cssi/SciSciGPT-SciSciNet huggingface dataset:
Self-Defense and the Mistaken Racist

How should the law of a liberal state respond when one person (D) kills another person (V), who is black, because D believes that V is about to kill him, but D would not have believed that V was about to kill him if V had been white? Should D be exonerated on grounds of self-defense? Some commentators argue that D's claim of self-defense should be rejected and that he should be convicted, either of murder or manslaughter, and punished accordingly. I disagree. I argue that denying D's claim of self-defense would be at odds with the principle that an actor should be punished, not for possessing or choosing to possess racist or otherwise illiberal beliefs or desires, but only for choosing to cause (or risk causing) harm when the law does not permit him to make such a choice. Moreover, insofar as this principle can fairly be characterized as one to which a liberal state must adhere, then a liberal state should acknowledge D's claim of self-defense.
Paradoxes of Health and Equality: When a Boy Becomes a Girl

This paper is about an unusual child custody dispute between the parents of a six-year-old child and the child welfare services of Franklin County, Ohio. The conflict emerged when the child's parents complied with their male child's professed desire to be treated as a girl by attempting to enroll the child in the first grade as a girl. The paper treats this case as an exemplary test-case of contemporary co-dependence between scientific-medical discourse and liberal-rights discourse. The paper analyzes the positions of the two sides of the custody dispute according to the classic modern distinction between mind and body. On the one hand, the child's parents alleged that the child should be treated as a girl, because the child's 'true self' in his/her effeminate mind. On the other hand, the child-welfare services counter-claimed that the child should be treated as a boy, because the child's 'true self' is located in the male body. The paper investigates the ways that each side attempted to justify its position in light of current debates regarding the foundation of 'gender-identity.' In particular, the paper focuses on medical-scientific theories about 'psychosexual development' that appeared in the US around mid-century and appeared in this case as legal claims about standards of parenthood such as 'best interest,' and parental 'neglect' and 'abuse.' The paper then focuses on the potential of liberal rights discourse, and specifically sex discrimination law to free individuals from institutional gender enforcement. The paper agrees that sex discrimination law can often produce satisfactory results, such as granting this child the liberty of free gender expression. However, the paper also critically analyzes sex discrimination law as a discourse that often reflects rather than rejects rigid mental health definitions of modern scientific discourses. In conclusion, the paper points to the limited horizons of rights as a liberating force, and demonstrates the need to think beyond rights in feminist and queer legal theory.
Minimum variance hedging when spot price changes are partially predictable.

Abstract In many markets, changes in the spot price are partially predictable. We show that when this is the case: (1) although unbiased, traditional regression estimates of the minimum variance hedge ratio are inefficient, (2) estimates of the riskiness of both hedged and unhedged positions are biased upward, and (3) estimates of the percentage risk reduction achievable through hedging are biased downward. For natural gas cross hedges, we find that both the inefficiency and bias are substantial. We further find that incorporating the expected change in the spot price, as measured by the futures-spot price spread at the beginning of the hedge, into the regression results in a substantial increase in efficiency and reduction in the bias.
THE GAUSS MAP OF A FREE BOUNDARY MINIMAL SURFACE

In this paper, we study the Gauss map of a free boundary minimal surface. The main theorem asserts that if components of the Gauss map are eigenfunctions of the Jacobi-Steklov operator, then the surface must be rotationally symmetric.
Predicting Camera Viewpoint Improves Cross-dataset Generalization for 3D Human Pose Estimation

Monocular estimation of 3d human pose has attracted increased attention with the availability of large ground-truth motion capture datasets. However, the diversity of training data available is limited and it is not clear to what extent methods generalize outside the specific datasets they are trained on. In this work we carry out a systematic study of the diversity and biases present in specific datasets and its effect on cross-dataset generalization across a compendium of 5 pose datasets. We specifically focus on systematic differences in the distribution of camera viewpoints relative to a body-centered coordinate frame. Based on this observation, we propose an auxiliary task of predicting the camera viewpoint in addition to pose. We find that models trained to jointly predict viewpoint and pose systematically show significantly improved cross-dataset generalization.
Migration and Informal Insurance: Evidence from a Randomized Controlled Trial and a Structural Model

We document that an experimental intervention offering transport subsidies for poor rural households to migrate seasonally in Bangladesh improved risk sharing. A theoretical model of endogenous migration and risk sharing shows that the effect of subsidizing migration depends on the underlying economic environment. If migration is risky, a temporary subsidy can induce an improvement in risk sharing and enable profitable migration. We estimate the model and find that the migration experiment increased welfare by 12.9%. Counterfactual analysis suggests that a permanent, rather than temporary, decline in migration costs in the same environment would result in a reduction in risk sharing.
Time-reversal odd transport in bilayer graphene: Hall conductivity and Hall viscosity

