cssi/SciSciGPT-SciSciNet huggingface dataset:
Convex Hull for Probabilistic Points

We analyze the correctness of an O(n log n) time divide-and-conquer algorithm for the convex hull problem when each input point is a location determined by a normal distribution. We show that the algorithm finds the convex hull of such probabilistic points to precision within some expected correctness determined by a user-given confidence value phi. In order to precisely explain how correct the resulting structure is, we introduce a new certificate error model for calculating and understanding approximate geometric error based on the fundamental properties of a geometric structure. We show that this new error model implies correctness under a robust statistical error model, in which each point lies within the hull with probability at least ϕ, for the convex hull problem.
Robust sensitivity analysis of the optimal value of linear programming

We propose a framework for sensitivity analysis of linear programs (LPs) in minimization form, allowing for simultaneous perturbations in the objective coecients and right-hand sides, where the perturbations are modeled in a compact, convex uncertainty set. This framework unies and extends multiple approaches for LP sensitivity analysis in the literature and has close ties to worst-case linear optimization and two-stage adaptive optimization. We dene the minimum (best-case) and maximum (worst-case) LP optimal values, p and p + , over the uncertainty set, and we discuss issues of niteness, attainability, and computational complexity. While p and p + are dicult to compute in general, we prove that they equal the optimal values of two separate, but related, copositive programs. We then develop tight, tractable conic relaxations to provide lower and upper bounds on p and p + , respectively. We also develop techniques to assess the quality of the bounds, and we validate our approach computationally on several examples from|and inspired by|the literature. We nd that the bounds on p and p + are very strong in practice and, in particular, are at least as strong as known results for specic cases from the literature.
An accurate and efficient analysis of a MBSFN network

A new accurate analysis is presented for an OFDM-based multicast-broadcast single-frequency network (MBSFN). The topology of the network is modeled by a constrained random spatial model involving a fixed number of base stations placed over a finite area with a minimum separation. The analysis is driven by a new closed-form expression for the conditional outage probability at each location of the network, where the conditioning is with respect to the network realization. The analysis accounts for the diversity combining of signals transmitted by different base stations of a given MBSFN area, and also accounts for the interference caused by the base stations of other MBSFN areas. The analysis features a flexible channel model, accounting for path loss, Nakagami fading, and correlated shadowing. The analysis is used to investigate the influence of the minimum base-station separation and provides insight regarding the optimal size of the MBSFN areas. In order to highlight the percentage of the network that will fail to successfully receive the broadcast, the area below an outage threshold (ABOT) is here used and defined as the fraction of the network that provides an outage probability (averaged over the fading) that meets a threshold.
Understanding Composition of Word Embeddings via Tensor Decomposition.

Word embedding is a powerful tool in natural language processing. In this paper we consider the problem of word embedding composition \--- given vector representations of two words, compute a vector for the entire phrase. We give a generative model that can capture specific syntactic relations between words. Under our model, we prove that the correlations between three words (measured by their PMI) form a tensor that has an approximate low rank Tucker decomposition. The result of the Tucker decomposition gives the word embeddings as well as a core tensor, which can be used to produce better compositions of the word embeddings. We also complement our theoretical results with experiments that verify our assumptions, and demonstrate the effectiveness of the new composition method.
Robust Polynomial Regression up to the Information Theoretic Limit

We consider the problem of robust polynomial regression, where one receives samples that are usually within a small additive error of a target polynomial, but have a chance of being arbitrary adversarial outliers. Previously, it was known how to efficiently estimate the target polynomial only when the outlier probability was subconstant in the degree of the target polynomial. We give an algorithm that works for the entire feasible range of outlier probabilities, while simultaneously improving other parameters of the problem. We complement our algorithm, which gives a factor 2 approximation, with impossibility results that show, for example, that a 1.09 approximation is impossible even with infinitely many samples.
Beating Fredman-Komlós for Perfect k-Hashing.

We say a subset C subseteq {1,2,...,k}^n is a k-hash code (also called k-separated) if for every subset of k codewords from C, there exists a coordinate where all these codewords have distinct values. Understanding the largest possible rate (in bits), defined as (log_2 |C|)/n, of a k-hash code is a classical problem. It arises in two equivalent contexts: (i) the smallest size possible for a perfect hash family that maps a universe of N elements into {1,2,...,k}, and (ii) the zero-error capacity for decoding with lists of size less than k for a certain combinatorial channel. A general upper bound of k!/k^{k-1} on the rate of a k-hash code (in the limit of large n) was obtained by Fredman and Komlos in 1984 for any k >= 4. While better bounds have been obtained for k=4, their original bound has remained the best known for each k >= 5. In this work, we present a method to obtain the first improvement to the Fredman-Komlos bound for every k >= 5, and we apply this method to give explicit numerical bounds for k=5, 6.
Training a Subsampling Mechanism in Expectation

We describe a mechanism for subsampling sequences and show how to compute its expected output so that it can be trained with standard backpropagation. We test this approach on a simple toy problem and discuss its shortcomings.
Supercritical elliptic problems on a perturbation of the ball

