cssi/SciSciGPT-SciSciNet huggingface dataset:
Shaping Parental Authority Over Children's Bodies

U.S. law treats parental decisions to size, shape, sculpt, and mine children’s bodies through the use of non-therapeutic medical and surgical interventions like decisions to send a child to a particular church or school. They are a matter of parental choice except in extraordinary cases involving grievous harm. This Article questions the assumption of parental rights that frames the current paradigm for medical decisionmaking for children. Focusing on cases involving eye surgery, human growth hormone, liposuction, and growth stunting, I argue that by allowing parents to subordinate their children’s interests to their own, the current paradigm distorts the parent-child relationship and objectifies children. I propose an alternative. Pushing analogies developed in family law and moral philosophy to respect children as complete but vulnerable human beings, I develop a trustee-based construct of the parent-child relationship, in which the parents are assigned trustee-like powers and responsibilities over a child’s welfare and future interests, and charged with fiduciary-like duties to the child. Application of the trustee-based construct separates medical decisions that belong to parents, from decisions that belong to children and those that should be made by a neutral third party. Introduction 2 I. Sculpting, Shaping, and Sizing Children: Focus Cases 5 A. Westernizing Asian Eyes 6 B. Hormones for Stature 7 C. Liposuction on a Twelve Year Old 8 D. Growth Stunting 9 II. The Law, Parental Rights, and Children’s Bodies 10 A. Background Law 11 B. Application in Shaping Cases 13 C. Room for Regulation 15 III. What is Really Wrong with Medical and Surgical Shaping of Children? 16 A. The Nonsubordination Priniciple as a Limit on Individual Rights 18 B. Children as Persons, Parental Rights 21 C. Medical and Surgical Shaping of Children is Different 24 IV. Concerning the Child: Another View of Parenthood 27 A. Alternative Models 28 B. Common Principles 31 V. Reconstructing the Role of the Parent in Medical Decisionmaking for Children ...... 32 A. Powers and Responsibilities of Trustees Generally 33 B. Powers and Responsibilities of Parent Trustees as Concerns Children’s Health.... 35 VI. Shaping Reconstructed 38 VII. Conclusion 39 * Associate Professor of Law, Albany Law School, Professor of Bioethics, Union/Mt. Sinai School of Medicine Program in Bioethics. © Alicia Ouellette 2009.
What the brain saw: The case of Trayvon Martin and the need for eyewitness identification reform

The shooting of Trayvon Martin has caused many to question what exactly led to the death of an unarmed seventeen-year-old African-American teenager. This essay provides at least one answer: the brain in creating and preserving memories can distort one's perception of events and people. In the courtroom, eyewitness testimony can be the most powerful and riveting information for a jury – and yet can contain that same inaccuracy of perception and memory. Bringing these two separate but connected insights together, this essay examines the role of memory and perception in the death of Trayvon Martin, along with eyewitness identification in criminal cases, ultimately calling for broad reform in our criminal justice system.
Belief propagation in monoidal categories

We discuss a categorical version of the celebrated belief propagation algorithm. This provides a way to prove that some algorithms which are known or suspected to be analogous, are actually identical when formulated generically. It also highlights the computational point of view in monoidal categories.
Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion

High-throughput spatial-transcriptomics RNA sequencing (sptRNA-seq) based on in-situ capturing technologies has recently been developed to spatially resolve transcriptome-wide mRNA expressions mapped to the captured locations in a tissue sample. Due to the low RNA capture efficiency by in-situ capturing and the complication of tissue section preparation, sptRNA-seq data often only provides an incomplete profiling of the gene expressions over the spatial regions of the tissue. In this paper, we introduce a graph-regularized tensor completion model for imputing the missing mRNA expressions in sptRNA-seq data, namely FIST, Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion. We first model sptRNA-seq data as a 3-way sparse tensor in genes (p-mode) and the (x, y) spatial coordinates (x-mode and y-mode) of the observed gene expressions, and then consider the imputation of the unobserved entries or fibers as a tensor completion problem in Canonical Polyadic Decomposition (CPD) form. To improve the imputation of highly sparse sptRNA-seq data, we also introduce a protein-protein interaction network to add prior knowledge of gene functions, and a spatial graph to capture the the spatial relations among the capture spots. The tensor completion model is then regularized by a Cartesian product graph of protein-protein interaction network and the spatial graph to capture the high-order relations in the tensor. In the experiments, FIST was tested on ten 10x Genomics Visium spatial transcriptomic datasets of different tissue sections with cross-validation among the known entries in the imputation. FIST significantly outperformed the state-of-the-art methods for single-cell RNAseq data imputation. We also demonstrate that both the spatial graph and PPI network play an important role in improving the imputation. In a case study, we further analyzed the gene clusters obtained from the imputed gene expressions to show that the imputations by FIST indeed capture the spatial characteristics in the gene expressions and reveal functions that are highly relevant to three different kinds of tissues in mouse kidney.
Plasma physics in strong-field regimes: Theories and simulations