This work explores a relationship between time-reversal odd transport quantities in graphene.
Privacy Preserving Distributed Energy Trading

The smart grid incentivizes distributed agents with local generation (e.g., smart homes, and microgrids) to establish multi-agent systems for enhanced reliability and energy consumption efficiency. Distributed energy trading has emerged as one of the most important multi-agent systems on the power grid by enabling agents to sell their excessive local energy to each other or back to the grid. However, it requests all the agents to disclose their sensitive data (e.g., each agent’s fine-grained local generation and demand load). In this paper, to the best of our knowledge, we propose the first privacy preserving distributed energy trading framework, Private Energy Market (PEM), in which all the agents privately compute an optimal price for their trading (ensured by a Nash Equilibrium), and allocate pairwise energy trading amounts without disclosing sensitive data (via novel cryptographic protocols). Specifically, we model the trading problem as a non-cooperative Stackelberg game for all the agents (i.e., buyers and sellers) to determine the optimal price, and then derive the pairwise trading amounts. Our PEM framework can privately perform all the computations among all the agents without a trusted third party. We prove the privacy, individual rationality, and incentive compatibility for the PEM framework. Finally, we conduct experiments on real datasets to validate the effectiveness and efficiency of the PEM.
Annotation of chromatin states in 66 complete mouse epigenomes during development

The morphologically and functionally distinct cell types of a multicellular organism are maintained by their unique epigenomes and gene expression programs. Phase III of the ENCODE Project profiled 66 mouse epigenomes across twelve tissues at daily intervals from embryonic day 11.5 to birth. Applying the ChromHMM algorithm to these epigenomes, we annotated eighteen chromatin states with characteristics of promoters, enhancers, transcribed regions, repressed regions, and quiescent regions. Our integrative analyses delineate the tissue specificity and developmental trajectory of the loci in these chromatin states. Approximately 0.3% of each epigenome is assigned to a bivalent chromatin state, which harbors both active marks and the repressive mark H3K27me3. Highly evolutionarily conserved, these loci are enriched in silencers bound by polycomb repressive complex proteins, and the transcription start sites of their silenced target genes. This collection of chromatin state assignments provides a useful resource for studying mammalian development.
DCI: learning causal differences between gene regulatory networks

Summary Designing interventions to control gene regulation necessitates modeling a gene regulatory network by a causal graph. Currently, large-scale expression datasets from different conditions, cell types, disease states and developmental time points are being collected. However, application of classical causal inference algorithms to infer gene regulatory networks based on such data is still challenging, requiring high sample sizes and computational resources. Here, we describe an algorithm that efficiently learns the differences in gene regulatory mechanisms between different conditions. Our difference causal inference (DCI) algorithm infers changes (i.e., edges that appeared, disappeared or changed weight) between two causal graphs given gene expression data from the two conditions. This algorithm is efficient in its use of samples and computation since it infers the differences between causal graphs directly without estimating each possibly large causal graph separately. We provide a user-friendly Python implementation of DCI and also enable the user to learn the most robust difference causal graph across different tuning parameters via stability selection. Finally, we show how to apply DCI to single-cell RNA-seq data from different conditions and cell states, and we also validate our algorithm by predicting the effects of interventions. Availability and implementation Python package freely available at http://uhlerlab.github.io/causaldag/dci. Supplementary information Supplementary information is available at Bioinformatics online.
Improved Upper and Lower Bounds for LR Drawings of Binary Trees

In SODA’99, Chan introduced a simple type of planar straight-line upward order-preserving drawings of binary trees, known as LR drawings: such a drawing is obtained by picking a root-to-leaf path, drawing the path as a straight line, and recursively drawing the subtrees along the paths. Chan proved that any binary tree with n nodes admits an LR drawing with \(O(n^{0.48})\) width. In SODA’17, Frati, Patrignani, and Roselli proved that there exist families of n-node binary trees for which any LR drawing has \(\varOmega (n^{0.418})\) width. In this paper, we improve Chan’s upper bound to \(O(n^{0.437})\) and Frati et al.’s lower bound to \(\varOmega (n^{0.429})\).
Local densities of diagonal integral ternary quadratic forms at odd primes

We give formulas for local densities of diagonal integral ternary quadratic forms at odd primes. Exponential sums and quadratic Gauss sums are used to obtain these formulas. These formulas (along w...
Strategic Defense Against Stealthy Link Flooding Attacks: A Signaling Game Approach