Abstract We examine the Henon equation − Δ u = | x | α u p in Ω ⊂ R N with u = 0 on ∂ Ω where 0 α . We show there exists a sequence { p k } k ⊂ [ N + 2 N − 2 , p α ( N ) ] with p 1 p 2 p 3 ⋯ , p k ↗ p α ( N ) such that for any N + 2 N − 2 ⩽ p p α ( N ) , which avoids { p k } k , there exists a positive classical solution of the Henon equation, provided Ω is a sufficiently small perturbation of the unit ball. We also examine the Lane–Emden–Fowler equation in the case of an exterior domain; i.e. − Δ u = u p in Ω , an exterior domain, with u = 0 on ∂ Ω . We show the existence of N + 2 N − 2 ⩽ p 1 p 2 p 3 ⋯ with p k → ∞ such that if N + 2 N − 2 p , which avoids { p k } k , then there exists a positive fast decay classical solution, provided Ω is a sufficiently small perturbation of the exterior of the unit ball.
Intuitionistic Existential Instantiation and Epsilon Symbol

A natural deduction system for intuitionistic predicate logic with existential instantiation rule presented here uses Hilbert’s \(\epsilon \)-symbol. It is conservative over intuitionistic predicate logic. We provide a completeness proof for a suitable Kripke semantics, sketch an approach to a normalization proof, survey related work and state some open problems. Our system extends intuitionistic systems with \(\epsilon \)-symbol due to Dragalin and Maehara.
A Continuous Relaxation of Beam Search for End-to-end Training of Neural Sequence Models

Beam search is a desirable choice of test-time decoding algorithm for neural sequence models because it potentially avoids search errors made by simpler greedy methods. However, typical cross entropy training procedures for these models do not directly consider the behaviour of the final decoding method. As a result, for cross-entropy trained models, beam decoding can sometimes yield reduced test performance when compared with greedy decoding. In order to train models that can more effectively make use of beam search, we propose a new training procedure that focuses on the final loss metric (e.g. Hamming loss) evaluated on the output of beam search. While well-defined, this "direct loss" objective is itself discontinuous and thus difficult to optimize. Hence, in our approach, we form a sub-differentiable surrogate objective by introducing a novel continuous approximation of the beam search decoding procedure. In experiments, we show that optimizing this new training objective yields substantially better results on two sequence tasks (Named Entity Recognition and CCG Supertagging) when compared with both cross entropy trained greedy decoding and cross entropy trained beam decoding baselines.
Space-Time Sampling for Network Observability

Abstract We will investigate under what conditions taking coarse samples from a network will contain the same information as a finer set of samples. Our goal is to estimate the initial state of a linear network of subsystems, which are distributed in a spatial domain, from noisy measurements. We develop a framework to produce feasible sets of spatio-temporal samples for the estimation problem, which essentially have a non-uniform space-time sampling pattern. If the number of sampling locations is comparable to the size of the network, the sampling pattern will have a high degree of redundancy. For these cases, using an efficient algorithm, we present a method for finding a feasible subset of samples that have a sparser space-time sampling pattern. It is shown that spatial samples can be traded for time samples: choosing to sample from a smaller set of subsystems must be compensated by taking more frequent time samples from those subsystems. Furthermore, we apply the Kadison-Singer paving solution to sparsify a feasible redundant sampling strategy with guaranteed estimation bounds. We support our theoretical findings via several numerical examples.
Stochastic Control via Entropy Compression

We consider an agent trying to bring a system to an acceptable state by repeated probabilistic action. Several recent works on algorithmizations of the Lovasz Local Lemma (LLL) can be seen as establishing sufficient conditions for the agent to succeed. Here we study whether such stochastic control is also possible in a noisy environment, where both the process of state-observation and the process of state-evolution are subject to adversarial perturbation (noise). The introduction of noise causes the tools developed for LLL algorithmization to break down since the key LLL ingredient, the sparsity of the causality (dependence) relationship, no longer holds. To overcome this challenge we develop a new analysis where entropy plays a central role, both to measure the rate at which progress towards an acceptable state is made and the rate at which noise undoes this progress. The end result is a sufficient condition that allows a smooth tradeoff between the intensity of the noise and the amenability of the system, recovering an asymmetric LLL condition in the noiseless case.
A Rokhlin type theorem for simple C⁎-algebras of finite nuclear dimension

Abstract We study Z -actions on unital simple separable stably finite C ⁎ -algebras of finite nuclear dimension. Assuming that the extreme boundary of the trace space is compact and finite dimensional, and that the induced action on the trace space is trivial, we show that strongly outer Z -actions have finite Rokhlin dimension in the sense of Hirshberg, Winter and Zacharias.
Preconditioned Iterative Methods for Eigenvalue Counts

