cssi/SciSciGPT-SciSciNet huggingface dataset:
Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification

Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods rely on regions of interest (ROIs) which require great efforts to annotate. Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning (MIL) for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned ROIs. We explore three different schemes to construct deep multi-instance networks for whole mammogram classification. Experimental results on the INbreast dataset demonstrate the robustness of proposed networks compared to previous work using segmentation and detection annotations. (Code: https://github.com/wentaozhu/deep-mil-for-whole-mammogram-classification.git).
Leveraging sensory data in estimating transformer lifetime

Transformer lifetime assessments plays a vital role in reliable operation of power systems. In this paper, leveraging sensory data, an approach in estimating transformer lifetime is presented. The winding hottest-spot temperature, which is the pivotal driver that impacts transformer aging, is measured hourly via a temperature sensor, then transformer loss of life is calculated based on the IEEE Std. C57.91-2011. A Cumulative Moving Average (CMA) model is subsequently applied to the data stream of the transformer loss of life to provide hourly estimates until convergence. Numerical examples demonstrate the effectiveness of the proposed approach for the transformer lifetime estimation, and explores its efficiency and practical merits.
A remark on partial sums involving the Mobius function

Let 〈𝒫〉⊂N be a multiplicative subsemigroup of the natural numbers N={1,2,3,…} generated by an arbitrary set 𝒫 of primes (finite or infinite). We give an elementary proof that the partial sums ∑ n∈〈𝒫〉:n≤x(μ(n))/n are bounded in magnitude by 1. With the aid of the prime number theorem, we also show that these sums converge to ∏ p∈𝒫(1−(1/p)) (the case where 𝒫 is all the primes is a well-known observation of Landau). Interestingly, this convergence holds even in the presence of nontrivial zeros and poles of the associated zeta function ζ𝒫(s)≔∏ p∈𝒫(1−(1/ps))−1 on the line {Re(s)=1}. As equivalent forms of the first inequality, we have ∣∑ n≤x:(n,P)=1(μ(n))/n∣≤1, ∣∑ n∣N:n≤x(μ(n))/n∣≤1, and ∣∑ n≤x(μ(mn))/n∣≤1 for all m,x,N,P≥1.
Uplink Performance Analysis in D2D-Enabled mmWave Cellular Networks

In this paper, we provide an analytical framework to analyze the uplink performance of device-to- device (D2D)-enabled millimeter wave (mmWave) cellular networks. Signal-to-interference-plus- noise ratio (SINR) outage probabilities are derived for both cellular and D2D links using tools from stochastic geometry. The distinguishing features of mmWave communications such as directional beamforming and having different path loss laws for line-of-sight (LOS) and non-line-of- sight (NLOS) links are incorporated into the outage analysis by employing a flexible mode selection scheme and Nakagami fading.
Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay

Despite huge success, deep networks are unable to learn effectively in sequential multitask learning settings as they forget the past learned tasks after learning new tasks. Inspired from complementary learning systems theory, we address this challenge by learning a generative model that couples the current task to the past learned tasks through a discriminative embedding space. We learn an abstract level generative distribution in the embedding that allows the generation of data points to represent the experience. We sample from this distribution and utilize experience replay to avoid forgetting and simultaneously accumulate new knowledge to the abstract distribution in order to couple the current task with past experience. We demonstrate theoretically and empirically that our framework learns a distribution in the embedding that is shared across all task and as a result tackles catastrophic forgetting.
Dynamics of Notch-Dependent Transcriptional Bursting in Its Native Context.

Summary Transcription is well known to be inherently stochastic and episodic, but the regulation of transcriptional dynamics is not well understood. Here, we analyze how Notch signaling modulates transcriptional bursting during animal development. Our focus is Notch regulation of transcription in germline stem cells of the nematode C. elegans. Using the MS2 system to visualize nascent transcripts and live imaging to record dynamics, we analyze bursting as a function of position within the intact animal. We find that Notch-dependent transcriptional activation is indeed “bursty”; that wild-type Notch modulates burst duration (ON-time) rather than duration of pauses between bursts (OFF-time) or mean burst intensity; and that a mutant Notch receptor, which is compromised for assembly into the Notch transcription factor complex, primarily modifies burst size (duration × intensity). These analyses thus visualize the effect of a canonical signaling pathway on metazoan transcriptional bursting in its native context.
Cultural stressors experienced by young Latinas with depressive symptoms living in a tumultuous sociopolitical climate in the United States

Abstract The purpose of this study is to describe the cultural stressors experienced by Latina young women with depressive symptoms from 2016 to 2018 in the United States. Twenty-four Latina young women (mean age = 16.7 years) participated in this qualitative descriptive study. Content analysis of interviews revealed four cultural stressors: Parental oversight, pressure to succeed, being treated differently, and fears of deportation. Experiences with cultural stressors varied across generational status. Clinicians should provide Latina young women with a safe space for discussing cultural stressors, assess how they are managing their stress, and advocate for policies that will benefit the well-being of Latina young women.
Query-focused Sentence Compression in Linear Time