With the increasing diversity of Distributed Denial-of-Service (DDoS) attacks, it is becoming extremely challenging to design a fully protected network. For instance, Stealthy Link Flooding Attack (SLFA) is a variant of DDoS attacks that strives to block access to a target area by flooding a small set of links, and it is shown that it can bypass traditional DDoS defense mechanisms. One potential solution to tackle such SLFAs is to apply Moving Target Defense (MTD) techniques in which network settings are dynamically changed to confuse/deceive attackers, thus making it highly expensive to launch a successful attack. However, since MTD comes with some overhead to the network, to find the best strategy (i.e., when and/or to what extent) of applying it has been a major challenge. The strategy is significantly influenced by the attacker's behavior that is often difficult to guess. In this work, we address the challenge of obtaining the optimal MTD strategy that effectively mitigates SLFAs while incurs a minimal overhead. We design the problem as a signaling game considering the network defender and the attacker as players. A belief function is established throughout the engagement of the attacker and the defender during this SLFA campaign, which is utilized to pick the best response/action for each player. We analyze the game model and derive a defense mechanism based on the equilibria of the game. We evaluate the technique on a Mininet-based network environment where an attacker is performing SLFAs and a defender applies MTD based on equilibria of the game. The results show that our signaling game-based dynamic defense mechanism can provide a similar level of protection against SLFAs like the extensive MTD solution, however, causing a significantly reduced overhead.
Phase-locking of laminar wake to periodic vibrations of a circular cylinder

The periodic wake flow past a bluff body immersed in a moving fluid can be synchronized to the harmonic vibrations of the body. A low-dimensional, phase-based analysis has been adopted to uncover the theoretical conditions necessary for synchronization between the wake and body oscillations. The present phase-based methodology allows for modifying the transient features of unsteady flows, thereby enabling the online control and analysis of aerodynamic forces on flyers/swimmers, vortex-induced vibrations, and fluid-structure interactions.
FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping.

Byzantine-robust federated learning aims to enable a service provider to learn an accurate global model when a bounded number of clients are malicious. The key idea of existing Byzantine-robust federated learning methods is that the service provider performs statistical analysis among the clients' local model updates and removes suspicious ones, before aggregating them to update the global model. However, malicious clients can still corrupt the global models in these methods via sending carefully crafted local model updates to the service provider. The fundamental reason is that there is no root of trust in existing federated learning methods. In this work, we bridge the gap via proposing FLTrust, a new federated learning method in which the service provider itself bootstraps trust. In particular, the service provider itself collects a clean small training dataset (called root dataset) for the learning task and the service provider maintains a model (called server model) based on it to bootstrap trust. In each iteration, the service provider first assigns a trust score to each local model update from the clients, where a local model update has a lower trust score if its direction deviates more from the direction of the server model update. Then, the service provider normalizes the magnitudes of the local model updates such that they lie in the same hyper-sphere as the server model update in the vector space. Our normalization limits the impact of malicious local model updates with large magnitudes. Finally, the service provider computes the average of the normalized local model updates weighted by their trust scores as a global model update, which is used to update the global model. Our extensive evaluations on six datasets from different domains show that our FLTrust is secure against both existing attacks and strong adaptive attacks.
Learning a mixture of two subspaces over finite fields

We study the problem of learning a mixture of two subspaces over $\mathbb{F}_2^n$. The goal is to recover the individual subspaces, given samples from a (weighted) mixture of samples drawn uniformly from the two subspaces $A_0$ and $A_1$. This problem is computationally challenging, as it captures the notorious problem of "learning parities with noise" in the degenerate setting when $A_1 \subseteq A_0$. This is in contrast to the analogous problem over the reals that can be solved in polynomial time (Vidal'03). This leads to the following natural question: is Learning Parities with Noise the only computational barrier in obtaining efficient algorithms for learning mixtures of subspaces over $\mathbb{F}_2^n$? The main result of this paper is an affirmative answer to the above question. Namely, we show the following results: 1. When the subspaces $A_0$ and $A_1$ are incomparable, i.e., $A_0$ and $A_1$ are not contained inside each other, then there is a polynomial time algorithm to recover the subspaces $A_0$ and $A_1$. 2. In the case when $A_1$ is a subspace of $A_0$ with a significant gap in the dimension i.e., $dim(A_1) \le \alpha dim(A_0)$ for $\alpha<1$, there is a $n^{O(1/(1-\alpha))}$ time algorithm to recover the subspaces $A_0$ and $A_1$. Thus, our algorithms imply computational tractability of the problem of learning mixtures of two subspaces, except in the degenerate setting captured by learning parities with noise.
The Counterfactual that Came to Pass: What if the Founders Had Not Constitutionalized the Privilege of the Writ of Habeas Corpus?