We describe preconditioned iterative methods for estimating the number of eigenvalues of a Hermitian matrix within a given interval. Such estimation is useful in a number of applications. It can also be used to develop an efficient spectrum-slicing strategy to compute many eigenpairs of a Hermitian matrix. Our method is based on the Lanczos- and Arnoldi-type of iterations. We show that with a properly defined preconditioner, only a few iterations may be needed to obtain a good estimate of the number of eigenvalues within a prescribed interval. We also demonstrate that the number of iterations required by the proposed preconditioned schemes is independent of the size and condition number of the matrix. The efficiency of the methods is illustrated on several problems arising from density functional theory based electronic structure calculations.
Face Detection with the Faster R-CNN

While deep learning based methods for generic object detection have improved rapidly in the last two years, most approaches to face detection are still based on the R-CNN framework [11], leading to limited accuracy and processing speed. In this paper, we investigate applying the Faster RCNN [26], which has recently demonstrated impressive results on various object detection benchmarks, to face detection. By training a Faster R-CNN model on the large scale WIDER face dataset [34], we report state-of-the-art results on the WIDER test set as well as two other widely used face detection benchmarks, FDDB and the recently released IJB-A.
The effects of leverage requirements and fire sales on financial contagion via asset liquidation strategies in financial networks

This paper provides a framework for modeling the financial system with multiple illiquid assets when liquidation of illiquid assets is caused by failure to meet a leverage requirement. This extends the network model of Cifuentes, Shin & Ferrucci (2005) which incorporates a single asset with fire sales and capital adequacy ratio. This also extends the network model of Feinstein (2015) which incorporates multiple illiquid assets with fire sales and no leverage ratios. We prove existence of equilibrium clearing payments and liquidation prices for a known liquidation strategy when leverage requirements are required. We also prove sufficient conditions for the existence of an equilibrium liquidation strategy with corresponding clearing payments and liquidation prices. Finally we calibrate network models to asset and liability data for 50 banks in the United States from 2007-2014 in order to draw conclusions on systemic risk as a function of leverage requirements.
Abelian networks III: The critical group

The critical group of an abelian network is a finite abelian group that governs the behavior of the network on large inputs. It generalizes the sandpile group of a graph. We show that the critical group of an irreducible abelian network acts freely and transitively on recurrent states of the network. We exhibit the critical group as a quotient of a free abelian group by a subgroup containing the image of the Laplacian, with equality in the case that the network is rectangular. We generalize Dhar's burning algorithm to abelian networks and estimate the running time of an abelian network on an arbitrary input up to a constant additive error.
Fairness in Supervised Learning: An Information Theoretic Approach

Automated decision making systems are increasingly being used in real-world applications. In these systems for the most part, the decision rules are derived by minimizing the training error on the available historical data. Therefore, if there is a bias related to a sensitive attribute such as gender, race, religion, etc. in the data, say, due to cultural/historical discriminatory practices against a certain demographic, the system could continue discrimination in decisions by including the said bias in its decision rule. We present an information theoretic framework for designing fair predictors from data, which aim to prevent discrimination against a specified sensitive attribute in a supervised learning setting. We use equalized odds as the criterion for discrimination, which demands that the prediction should be independent of the protected attribute conditioned on the actual label. To ensure fairness and generalization simultaneously, we compress the data to an auxiliary variable, which is used for the prediction task. This auxiliary variable is chosen such that it is decontaminated from the discriminatory attribute in the sense of equalized odds. The final predictor is obtained by applying a Bayesian decision rule to the auxiliary variable.
A Cylindrical Basis Function for Solving Partial Differential Equations on Manifolds

Numerical solution of partial differential equations (PDEs) on manifolds continues to generate a lot of interest among scientists in the natural and applied sciences. On the other hand, recent developments of 3D scanning and computer vision technologies have produced a large number of 3D surface models represented as point clouds. Herein, we develop a simple and efficient method for solving PDEs on closed surfaces represented as point clouds. By projecting the radial vector of standard radial basis function(RBF) kernels onto the local tangent plane, we are able to produce a representation of functions that permits the replacement of surface differential operators with their Cartesian equivalent. We demonstrate, numerically, the efficiency of the method in discretizing the Laplace Beltrami operator.
Hierarchical H2 Control of Large-Scale Network Dynamic Systems

Standard H2 optimal control of networked dynamic systems tend to become unscalable with network size. Structural constraints can be imposed on the design to counteract this problem albeit at the risk of making the solution non-convex. In this paper, we present a special class of structural constraints such that the H2 design satisfies a quadratic invariance condition, and therefore can be reformulated as a convex problem. This special class consists of structured and weighted projections of the input and output spaces. The choice of these projections can be optimized to match the closed-loop performance of the reformulated controller with that of the standard H2 controller. The advantage is that unlike the latter, the reformulated controller results in a hierarchical implementation which requires significantly lesser number of communication links, while also admitting model and controller reduction that helps the design to scale computationally. We illustrate our design with simulations of a 500-node network.
Energy disaggregation via adaptive filtering