In strong electromagnetic fields, unique plasma phenomena and applications emerge whose description requires recently developed theories and simulations [Y. Shi, Ph.D. thesis, Princeton University (2018)]. In the classical regime, to quantify effects of strong magnetic fields on three-wave interactions, a convenient formula is derived by solving the fluid model to the second order in general geometry. As an application, magnetic resonances are exploited to mediate laser pulse compression, whereby higher intensity pulses can be produced in wider frequency ranges, as confirmed by particle-in-cell simulations. In even stronger fields, relativistic-quantum effects become important, and a plasma model based on scalar quantum electrodynamics (QED) is developed which unveils observable corrections to Faraday rotation and cyclotron absorption in strongly magnetized plasmas. Beyond the perturbative regime, lattice QED is extended as a numerical tool for plasma physics, using which the transition from wakefield acceleration to electron-positron pair production is captured when laser intensity exceeds the Schwinger threshold.
Simultaneous Denoising and Localization Network for Photoacoustic Target Localization

A significant research problem of recent interest is the localization of targets like vessels, surgical needles, and tumors in photoacoustic (PA) images.To achieve accurate localization, a high photoacoustic signal-to-noise ratio (SNR) is required. However, this is not guaranteed for deep targets, as optical scattering causes an exponential decay in optical fluence with respect to tissue depth. To address this, we develop a novel deep learning method designed to explicitly exhibit robustness to noise present in photoacoustic radio-frequency (RF) data. More precisely, we describe and evaluate a deep neural network architecture consisting of a shared encoder and two parallel decoders. One decoder extracts the target coordinates from the input RF data while the other boosts the SNR and estimates clean RF data. The joint optimization of the shared encoder and dual decoders lends significant noise robustness to the features extracted by the encoder, which in turn enables the network to contain detailed information about deep targets that may be obscured by noise. Additional custom layers and newly proposed regularizers in the training loss function (designed based on observed RF data signal and noise behavior) serve to increase the SNR in the cleaned RF output and improve model performance. To account for depth-dependent strong optical scattering, our network was trained with simulated photoacoustic datasets of targets embedded at different depths inside tissue media of different scattering levels. The network trained on this novel dataset accurately locates targets in experimental PA data that is clinically relevant with respect to the localization of vessels, needles, or brachytherapy seeds. We verify the merits of the proposed architecture by outperforming the state of the art on both simulated and experimental datasets.
Effective Interfaces for Student-Driven Revision Sessions for Argumentative Writing

We present the design and evaluation of a web-based intelligent writing assistant that helps students recognize their revisions of argumentative essays. To understand how our revision assistant can best support students, we have implemented four versions of our system with differences in the unit span (sentence versus sub-sentence) of revision analysis and the level of feedback provided (none, binary, or detailed revision purpose categorization). We first discuss the design decisions behind relevant components of the system, then analyze the efficacy of the different versions through a Wizard of Oz study with university students. Our results show that while a simple interface with no revision feedback is easier to use, an interface that provides a detailed categorization of sentence-level revisions is the most helpful based on user survey data, as well as the most effective based on improvement in writing outcomes.
Smoothings and rational double point adjacencies for cusp singularities

A cusp singularity is a surface singularity whose minimal resolution is a cycle of smooth rational curves meeting transversely. Cusp singularities come in dual pairs. Looijenga proved in 1981 that if a cusp singularity is smoothable, the minimal resolution of the dual cusp is the anticanonical divisor of some smooth rational surface. In 1983, the second author and Miranda gave a criterion for smoothability of a cusp singularity, in terms of the existence of a K‑trivial semistable model for the central fiber of such a smoothing. We study these “Type III degenerations” of rational surfaces with an anticanonical divisor—their deformations, birational geometry, and monodromy. Looijenga’s original paper also gave a description of the rational double point configurations to which a cusp singularity deforms, but only in the case where the resolution of the dual cusp has cycle length $5$ or less. We generalize this classification to an arbitrary cusp singularity, giving an explicit construction of a semistable simultaneous resolution of such an adjacency. The main tools of the proof are (1) formulas for the monodromy of a Type III degeneration, (2) a construction via surgeries on integral-affine surfaces of a degeneration with prescribed monodromy, (3) surjectivity of the period map for Type III central fibers, and (4) a theorem of Shepherd–Barron producing a simultaneous contraction to the adjacency.
Crowdsourcing Last-Mile Deliveries

Problem definition: Because of the emergence and development of e-commerce, customers demand faster and cheaper delivery services. However, many retailers find it challenging to efficiently provide...
Should College Dropout Prediction Models Include Protected Attributes