What if, this Essay asks, the Founding generation had not constitutionalized the privilege of the writ of habeas corpus? As is explored below, in many respects, the legal framework within which we are detaining suspected terrorists in this country today — particularly suspected terrorists who are citizens — suggests that our current legal regime stands no differently than the English legal framework from which it sprang some two-hundred-plus years ago. That framework, by contrast to our own, does not enshrine the privilege of the writ of habeas corpus as a right enjoyed by reason of a binding and supreme constitution. Instead, English law views the privilege as a right that exists at the pleasure of Parliament and is, accordingly, subject to legislative override. As is also shown below, a comparative inquiry into the existing state of detention law in this country and in the United Kingdom reveals a notable contrast — namely, notwithstanding their lack of a constitutionally based right to the privilege, British citizens detained in the United Kingdom without formal charges on suspicion of terrorist activities enjoy the benefit of far more legal protections than their counterparts in this country.
Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines.

Human cancer cell line profiling and drug sensitivity studies provide valuable information about the therapeutic potential of drugs and their possible mechanisms of action. The goal of those studies is to translate the findings from in vitro studies of cancer cell lines into in vivo therapeutic relevance and, eventually, patients’ care. Tremendous progress has been made. In this work, we built predictive models for 453 drugs using data on gene expression and drug sensitivity (IC50) from cancer cell lines. We identified many known drug-gene interactions and uncovered several potentially novel drug-gene associations. Importantly, we further applied these predictive models to ~ 17,000 bulk RNA-seq samples from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database to predict drug sensitivity for both normal and tumor tissues. We created a web site for users to visualize and download our predicted data ( https://manticore.niehs.nih.gov/cancerRxTissue ). Using trametinib as an example, we showed that our approach can faithfully recapitulate the known tumor specificity of the drug. We demonstrated that our approach can predict drugs that 1) are tumor-type specific; 2) elicit higher sensitivity from tumor compared to corresponding normal tissue; 3) elicit differential sensitivity across breast cancer subtypes. If validated, our prediction could have relevance for preclinical drug testing and in phase I clinical design.
Crisscrossed Captions: Extended Intramodal and Intermodal Semantic Similarity Judgments for MS-COCO

By supporting multi-modal retrieval training and evaluation, image captioning datasets have spurred remarkable progress on representation learning. Unfortunately, datasets have limited cross-modal associations: images are not paired with other images, captions are only paired with other captions of the same image, there are no negative associations and there are missing positive cross-modal associations. This undermines research into how inter-modality learning impacts intra-modality tasks. We address this gap with Crisscrossed Captions (CxC), an extension of the MS-COCO dataset with human semantic similarity judgments for 267,095 intra- and inter-modality pairs. We report baseline results on CxC for strong existing unimodal and multimodal models. We also evaluate a multitask dual encoder trained on both image-caption and caption-caption pairs that crucially demonstrates CxC’s value for measuring the influence of intra- and inter-modality learning.
A Multi-platform Study of Crowd Signals Associated with Successful Online Fundraising

The growing popularity of online fundraising (aka "crowdfunding") has attracted significant research on the subject. In contrast to previous studies that attempt to predict the success of crowdfunded projects based on specific characteristics of the projects and their creators, we present a more general approach that focuses on crowd dynamics and is robust to the particularities of different crowdfunding platforms. We rely on a multi-method analysis to investigate the correlates, predictive importance, and quasi-causal effects of features that describe crowd dynamics in determining the success of crowdfunded projects. By applying a multi-method analysis to a study of fundraising in three different online markets, we uncover universal crowd dynamics that ultimately decide which projects will succeed. In all analyses and across three markets, we consistently find that funders' behavioural signals (1) are significantly correlated with fundraising success; (2) approximate fundraising outcomes better than the characteristics of projects and their creators such as credit grade, company valuation, and subject domain; and (3) have significant quasi-causal effects on fundraising outcomes while controlling for potentially confounding project variables. By showing that universal features deduced from crowd behaviour are predictive of fundraising success on different crowdfunding platforms, our work provides design-relevant insights about novel types of collective decision-making online. This research inspires thus potential ways to leverage cues from the crowd and catalyses research into crowd-aware system design.
Ideal Free Dispersal under General Spatial heterogeneity and Time Periodicity

A population is said to have an ideal free distribution in a spatially heterogeneous but temporally constant environment if each of its members has chosen a fixed spatial location in a way that opt...
Semiparametric Efficiency in Convexity Constrained Single Index Model

We consider estimation and inference in a single index regression model with an unknown convex link function. We introduce a convex and Lipschitz constrained least squares estimator (CLSE) for both...
Confidence as a Driver of Private Investment in Selected Countries of Central America

This paper argues that structural weaknesses may make private investment particularly sensitive to business confidence relative to other traditional investment drivers and global shocks. It gauges the importance of confidence over recent years in selected countries in Central America, including Costa Rica, the Dominican Republic, El Salvador, and Guatemala. Using a vector error correction model to carry out the empirical work, a system representing global activity and the domestic economy, including a set of investment drivers (interest rates, unit labor costs, and confidence) is analyzed. The findings suggest that confidence has been, on average, the most important driver of investment in these countries, exceeded only by global factors. Since confidence, arguably, can be influenced by policymakers' decisions, structural reforms to improve the business climate and reduce uncertainty play an important role in promoting investment and economic growth.
CHSSI software for geometrically complex unsteady aerodynamic applications