The energy disaggregation problem is recovering device level power consumption signals from the aggregate power consumption signal for a building. We show in this paper how the disaggregation problem can be reformulated as an adaptive filtering problem. This gives both a novel disaggregation algorithm and a better theoretical understanding for disaggregation. In particular, we show how the disaggregation problem can be solved online using a filter bank and discuss its optimality.
The Effects of Amiodarone on Thyroid Function in Pediatric and Young Adult Patients

Context Amiodarone is used in patients with arrhythmias, but thyroid dysfunction [amiodarone-induced thyrotoxicosis (AIT) or amiodarone-induced hypothyroidism (AIH)] is a common adverse effect. As the onset of AIT and AIH has not been studied in children, the timing of dysfunction and long-term monitoring are not known in this population. Objective To describe the incidence and timing of amiodarone-induced thyroid dysfunction in children and adolescents, with a secondary aim to identify risk factors for amiodarone-induced thyroid dysfunction, and to identify variance in thyroid hormone surveillance and treatment. Design Retrospective review of thyroid dysfunction in children and young adults treated with amiodarone between 2007 and 2018. Setting Children's Hospital of Philadelphia. Patients Children and young adults treated with amiodarone. Main outcome measures Prevalence of amiodarone-induced thyroid dysfunction. Results Of 484 patients, 190 had thyroid-function testing; 17.3% were found to have subclinical hypothyroidism, and 13.7% testing developed hypothyroidism. Hyperthyroidism occurred in 2.1%. In patients with subclinical hypothyroidism, 63% returned to normal thyroid function without thyroid hormone replacement. Only 26% of patients with hypothyroidism had spontaneous normalization of thyroid function. Twenty-five percent of AIT patients had spontaneous normalization of thyroid function. Conclusions This study looks at a pediatric and young-adult population in an effort to describe the natural history of amiodarone-induced thyroid dysfunction. Based on our data, we recommend that a complete thyroid-function panel be obtained within the first week and then at weekly intervals for the first 5 weeks after initiation. The majority of thyroid dysfunction was noted within the first 35 days of treatment.
Multivitamins and Nutritional Adequacy in Middle-Aged to Older Americans by Obesity Status.

Multivitamins are the most commonly consumed dietary supplement in the United States and worldwide. Micronutrient insufficiency and clinical deficiency are more common in middle-aged to older adult...
Efficient Network Construction Through Structural Plasticity

Deep Neural Networks (DNNs) on hardware is facing excessive computation cost due to the massive number of parameters. A typical training pipeline to mitigate over-parameterization is to pre-define a DNN structure with redundant learning units (filters and neurons) with the goal of high accuracy, then to prune redundant learning units after training with the purpose of efficient inference. We argue that it is sub-optimal to introduce redundancy into training in order to reduce redundancy later in inference. Moreover, the fixed network structure further results in poor adaption to dynamic tasks, such as lifelong learning. In contrast, structural plasticity plays an indispensable role in mammalian brains to achieve compact and accurate learning. Throughout the lifetime, active connections are continuously created while those that are no longer important are degenerated. Inspired by such observation, we propose a training scheme, namely Continuous Growth and Pruning (CGaP), where we start the training from a small network seed, then literally execute continuous growth by adding important learning units and finally prune secondary ones for efficient inference. The inference model generated from CGaP is sparse in the structure, largely decreasing the inference power and latency when deployed on hardware platforms. With popular DNN structures on representative datasets, the efficacy of CGaP is benchmarked by both algorithmic simulation and architectural modeling on Field-programmable Gate Arrays (FPGA). For example, CGaP decreases the FLOPs, model size, DRAM access energy and inference latency by 63.3%, 64.0%, 11.8% and 40.2%, respectively, for ResNet-110 on CIFAR-10.
Random clique covers for graphs with local density and global sparsity

Large real-world graphs tend to be sparse, but they often contain many densely connected subgraphs and exhibit high clustering coefficients. While recent random graph models can capture this sparsity, they ignore the local density, or vice versa. We develop a Bayesian nonparametric graph model based on random edge clique covers, and show that this model can capture power law degree distribution, global sparsity and non-vanishing local clustering coefficient. This distribution can be used directly as a prior on observed graphs, or as part of a hierarchical Bayesian model for inferring latent graph structures.
Comparative technoeconomic process analysis of industrial-scale microencapsulation of bioactives in cross-linked alginate

Abstract Calcium alginate microencapsulation is a popular laboratory technique, but implementing the traditional external gelation process in the food industry requires a series of costly unit operations. Alternatively, a consolidated process accomplishes alginate cross-linking in situ during spray-drying to form cross-linked alginate microcapsules (‘the CLAMs process’). This work conducted technoeconomic analysis in SuperPro Designer to compare the process economics of both microencapsulation systems, initially for encapsulating emulsified fish oil. At all production scales examined, the capital investment and annual operating cost were lower for the CLAMs process. The CLAMs process' economic advantage stemmed from reducing the number of unit procedures. Upon modifying the models for microencapsulating hydrophilic cargo (e.g. enzymes, vitamins, microbial concentrates), the CLAMs process remained favorable at all cargo material costs and cargo loadings examined. This work demonstrates the utility of technoeconomic analysis for evaluating microencapsulation processes and may justify applying the CLAMs process at the industrial scale.
How to Build User Simulators to Train RL-based Dialog Systems.

