientists have recently turned to analyzing text using tools from natural language processing like word embeddings to measure concepts like ideology, bias, and affinity. However, word embeddings are difficult to use in the regression framework familiar to social scientists: embeddings are are neither identified, nor directly interpretable. I offer two advances on standard embedding models to remedy these problems. First, I develop Bayesian Word Embeddings with Automatic Relevance Determination priors, relaxing the assumption that all embedding dimensions have equal weight. Second, I apply work identifying latent variable models to anchor embeddings, identifying them, and making them interpretable and usable in a regression. I then apply this model and anchoring approach to two cases, the shift in internationalist rhetoric in the American presidents’ inaugural addresses, and the relationship between bellicosity in American foreign policy decision-makers’ deliberations. I find that inaugural addresses became less internationalist after 1945, which goes against the conventional wisdom, and that an increase in bellicosity is associated with an increase in hostile actions by the United States, showing that elite deliberations are not cheap talk, and helping confirm the validity of the model.
Attack Strength vs. Detectability Dilemma in Adversarial Machine Learning

As the prevalence and everyday use of machine learning algorithms, along with our reliance on these algorithms grow dramatically, so do the efforts to attack and undermine these algorithms with malicious intent, resulting in a growing interest in adversarial machine learning. A number of approaches have been developed that can render a machine learning algorithm ineffective through poisoning or other types of attacks. Most attack algorithms typically use sophisticated optimization approaches, whose objective function is designed to cause maximum damage to accuracy or performance of the algorithm with respect to some task. In this effort, we show that while such an objective function is indeed brutally effective in causing maximum damage on an embedded feature selection task, it often results in an attack mechanism that can be easily detected with an embarrassingly simple novelty or outlier detection algorithm. We then propose an equally simple yet elegant solution by adding a regularization term to the attacker's objective function that penalizes outlying attack points.
ASP Vision: Optically Computing the First Layer of Convolutional Neural Networks Using Angle Sensitive Pixels

Deep learning using convolutional neural networks (CNNs) is quickly becoming the state-of-the-art for challenging computer vision applications. However, deep learning's power consumption and bandwidth requirements currently limit its application in embedded and mobile systems with tight energy budgets. In this paper, we explore the energy savings of optically computing the first layer of CNNs. To do so, we utilize bio-inspired Angle Sensitive Pixels (ASPs), custom CMOS diffractive image sensors which act similar to Gabor filter banks in the V1 layer of the human visual cortex. ASPs replace both image sensing and the first layer of a conventional CNN by directly performing optical edge filtering, saving sensing energy, data bandwidth, and CNN FLOPS to compute. Our experimental results (both on synthetic data and a hardware prototype) for a variety of vision tasks such as digit recognition, object recognition, and face identification demonstrate 97% reduction in image sensor power consumption and 90% reduction in data bandwidth from sensor to CPU, while achieving similar performance compared to traditional deep learning pipelines.
Collisions and spirals of Loewner traces

We analyze Loewner traces driven by functions asymptotic to K\sqrt{1-t}. We prove a stability result when K is not 4 and show that K=4 can lead to non locally connected hulls. As a consequence, we obtain a driving term \lambda(t) so that the hulls driven by K\lambda(t) are generated by a continuous curve for all K > 0 with K not equal to 4 but not when K = 4, so that the space of driving terms with continuous traces is not convex. As a byproduct, we obtain an explicit construction of the traces driven by K\sqrt{1-t} and a conceptual proof of the corresponding results of Kager, Nienhuis and Kadanoff, math-ph/0309006
Iterative machine teaching

In this paper, we consider the problem of machine teaching, the inverse problem of machine learning. Different from traditional machine teaching which views the learners as batch algorithms, we study a new paradigm where the learner uses an iterative algorithm and a teacher can feed examples sequentially and intelligently based on the current performance of the learner. We show that the teaching complexity in the iterative case is very different from that in the batch case. Instead of constructing a minimal training set for learners, our iterative machine teaching focuses on achieving fast convergence in the learner model. Depending on the level of information the teacher has from the learner model, we design teaching algorithms which can provably reduce the number of teaching examples and achieve faster convergence than learning without teachers. We also validate our theoretical findings with extensive experiments on different data distribution and real image datasets.
Learning Hierarchical Discourse-level Structure for Fake News Detection