Search applications often display shortened sentences which must contain certain query terms and must fit within the space constraints of a user interface. This work introduces a new transition-based sentence compression technique developed for such settings. Our query-focused method constructs length and lexically constrained compressions in linear time, by growing a subgraph in the dependency parse of a sentence. This theoretically efficient approach achieves an 11x empirical speedup over baseline ILP methods, while better reconstructing gold constrained shortenings. Such speedups help query-focused applications, because users are measurably hindered by interface lags. Additionally, our technique does not require an ILP solver or a GPU.
Learning Mixtures of Plackett-Luce Models from Structured Partial Orders

Mixtures of ranking models have been widely used for heterogeneous preferences. However, learning a mixture model is highly nontrivial, especially when the dataset consists of partial orders. In such cases, the parameter of the model may not be even identifiable. In this paper, we focus on three popular structures of partial orders: ranked top-$l_1$, $l_2$-way, and choice data over a subset of alternatives. We prove that when the dataset consists of combinations of ranked top-$l_1$ and $l_2$-way (or choice data over up to $l_2$ alternatives), mixture of $k$ Plackett-Luce models is not identifiable when $l_1+l_2\le 2k-1$ ($l_2$ is set to $1$ when there are no $l_2$-way orders). We also prove that under some combinations, including ranked top-$3$, ranked top-$2$ plus $2$-way, and choice data over up to $4$ alternatives, mixtures of two Plackett-Luce models are identifiable. Guided by our theoretical results, we propose efficient generalized method of moments (GMM) algorithms to learn mixtures of two Plackett-Luce models, which are proven consistent. Our experiments demonstrate the efficacy of our algorithms. Moreover, we show that when full rankings are available, learning from different marginal events (partial orders) provides tradeoffs between statistical efficiency and computational efficiency.
The tree-structured distributed network front-end processor architecture

Contemporary front-end processors, such as the IBM 3705, 1 Memorex 1380, 2 Burroughs Data Communications Processor 3 and the MERIT Communications Controller, 4 share the same general architecture depicted in Figure 1 . Communication lines are associated on a one-on-one basis with line adapters. These are connected in small groups (clusters) to the local processor, and perhaps to the local memory, through line interface bases. In a similar manner, the host system input/output channels are associated on a one-on- one basis with channel adapters which are connected to the local processor and memory. The architecture can be thought of as a minicomputer system in which the channel adapters and line interface base/line adapter subsystems are input/output devices. In fact, many front-end processors are configured in precisely this fashion.
Neural sensitization improves encoding fidelity in the primate retina.

An animal’s motion through the environment can induce large and frequent fluctuations in light intensity on the retina. These fluctuations pose a major challenge to neural circuits tasked with encoding visual information, as they can cause cells to adapt and lose sensitivity. Here, we report that sensitization, a short-term plasticity mechanism, solves this difficult computational problem by maintaining neuronal sensitivity in the face of these fluctuations. The numerically dominant output pathway in the macaque monkey retina, the midget (parvocellular-projecting) pathway, undergoes sensitization under specific conditions, including simulated eye movements. Sensitization is present in the excitatory synaptic inputs from midget bipolar cells and is mediated by presynaptic disinhibition from a wide-field mechanism extending >0.5 mm along the retinal surface. Direct physiological recordings and a computational model indicate that sensitization in the midget pathway supports accurate sensory encoding and prevents a loss of responsiveness during dynamic visual processing. Light intensity on the retina can fluctuate rapidly during natural vision, posing a challenge for encoding visual information. Here, the authors report that mechanisms of sensitization/facilitation maintain the sensitivity of the numerically dominant neural pathway in the primate retina during dynamic vision.
Risk of seizures induced by intracranial research stimulation: analysis of 770 stimulation sessions