User simulators are essential for training reinforcement learning (RL) based dialog models. The performance of the simulator directly impacts the RL policy. However, building a good user simulator that models real user behaviors is challenging. We propose a method of standardizing user simulator building that can be used by the community to compare dialog system quality using the same set of user simulators fairly. We present implementations of six user simulators trained with different dialog planning and generation methods. We then calculate a set of automatic metrics to evaluate the quality of these simulators both directly and indirectly. We also ask human users to assess the simulators directly and indirectly by rating the simulated dialogs and interacting with the trained systems. This paper presents a comprehensive evaluation framework for user simulator study and provides a better understanding of the pros and cons of different user simulators, as well as their impacts on the trained systems.
Exploiting Computation Power of Blockchain for Biomedical Image Segmentation

Biomedical image segmentation based on Deep neural network (DNN) is a promising approach that assists clinical diagnosis. This approach demands enormous computation power because these DNN models are complicated, and the size of the training data is usually very huge. As blockchain technology based on Proof-of-Work (PoW) has been widely used, an immense amount of computation power is consumed to maintain the PoW consensus. In this paper, we propose a design to exploit the computation power of blockchain miners for biomedical image segmentation, which lets miners perform image segmentation as the Proof-of-Useful-Work (PoUW) instead of calculating useless hash values. This work distinguishes itself from other PoUW by addressing various limitations of related others. As the overhead evaluation shown in Section 5 indicates, for U-net and FCN, the average overhead of digital signature is 1.25 seconds and 0.98 seconds, respectively, and the average overhead of network is 3.77 seconds and 3.01 seconds, respectively. These quantitative experiment results prove that the overhead of the digital signature and network is small and comparable to other existing PoUW designs.
Pediatric firearm-related traumatic brain injury in United States trauma centers.

OBJECTIVE Pediatric firearm injury is a leading cause of death and disability in the youth of the United States. The epidemiology of and outcomes following gunshot wounds to the head (GSWHs) are in need of systematic characterization. Here, the authors analyzed pediatric GSWHs from a population-based sample to identify predictors of prolonged hospitalization, morbidity, and death. METHODS All patients younger than 18 years of age and diagnosed with a GSWH in the National Sample Program (NSP) of the National Trauma Data Bank (NTDB) in 2003-2012 were eligible for inclusion in this study. Variables of interest included injury intent, firearm type, site of incident, age, sex, race, health insurance, geographic region, trauma center level, isolated traumatic brain injury (TBI), hypotension in the emergency department, Glasgow Coma Scale (GCS) score, and Injury Severity Score (ISS). Risk predictors for a prolonged hospital stay, morbidity, and mortality were identified. Odds ratios, mean increases or decreases (B), and 95% confidence intervals were reported. Statistical significance was assessed at α < 0.001 accounting for multiple comparisons. RESULTS In a weighted sample of 2847 pediatric patients with GSWHs, the mean age was 14.8 ± 3.3 years, 79.2% were male, and 59.0% had severe TBI (GCS score 3-8). The mechanism of assault (63.0%), the handgun as firearm (45.6%), and an injury incurred in a residential area (40.6%) were most common. The mean hospital length of stay was 11.6 ± 14.4 days for the survivors, for whom suicide injuries involved longer hospitalizations (B = 5.9-day increase, 95% CI 3.3-8.6, p < 0.001) relative to those for accidental injuries. Mortality was 45.1% overall but was greater with injury due to suicidal intent (mortality 71.5%, p < 0.001) or caused by a shotgun (mortality 56.5%, p < 0.001). Lower GCS scores, higher ISSs, and emergency room hypotension predicted poorer outcomes. Patients with private insurance had lower mortality odds than those with Medicare/Medicaid (OR 2.4, 95% CI 1.7-3.4, p < 0.001) or government insurance (OR 3.6, 95% CI 2.2-5.8, p < 0.001). Management at level II centers, compared to level I, was associated with lower odds of returning home (OR 0.3, 95% CI 0.2-0.5, p < 0.001). CONCLUSIONS From 2003 to 2012, with regard to pediatric TBI hospitalizations due to GSWHs, their proportion remained stable, those caused by accidental injuries decreased, and those attributable to suicide increased. Overall mortality was 45%. Hypotension, cranial and overall injury severity, and suicidal intent were associated with poor prognoses. Patients treated at level II trauma centers had lower odds of being discharged home. Given the spectrum of risk factors that predispose children to GSWHs, emphasis on screening, parental education, and standardization of critical care management is needed to improve outcomes.
Skinning bounds along thick rays

We show that the diameter of the skinning map of an acylindrical hyperbolic 3-manifold M is bounded on &#x1d700;-thick Teichmuller geodesics by a constant depending only on &#x1d700; and the topological type of ∂M.
Timbs v. Indiana: Toward the Regulation of Mercenary Criminal Justice