Early identification of college dropouts can provide tremendous value for improving student success and institutional effectiveness, and predictive analytics are increasingly used for this purpose. However, ethical concerns have emerged about whether including protected attributes in these prediction models discriminates against underrepresented student groups and exacerbates existing inequities. We examine this issue in the context of a large U.S. research university with both residential and fully online degree-seeking students. Based on comprehensive institutional records for the entire student population across multiple years (N = 93,457), we build machine learning models to predict student dropout after one academic year of study and compare the overall performance and fairness of model predictions with or without four protected attributes (gender, URM, first-generation student, and high financial need). We find that including protected attributes does not impact the overall prediction performance and it only marginally improves the algorithmic fairness of predictions. These findings suggest that including protected attributes is preferable. We offer guidance on how to evaluate the impact of including protected attributes in a local context, where institutional stakeholders seek to leverage predictive analytics to support student success.
Characterizing Fairness Over the Set of Good Models Under Selective Labels

Algorithmic risk assessments are used to inform decisions in a wide variety of high-stakes settings. Often multiple predictive models deliver similar overall performance but differ markedly in their predictions for individual cases, an empirical phenomenon known as the "Rashomon Effect." These models may have different properties over various groups, and therefore have different predictive fairness properties. We develop a framework for characterizing predictive fairness properties over the set of models that deliver similar overall performance, or "the set of good models." Our framework addresses the empirically relevant challenge of selectively labelled data in the setting where the selection decision and outcome are unconfounded given the observed data features. Our framework can be used to 1) replace an existing model with one that has better fairness properties; or 2) audit for predictive bias. We illustrate these uses cases on a real-world credit-scoring task and a recidivism prediction task.
Cryo‐EM Structures of Outer‐arm Dynein Array Bound to Microtubule Doublet Reveal a Mechanism for Motor Coordination

Thousands of outer-arm dyneins (OADs) are arrayed in the axoneme to drive a rhythmic ciliary beat. Using electron microscopy, we determined the structure of OAD array bound to microtubule doublets (MTDs) in near-atomic details and illuminate how OADs coordinate with each other to move one step forward. OAD prefers a specific pattern of MTD protofilaments for its distinct microtubule-binding domains. Upon MTD binding, free OADs are induced to adopt a stable parallel conformation, primed for array formation. Extensive tail-to-head (TTH) interactions between OADs are observed, which need to be broken for ATP turnover by the dynein motor. ATP-hydrolysis in turn relaxes the TTH interfaces to sequentially effectuate free nucleotide cycle of downstream OADs. These findings lead to a model for how conformational changes of OADs produce coordinated actions.
Intermediate intrinsic density and randomness

Given any 1-random set $X$ and any $r\in(0,1)$, we construct a set of intrinsic density $r$ which is computable from $r\oplus X$. For almost all $r$, this set will be the first known example of an intrinsic density $r$ set which cannot compute any $r$-Bernoulli random set. To achieve this, we shall formalize the {\tt into} and {\tt within} noncomputable coding methods which work well with intrinsic density.
Solving Inverse Problems with a Flow-based Noise Model

We study image inverse problems with a normalizing flow prior. Our formulation views the solution as the maximum a posteriori estimate of the image conditioned on the measurements. This formulation allows us to use noise models with arbitrary dependencies as well as non-linear forward operators. We empirically validate the efficacy of our method on various inverse problems, including compressed sensing with quantized measurements and denoising with highly structured noise patterns. We also present initial theoretical recovery guarantees for solving inverse problems with a flow prior.
Non-Parametric Few-Shot Learning for Word Sense Disambiguation.

Word sense disambiguation (WSD) is a long-standing problem in natural language processing. One significant challenge in supervised all-words WSD is to classify among senses for a majority of words that lie in the long-tail distribution. For instance, 84% of the annotated words have less than 10 examples in the SemCor training data. This issue is more pronounced as the imbalance occurs in both word and sense distributions. In this work, we propose MetricWSD, a non-parametric few-shot learning approach to mitigate this data imbalance issue. By learning to compute distances among the senses of a given word through episodic training, MetricWSD transfers knowledge (a learned metric space) from high-frequency words to infrequent ones. MetricWSD constructs the training episodes tailored to word frequencies and explicitly addresses the problem of the skewed distribution, as opposed to mixing all the words trained with parametric models in previous work. Without resorting to any lexical resources, MetricWSD obtains strong performance against parametric alternatives, achieving a 75.1 F1 score on the unified WSD evaluation benchmark (Raganato et al., 2017b). Our analysis further validates that infrequent words and senses enjoy significant improvement.
Immature platelets as a biomarker for disease severity and mortality in COVID-19 patients.