OBJECTIVE Patients with medically refractory epilepsy often undergo intracranial electroencephalography (iEEG) monitoring to identify a seizure focus and determine their candidacy for surgical intervention. This clinically necessary monitoring period provides an increasingly utilized research opportunity to study human neurophysiology, however ethical concerns demand a thorough appreciation of the associated risks. We measured the incidence of research stimulation-associated seizures in a large multi-institutional dataset in order to determine whether brain stimulation was statistically associated with seizure incidence and identify potential risk factors for stimulation-associated seizures. APPROACH 188 subjects undergoing iEEG monitoring across ten institutions participated in 770 research stimulation sessions over 3.5 yr. Seizures within 30 min of a stimulation session were included in our retrospective analysis. We analyzed stimulation parameters, seizure incidence, and typical seizure patterns, to assess the likelihood that recorded seizures were stimulation-induced, rather than events that occurred by chance in epilepsy patients prone to seizing. MAIN RESULTS In total, 14 seizures were included in our analysis. All events were single seizures, and no adverse events occurred. The mean amplitude of seizure-associated stimulation did not differ significantly from the mean amplitude delivered in sessions without seizures. In order to determine the likelihood that seizures were stimulation induced, we used three sets of analyses: visual iEEG analysis, statistical frequency, and power analyses. We determined that three of the 14 seizures were likely stimulation-induced, five were possibly stimulation-induced, and six were unlikely stimulation-induced. Overall, we estimate a rate of stimulation-induced seizures between 0.39% and 1.82% of sessions. SIGNIFICANCE The rarity of stimulation-associated seizures and the fact that none added morbidity or affected the clinical course of any patient are important findings for understanding the feasibility and safety of intracranial stimulation for research purposes.
Exponential separations in local differential privacy

We prove a general connection between the communication complexity of two-player games and the sample complexity of their multi-player locally private analogues. We use this connection to prove sample complexity lower bounds for locally differentially private protocols as straightforward corollaries of results from communication complexity. In particular, we 1) use a communication lower bound for the hidden layers problem to prove an exponential sample complexity separation between sequentially and fully interactive locally private protocols, and 2) use a communication lower bound for the pointer chasing problem to prove an exponential sample complexity separation between k-round and (k + 1)-round sequentially interactive locally private protocols, for every k.
Animating Tree Branch Breaking and Flying Effects for a 3D Interactive Visualization System for Hurricanes and Storm Surge Flooding

This paper illustrates branch breaking and flying effect animation for trees in our 3D interactive visualization system for hurricanes and storm surge flooding. The tree branch breaking and flying effect animation extends our current tree animation framework by estimating the destination quadrant, direction (angle) and traveling speed with which a branch breaks and flies across the terrain. These calculations thus assist in simulating the potential threats to property and human lives posed by the flying branches during a hurricane, and add to the realism and appeal of our system. We utilize 3D tree models created with 3DSmax and implement the animation effects via OpenGL and nVidia CG shader language.
Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment.

The interpretation of chest radiographs is an essential task for the detection of thoracic diseases and abnormalities. However, it is a challenging problem with high inter-rater variability and inherent ambiguity due to inconclusive evidence in the data, limited data quality or subjective definitions of disease appearance. Current deep learning solutions for chest radiograph abnormality classification are typically limited to providing probabilistic predictions, relying on the capacity of learning models to adapt to the high degree of label noise and become robust to the enumerated causal factors. In practice, however, this leads to overconfident systems with poor generalization on unseen data. To account for this, we propose an automatic system that learns not only the probabilistic estimate on the presence of an abnormality, but also an explicit uncertainty measure which captures the confidence of the system in the predicted output. We argue that explicitly learning the classification uncertainty as an additional measure to the predicted output, is essential to account for the inherent variability characteristic of this data. Experiments were conducted on two datasets of chest radiographs of over 85,000 patients. Sample rejection based on the predicted uncertainty can significantly improve the ROC-AUC, e.g., by 8% to 0.91 with an expected rejection rate of under 25%. Eliminating training samples using uncertainty-driven bootstrapping, enables a significant increase in robustness and accuracy. In addition, we present a multi-reader study showing that the predictive uncertainty is indicative of reader errors.
Resilience in multi-robot target tracking through reconfiguration

We address the problem of maintaining resource availability in a networked multi-robot system performing distributed target tracking. In our model, robots are equipped with sensing and computational resources enabling them to track a target’s position using a Distributed Kalman Filter (DKF). We use the trace of each robot’s sensor measurement noise covariance matrix as a measure of sensing quality. When a robot’s sensing quality deteriorates, the systems communication graph is modified by adding edges such that the robot with deteriorating sensor quality may share information with other robots to improve the team’s target tracking ability. This computation is performed centrally and is designed to work without a large change in the number of active communication links. We propose two mixed integer semi-definite programming formulations (an ‘agent-centric’ strategy and a ‘team-centric’ strategy) to achieve this goal. We implement both formulations and a greedy strategy in simulation and show that the team-centric strategy outperforms the agent-centric and greedy strategies.
DIFFERENTIAL EFFECTS OF VARYING DOSES OF DIETARY NITRATE ON MUSCLE FUNCTION AND BLOOD PRESSURE IN OLDER SUBJECTS