On the one hand, nowadays, fake news articles are easily propagated through various online media platforms and have become a grand threat to the trustworthiness of information. On the other hand, our understanding of the language of fake news is still minimal. Incorporating hierarchical discourse-level structure of fake and real news articles is one crucial step toward a better understanding of how these articles are structured. Nevertheless, this has rarely been investigated in the fake news detection domain and faces tremendous challenges. First, existing methods for capturing discourse-level structure rely on annotated corpora which are not available for fake news datasets. Second, how to extract out useful information from such discovered structures is another challenge. To address these challenges, we propose Hierarchical Discourse-level Structure for Fake news detection. HDSF learns and constructs a discourse-level structure for fake/real news articles in an automated and data-driven manner. Moreover, we identify insightful structure-related properties, which can explain the discovered structures and boost our understating of fake news. Conducted experiments show the effectiveness of the proposed approach. Further structural analysis suggests that real and fake news present substantial differences in the hierarchical discourse-level structures.
Deep Fully-Connected Networks for Video Compressive Sensing

Abstract In this work we present a deep learning framework for video compressive sensing. The proposed formulation enables recovery of video frames in a few seconds at significantly improved reconstruction quality compared to previous approaches. Our investigation starts by learning a linear mapping between video sequences and corresponding measured frames which turns out to provide promising results. We then extend the linear formulation to deep fully-connected networks and explore the performance gains using deeper architectures. Our analysis is always driven by the applicability of the proposed framework on existing compressive video architectures. Extensive simulations on several video sequences document the superiority of our approach both quantitatively and qualitatively. Finally, our analysis offers insights into understanding how dataset sizes and number of layers affect reconstruction performance while raising a few points for future investigation.
The acyclicity of the Frobenius functor for modules of finite flat dimension

Let R be a commutative Noetherian local ring of prime characteristic p and f:R⟶Rf:R⟶R the Frobenius ring homomorphism. For e≥1e≥1 let R(e)R(e) denote the ring R viewed as an R -module via fefe. Results of Peskine, Szpiro, and Herzog state that for finitely generated modules M, M has finite projective dimension if and only if ToriR(R(e),M)=0 for all i>0i>0 and all (equivalently, infinitely many) e≥1e≥1. We prove this statement holds for arbitrary modules using the theory of flat covers and minimal flat resolutions.
Multigraph Hyperplane Arrangements and Parking Functions

Pak and Stanley introduced a labeling of the regions of a k-Shi arrangement by k-parking functions and proved its bijectivity. Duval, Klivans, and Martin considered a modification of this construction associated with a graph G. They introduced the G-Shi arrangement and a labeling of its regions by G-parking functions. They conjectured that their labeling is surjective, i.e., that every G-parking function appears as a label of a region of the G-Shi arrangement. Later Hopkins and Perkinson proved this conjecture. In particular, this provided a new proof of the bijectivity of Pak-Stanley labeling in the k = 1 case. We generalize Hopkins-Perkinson’s construction to the case of arrangements associated with oriented multigraphs. In particular, our construction provides a simple straightforward proof of the bijectivity of the original Pak-Stanley labeling for arbitrary k.
Bousfield Localization and Algebras over Colored Operads

We provide a very general approach to placing model structures and semi-model structures on algebras over symmetric colored operads. Our results require minimal hypotheses on the underlying model category $\mathcal {M}$ , and these hypotheses vary depending on what is known about the colored operads in question. We obtain results for the classes of colored operad which are cofibrant as a symmetric collection, entrywise cofibrant, or arbitrary. As the hypothesis on the operad is weakened, the hypotheses on $\mathcal {M}$ must be strengthened. Via a careful development of the categorical algebra of colored operads we provide a unified framework which allows us to build (semi-)model structures for all three of these classes of colored operads. We then apply these results to provide conditions on $\mathcal {M}$ , on the colored operad O, and on a class $\mathcal {C}$ of morphisms in $\mathcal {M}$ so that the left Bousfield localization of $\mathcal {M}$ with respect to $\mathcal {C}$ preserves O-algebras. Even the strongest version of our hypotheses on $\mathcal {M}$ is satisfied for model structures on simplicial sets, chain complexes over a field of characteristic zero, and symmetric spectra. We obtain results in these settings allowing us to place model structures on algebras over any colored operad, and to conclude that monoidal Bousfield localizations preserve such algebras.
Text-Independent Speaker Verification Using 3D Convolutional Neural Networks