In Timbs v. Indiana, the Supreme Court unanimously held that the Eighth Amendment's Excessive Fines Clause is an incorporated protection under the Fourteenth Amendment and therefore regulates state and local governments. The unanimous result, wedding liberal and conservative Justices alike, was backed by an ideologically diverse group of amici, including the ACLU, the U.S. Chamber of Commerce, and the Cato Institute. The government practice giving rise to the litigation — civil asset forfeiture — has been subject to widespread criticism, fueled by troubling accounts of what has come to be known as “policing for profit.” Reaction to Timbs ran the gamut from regarding it as “huge” to being a decision having little impact. As I discuss in this symposium contribution, Timbs is important both because it provides a new federal constitutional basis to regulate government targeting of criminal defendants for revenue generation and signals the Court’s broader recognition of the problematic nature of the practice.
Secret Key Generation via Pulse-Coupled Synchronization

A novel framework for sharing common randomness and generating secret keys in wireless networks is considered. In particular, a network of users equipped with pulse oscillators (POs) and coupling mechanisms in between is considered. Such mechanisms exist in synchronized biological and natural systems, and have been exploited to provide synchronization in distributed networks. We show that naturally-existing initial random phase differences between the POs in the network can be utilized to provide almost identical common randomness to the users. This randomness is extracted from the synchronization time in the network. Bounds on the entropy of such randomness are derived for a two-user system and a conjecture is made for a general n-user system. Then, a three-terminal scenario is considered including two legitimate users and a passive eavesdropper, referred to as Eve. Since in a practical setting Eve receives pulses with propagation delays, she can not identify the exact synchronization time. A simplified model is then considered for Eve’s receiver and then a bound on the rate of secret key generation is derived. Also, it is shown, under certain conditions, that the proposed protocol is resilient to an active jammer equipped with a similar pulse generation mechanism.
An information processing approach to distributed detection

We apply the recent theory of information processing to a hybrid distributed detection architecture that combines the traditional parallel and tandem architectures. Central to this theory is the Kullback-Leibler discrimination distance and quantity known as the information transfer ratio, defined as the ratio of the KL distances between the distributions characterizing the input and output of a system. We characterize the asymptotic performance of proposed hybrid system and compare it with the performance of the parallel, tandem and centralized architectures. We conclude with an illustrative example.
Transcriptome Profiling Reveals Matrisome Alteration as a Key Feature of Ovarian Cancer Progression.

Background: Ovarian cancer is the most lethal gynecologic malignancy. There is a lack of comprehensive investigation of disease initiation and progression, including gene expression changes during early metastatic colonization. Methods: RNA-sequencing (RNA-seq) was done with matched primary tumors and fallopian tubes (n = 8 pairs) as well as matched metastatic and primary tumors (n = 11 pairs) from ovarian cancer patients. Since these are end point analyses, it was combined with RNA-seq using high-grade serous ovarian cancer cells seeded on an organotypic three-dimensional (3D) culture model of the omentum, mimicking early metastasis. This comprehensive approach revealed key changes in gene expression occurring in ovarian cancer initiation and metastasis, including early metastatic colonization. Results: 2987 genes were significantly deregulated in primary tumors compared to fallopian tubes, 845 genes were differentially expressed in metastasis compared to primary tumors and 304 genes were common to both. An assessment of patient metastasis and 3D omental culture model of early metastatic colonization revealed 144 common genes that were altered during early colonization and remain deregulated even in the fully developed metastasis. Deregulation of the matrisome was a key process in early and late metastasis. Conclusion: These findings will help in understanding the key pathways involved in ovarian cancer progression and eventually targeting those pathways for therapeutic interventions.
Postural Representations of the Hand in the Primate Sensorimotor Cortex

Manual dexterity requires proprioceptive feedback about the state of the hand. To date, study of the neural basis of proprioception in the cortex has focused primarily on reaching movements to the exclusion of hand-specific behaviors such as grasping. To fill this gap, we record both time-varying hand kinematics and neural activity evoked in somatosensory and motor cortices as monkeys grasp a variety of objects. We find that neurons in the somatosensory cortex, as well as in the motor cortex, preferentially track time-varying postures of multi-joint combinations spanning the entire hand. This contrasts with neural responses during reaching movements, which preferentially track time-varying movement kinematics of the arm, such as velocity and speed of the limb, rather than its time-varying postural configuration. These results suggest different representations of arm and hand movements suited to the different functional roles of these two effectors.
Hessian transport gradient flows

We derive new gradient flows of divergence functions in the probability space embedded with a class of Riemannian metrics. The Riemannian metric tensor is built from the transported Hessian operator of an entropy function. The new gradient flow is a generalized Fokker–Planck equation and is associated with a stochastic differential equation that depends on the reference measure. Several examples of Hessian transport gradient flows and the associated stochastic differential equations are presented, including the ones for the reverse Kullback–Leibler divergence, $$\alpha $$-divergence, Hellinger distance, Pearson divergence, and Jenson–Shannon divergence.
Sato-Tate distributions on Abelian surfaces

We prove a few new cases of the Sato-Tate conjecture for abelian surfaces, using a new automorphy theorem of Allen et al. Then in the unproven cases, we use partial results to describe nontrivial asymptotics on the trace of Frobenius, and prove their optimality given current knowledge.
BAGSE: a Bayesian hierarchical model approach for gene set enrichment analysis.