We have recently demonstrated that dietary nitrate, a source of nitric oxide via the enterosalivary pathway, can improve muscle contractile function in healthy older men and women. Nitrate ingestion has also been shown to reduce blood pressure in older individuals. However, the optimal dose for eliciting these beneficial effects is unknown. We therefore performed a randomized, double-blind, crossover study to determine the effects of ingesting 3.3 mL/kg of beetroot juice (BRJ) containing 0, 212, or 425 µmol/kg of nitrate in six healthy older (age 69±3 y) subjects. Maximal knee extensor speed (Vmax) and power (Pmax) were measured 2 h after BRJ ingestion using isokinetic dynamometry; blood pressure was monitored periodically throughout each study. Mean arterial pressure (in mmHg) was lower (P<0.05) after the high (80±4) vs. the low (84±3) or placebo (88±2) doses. Vmax (in rad/s), however, was higher (P<0.05) after the low dose (11.7±0.8), but not the high dose (10.8±1.0), compared to the placebo (10.5±1.0). Pmax (in W/kg) also tended to be higher (P=0.11) in the low (3.9±0.5) compared to the placebo (3.7±0.5) or high (3.7±0.5) trials. Five out of six subjects achieved a higher Vmax and Pmax after the low vs. the high dose. We conclude that dietary nitrate has differential effects on muscle function and blood pressure in older individuals. A high dose of nitrate intake further lowers blood pressure but does not enhance muscle contractility as much as a lower dose. Supported by Indiana University Purdue University Indianapolis and by the NIA (R21 AG053606)
Adaptive spectral solution method for the Landau and Lenard-Balescu equations

Abstract We present an adaptive spectral method for solving the Landau/Fokker-Planck equation for electron-ion systems. The heart of the algorithm is an expansion in Laguerre polynomials, which has several advantages, including automatic conservation of both energy and particles without the need for any special discretization or time-stepping schemes. One drawback of such an expansion is the O ( N 3 ) memory requirement, where N is the number of polynomials used. This can impose an inconvenient limit in cases of practical interest, such as when two particle species have widely separated temperatures. The algorithm we describe here addresses this problem by periodically re-projecting the solution onto a judicious choice of new basis functions that are still Laguerre polynomials but have arguments adapted to the current physical conditions. This results in a reduction in the number of polynomials needed, at the expense of increased solution time. Because the equations are solved with little difficulty, this added time is not of much concern compared to the savings in memory. To demonstrate the algorithm, we solve several relaxation problems that could not be computed with the spectral method without re-projection. Another major advantage of this method is that it can be used for collision operators more complicated than that of the Landau equation, and we demonstrate this here by using it to solve the non-degenerate quantum Lenard-Balescu (QLB) equation for a hydrogen plasma. We conclude with some comparisons of temperature relaxation problems solved with the latter equation and the Landau equation with a Coulomb logarithm inspired by the properties of the QLB operator. We find that with this choice of Coulomb logarithm, there is little difference between using the two equations for these particular systems.
Loss aversion and sunk cost sensitivity in all-pay auctions for charity: Theory and experiments

Abstract This paper theoretically and experimentally studies the role of two behavioral biases in all-pay auctions for charity. The theory is developed to predict the effect of loss aversion in the first-price all-pay auction and sunk cost sensitivity in the war of attrition. Using controlled laboratory experiments, auction treatments are designed to test for the presence of these biases. In support of the theory, the results indicate that revenues in incremental bidding mechanisms such as the war of attrition rely on bidders who are sunk cost sensitive. It is shown that this behavioral response can be curbed significantly with a commitment device. Likewise, the results of the experimental first-price all-pay auctions find evidence of loss aversion, which reduces bidders’ average bid. These findings help explain the inconsistencies in revenues from previous all-pay auction studies and indicate a mechanism preference based on the distribution of these behavioral characteristics.
Rapid uncertainty propagation and chance‐constrained path planning for small unmanned aerial vehicles

With the number of small Unmanned Aircraft Systems (sUAS) in the national airspace projected to increase in the next few years, there is growing interest in a traffic management system capable of handling the demands of this aviation sector. It is expected that such a system will involve trajectory prediction, uncertainty propagation, and path planning algorithms. In this work, we use linear covariance propagation in combination with a quadratic programming-based collision detection algorithm to rapidly validate declared flight plans. Additionally, these algorithms are combined with a Dynamic, Informed RRT* algorithm, resulting in a computationally efficient algorithm for chance-constrained path planning. Detailed numerical examples for both fixed-wing and quadrotor sUAS models are presented.
Effects of Online Course Load on Degree Completion, Transfer, and Dropout among Community College Students of the State University of New York

Research suggests that some students are at risk of lower levels of academic performance when studying online compared to students who take coursework only in the classroom. Community college students appear to be among those that struggle in online settings. In this paper, we hypothesize that online course load may influence outcomes for such students, especially those at risk for lower levels of degree attainment. To examine this, we conducted a statewide study using data from the 30 community colleges (n=45,557) of the State University of New York, to understand online course-load effects on degree completion, transfer, and dropout. We conclude that when controlling for covariates known to impact degree completion, on average, community college students who successfully complete online courses nearly double their chances (odds ratio=1.72) of earning a degree or transferring to a 4-year college. However, racial minority students had reduced outcomes and additional research is warranted.
Looking at Hands in Autonomous Vehicles: A ConvNet Approach Using Part Affinity Fields