In this paper, a novel method using 3D Convolutional Neural Network (3D-CNN) architecture has been proposed for speaker verification in the text-independent setting. One of the main challenges is the creation of the speaker models. Most of the previously-reported approaches create speaker models based on averaging the extracted features from utterances of the speaker, which is known as the d-vector system. In our paper, we propose an adaptive feature learning by utilizing the 3D-CNN s for direct speaker model creation in which, for both development and enrollment phases, an identical number of spoken utterances per speaker is fed to the network for representing the speakers' utterances and creation of the speaker model. This leads to simultaneously capturing the speaker-related information and building a more robust system to cope with within-speaker variation. We demonstrate that the proposed method significantly outperforms the traditional d-vector verification system. Moreover, the proposed system can also be an alternative to the traditional d-vector system which is a one-shot speaker modeling system by utilizing 3D-CNNs.
Stability of Overintegration Methods for Nodal Discontinuous Galerkin Spectral Element Methods

We perform stability analyses for discontinuous Galerkin spectral element approximations of linear variable coefficient hyperbolic systems in three dimensional domains with curved elements. Although high order, the precision of the quadratures used are typically too low with respect to polynomial order associated with their arguments, which introduces errors that can destabilize an approximation, especially when the solution is underresolved. We show that using a larger number of points in the volume quadrature, often called “overintegration”, can eliminate the destabilizing errors associated with the volume, but introduces new errors at the surfaces that can also destabilize the solution. Increased quadrature precision on both the volume and surface terms, on the other hand, leads to a stable approximation. The results support the findings of Mengaldo et al. (J Comput Phys 299:56–81, 2015) who found that fully consistent integration was more robust for the solution of compressible flows than the volume only version.
Glazman–Krein–Naimark theory, left-definite theory and the square of the Legendre polynomials differential operator

Abstract As an application of a general left-definite spectral theory, Everitt, Littlejohn and Wellman, in 2002, developed the left-definite theory associated with the classical Legendre self-adjoint second-order differential operator A in L 2 ( − 1 , 1 ) which has the Legendre polynomials { P n } n = 0 ∞ as eigenfunctions. As a consequence, they explicitly determined the domain D ( A 2 ) of the self-adjoint operator A 2 . However, this domain, in their characterization, does not contain boundary conditions. In fact, this is a general feature of the left-definite approach developed by Littlejohn and Wellman. Yet, the square of the second-order Legendre expression is in the limit-4 case at each end point x = ± 1 in L 2 ( − 1 , 1 ) so D ( A 2 ) should exhibit four boundary conditions. In this paper, we show that this domain can, in fact, be expressed using four separated boundary conditions using the classical GKN (Glazman–Krein–Naimark) theory. In addition, we determine a new characterization of D ( A 2 ) that involves four non-GKN boundary conditions. These new boundary conditions are surprisingly simple – and natural – and are equivalent to the boundary conditions obtained from the GKN theory.
Cross-Cultural and Cultural-Specific Production and Perception of Facial Expressions of Emotion in the Wild

Automatic recognition of emotion from facial expressions is an intense area of research, with a potentially long list of important application. Yet, the study of emotion requires knowing which facial expressions are used within and across cultures in the wild, not in controlled lab conditions; but such studies do not exist. Which and how many cross-cultural and cultural-specific facial expressions do people commonly use? And, what affect variables does each expression communicate to observers? If we are to design technology that understands the emotion of users, we need answers to these two fundamental questions. In this paper, we present the first large-scale study of the production and visual perception of facial expressions of emotion in the wild. We find that of the 16,384 possible facial configurations that people can theoretically produce, only 35 are successfully used to transmit emotive information across cultures, and only 8 within a smaller number of cultures. Crucially, we find that visual analysis of cross-cultural expressions yields consistent perception of emotion categories and valence, but not arousal. In contrast, visual analysis of cultural-specific expressions yields consistent perception of valence and arousal, but not of emotion categories. Additionally, we find that the number of expressions used to communicate each emotion is also different, e.g., 17 expressions transmit happiness, but only 1 is used to convey disgust.
A Nearly-Linear Time Algorithm for Submodular Maximization with a Knapsack Constraint