A comprehensive package of scalable overset grid CFD software is reviewed. The software facilitates accurate simulation of complete aircraft aerodynamics, including viscous effects, unsteadiness, and relative motion between component parts. The software significantly lowers the manpower and computer costs normally associated with such efforts. The software is discussed in terms of current capabilities and planned future enhancements.
Directed percolation and numerical stability of simulations of digital memcomputing machines

Digital memcomputing machines (DMMs) are a novel, non-Turing class of machines designed to solve combinatorial optimization problems. They can be physically realized with continuous-time, non-quantum dynamical systems with memory (time non-locality), whose ordinary differential equations (ODEs) can be numerically integrated on modern computers. Solutions of many hard problems have been reported by numerically integrating the ODEs of DMMs, showing substantial advantages over state-of-the-art solvers. To investigate the reasons behind the robustness and effectiveness of this method, we employ three explicit integration schemes (forward Euler, trapezoid, and Runge–Kutta fourth order) with a constant time step to solve 3-SAT instances with planted solutions. We show that (i) even if most of the trajectories in the phase space are destroyed by numerical noise, the solution can still be achieved; (ii) the forward Euler method, although having the largest numerical error, solves the instances in the least amount of function evaluations; and (iii) when increasing the integration time step, the system undergoes a “solvable–unsolvable transition” at a critical threshold, which needs to decay at most as a power law with the problem size, to control the numerical errors. To explain these results, we model the dynamical behavior of DMMs as directed percolation of the state trajectory in the phase space in the presence of noise. This viewpoint clarifies the reasons behind their numerical robustness and provides an analytical understanding of the solvable–unsolvable transition. These results land further support to the usefulness of DMMs in the solution of hard combinatorial optimization problems.
Uncoded Binary Signaling through Modulo AWGN Channel

Modulo-wrapping receivers have attracted interest in several areas of digital communications, including precoding and lattice coding. The asymptotic capacity and error performance of the modulo AWGN channel have been well established. However, due to underlying assumptions of the asymptotic analyses, these findings might not always be realistic in physical world applications, which are often dimension- or delay-limited. In this work, the optimum ways to achieve minimum probability of error for binary signaling through a scalar modulo AWGN channel is examined under different scenarios where the receiver has access to full or partial information. In case of partial information at the receiver, an iterative estimation rule is proposed to reduce the error rate, and the performance of different estimators are demonstrated in simulated experiments.
Spatial profiles of photon chemical potential in near-field thermophotovoltaic cells

Emitted photons stemming from the radiative recombination of electron–hole pairs carry chemical potential in radiative energy converters. This luminescent effect can substantially alter the local net photogeneration in near-field thermophotovoltaic cells. Several assumptions involving the luminescent effect are commonly made in modeling photovoltaic devices; in particular, the photon chemical potential is assumed to be zero or a constant prescribed by the bias voltage. The significance of photon chemical potential depends upon the emitter temperature, the semiconductor properties, and the injection level. Hence, these assumptions are questionable in near-field thermophotovoltaic devices with nanoscale separation distances between the emitter and the cell. In the present work, an iterative solver that combines fluctuational electrodynamics with the drift-diffusion model is developed to tackle the coupled photon and charge transport problem, enabling the determination of the spatial profile of photon chemical potential beyond the detailed balance approach. The difference between the results obtained by allowing the photon chemical potential to vary spatially and by assuming a constant value demonstrates the limitations of the conventional approaches. This study is critically important for performance evaluation of near-field thermophotovoltaic systems with nanoscale vacuum gaps.
A refined lifting theorem for supersingular Galois representations

Abstract Let p ≥ 5 be a prime number, F a finite field of characteristic p and let χ ¯ be the mod-p cyclotomic character. Let ρ ¯ : G Q → GL 2 ( F ) be a Galois representation such that the local representation ρ ¯ ↾ G Q p is flat and irreducible. Further, assume that det ρ ¯ = χ ¯ . The celebrated theorem of Khare and Wintenberger asserts that if ρ ¯ satisfies some natural conditions, there exists a normalized Hecke-eigencuspform f = ∑ n ≥ 1 a n q n and a prime p | p in its field of Fourier coefficients such that the associated p -adic representation ρ f , p lifts ρ ¯ . In this manuscript we prove a refined version of this theorem, namely, that one may control the valuation of the p-th Fourier coefficient of f. The main result is of interest from the perspective of the p-adic Langlands program.
Global Optimality Beyond Two Layers: Training Deep ReLU Networks via Convex Programs