In the context of autonomous driving, where humans may need to take over in the event where the computer may issue a takeover request, a key step towards driving safety is the monitoring of the hands to ensure the driver is ready for such a request. This work, focuses on the first step of this process, which is to locate the hands. Such a system must work in real-time and under varying harsh lighting conditions. This article introduces a fast ConvNet approach, based on the work of original work of OpenPose by Cao, et al. for full body joint estimation. The network is modified with fewer parameters and retrained using our own day-time naturalistic autonomous driving dataset to estimate joint and affinity heatmaps for driver and passenger’s wrist and elbows, for a total of 8 joint classes and part affinity fields between each wrist-elbow pair. The approach runs real-time on real-world data at 40 fps on multiple drivers and passengers. The system is extensively evaluated both quantitatively and qualitatively, showing at least 95% detection performance on joint localization and arm-angle estimation.
Measuring and Modulating Substrate Confinement during Nitrogen-Atom Transfer in a Ru2-Based Metal-Organic Framework.

The porosity and synthetic tunability of metal-organic frameworks (MOFs) has motivated interest in application of these materials as designer heterogeneous catalysts. While understanding substrate ...
Long-Term Protection of Rhesus Macaques from Zika Virus Reinfection.

By the end of the 2016 Zika virus (ZIKV) outbreak, it is estimated that there were up to 100 million infections in the Americas. In approximately one in seven infants born to mothers infected during pregnancy, ZIKV has been linked to microcephaly, developmental delays, or other congenital disorders collectively known as congenital Zika syndrome, as well as Guillain-Barre syndrome, in ZIKV-infected adults. It is a global health priority to develop a vaccine against ZIKV that elicits long-lasting immunity; however, the durability of immunity to ZIKV is unknown. Previous studies in mice and nonhuman primates have been crucial in vaccine development but have not defined the duration of immunity generated by ZIKV infection. In this study, we rechallenged five rhesus macaques with ZIKV 22 to 28 months after a primary ZIKV infection. We show that primary ZIKV infection generates high titers of neutralizing antibodies that protect from detectable plasma viremia following rechallenge and persist for at least 22 to 28 months. While additional longitudinal studies are necessary with longer time frames, this study establishes a new experimentally defined minimal length of protective ZIKV immunity.IMPORTANCE ZIKV emerged as a vector-borne pathogen capable of causing illness in infected adults and congenital birth defects in infants born to mothers infected during pregnancy. Despite the decrease in ZIKV cases since the 2015-2016 epidemic, questions concerning the prevalence and longevity of protective immunity have left vulnerable communities fearful that they may become the center of next ZIKV outbreak. Although preexisting herd immunity in regions of past outbreaks may dampen the potential for future outbreaks to occur, we currently do not know the longevity of protective immunity to ZIKV after a person becomes infected. Here, we establish a new experimentally defined minimal length of protective ZIKV immunity. We show that five rhesus macaques initially infected with ZIKV 22 to 28 months prior to rechallenge elicit a durable immune response that protected from detectable plasma viremia. This study establishes a new minimal length of protective immunity.
The Liquid Metallic Hydrogen Model of the Sun and the Solar Atmosphere VII. Further Insights into the Chromosphere and Corona

In the liquid metallic hydrogen model of the Sun, the chromosphere is responsible for the capture of atomic hydrogen in the solar atmosphere and its eventual re-entry onto the photospheric surface (P.M. Robitaille. The Liquid Metallic Hydrogen Model of the Sun and the Solar Atmosphere IV. On the Nature of the Chromosphere. Prog. Phys., 2013, v. 3, L15–L21). As for the corona, it represents a diffuse region containing both gaseous plasma and condensed matter with elevated electron affinity (P.M. Robitaille. The Liquid Metallic Hydrogen Model of the Sun and the Solar Atmosphere V. On the Nature of the Corona. Prog. Phys., 2013, v. 3, L22–L25). Metallic hydrogen in the corona is thought to enable the continual harvest of electrons from the outer reaches of the Sun, thereby preserving the neutrality of the solar body. The rigid rotation of the corona is offered as the thirty-third line of evidence that the Sun is comprised of condensed matter. Within the context of the gaseous models of the Sun, a 100 km thick transition zone has been hypothesized to exist wherein temperatures increase dramatically from 10–10 K. Such extreme transitional temperatures are not reasonable given the trivial physical scale of the proposed transition zone, a region adopted to account for the ultra-violet emission lines of ions such as C IV, O IV, and Si IV. In this work, it will be argued that the transition zone does not exist. Rather, the intermediate ionization states observed in the solar atmosphere should be viewed as the result of the simultaneous transfer of protons and electrons onto condensed hydrogen structures, CHS. Line emissions from ions such as C IV, O IV, and Si IV are likely to be the result of condensation reactions, manifesting the involvement of species such as CH4, SiH4, H3O in the synthesis of CHS in the chromosphere. In addition, given the presence of a true solar surface at the level of the photosphere in the liquid metallic hydrogen model, it follows that the great physical extent of the chromosphere is supported by gas pressure, much like the atmosphere of the Earth. This constitutes the thirty-fourth line of evidence that the Sun is comprised of condensed matter.
The Trade-Off Between Privacy and Fidelity via Ehrhart Theory