COVID-19, caused by SARS-CoV-2, is a contagious life-threatening viral disease that has killed more than three million people worldwide to date. Attempts have been made to identify biomarker(s) to stratify disease severity and improve treatment and resource allocation. Patients with SARS-COV-2 infection manifest with a higher inflammatory response and platelet hyperreactivity; this raises the question of the role of thrombopoiesis in COVID-19 infection. Immature platelet fraction (IPF, %) and immature platelet counts (IPC, ×109 /l) can be used to assess thrombopoiesis. This study investigates whether the level of thrombopoiesis correlates with COVID-19 severity. A large cohort of 678 well-characterized COVID-19 patients was analyzed, including 658 (97%) hospitalized and 139 (21%) admitted to the intensive care unit (ICU). Elevated percentage IPF at presentation was predictive of length of hospitalization (P < 0·01) and ICU admission (P < 0·05). Additionally, percentage IPF at the peak was significantly higher among ICU patients than non-ICU patients (6·9 ± 5·1 vs 5·3 ± 8·4, P < 0·01) and among deceased patients than recovered patients (7·9 ± 6·3 vs 5·4 ± 7·8, P < 0·01). Furthermore, IPC at the peak was significantly higher among ICU patients than non-ICU patients (18·5 ± 16·2 vs. 13·2 ± 8·3, P < 0·05) and among patients on a ventilator than those not (22·1 ± 20·1 vs.13·4 ± 8·4, P < 0·05). Our study demonstrated that elevated initial and peak values of percentage IPF and IPC might serve as prognostic biomarkers for COVID-19 progression to severe conditions.
Adaptive and maladaptive expression plasticity underlying herbicide resistance in an agricultural weed

Plastic phenotypic responses to environmental change are common, yet we lack a clear understanding of the fitness consequences of these plastic responses. Here, we use the evolution of herbicide resistance in the common morning glory (Ipomoea purpurea) as a model for understanding the relative importance of adaptive and maladaptive gene expression responses to herbicide. Specifically, we compare leaf gene expression changes caused by herbicide to the expression changes that evolve in response to artificial selection for herbicide resistance. We identify a number of genes that show plastic and evolved responses to herbicide and find that for the majority of genes with both plastic and evolved responses, plastic responses appear to be adaptive. We also find that selection for herbicide response increases gene expression plasticity. Overall, these results show the importance of adaptive plasticity for herbicide resistance in a common weed and that expression changes in response to strong environmental change can be adaptive.
A Spectrum Synthesis Program for Binary Stars

A new program produces synthetic spectra of binary stars at arbitrary values of orbital longitude, including longitudes of partial or complete eclipse. The stellar components may be distorted, either tidally or rotationally, or both. Either or both components may be rotating nonsynchronously. We illustrate the program performance with two cases: EE Peg, an eclipsing binary with small distortion, and SX Aur, an eclipsing binary that is close to contact.
Learning from Censored and Dependent Data: The case of Linear Dynamics

Observations from dynamical systems often exhibit irregularities, such as censoring, where values are recorded only if they fall within a certain range. Censoring is ubiquitous in practice, due to saturating sensors, limit-of-detection effects, image frame effects, and combined with the temporal dependencies within the data, makes the task of system identification particularly challenging. In light of recent developments on learning linear dynamical systems (LDSs), and on censored statistics with independent data, we revisit the decades-old problem of learning an LDS, from censored observations (Lee and Maddala (1985), Zeger and Brookmeyer (1986)). Here, the learner observes the state x_t \in R^d if and only if x_t belongs to some set S_t \in R^d. We develop the first computationally and statistically efficient algorithm for learning the system, assuming only oracle to the sets St. Our algorithm, Stochastic Online Newton with Switching Gradients, is a novel second-order method that builds on the Online Newton Step (ONS) of Hazan et al. (2007). Our Switching-Gradient scheme does not always use (stochastic) gradients of the function we want to optimize, which we call censor-aware function. Instead, in each iteration, it performs a simple test to decide whether to use the censor-aware, or another censor-oblivious function, for getting a stochastic gradient. In our analysis, we consider a “generic” Online Newton method, which uses arbitrary vectors instead of gradients, and we prove an error-bound for it. This can be used to appropriately design these vectors, Leading to our Switching-Gradient scheme. This framework significantly deviates from the recent long line of works on censored statistics (e.g, Daskalakis et al. (2018); Kontonis et al. (2019); Daskalakis et al. (2019), which apply Stochastic Gradient Descent (SGD), and their analysis reduces to establishing conditions for off-the-shelf SGD-bounds. Our approach enables to relax these conditions, and gives rise to phenomena that might appear counterintuitive, given the previous works. Specifically, our method makes progress even when the current “survival probability” is exponentially small. We believe that our analysis framework will have applications in more settings where the data are subject to censoring.
Braid actions on quantum toroidal superalgebras

Abstract We prove that the quantum toroidal algebras E s associated with different root systems s of gl m | n type are isomorphic. We also show the existence of Miki automorphism of E s , which exchanges the vertical and horizontal subalgebras. To obtain these results, we establish an action of the toroidal braid group on the direct sum ⊕ s E s of all such algebras.
Improving Adversarial Robustness via Probabilistically Compact Loss with Logit Constraints.