Understanding the fundamental mechanism behind the success of deep neural networks is one of the key challenges in the modern machine learning literature. Despite numerous attempts, a solid theoretical analysis is yet to be developed. In this paper, we develop a novel unified framework to reveal a hidden regularization mechanism through the lens of convex optimization. We first show that the training of multiple three-layer ReLU sub-networks with weight decay regularization can be equivalently cast as a convex optimization problem in a higher dimensional space, where sparsity is enforced via a group $\ell_1$-norm regularization. Consequently, ReLU networks can be interpreted as high dimensional feature selection methods. More importantly, we then prove that the equivalent convex problem can be globally optimized by a standard convex optimization solver with a polynomial-time complexity with respect to the number of samples and data dimension when the width of the network is fixed. Finally, we numerically validate our theoretical results via experiments involving both synthetic and real datasets.
IPO quantity revisions

Abstract During an IPO offering, issuers may revise both the offer price and quantity (number of shares). These revisions are of comparable magnitude and are nearly uncorrelated. We show theoretically that these two revisions should be interpreted differently. Shocks to growth options induce price and quantity to move together, whereas shocks to assets-in-place induce movements in opposing directions. Using public shocks that proxy for these two shocks, we find evidence of the predicted comovements. Post-IPO investment behavior is predicted by these revisions in a manner consistent with our model. Unexpected investment is positively associated with both price and quantity revisions, but only when the revisions are aligned. Viewed jointly, price and quantity revisions disentangle the nature of information received during the offering.
DSXplore: Optimizing Convolutional Neural Networks via Sliding-Channel Convolutions

As the key advancement of the convolutional neural networks (CNNs), depthwise separable convolutions (DSCs) are becoming one of the most popular techniques to reduce the computations and parameters size of CNNs meanwhile maintaining the model accuracy. It also brings profound impact to improve the applicability of the compute- and memory-intensive CNNs to a broad range of applications, such as mobile devices, which are generally short of computation power and memory. However, previous research in DSCs are largely focusing on compositing the limited existing DSC designs, thus, missing the opportunities to explore more potential designs that can achieve better accuracy and higher computation/parameter reduction. Besides, the off-the-shelf convolution implementations offer limited computing schemes, therefore, lacking support for DSCs with different convolution patterns.To this end, we introduce, DSXplore, the first optimized design for exploring DSCs on CNNs. Specifically, at the algorithm level, DSXplore incorporates a novel factorized kernel–sliding-channel convolution (SCC), featured with input-channel overlapping to balance the accuracy performance and the reduction of computation and memory cost. SCC also offers enormous space for design exploration by introducing adjustable kernel parameters. Further, at the implementation level, we carry out an optimized GPU-implementation tailored for SCC by leveraging several key techniques, such as the input-centric backward design and the channel-cyclic optimization. Intensive experiments on different datasets across mainstream CNNs show the advantages of DSXplore in balancing accuracy and computation/parameter reduction over the standard convolution and the existing DSCs.
The Effect of Backward-facing Step Height on Instability Growth and Breakdown in Swept-Wing Boundary-Layer Transition

A low-speed experiment was performed on a swept at plate model with an imposed pressure gradient to determine the effect of a backward-facing step on transition in a stationary-cross flow dominated flow. Detailed hot-wire boundary-layer measurements were performed for three backward-facing step heights of approximately 36, 45, and 49% of the boundary-layer thickness at the step. These step heights correspond to a subcritical, nearly-critical, and critical case. Three leading-edge roughness configurations were tested to determine the effect of stationary-cross flow amplitude on transition. The step caused a local increase in amplitude of the stationary cross flow for the two larger step height cases, but farther downstream the amplitude decreased and remained below the baseline amplitude. The smallest step caused a slight local decrease in amplitude of the primary stationary cross flow mode, but the amplitude collapsed back to the baseline case far downstream of the step. The effect of the step on the amplitude of the primary cross flow mode increased with step height, however, the stationary cross flow amplitudes remained low and thus, stationary cross flow was not solely responsible for transition. Unsteady disturbances were present downstream of the step for all three step heights, and the amplitudes increased with increasing step height. The only exception is that the lower frequency (traveling crossflow-like) disturbance was not present in the lowest step height case. Positive and negative spikes in instantaneous velocity began to occur for the two larger step height cases and then grew in number and amplitude downstream of reattachment, eventually leading to transition. The number and amplitude of spikes varied depending on the step height and cross flow amplitude. Despite the low amplitude of the disturbances in the intermediate step height case, breakdown began to occur intermittently and the flow underwent a long transition region.
Positivity effect in aging: evidence for the primacy of positive responses to emotional ambiguity.