As an increasing amount of data is gathered nowadays and stored in databases, the question arises of how to protect the privacy of individual records in a database even while providing accurate answers to queries on the database. Differential Privacy (DP) has gained acceptance as a framework to quantify vulnerability of algorithms to privacy breaches. We consider the problem of how to sanitize an entire database via a DP mechanism, on which unlimited further querying is performed. While protecting privacy, it is important that the sanitized database still provide accurate responses to queries. The central contribution of this work is to characterize the amount of information preserved in an optimal DP database sanitizing mechanism (DSM). We precisely characterize the utility-privacy trade-off of mechanisms that sanitize databases in the asymptotic regime of large databases. We study this in an information-theoretic framework by modeling a generic distribution on the data, and a measure of fidelity between the histograms of the original and sanitized databases. We consider the popular $\mathbb {L}_{1}-$ distortion metric, i.e., the total variation norm that leads to the formulation as a linear program (LP). This optimization problem is prohibitive in complexity with the number of constraints growing exponentially in the parameters of the problem. Our focus on the asymptotic regime enables us characterize precisely, the limit of the sequence of solutions to this optimization problem. Leveraging tools from discrete geometry, analytic combinatorics, and duality theorems of optimization, we fully characterize this limit in terms of a power series whose coefficients are the number of integer points on a multidimensional convex cross-polytope studied by Ehrhart in 1967. Employing Ehrhart theory, we determine a simple closed form computable expression for the asymptotic growth of the optimal privacy-fidelity trade-off to infinite precision. At the heart of the findings is a deep connection between the minimum expected distortion and a fundamental construct in Ehrhart theory - Ehrhart series of an integral convex polytope.
Boundary Solutions of the Quantum Yang–Baxter Equation and Solutions in Three Dimensions

Boundary solutions to the quantum Yang–Baxter (qYB) equation are defined to be those in the boundary of (but not in) the variety of solutions to the ‘modified’ qYB equation, the latter being analogous to the modified classical Yang–Baxter (cYB) equation. We construct, for a large class of solutions r to the modified cYB equation, explicit ‘boundary quantizations’, i.e., boundary solutions to the qYB equation of the form I + tr + t2r2 +⋯. In the last section we list and give quantizations for all classical r-matrices in sl(3) ∧ sl(3).
Deep Imitative Models for Flexible Inference, Planning, and Control

Imitation Learning (IL) is an appealing approach to learn desirable autonomous behavior. However, directing IL to achieve arbitrary goals is difficult. In contrast, planning-based algorithms use dynamics models and reward functions to achieve goals. Yet, reward functions that evoke desirable behavior are often difficult to specify. In this paper, we propose "Imitative Models" to combine the benefits of IL and goal-directed planning. Imitative Models are probabilistic predictive models of desirable behavior able to plan interpretable expert-like trajectories to achieve specified goals. We derive families of flexible goal objectives, including constrained goal regions, unconstrained goal sets, and energy-based goals. We show that our method can use these objectives to successfully direct behavior. Our method substantially outperforms six IL approaches and a planning-based approach in a dynamic simulated autonomous driving task, and is efficiently learned from expert demonstrations without online data collection. We also show our approach is robust to poorly-specified goals, such as goals on the wrong side of the road.
Arctic sea ice state estimation from thermodynamic PDE model

Abstract Recent rapid loss of the Arctic sea ice motivates the study of the Arctic sea ice thickness. Global climate model that describes the ice thickness evolution requires an accurate spatial temperature profile of the Arctic sea ice. However, measuring the complete temperature profile is not feasible within and throughout the Arctic icecap. Instead, measuring the ice thickness is doable with the acquisition of data from submarine and satellite devices. In this paper, we develop a backstepping observer algorithm to estimate the temperature profile for the Arctic sea ice model via available measurements of sea ice thickness and sea ice surface temperature. The observer is designed in a rigorous manner to drive the temperature profile estimation error to zero, for a salinity-free sea ice model. Moreover, the proposed observer is used to estimate the temperature profile of the original sea ice model with salinity via numerical simulation. The simulation results illustrate that our observer design achieves a successful estimation of the sea ice temperature profile in three days, which is ten times faster than the straightforward open-loop algorithm.
On the Modulus of C. J. Read's Operator