We consider the problem of maximizing a monotone submodular function subject to a knapsack constraint. Our main contribution is an algorithm that achieves a nearly-optimal, 1 - 1/e - epsilon approximation, using (1/epsilon)^{O(1/epsilon^4)} n log^2{n} function evaluations and arithmetic operations. Our algorithm is impractical but theoretically interesting, since it overcomes a fundamental running time bottleneck of the multilinear extension relaxation framework. This is the main approach for obtaining nearly-optimal approximation guarantees for important classes of constraints but it leads to Omega(n^2) running times, since evaluating the multilinear extension is expensive. Our algorithm maintains a fractional solution with only a constant number of entries that are strictly fractional, which allows us to overcome this obstacle.
End-to-end Audiovisual Speech Activity Detection with Bimodal Recurrent Neural Models

Abstract Speech activity detection (SAD) plays an important role in current speech processing systems, including automatic speech recognition (ASR). SAD is particularly difficult in environments with acoustic noise. A practical solution is to incorporate visual information, increasing the robustness of the SAD approach. An audiovisual system has the advantage of being robust to different speech modes (e.g., whisper speech) or background noise. Recent advances in audiovisual speech processing using deep learning have opened opportunities to capture in a principled way the temporal relationships between acoustic and visual features. This study explores this idea, proposing the bimodal recurrent neural network (BRNN) framework for SAD. The approach models the temporal dynamic of the sequential audiovisual data, improving the accuracy and robustness of the proposed SAD system. Instead of estimating hand-crafted features, the study investigates an end-to-end training approach, where acoustic and visual features are directly learned from the raw data during training. The experimental evaluation considers a large audiovisual corpus with over 60.8 h of recordings, collected from 105 speakers. The results demonstrate that the proposed framework leads to absolute improvements up to 1.2% under practical scenarios over a VAD baseline using only audio implemented with deep neural network (DNN). The proposed approach achieves 92.7% F1-score when it is evaluated using the sensors from a portable tablet under noisy acoustic environment, which is only 1.0% lower than the performance obtained under ideal conditions (e.g., clean speech obtained with a high definition camera and a close-talking microphone).
Disproof of a packing conjecture of Alon and Spencer

A 1992 conjecture of Alon and Spencer says, roughly, that the ordinary random graph $G_{n,1/2}$ typically admits a covering of a constant fraction of its edges by edge-disjoint, nearly maximum cliques. We show that this is not the case. The disproof is based on some (partial) understanding of a more basic question: for $k\ll \sqrt{n}$ and $A_1\dots A_t$ chosen uniformly and independently from the $k$-subsets of $\{1\dots n\}$, what can one say about \[ \mathbb{P}(|A_i\cap A_j|\leq 1 ~\forall i\neq j)? \] Our main concern is trying to understand how closely the answers to this and a related question about matchings follow heuristics gotten by pretending that certain (dependent) choices are made independently.
hypeR: an R package for geneset enrichment workflows.

Summary Geneset enrichment is a popular method for annotating high-throughput sequencing data. Existing tools fall short in providing the flexibility to tackle the varied challenges researchers face in such analyses, particularly when analyzing many signatures across multiple experiments. We present a comprehensive R package for geneset enrichment workflows that offers multiple enrichment, visualization, and sharing methods in addition to novel features such as hierarchical geneset analysis and built-in markdown reporting. hypeR is a one-stop solution to performing geneset enrichment for a wide audience and range of use cases. Availability and implementation The most recent version of the package is available at https://github.com/montilab/hypeR.
Late Breaking Results: New Computational Results and Hardware Prototypes for Oscillator-based Ising Machines

In this paper, we report new results on a novel Ising machine technology for solving combinatorial optimization problems using networks of coupled self-sustaining oscillators. Specifically, we present several working hardware prototypes using CMOS electronic oscillators, built on breadboards/perfboards and PCBs, implementing Ising machines consisting of up to 240 spins with programmable couplings. We also report that, just by simulating the differential equations of such Ising machines of larger sizes, good solutions can be achieved easily on benchmark optimization problems, demonstrating the effectiveness of oscillator-based Ising machines.
CUT&RUNTools: a flexible pipeline for CUT&RUN processing and footprint analysis

We introduce CUT&RUNTools as a flexible, general pipeline for facilitating the identification of chromatin-associated protein binding and genomic footprinting analysis from antibody-targeted CUT&RUN primary cleavage data. CUT&RUNTools extracts endonuclease cut site information from sequences of short-read fragments and produces single-locus binding estimates, aggregate motif footprints, and informative visualizations to support the high-resolution mapping capability of CUT&RUN. CUT&RUNTools is available at https://bitbucket.org/qzhudfci/cutruntools/ .
Intervertebral disc swelling maintains strain homeostasis throughout the annulus fibrosus: A finite element analysis of healthy and degenerated discs.