Motivation Gene set enrichment analysis has been shown to be effective in identifying relevant biological pathways underlying complex diseases. Existing approaches lack the ability to quantify the enrichment levels accurately, hence preventing the enrichment information to be further utilized in both upstream and downstream analyses. A modernized and rigorous approach for gene set enrichment analysis that emphasizes both hypothesis testing and enrichment estimation is much needed. Results We propose a novel computational method, Bayesian Analysis of Gene Set Enrichment (BAGSE), for gene set enrichment analysis. BAGSE is built on a Bayesian hierarchical model and fully accounts for the uncertainty embedded in the association evidence of individual genes. We adopt an empirical Bayes inference framework to fit the proposed hierarchical model by implementing an efficient EM algorithm. Through simulation studies, we illustrate that BAGSE yields accurate enrichment quantification while achieving similar power as the state-of-the-art methods. Further simulation studies show that BAGSE can effectively utilize the enrichment information to improve the power in gene discovery. Finally, we demonstrate the application of BAGSE in analyzing real data from a differential expression experiment and a transcriptome-wide association study. Our results indicate that the proposed statistical framework is effective in aiding the discovery of potentially causal pathways and gene networks. Availability and implementation BAGSE is implemented using the C++ programing language and is freely available from https://github.com/xqwen/bagse/. Simulated and real data used in this paper are also available at the Github repository for reproducibility purposes. Supplementary information Supplementary data are available at Bioinformatics online.
Leiomyoma of the Spermatic Cord

A case of leiomyoma of the spermatic cord is described, and the English literature is reviewed.
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders

Abstract Nearly all model-reduction techniques project the governing equations onto a linear subspace of the original state space. Such subspaces are typically computed using methods such as balanced truncation, rational interpolation, the reduced-basis method, and (balanced) proper orthogonal decomposition (POD). Unfortunately, restricting the state to evolve in a linear subspace imposes a fundamental limitation to the accuracy of the resulting reduced-order model (ROM). In particular, linear-subspace ROMs can be expected to produce low-dimensional models with high accuracy only if the problem admits a fast decaying Kolmogorov n-width (e.g., diffusion-dominated problems). Unfortunately, many problems of interest exhibit a slowly decaying Kolmogorov n-width (e.g., advection-dominated problems). To address this, we propose a novel framework for projecting dynamical systems onto nonlinear manifolds using minimum-residual formulations at the time-continuous and time-discrete levels; the former leads to manifold Galerkin projection, while the latter leads to manifold least-squares Petrov–Galerkin (LSPG) projection. We perform analyses that provide insight into the relationship between these proposed approaches and classical linear-subspace reduced-order models; we also derive a posteriori discrete-time error bounds for the proposed approaches. In addition, we propose a computationally practical approach for computing the nonlinear manifold, which is based on convolutional autoencoders from deep learning. Finally, we demonstrate the ability of the method to significantly outperform even the optimal linear-subspace ROM on benchmark advection-dominated problems, thereby demonstrating the method's ability to overcome the intrinsic n-width limitations of linear subspaces.
Light-Guided Surface Plasmonic Bubble Movement via Contact Line De-Pinning by In-Situ Deposited Plasmonic Nanoparticle Heating

Precise spatiotemporal control of surface bubble movement can benefit a wide range of applications like high-throughput drug screening, combinatorial material development, microfluidic logic, colloidal and molecular assembly, and so forth. In this work, we demonstrate that surface bubbles on a solid surface are directed by a laser to move at high speeds (>1.8 mm/s), and we elucidate the mechanism to be the depinning of the three-phase contact line (TPCL) by rapid plasmonic heating of nanoparticles (NPs) deposited in situ during bubble movement. On the basis of our observations, we deduce a stick-slip mechanism based on asymmetric fore-aft plasmonic heating: local evaporation at the front TPCL due to plasmonic heating depins and extends the front TPCL, followed by the advancement of the trailing TPCL to resume a spherical bubble shape to minimize surface energy. The continuous TPCL drying during bubble movement also enables well-defined contact line deposition of NP clusters along the moving path. Our finding is beneficial to various microfluidics and pattern writing applications.
Repressive H3K9me2 protects lifespan against the transgenerational burden of COMPASS activity in C. elegans