Let \(T:\ell _1 \to \ell _1 \) be the quasinilpotent operator without an invariant subspace constructed by C. J. Read. We prove that the modulus of this operator has an invariant subspace (and even an eigenvector). This answers a question posed by Y. Abramovich, C. Aliprantis and O. Burkinshaw.
The masterpieces of John Forbes Nash Jr

In this set of notes I follow Nash’s four groundbreaking works on real algebraic manifolds, on isometric embeddings of Riemannian manifolds and on the continuity of solutions to parabolic equations. My aim has been to stay as close as possible to Nash’s original arguments, but at the same time present them with a more modern language and notation. Occasionally I have also provided detailed proofs of the points that Nash leaves to the reader.
Attenuated dopamine receptor signaling in nucleus accumbens core in a rat model of chemically-induced neuropathy

Neuropathy is major source of chronic pain that can be caused by mechanically or chemically induced nerve injury. Intraplantar formalin injection produces local necrosis over a two-week period and has been used to model neuropathy in rats. To determine whether neuropathy alters dopamine (DA) receptor responsiveness in mesolimbic brain regions, we examined dopamine D1-like and D2-like receptor (D1/2R) signaling and expression in male rats 14 days after bilateral intraplantar formalin injections into both rear paws. D2R-mediated G-protein activation and expression of the D2R long, but not short, isoform were reduced in nucleus accumbens (NAc) core, but not in NAc shell, caudate-putamen or ventral tegmental area of formalin- compared to saline-treated rats. In addition, D1R-stimulated adenylyl cyclase activity was also reduced in NAc core, but not in NAc shell or prefrontal cortex, of formalin-treated rats, whereas D1R expression was unaffected. Other proteins involved in dopamine neurotransmission, including dopamine uptake transporter and tyrosine hydroxylase, were unaffected by formalin treatment. In behavioral tests, the potency of a D2R agonist to suppress intracranial self-stimulation (ICSS) was decreased in formalin-treated rats, whereas D1R agonist effects were not altered. The combination of reduced D2R expression and signaling in NAc core with reduced suppression of ICSS responding by a D2R agonist suggest a reduction in D2 autoreceptor function. Altogether, these results indicate that intraplantar formalin produces attenuation of highly specific DA receptor signaling processes in NAc core of male rats and suggest the development of a neuropathy-induced allostatic state in both pre- and post-synaptic DA receptor function.
Secrecy capacity of the wiretap channel with noisy feedback

In this work, the role of noisy feedback in enhancing the secrecy capacity of the wiretap channel is investigated. A model is considered in which the feed-forward and feedback signals share the same noisy channel. More specifically, a discrete memoryless modulo-additive channel with a full-duplex destination node is considered first, and it is shown that a judicious use of feedback increases the perfect secrecy capacity to the capacity of the source-destination channel in the absence of the wiretapper. In the achievability scheme, the feedback signal corresponds to a private key, known only to the destination. Then a half-duplex system is considered, for which a novel feedback technique that always achieves a positive perfect secrecy rate (even when the source-wiretapper channel is less noisy than the source-destination channel) is proposed. These results hinge on the modulo-additive property of the channel, which is exploited by the destination to perform encryption over the channel without revealing its key to the source.
Enumerating cliques in direct product graphs

The unitary Cayley graph of $\mathbb Z/n\mathbb Z$, denoted $G_{\mathbb Z/n\mathbb Z}$, is the graph with vertices $0,1,\ldots,$ $n-1$ in which two vertices are adjacent if and only if their difference is relatively prime to $n$. These graphs are central to the study of graph representations modulo integers, which were originally introduced by Erdős and Evans. We give a brief account of some results concerning these beautiful graphs and provide a short proof of a simple formula for the number of cliques of any order $m$ in the unitary Cayley graph $G_{\mathbb Z/n\mathbb Z}$. This formula involves an exciting class of arithmetic functions known as Schemmel totient functions, which we also briefly discuss. More generally, the proof yields a formula for the number of cliques of order $m$ in a direct product of balanced complete multipartite graphs.
Convergence of a stochastic subgradient method with averaging for nonsmooth nonconvex constrained optimization

We prove convergence of a single time-scale stochastic subgradient method with subgradient averaging for constrained problems with a nonsmooth and nonconvex objective function having the property of generalized differentiability. As a tool of our analysis, we also prove a chain rule on a path for such functions.
Computational Simulation and Analysis of Major Control Parameters of Time-Dependent PV/T Collectors