Abstract Older compared to younger adults show greater amygdala activity to positive emotions, and are more likely to interpret emotionally ambiguous stimuli (e.g., surprised faces) as positive. While some evidence suggests this positivity effect results from a top-down, effortful mechanism, others suggest it may emerge as the default or initial response. The amygdala is a key node in rapid, bottom-up processing and patterns of amygdala activity over time (e.g., habituation) can shed light on the mechanisms underlying the positivity effect. Younger and older adults passively viewed neutral and surprised faces in an MRI. Only in older adults, amygdala habituation was associated with the tendency to interpret surprised faces as positive or negative (valence bias), where a more positive bias was associated with greater habituation. Interestingly, although a positive bias in younger adults was associated with slower responses, consistent with an initial negativity hypothesis in younger adults, older adults showed faster categorizations of positivity. Together, we propose that there may be a switch to a primacy of positivity in aging.
Flexural Wave Modulation and Mitigation in Airfoils Using Acoustic Black Holes

This study introduces a framework for the design and implementation of acoustic black holes (ABHs) in airfoils. A generalized multiparameter damped-ABH generation function is mapped onto airfoils based on the National Advisory Committee for Aeronautics series. Representative geometries and a uniformly distributed baseline, all with the same mass of structure and damping are fabricated using multimaterial PolyJet three-dimensional printing. Laser Doppler vibrometer measurements along the airfoil chord in response to a broadband 0.1--12 kHz excitation show a decrease in trailing-edge vibrations by as much as 10 dB, a broadband 5-dB reduction across the entire chord, substantial spatial and temporal modulation of flexural waves by ABH foils. Finite-element analysis (FEA) models are developed and validated based on the measured data. Furthermore, a parametric FEA study is performed on a set of comparable designs to elucidate the scope of modulation achievable. These findings are applicable to trailing-edge noise reduction, flow control, structural enhancement, and energy harvesting for airfoils.
Group Fairness by Probabilistic Modeling with Latent Fair Decisions.

Machine learning systems are increasingly being used to make impactful decisions such as loan applications and criminal justice risk assessments, and as such, ensuring fairness of these systems is critical. This is often challenging as the labels in the data are biased. This paper studies learning fair probability distributions from biased data by explicitly modeling a latent variable that represents a hidden, unbiased label. In particular, we aim to achieve demographic parity by enforcing certain independencies in the learned model. We also show that group fairness guarantees are meaningful only if the distribution used to provide those guarantees indeed captures the real-world data. In order to closely model the data distribution, we employ probabilistic circuits, an expressive and tractable probabilistic model, and propose an algorithm to learn them from incomplete data. We show on real-world datasets that our approach not only is a better model of how the data was generated than existing methods but also achieves competitive accuracy. Moreover, we also evaluate our approach on a synthetic dataset in which observed labels indeed come from fair labels but with added bias, and demonstrate that the fair labels are successfully retrieved.
Concentration, Retail Markups, and Countervailing Power: Evidence from Retail Lotteries

In this note, we investigate the causal link between market concentration and markups in a retail setting. We study the Washington retail cannabis industry, which features exogenous variation in market concentration that resulted from retail licenses being awarded via lotteries. We observe markups directly. We find a negative causal relationship between markups and concentration, where more concentrated markets have significantly lower markups and wholesale prices. The results provide direct evidence of countervailing buyer power by retailers. These results highlight the value of using industry specific data and rich models of competition to advance the debate on concentration and markups.
Black-Box Control for Linear Dynamical Systems

We consider the problem of black-box control: the task of controlling an unknown linear time-invariant dynamical system from a single trajectory without a stabilizing controller. Under the assumption that the system is controllable, we give the first {\it efficient} algorithm that is capable of attaining sublinear regret under the setting of online nonstochastic control. This resolves an open problem since the work of Abbasi-Yadkori and Szepesvari(2011) on the stochastic LQR problem, and in a more challenging setting that allows for adversarial perturbations and adversarially chosen changing convex loss functions. We give finite-time regret bounds for our algorithm on the order of $2^{poly(d)} + \tilde{O}(poly(d) T^{2/3})$ for general nonstochastic control, and $2^{poly(d)} + \tilde{O}(poly(d) \sqrt{T})$ for black-box LQR. To complete the picture, we investigate the complexity of the online black-box control problem and give a matching regret lower bound of $2^{\Omega(d)}$, showing that the exponential cost is inevitable. This lower bound holds even in the noiseless setting, and applies to any, randomized or deterministic, black-box control method.
Towards A Process Model for Co-Creating AI Experiences

Thinking of technology as a design material is appealing. It encourages designers to explore the material’s properties to understand its capabilities and limitations—a prerequisite to generative design thinking. However, as a material, AI resists this approach because its properties only emerge as part of the user experience design. Therefore, designers and AI engineers must collaborate in new ways to create both the material and its application experience. We investigate the co-creation process through a design study with 10 pairs of designers and engineers. We find that design ‘probes’ with user data are a useful tool in defining AI materials. Through data probes, designers construct designerly representations of the envisioned AI experience (AIX) to identify desirable AI characteristics. Data probes facilitate divergent design thinking, material testing, and design validation. Based on our findings, we propose a process model for co-creating AIX and offer design considerations for incorporating data probes in AIX design tools.
Fitness Estimation for Viral Variants in the Context of Cellular Coinfection.