Convolutional neural networks (CNNs) have achieved state-of-the-art performance on various tasks in computer vision. However, recent studies demonstrate that these models are vulnerable to carefully crafted adversarial samples and suffer from a significant performance drop when predicting them. Many methods have been proposed to improve adversarial robustness (e.g., adversarial training and new loss functions to learn adversarially robust feature representations). Here we offer a unique insight into the predictive behavior of CNNs that they tend to misclassify adversarial samples into the most probable false classes. This inspires us to propose a new Probabilistically Compact (PC) loss with logit constraints which can be used as a drop-in replacement for cross-entropy (CE) loss to improve CNN's adversarial robustness. Specifically, PC loss enlarges the probability gaps between true class and false classes meanwhile the logit constraints prevent the gaps from being melted by a small perturbation. We extensively compare our method with the state-of-the-art using large scale datasets under both white-box and black-box attacks to demonstrate its effectiveness. The source codes are available at https://github.com/xinli0928/PC-LC.
The Golgi-associated retrograde protein (GARP) complex plays an essential role in the maintenance of the Golgi glycosylation machinery.

This study uncovers that the Golgi-associated retrograde protein (GARP) complex is essential for the recycling and stability of Golgi glycosylation machinery.
Characterization and Mitigation of Electromigration Effects in TSV-Based Power Delivery Network Enabled 3D-Stacked DRAMs

With 3D-stacked DRAM architectures becoming more prevalent, it has become important to find ways to characterize and mitigate the adverse effects that can hinder their inherent access parallelism and throughput. One example of such adversities is the electromigration (EM) effects in the through-silicon vias (TSVs) of the power delivery network (PDN) of 3D-stacked DRAM architectures. Several prior works have addressed the effects of EM in TSVs of 3D integrated circuits. However, no prior work has addressed the effects of EM in the PDN TSVs on the performance and lifetime of 3D-stacked DRAMs. In this paper, we characterize the effects of EM in PDN TSVs on a Hybrid Memory Cube (HMC) architecture employing the conventional PDN design with clustered layout of power and ground TSVs. We then present a new PDN design with a distributed layout of power and ground TSVs and show that it can mitigate the adverse effects of EM on the HMC architecture performance without requiring additional power and ground pins. Our benchmark-driven simulation-based analysis shows that compared to the clustered PDN layout, our proposed distributed PDN layout improves the EM-affected lifetime of the HMC architecture by up to 10 years. During this useful lifetime, the HMC architecture also yields up to 1.10× less average latency, 1.43× more throughput, and 1.51× less energy-delay product (EDP).
Carbon to Diamond: An Incident Remediation Assistant System From Site Reliability Engineers' Conversations in Hybrid Cloud Operations.

Conversational channels are changing the landscape of hybrid cloud service management. These channels are becoming important avenues for Site Reliability Engineers (SREs) %Subject Matter Experts (SME) to collaboratively work together to resolve an incident or issue. Identifying segmented conversations and extracting key insights or artefacts from them can help engineers to improve the efficiency of the incident remediation process by using information retrieval mechanisms for similar incidents. However, it has been empirically observed that due to the semi-formal behavior of such conversations (human language) they are very unique in nature and also contain lot of domain-specific terms. This makes it difficult to use the standard natural language processing frameworks directly, which are popularly used in standard NLP tasks. %It is important to identify the correct keywords and artefacts like symptoms, issue etc., present in the conversation chats. In this paper, we build a framework that taps into the conversational channels and uses various learning methods to (a) understand and extract key artefacts from conversations like diagnostic steps and resolution actions taken, and (b) present an approach to identify past conversations about similar issues. Experimental results on our dataset show the efficacy of our proposed method.
NeuTex: Neural Texture Mapping for Volumetric Neural Rendering

Recent work [28], [5] has demonstrated that volumetric scene representations combined with differentiable volume rendering can enable photo-realistic rendering for challenging scenes that mesh reconstruction fails on. However, these methods entangle geometry and appearance in a "black-box" volume that cannot be edited. Instead, we present an approach that explicitly disentangles geometry—represented as a continuous 3D volume—from appearance—represented as a continuous 2D texture map. We achieve this by introducing a 3D-to-2D texture mapping (or surface parameterization) network into volumetric representations. We constrain this texture mapping network using an additional 2D-to-3D inverse mapping network and a novel cycle consistency loss to make 3D surface points map to 2D texture points that map back to the original 3D points. We demonstrate that this representation can be reconstructed using only multi-view image supervision and generates high-quality rendering results. More importantly, by separating geometry and texture, we allow users to edit appearance by simply editing 2D texture maps.
High-dimensional black-box optimization under uncertainty