In Caenorhabditis elegans, mutations in WDR-5 and other components of the COMPASS H3K4 methyltransferase complex extend lifespan and enable its inheritance. Here, we show that wdr-5 mutant longevity is itself a transgenerational trait that corresponds with a global enrichment of the heterochromatin factor H3K9me2 over twenty generations. In addition, we find that the transgenerational aspects of wdr-5 mutant longevity require the H3K9me2 methyltransferase MET-2, and can be recapitulated by removal of the putative H3K9me2 demethylase JHDM-1. Finally, we show that the transgenerational acquisition of longevity in jhdm-1 mutants is associated with accumulating genomic H3K9me2 that is inherited by their long-lived wild-type descendants at a subset of loci. These results suggest that heterochromatin facilitates the transgenerational establishment and inheritance of a complex trait. Based on these results, we propose that transcription-coupled H3K4me via COMPASS limits lifespan by encroaching upon domains of heterochromatin in the genome.
Liquidity Choice and Misallocation of Credit

This paper studies a novel type of misallocation of credit between investments of varying liquidity. One type of investment is more liquid, i.e., its return is more pledgeable, and the other is more productive. Low liquidities of both investment types imply that the allocation of credit is constrained inefficient and that there is overinvestment in the liquid type. Constrained inefficient equilibria feature non-positive, i.e., one less than or equal the economy’s growth rate, and yet too high interest rate, too much investment and too little consumption. Financial development can reduce long-term welfare and output in a constrained inefficient equilibrium if it raises the liquidity of the liquid type. I show a maximum liquid asset ratio or a simple debt tax can achieve constrained efficiency. Introducing government bonds can make Pareto improvement whenever it does not raise the interest rate.
Sea star inspired crawling and bouncing

The oral surface of sea stars is lined with arrays of tube feet that enable them to achieve highly controlled locomotion on various terrains. The activity of the tube feet is orchestrated by a nerv...
Modeling of Impact Ionization and Charge Trapping in SuperCDMS HVeV Detectors

A model for charge trapping and impact ionization and an experiment to measure these parameters are presented for the SuperCDMS HVeV detector. A procedure to isolate and quantify the main sources of noise (bulk and surface charge leakage) in the measurements is also described. This sets the stage to precisely measure the charge trapping and impact ionization probabilities in order to incorporate this model into future dark matter searches.
A thermophilic phage uses a small terminase protein with a fixed helix-turn-helix geometry.

Tailed bacteriophages use a DNA-packaging motor to encapsulate their genome during viral particle assembly. The small terminase (TerS) component of this DNA-packaging machinery acts as a molecular matchmaker that recognizes both the viral genome and the main motor component, the large terminase (TerL). However, how TerS binds DNA and the TerL protein remains unclear. Here we identified gp83 of the thermophilic bacteriophage P74-26 as the TerS protein. We found that TerSP76-26 oligomerizes into a nonamer that binds DNA, stimulates TerL ATPase activity, and inhibits TerL nuclease activity. A cryo-EM structure of TerSP76-26 revealed that it forms a ring with a wide central pore and radially arrayed helix–turn–helix domains. The structure further showed that these helix–turn–helix domains, which are thought to bind DNA by wrapping the double helix around the ring, are rigidly held in an orientation distinct from that seen in other TerS proteins. This rigid arrangement of the putative DNA-binding domain imposed strong constraints on how TerSP76-26 can bind DNA. Finally, the TerSP76-26 structure lacked the conserved C-terminal β-barrel domain used by other TerS proteins for binding TerL. This suggests that a well-ordered C-terminal β-barrel domain is not required for TerSP76-26 to carry out its matchmaking function. Our work highlights a thermophilic system for studying the role of small terminase proteins in viral maturation and presents the structure of TerSP76-26, revealing key differences between this thermophilic phage and its mesophilic counterparts.
On Code Design for Wireless Channels with Additive Radar Interference

This paper considers the problem of code design for a channel where communications and radar systems coexist, modeled as having both Additive White Gaussian Noise (AWGN) and Additive Radar Interference (ARI). The question of how to adapt or re-design convolutional codes (decoded by the Viterbi algorithm) and LDPC codes (decoded by the sum-product algorithm and optimized by using the EXIT chart method) to effectively handle the overall non-Gaussian ARI noise is investigated. Interestingly, it is found that codes optimized for AWGN-only noise and decoded using a decoding metric that accounts for both the Signal-to-Noise Ratio (SNR) of the useful signal and the Interference-to-Noise Ratio (INR) of the radar signal is only slightly outperformed by codes that are optimized for the AWGN+ARI channel.
THE MUON ANOMALY: EXPERIMENT AND THEORY

A summary is given of the present status of the theory and experiment of the anomalous magnetic moment of the muon. A difference between predicted and measured values is an indication of physics beyond the Standard Model. A new experimental measurements has produced a value which differs from a recent Standard Model prediction by about 1.6 standard deviations. When first announced, the discrepancy was about 2.6 standard deviations, but theorists have recently found an error in the sign of the largest term in standard model hadronic light-by-light contribution which reduces the difference. Additional data are being analyzed and elements of the theory are being scrutinized to provide, in the future, a sharper test of theory.