Abstract Tissues in the intervertebral disc have a large capacity to absorb water, partially due to the high glycosaminoglycan (GAG) content, which decreases linearly from the nucleus pulposus (NP) in the center to the outer annulus. Our recent work showed that fiber network and GAG distribution contributes to development of residual stresses and strains that were compressive in the inner annulus to tensile in the outer annulus. GAG loss in the inner annulus, as observed with early to moderate degeneration, reduced swelling capacity and circumferential-direction stress by over 50%. However, our previous model was not capable of evaluating interactions between the NP and annulus fibrosus (AF) during swelling. In this study, we evaluated the effect of degeneration (GAG content or swelling capacity) on residual stress development throughout the disc. Simulations of moderate to severe degeneration showed a 40% decrease in NP swelling capacity, with a 25% decrease in AF and cartilaginous endplate swelling. Together, these changes in tissue swelling resulted in a decrease in NP pressure (healthy = 0.21 MPa; severe degeneration = 0.03 MPa) that was comparable to observations in human discs. There was a 60% decrease in circumferential-direction residual deformations with early degeneration. Radial-direction stretch switched from compressive to tensile with degeneration, which may increase the risk for tears or delamination. Degeneration had a significant impact on residual stress/stretch and fiber stretch in the posterior AF, which is important for understanding herniation risk. In conclusion, degenerative changes in disc geometry and intradiscal deformations was recreated by only altering NP and AF GAG composition. Since most computational models simulate degeneration by altering material stiffness, this work highlights the importance of directly simulating biochemical composition and distribution to study disc biomechanics with degeneration. Statement of significance Tissues in the intervertebral disc have a large swelling capacity, due to its high glycosaminoglycan content. Our recent work demonstrated the importance of fiber network and glycosaminoglycan distribution residual stresses and strains development. In this study, we evaluated the effect of swelling on intradiscal deformations between the nucleus pulposus and annulus fibrosus. We also investigated the effect of degenerative glycosaminoglycan loss on swelling-based intradiscal deformations of the intact disc and its subcomponents. Decreases in nucleus glycosaminoglycan content resulted in morphological changes observed with degenerated discs and may help to explain mechanisms behind the increases in annular tears and mechanical dysfunction with degeneration.
Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change

Microorganisms are critical in terrestrial carbon cycling because their growth, activity and interactions with the environment largely control the fate of recent plant carbon inputs as well as protected soil organic carbon [1, 2]. Soil carbon stocks reflect a balance between microbial decomposition of organic carbon and stabilisation of microbial assimilated carbon. The balance can shift under altered environmental conditions [3], and new research suggests that knowledge of microbial physiology may be critical for projecting changes in soil carbon and improving the prognosis of climate change feedbacks [4–7]. Still, predicting the ecosystem implications of microbial processes remains a challenge. Here we argue that this challenge can be met by identifying microbial life history strategies based on an organism’s phenotypic characteristics, or traits, and representing these strategies in ecosystem models. What are the key microbial traits for soil carbon cycling under environmental change? Microbial growth and survival in soil are impacted by multiple traits that determine responses to varying resource availability and fluctuating abiotic conditions [8]. Cellular maintenance activities (those that do not produce growth) include production of extracellular enzymes to degrade and acquire resources, biomolecular repair mechanisms, maintenance of cellular integrity, osmotic balance, defence, antagonism, cell signalling and motility [9–11]. It is conceivable that microbial investment into maintenance activities would be generally high in soils, with their highly heterogeneous and temporally variable resource distribution and stressful abiotic conditions like extremes of moisture, temperature, pH and salinity [12, 13]. Selective pressures in suboptimal environmental conditions could lead to greater cellular-level physiological allocation to maintenance relative to growth traits (Fig. 1) thereby impacting soil carbon cycling processes. Open in a separate window Fig. 1 Schematic showing cellular C flux that includes depolymerisation, substrate uptake, assimilation, dissimilation, biomass synthesis and non-growth production. Extracellular enzyme production represents investment in resource acquisition, stress protein production is linked to stress tolerance mechanisms, and biomass production reflects higher growth yield. Forked arrows signify metabolic points where hypothesised tradeoffs in traits might occur. The expected empirical relationships among the key traits are also shown
Launderability of surface-insulated cut and sew E-textiles.