Abstract Optimizing expensive black-box systems with limited data is an extremely challenging problem. As a resolution, we present a new surrogate optimization approach by addressing two gaps in prior research—unimportant input variables and inefficient treatment of uncertainty associated with the black-box output. We first design a new flexible non-interpolating parsimonious surrogate model using a partitioning-based multivariate adaptive regression splines approach, Tree Knot MARS (TK-MARS). The proposed model is specifically designed for optimization by capturing the structure of the function, bending at near-optimal locations, and is capable of screening unimportant input variables. Furthermore, we develop a novel replication approach called Smart-Replication, to overcome the uncertainty associated with the black-box output. The Smart-Replication approach identifies promising input points to replicate and avoids unnecessary evaluations of other data points. Smart-Replication is agnostic to the choice of a surrogate and can adapt itself to an unknown noise level. Finally to demonstrate the effectiveness of our proposed approaches we consider different complex global optimization test functions from the surrogate optimization literature. The results indicate that TK-MARS outperforms original MARS within a surrogate optimization algorithm and successfully detects important variables. The results also show that although non-interpolating surrogates can mitigate uncertainty, replication is still beneficial for optimizing highly complex black-box functions. The robustness and the quality of the final optimum solution found through Smart-Replication are competitive with that using no replications in environments with low levels of noise and using a fixed number of replications in highly noisy environments.
FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds

Scene flow depicts the dynamics of a 3D scene, which is critical for various applications such as autonomous driving, robot navigation, AR/VR, etc. Conventionally, scene flow is estimated from dense/regular RGB video frames. With the development of depth-sensing technologies, precise 3D measurements are available via point clouds which have sparked new research in 3D scene flow. Nevertheless, it remains challenging to extract scene flow from point clouds due to the sparsity and irregularity in typical point cloud sampling patterns. One major issue related to irregular sampling is identified as the randomness during point set abstraction/feature extraction—an elementary process in many flow estimation scenarios. A novel Spatial Abstraction with Attention (SA2) layer is accordingly proposed to alleviate the unstable abstraction problem. Moreover, a Temporal Abstraction with Attention (TA2) layer is proposed to rectify attention in temporal domain, leading to benefits with motions scaled in a larger range. Extensive analysis and experiments verified the motivation and significant performance gains of our method, dubbed as Flow Estimation via Spatial-Temporal Attention (FESTA), when compared to several state-of-the-art benchmarks of scene flow estimation.
What Is Cultural Cognition, and Why Does It Matter?

By all accounts, we currently live in a polarized political state in which virtually every fact is contestable. From climate change to vaccine efficacy, people feel free to choose their own facts t...
Growing Oligopolies, Prices, Output, and Productivity

American industries have grown more concentrated over the last 40 years. In the absence of productivity innovation, this should lead to price hikes and output reductions, decreasing consumer welfare. With US census data from 1972 to 2012, I use price data to disentangle revenue from output. Industry-level estimates show that concentration increases are positively correlated to productivity and real output growth, uncorrelated with price changes and overall payroll, and negatively correlated with labor's revenue share. I rationalize these results in a simple model of competition. Productive industries (with growing oligopolists) expand real output and hold down prices, raising consumer welfare, while maintaining or reducing their workforces, lowering labor's share of output.
Automated Malware Design for Cyber Physical Systems

The design of attacks for cyber physical systems is critical to assess CPS resilience at design time and run-time, and to generate rich datasets from testbeds for research. Attacks against cyber physical systems distinguish themselves from IT attacks in that the main objective is to harm the physical system. Therefore, both cyber and physical system knowledge are needed to design such attacks. The current practice to generate attacks either focuses on the cyber part of the system using IT cyber security existing body of knowledge, or uses heuristics to inject attacks that could potentially harm the physical process. In this paper, we present a systematic approach to automatically generate integrity attacks from the CPS safety and control specifications, without knowledge of the physical system or its dynamics. The generated attacks violate the system operational and safety requirements, hence present a genuine test for system resilience. We present an algorithm to automate the malware payload development. Several examples are given throughout the paper to illustrate the proposed approach.
Natural Emergence of Heterogeneous Strategies in Artificially Intelligent Competitive Teams

Multi agent strategies in mixed cooperative-competitive environments can be hard to craft by hand because each agent needs to coordinate with its teammates while competing with its opponents. Learning based algorithms are appealing but they require a competitive opponent to train against, which is often not available. Many scenarios require heterogeneous agent behavior for the team’s success and this increases the complexity of the learning algorithm. In this work, we develop a mixed cooperative-competitive multi agent environment called FortAttack in which two teams compete against each other for success. We show that modeling agents with Graph Neural Networks (GNNs) and training them with Reinforcement Learning (RL) from scratch, leads to the co-evolution of increasingly complex strategies for each team. Through competition in Multi-Agent Reinforcement Learning (MARL), we observe a natural emergence of heterogeneous behavior among homogeneous agents when such behavior can lead to the team’s success. Such heterogeneous behavior from homogeneous agents is appealing because any agent can replace the role of another agent at test time. Finally, we propose ensemble training, in which we utilize the evolved opponent strategies to train a single policy for friendly agents. We were able to train a large number of agents on a commodity laptop, which shows the scalability and efficiency of our approach. The code and a video presentation are available online (Code: https://github.com/Ankur-Deka/Emergent-Multiagent-Strategies, Video: https://youtu.be/ltHgKYc0F-E).
A continuous-time semi-Markov model for animal movement in a dynamic environment