In order to improve performance of photovoltaic/thermal (or PV/T for simplicity) collectors, this paper firstly validated a previous computational thermal model and then introduced an improved computational thermal model to investigate the effects of the major control parameters on the thermal performance of PV/T collectors, including solar cell temperature, back surface temperature, and outlet water temperature. Besides, a computational electrical model of PV/T system was also introduced to elaborate the relationship of voltage, current and power of a PV module (MSX60 polycrystalline solar cell) used in an experiment in the literature. Simulation results agree with the experimental data very well. The effects of the time-steps from 1 hour to minute, which is closed to the real time, were also reported. At last, several suggestions to improve the efficiency of PV/T system were illustrated.
On Numerical Stabilization in Modeling Double-Diffusive Viscous Fingering

A firm understanding and control of viscous fingering (VF) and miscible displacement will be vital to a wide range of industrial, environmental, and pharmaceutical applications, such as geological carbon dioxide sequestration, enhanced oil recovery, and drug delivery. We restrict our study to VF, a well-known hydrodynamic instability, in miscible fluid systems but consider double-diffusive (DD) effects—the combined effect of compositional changes because of solute transport and temperature. One often uses numerical formulations to study VF with DD effects. The primary aim of the current study is to show that popular formulations have limitations to study VF with DD effect. These limitations include exhibiting node-to-node spurious oscillations, violating physical constraints such as the nonnegativity of the concentration field or mathematical principles such as the maximum principle, and suppressing physical instabilities. We will use several popular stabilized finite element formulations—the SUPG formulations and three modifications based on the SOLD approach—in our study. Using representative numerical results, we will illustrate two critical limitations. First, we document that these formulations do not respect the nonnegative constraint and the maximum principle for the concentration field. We will also show the impact of these violations on how viscous fingers develop. Second, we show that these stabilized formulations, often used to suppress numerical instabilities, may also suppress physical instabilities, such as viscous fingering. Our study will be valuable to practitioners who use existing numerical formulations and to computational mathematicians who develop new formulations. This figure shows the unphysical concentration values got using the SUPG stabilized formulation on the quarter five-spot problem. The numerical solution for the concentration field violated the nonnegative constraint (left) and the maximum principle (right). The mathematical model comprises coupled flow-thermal-transport equations.
Cross-linguistic f0 differences in bilingual speakers of English and Korean

Languages may differ in fundamental frequency of voicing (f0), even when they are spoken by a bilingual individual. However, little is known in bilingual/L2 acquisition research about simultaneous bilinguals. With the expectation that speakers who acquired two languages early use f0 differently for each language, this study measured f0 in English-Korean early bilinguals' natural speech. The f0 level was higher for Korean than English, regardless of gender, age, or generational status (early and late bilinguals did not differ). The f0 span showed a language-gender interaction: males' span was larger in Korean, while females' span was larger in English. This study demonstrates that languages differ in f0 independent of speaker anatomy and suggests that children may acquire these differences in early childhood.
On the Relationship between the African Easterly Jet, Saharan Mineral Dust Aerosols, and West African Precipitation

AbstractThe relationship between the African easterly jet (AEJ), Saharan mineral dust (SMD) aerosols, and West African precipitation (WAP) is examined using European Centre for Medium-Range Weather...
A comparison of anti-whiplash seats during low/moderate speed rear-end collisions.

Objectives: The Insurance Institute for Highway Safety (IIHS) rates automotive seats as good, acceptable, marginal, and poor on their abilities to prevent whiplash injuries during rear-end collisio...
Extracting Molecular Entities and Their Interactions from Pathway Figures Based on Deep Learning

Many new molecular mechanisms of genomics, pharmacogenomics, immunology, and other fields are reflected in pathway figures and need to be curated for various applications, especially in precision medicine. Current manual curation approaches are inadequate in keeping up with the pace of biomedical literature growth. Compared with textual representations, pathway figures in the biomedical literature often contain more direct representations of the mechanisms. However, no systematic method for curating pathway figures exists in publications due to date. Here, we propose a pathway curation pipeline, which integrates a deep learning model with an optical character recognition method and an image processing strategy to capture the locations, names, and interactions of pathway entities in the figure. Our pipeline was evaluated on the figures from PubMed publications. The results demonstrate that our model can effectively retrieve molecular entities and their interactions from pathway figures at a large scale. The proposed pipeline provides a complementary approach to text-mining in biological literature mining. In future work, we will combine our method with text-mining tools to enrich extracted information and reconstruct pathway mechanisms more precisely.
Silicate Melting and Vaporization During Rocky Planet Formation

Author(s): Davies, E. J; Carter, P. J; Root, S.; Kraus, R. G; Spaulding, D. K; Stewart, S. T; Jacobsen, S. B