E-textiles that enable distribution of electronic components have advantages for wearable technology, in that functionality, power, and networking can be spread over a much larger area while preserving hand-feel and wearability. However, textile-embedded circuitry often must be machine-washable to conform to user expectations for care and maintenance, particularly for garments. In this study, we evaluate the robustness to home laundering of a previously-developed cut-and-sew technique for assembling e-textile circuits. Alternative surface insulation materials, textile substrate properties, and soldered component joints are evaluated. After around 1000 minutes (16.67 hours) of rigorous washing and drying, we measured a best-case 0% failure rate for component solder joints, and a best-case 0.38 ohm/m maximum increase in trace resistance. Liquid silicone seam sealer was effective in protecting 100% of solder joints. Two tape-type alternative surface insulation materials were effective in protecting bare traces and component attachment points respectively. Overall, results demonstrate the feasibility of producing insulated, washable cut-and-sew circuits for smart garment manufacturing.
A self-coherence based anti-jam GPS receiver

In this paper, we propose an anti-jam GPS receiver that is based on the self-coherence feature of the GPS signal. Due to the repetition of the spreading code within a navigation symbol, GPS signals exhibit strong self-coherence between the code chips separated by an integer multiple of the spreading gain. The proposed receiver utilizes the inherent self-coherence property of the GPS C/A-code to suppress interferers that have different structures from that of the GPS signal. The multi-antenna GPS receiver provides the optimum set of beamforming coefficients by maximizing the cross-correlation between the beamformer output and a reference signal.
Choice, Capital, and Competition: Private Mortgage Insurance Application and Availability

The financial health of the mortgage insurance industry is vital for the flow of mortgage credit to low-wealth borrowers. Private mortgage insurance competes with insurance offered through the fede...
A data-driven method for the steady state of randomly perturbed dynamics

We demonstrate a data-driven method to solve for the invariant probability density function of a randomly perturbed dynamical system. The key idea is to replace the boundary condition of numerical schemes by a least squares problem corresponding to a reference solution, which is generated by Monte Carlo simulation. With this method we can solve for the invariant probability density function in any local area with high accuracy, regardless of whether the attractor is covered by the numerical domain.
Human Endometrial Transcriptome and Progesterone Receptor Cistrome Reveal Important Pathways and Epithelial Regulators.

Context Poor uterine receptivity is one major factor leading to pregnancy loss and infertility. Understanding the molecular events governing successful implantation is hence critical in combating infertility. Objective To define Progesterone Receptor (PGR)-regulated molecular mechanisms and epithelial roles in receptivity. Design RNA-sequencing and PGR-ChIP-seq were conducted in parallel to identify PGR-regulated pathways during the Window of implantation (WOI) in endometrium of fertile women. Setting Endometrial biopsies from the proliferative and mid-secretory phases were analyzed. Patients or other participants Participants were fertile, reproductive aged (18-37 years) women with normal cycle length, and without any history of dysmenorrhea, infertility, or irregular cycles. In total, 42 endometrial biopsies obtained from 42 women were analyzed in this study. Interventions There were no interventions during this study. Main outcome measures Here we measured the alterations in gene expression and PGR occupancy in the genome during the WOI, based on the hypothesis that PGR binds uterine chromatin cycle dependently to regulate genes involved in uterine cell differentiation and function. Results 653 genes were identified with regulated PGR binding and differential expression during the WOI. These were involved in regulating inflammatory response, xenobiotic metabolism, epithelial mesenchymal transition, cell death, interleukin/Signal Transducer And Activator Of Transcription (STAT) signaling, estrogen response, and Mammalian target of rapamycin complex 1 (MTORC1) response. Transcriptome of the epithelium identified 3052 differentially expressed genes, of which 658 were uniquely regulated. Transcription factors Interferon Regulatory Factor 8 (IRF8) and Myocyte Enhancer Factor 2C (MEF2C) were found to be regulated in the epithelium during the WOI at the protein level, suggesting potentially important functions that are previously unrecognized. Conclusion PGR binds the genomic regions of genes regulating critical processes in uterine receptivity and function.
Single-clock, single-latch, scan design