We consider an extension to discrete-space, continuous-time models for animal movement that have previously been presented in the literature. The extension from a continuous-time Markov formulation to a continuous-time semi-Markov formulation allows for the inclusion of temporally dynamic habitat conditions as well as temporally changing movement responses by animals to that environment. We show that, with only a little additional consideration, the Poisson likelihood calculation for the Markov version can still be used within the multiple imputation framework commonly employed for analysis of telemetry data. In addition, we consider a Bayesian model selection methodology within the imputation framework. The model selection method uses a Laplace approximation to the posterior model probability to provide a computationally feasible approach. The full methodology is then used to analyze movements of 15 weaned northern fur seal (Callorhinus ursinus) pups with respect to surface winds, geostrophic currents and sea surface temperature. The highest posterior model probabilities belonged to those models containing only winds and current; SST was not a significant factor for modeling their movement.
Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpus

Recent literature has underscored the importance of dataset documentation work for machine learning, and part of this work involves addressing "documentation debt" for datasets that have been used widely but documented sparsely. This paper aims to help address documentation debt for BookCorpus, a popular text dataset for training large language models. Notably, researchers have used BookCorpus to train OpenAI's GPT-N models and Google's BERT models, even though little to no documentation exists about the dataset's motivation, composition, collection process, etc. We offer a preliminary datasheet that provides key context and information about BookCorpus, highlighting several notable deficiencies. In particular, we find evidence that (1) BookCorpus likely violates copyright restrictions for many books, (2) BookCorpus contains thousands of duplicated books, and (3) BookCorpus exhibits significant skews in genre representation. We also find hints of other potential deficiencies that call for future research, including problematic content, potential skews in religious representation, and lopsided author contributions. While more work remains, this initial effort to provide a datasheet for BookCorpus adds to growing literature that urges more careful and systematic documentation for machine learning datasets.
Affine Projections of Polynomials

An m-variate polynomial f is said to be an affine projection of some n-variate polynomial g if there exists an n×m matrix A and an n-dimensional vector b such that f(x) = g(Ax + b). In other words, if f can be obtained by replacing each variable of g by an affine combination of the variables occurring in f , then it is said to be an affine projection of g. Given f and g can we determine whether f is an affine projection of g? Some well known problems (such as VP versus VNP and matrix multiplication for example) are instances of this problem. The intention of this paper is to understand the complexity of the corresponding computational problem: given polynomials f and g find A and b such that f = g(Ax + b), if such an (A,b) exists. We first show that this is an NP-hard problem. We then focus our attention on instances where g is a member of some fixed, well known family of polynomials so that the input consists only of the polynomial f(x) having m variables and degree d. We consider the situation where f(x) is given to us as a blackbox (i.e. for any point a ∈ F we can query the blackbox and obtain f(a) in one step) and devise randomized algorithms with running time poly(mnd) in the following special cases: (1) when f = Permn(Ax + b) and A satisfies rank(A) = n. Here Permn is the permanent polynomial. (2) when f = Detn(Ax + b) and A satisfies rank(A) = n. Here Detn is the determinant polynomial. (3) when f = Pown,d(Ax + b) and A is a random n×m matrix with d = n. Here Pown,d is the power-symmetric polynomial of degree d. (4) when f = SPSn,d(Ax + b) and A is a random (nd) × m matrix with n constant. Here SPSn,d is the sum-of-products polynomials of degree d with n terms. Acknowledgments. The author would like to thank : Ketan Mulmuley and Milind Sohoni for suggesting the use of the corresponding lie algebras for equivalence to the determinant, K V Subrahmanyam for his nice lectures on representation theory and explaining aspects of the GCT approach, Michael Forbes for pointing out the relationship between symmetric rank and tensor rank, Shubhangi Saraf and Srikanth Srinivasan for discussions pertaining to projections of the sum of products polynomial. The author would also like to thank the organizers of the Geometric Complexity Theory workshop for their kind invitation. ∗Microsoft Research India, neeraka@microsoft.com
The cost of looking natural: Why the no-makeup movement may fail to discourage cosmetic use

Consumers seek naturalness across many domains, including physical appearance. It seems that the desire for natural beauty would discourage artificial appearance-enhancement consumption, such as cosmetic use. However, across an analysis of the “no-makeup movement” on Twitter and Nielsen cosmetic sales (Study 1a), an image analysis of #nomakeup selfies using machine learning approaches (Study 1b), and three experiments (Studies 2–4), we find that calls to look natural can be associated with increased artificial beauty practices. Drawing from attribution theory, we theorize that calls to look natural maintain the value of attractiveness while adding the consumer concern that others will discount their attractiveness if overt effort is present. Thus, rather than investing less effort, consumers may engage in a self-presentational strategy wherein they construct an appearance of naturalness to signal low effort to others, thereby augmenting their attractiveness. This work contributes to attribution and self-presentation theory and offers practical implications for naturalness consumption.
CURRENT STATUS OF PROJECTIVE TEST THEORIES AND PROJECTIVE TECHNIQUES