Animal models are frequently used to characterize the within-host dynamics of emerging zoonotic viruses. More recent studies have also deep-sequenced longitudinal viral samples originating from experimental challenges to gain a better understanding of how these viruses may evolve in vivo and between transmission events. These studies have often identified nucleotide variants that can replicate more efficiently within hosts and also transmit more effectively between hosts. Quantifying the degree to which a mutation impacts viral fitness within a host can improve identification of variants that are of particular epidemiological concern and our ability to anticipate viral adaptation at the population level. While methods have been developed to quantify the fitness effects of mutations using observed changes in allele frequencies over the course of a host’s infection, none of the existing methods account for the possibility of cellular coinfection. Here, we develop mathematical models to project variant allele frequency changes in the context of cellular coinfection and, further, integrate these models with statistical inference approaches to demonstrate how variant fitness can be estimated alongside cellular multiplicity of infection. We apply our approaches to empirical longitudinally sampled H5N1 sequence data from ferrets. Our results indicate that previous studies may have significantly underestimated the within-host fitness advantage of viral variants. These findings underscore the importance of considering the process of cellular coinfection when studying within-host viral evolutionary dynamics.
Tensile behavior of stainless steel 304L to Ni-20Cr functionally graded material: Experimental characterization and computational simulations

Abstract This study presents experimental and computational analyses of the phase composition and mechanical behavior of a functionally graded material (FGM) grading from 100 vol% 304L stainless steel (SS304L) to 100 vol% Ni-20Cr (NiCr) in 10 vol% increments. The mechanical behavior of the FGM was evaluated by extracting tensile specimens from SS304L-rich and NiCr-rich regions of the FGM, with the former behaving in a ductile manner and the latter being relatively brittle. The brittle response of the NiCr-rich sample was explained by the presence of the eutectic FCC+BCC phase in this sample, as identified by Scheil-Gulliver simulations along with experimental phase and composition analyses. The spatially varying yield and flow behavior of the FGM was determined through comparison of finite element analysis simulations with experimentally measured stress-strain curves and surface strain contours. The results highlight that the overall ductile behavior of SS304L and brittle behavior of NiCr are preserved in the SS304L/NiCr FGM but the formation of the brittle eutectic phase further reduced the ductility within the regions where it formed (90 vol% NiCr/10 vol% SS304L).
Probiotic supplementation reduces inflammatory profiles but does not prevent oral immune perturbations during SIV infection

HIV/SIV infections lead to massive loss of mucosal CD4 + T cells and breakdown of the epithelial mucosa resulting in severe microbial dysbiosis and chronic immune activation that ultimately drive disease progression. Moreover, disruption of one of the most understudied mucosal environments, the oral cavity, during HIV-induced immunosuppression results in significant microbial and neoplastic co-morbidities and contributes to and predicts distal disease complications. In this study we evaluated the effects of oral probiotic supplementation (PBX), which can stimulate and augment inflammatory or anti-inflammatory pathways, on early SIV infection of rhesus macaques. Our study revealed that similar to the GI mucosae, oral CD4 + T cells were rapidly depleted, and as one of the first comprehensive analyses of the oral microflora in SIV infection, we also observed significant modulation among two genera, Porphyromonas and Actinobacillus, early after infection. Interestingly, although PBX therapy did not substantially protect against oral dysbiosis or ameliorate cell loss, it did somewhat dampen inflammation and T cell activation. Collectively, these data provide one of the most comprehensive evaluations of SIV-induced changes in oral microbiome and CD4 + T cell populations, and also suggest that oral PBX may have some anti-inflammatory properties in lentivirus infections.
Magnetoresistance driven by the magnetic Berezinskii-Kosterlitz-Thouless transition

While the Berezinskii-Kosterlitz-Thouless transition (BKT) has been under intense scrutiny for decades, unambiguous experimental signatures in magnetic systems remain elusive. Here, we investigate the interplay between electronic and magnetic degrees of freedom near the BKT transition. Focusing on a metal with easy-plane ferromagnetic order, we establish a framework that accounts both for the coupling between the charge current and the flow of topological magnetic defects and for electron scattering on their inhomogeneous spin texture. We show that electron scattering is responsible for a temperature-dependent magnetoresistance effect scaling as the density of the topological defects, which is expected to increase dramatically above the BKT transition temperature. Our findings call for further experimental investigations.