This correspondence describes a new single-latch scan design that uses a single clock for both scan and functional operations. A test mode signal differentiates between normal and test operations. This new design enjoys savings in circuits, pins, test time, and also enjoys the benefits of a high-speed scan capability.
Liability Design for Autonomous Vehicles and Human-Driven Vehicles: A Hierarchical Game-Theoretic Approach

Abstract Autonomous vehicles (AVs) are inevitably entering our lives with potential benefits for improved traffic safety, mobility, and accessibility. However, AVs’ benefits also introduce a serious potential challenge, in the form of complex interactions with human-driven vehicles (HVs). The emergence of AVs introduces uncertainty in the behavior of human actors and in the impact of the AV manufacturer on autonomous driving design. This paper thus aims to investigate how AVs affect road safety and to design socially optimal liability rules in comparative negligence for AVs and human drivers. A unified game is developed, including a Nash game between human drivers, a Stackelberg game between the AV manufacturer and HVs, and a Stackelberg game between the lawmaker and other users. We also establish the existence and uniqueness of the equilibrium of the game. The game is then simulated with numerical examples to investigate the emergence of human drivers’ moral hazard, the AV manufacturer’s role in traffic safety, and the lawmaker’s role in liability design. Our findings demonstrate that human drivers could develop moral hazard if they perceive their road environment has become safer and an optimal liability rule design is crucial to improve social welfare with advanced transportation technologies. More generally, the game-theoretic model developed in this paper provides an analytical tool to assist policy-makers in AV policymaking and hopefully mitigate uncertainty in the existing regulation landscape about AV technologies.
4D Flow MRI Pressure Estimation Using Velocity Measurement-Error-Based Weighted Least-Squares

This work introduces a 4D flow magnetic resonance imaging (MRI) pressure reconstruction method which employs weighted least-squares (WLS) for pressure integration. Pressure gradients are calculated from the velocity fields, and velocity errors are estimated from the velocity divergence for incompressible flow. Pressure gradient errors are estimated by propagating the velocity errors through Navier-Stokes momentum equation. A weight matrix is generated based on the pressure gradient errors, then employed for pressure reconstruction. The pressure reconstruction method was demonstrated and analyzed using synthetic velocity fields as well as Poiseuille flow measured using in vitro 4D flow MRI. Performance of the proposed WLS method was compared to the method of solving the pressure Poisson equation which has been the primary method used in the previous studies. Error analysis indicated that the proposed method is more robust to velocity measurement errors. Improvement on pressure results was found to be more significant for the cases with spatially-varying velocity error level, with reductions in error ranging from 50% to over 200%. Finally, the method was applied to flow in patient-specific cerebral aneurysms. Validation was performed with in vitro flow data collected using Particle Tracking Velocimetry (PTV) and in vivo flow measurement obtained using 4D flow MRI. Pressure calculated by WLS, as opposed to the Poisson equation, was more consistent with the flow structures and showed better agreement between the in vivo and in vitro data. These results suggest the utility of WLS method to obtain reliable pressure field from clinical flow measurement data.
VLSS redux: Software improvements applied to the Very Large Array Low‐Frequency Sky Survey

[1] We present details of improvements to data processing and analysis which were recently used for a re-reduction of the Very Large Array (VLA) Low-Frequency Sky Survey (VLSS) data. Algorithms described are implemented in the data-reduction package Obit, and include smart-windowing to reduce clean bias, improved automatic radio frequency interference removal, improved bright-source peeling, and higher-order Zernike fits to model the ionospheric phase contributions. An additional, but less technical improvement was using the original VLSS catalog as a same-frequency/same-resolution reference for calculating ionospheric corrections, allowing more accuracy and a higher percentage of data for which solutions are found. We also discuss new algorithms for extracting a source catalog and analyzing ionospheric fluctuations present in the data. The improved reduction techniques led to substantial improvements including images of six previously unpublished fields (1% of the survey area) and reducing the clean bias by 50%. The largest angular size imaged has been roughly doubled, and the number of cataloged sources is increased by 35% to 95,000.