Abstract In order to sharpen the present review of the current status of projective test theory and projective techniques, it is focused on the more recent developments in the United States over the past several years. To begin the review, however, we first need to look back to the important beginnings of projective techniques in the brilliant contributions of Herman Rorschach.
The Spillover Effect of Consolidating Securitization Entities on Small Business Lending

I investigate spillover effects of a new regulation for securitization on supply of small business loans, which are rarely securitized. The regulation requires banks to consolidate securitization entities previously accorded off-balance-sheet treatment and subjects them to full regulatory capital charges. Controlling for local credit demand during within-county-year analyses, I find that banks that bring securitization entities onto books reduce small business lending in comparison to banks that do not consolidate entities but lend to the same market. After consolidations, counties with a greater market share of affected banks receive less total small business credit and experience slower small business growth. The findings highlight potential erosion of the efficacy of the new regulation to hold banks accountable for risk in securitizations and unintended consequences on the supply of non-securitized credit and the broader economy.
Plasticity and artificial selection for developmental mode in a poecilogonous sea slug.

The contribution of phenotypically plastic traits to evolution depends on the degree of environmental influence on the target of selection (the phenotype) as well as the underlying genetic structure of the trait and plastic response. Likewise, maternal effects can help or hinder evolution through affects to the response to selection. The sacoglossan sea slug Alderia willowi exhibits intraspecific variation for developmental mode (= poecilogony) that is environmentally modulated with populations producing more yolk-feeding (lecithotrophic) larvae during the summer, and more planktonic-feeding (planktotrophic) larvae in the winter. I found significant family-level variation in the reaction norms between 17 maternal families of A. willowi when reared in a split-brood design in low (16 ppt) versus high (32 ppt) salinity, conditions which mimic seasonal variation in salinity of natural populations. I documented a significant response to selection for lecithotrophic larvae in high and low salinity. The slope of the reaction norm was maintained following one generation of selection for lecithotrophy. When the maternal environment was controlled in the laboratory, I found significant maternal effects, which reduced the response to selection. These results suggest there is standing genetic variation for egg-mass type in A. willowi, but the ability of selection to act on that variation may depend on the environment in which the phenotype is expressed in preceding generations.
SCANNING PROBE MICROSCOPY STUDY OF LAYERED DICHALCOGENIDE RES2

The surface of the layered chalcogenide ReS{sub 2} was examined by atomic force microscopy (AFM) and scanning tunneling microscopy (STM), and the structure of ReS{sub 2} was determined by single crystal X-ray diffraction measurements. The observed atomic-resolution AFM and STM images were analyzed by calculating the total and partial electron density distributions [{rho}(r{sub o}) and {rho}(r{sub o}e{sub r}) respectively] of a single ReS{sub 2} layer taken from the bulk crystal structure. Our results show that the STM images are associated with the surface S atoms. The {rho}(r{sub o},e{sub f}) plots of the two tunneling processes differ in topography from the {rho}(r{sub o}) plot so that the surface atomic structure cannot be unambiguously deduced from the analysis of atomic-resolution STM images alone. Calculations of the {rho}(r{sub o}) and {rho}(r{sub o},e{sub f}) plots are essential for properly interpreting the atomic-resolution AFM and STM images. 27 refs., 11 figs., 1 tab.
Numerical dispersion effects on the energy cascade in large-eddy simulation

This work focuses on characterizing the effects of numerical dispersion error on the energy cascade in large-eddy simulation (LES) of convecting homogeneous isotropic turbulence. Numerical energy and transfer spectra reveal that energy is not transferred at the appropriate rate to wavemodes where significant dispersion error is present, leading to a deficiency of energy in highly dispersive modes and an accompanying pile up of energy in the well resolved modes. An asymptotic analysis indicates that dispersion error causes a phase decoherence between triad interacting wavemodes, leading to a reduction in the mean energy transfer rate for these scales.
More informative disclosures, less informative prices? Portfolio and price formation around quarter-ends

Abstract Fund trades and stock prices vary systematically with the quarterly reporting cycle. Funds accelerate trades that complete the building of existing positions at quarter-end but delay trades that initiate the building of new positions until the start of the new quarter. Evidence suggests these trade dynamics are driven by a dual desire to make disclosures more informative about future holdings but avoid disclosing incomplete positions. Consistent with disclosure-based motives unrelated to new information about intrinsic values driving these quarterly trade dynamics, both stock price informativeness and commissions paid by funds drop at quarter-end.