cssi/SciSciGPT-SciSciNet huggingface dataset:
Using Law Against Itself: Bush v. Gore Applied in the Courts

The decisions in Bush v. Palm Beach County Canvassing Bd. ("Bush I") and Bush v. Gore, ("Bush II") evidence the extent to which it now appears unremarkable for courts to play a role in even the most basic of political issues. While the doctrinal value of the Bush decisions is certainly important, the Bush decisions are far more valuable for their endorsement of methodologies available to anyone seeking political advantage under the cover of judicial legitimacy. This article explores those principles, practices and procedures. I start with an appropriate theoretical context. For that purpose I look to LatCrit theory rather than more ideologically traditional or doctrinal theory. Ironically, this expression of critical theory, grounded in progressive political programs, provides the best conceptual basis for interpreting the ostensibly traditionalist Bush cases. I use this theoretical context to identify eight core methodological lessons of the Bush cases: (1) "Be Consciously Political;" (2) "Be Literal;" (3) "Attack Precedent;" (4) "Create Contradiction;" (5) "Appropriate;" (6) "Exploit Uncertainty and Sentimentality;" (7) "Recruit Legitimacy;" and (8) "If All Else Fails, Overwhelm Law Through its Own Devices." I then explore the way these lessons have been internalized by the courts and applied in nearly one hundred published and unpublished judicial opinions issued since the publication of the Bush cases. Those opinions are windows through which one can see the ways in which litigants have attempted to extract meaning from the Bush decisions far beyond their officially sanctioned reading, and the ways in which judges have attempted to situate the Bush decisions within the body of American case law. In their least dangerous sense, the lessons, as applied, suggest the ways in which the juridification of politics has become institutionalized in federal and state courts. The courts now rival the legislature as the venue par excellence for the resolution of political issues of every description. But the lessons also suggest the naturalization, within an ostensibly conservative political jurisprudence, of methodologies of legal perversion, of the use of law against itself. Having made a vocation of criticizing the political left for the nihilistic evils of critical and other progressive approaches to law, a so-called conservative court has gone a long way to implement a jurispruence of ultimate relativism and indeterminacy. Now that is irony!
Mechanism Design for Large Scale Network Utility Maximization

Network utility maximization (NUM) is a general framework for designing distributed optimization algorithms for networks. Existing studies proposed (economic) mechanisms to solve the NUM but largely neglected the issue of large-scale implementation. In this paper, we present the Large-Scale Vickery-Clark-Grove (VCG) Mechanism for NUM with a simpler payment rule. The Large-Scale VCG Mechanism maximizes the network utility and achieves individual rationality and budget balance. We show that, as the number of agents approaches infinity, each agent's incentive to misreport converges quadratically to zero. For practical implementation, we introduce a modified mechanism that possesses an additional important technical property, superimposability, which makes it able to be built upon any (potentially distributed) algorithm that optimally solves the NUM Problem and ensures agents to obey the algorithm.
Why Security Defects Go Unnoticed during Code Reviews?: A Case-Control Study of the Chromium OS Project

Peer code review has been found to be effective in identifying security vulnerabilities. However, despite practicing mandatory code reviews, many Open Source Software (OSS) projects still encounter a large number of post-release security vulnerabilities, as some security defects escape those. Therefore, a project manager may wonder if there was any weakness or inconsistency during a code review that missed a security vulnerability. Answers to this question may help a manager pinpointing areas of concern and taking measures to improve the effectiveness of his/her project's code reviews in identifying security defects. Therefore, this study aims to identify the factors that differentiate code reviews that successfully identified security defects from those that missed such defects. With this goal, we conduct a case-control study of Chromium OS project. Using multi-stage semi-automated approaches, we build a dataset of 516 code reviews that successfully identified security defects and 374 code reviews where security defects escaped. The results of our empirical study suggest that the are significant differences between the categories of security defects that are identified and that are missed during code reviews. A logistic regression model fitted on our dataset achieved an AUC score of 0.91 and has identified nine code review attributes that influence identifications of security defects. While time to complete a review, the number of mutual reviews between two developers, and if the review is for a bug fix have positive impacts on vulnerability identification, opposite effects are observed from the number of directories under review, the number of total reviews by a developer, and the total number of prior commits for the file under review.
A coding theorem for the rate-distortion-perception function

The rate-distortion-perception function (RDPF; Blau and Michaeli, 2019) has emerged as a useful tool for thinking about realism and distortion of reconstructions in lossy compression. Unlike the rate-distortion function, however, it is unknown whether encoders and decoders exist that achieve the rate suggested by the RDPF. Building on results by Li and El Gamal (2018), we show that the RDPF can indeed be achieved using stochastic, variable-length codes. For this class of codes, we also prove that the RDPF lower-bounds the achievable rate
Using Transformer Based Ensemble Learning to Classify Scientific Articles

Many time reviewers fail to appreciate novel ideas of a researcher and provide generic feedback. Thus, proper assignment of reviewers based on their area of expertise is necessary. Moreover, reading each and every paper from end-to-end for assigning it to a reviewer is a tedious task. In this paper, we describe a system which our team FideLIPI submitted in the shared task of SDPRA-2021 (https://sdpra-2021.github.io/website/ (accessed January 25, 2021)) [14]. It comprises four independent sub-systems capable of classifying abstracts of scientific literature to one of the given seven classes. The first one is a RoBERTa [10] based model built over these abstracts. Adding topic models/Latent dirichlet allocation (LDA) [2] based features to the first model results in the second sub-system. The third one is a sentence level RoBERTa [10] model. The fourth one is a Logistic Regression model built using Term Frequency Inverse Document Frequency (TF-IDF) features. We ensemble predictions of these four sub-systems using majority voting to develop the final system which gives a F1 score of 0.93 on the test and validation set. This outperforms the existing State Of The Art (SOTA) model SciBERT’s [1] in terms of F1 score on the validation set. Our codebase is available at https://github.com/SDPRA-2021/shared-task/tree/main/FideLIPI.
Thermal analysis of a conceptual design for a 250 We GPHS/FPSE space power system

A thermal analysis has been performed for a 250-We space nuclear power system which combines the US Department of Energy's general purpose heat source (GPHS) modules with a state-of-the-art free-piston Stirling engine (FPSE). The focus of the analysis is on the temperature of the indium fuel clad within the GPHS modules. The thermal analysis results indicate fuel clad temperatures slightly higher than the design goal temperature of 1573 K. The results are considered favorable due to numerous conservative assumptions used. To demonstrate the effects of the conservatism, a brief sensitivity analysis is performed in which a few of the key system parameters are varied to determine their effect on the fuel clad temperatures. It is shown that thermal analysis of a more detailed thermal mode should yield fuel clad temperatures below 1573 K.
What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization.

Summarization of clinical narratives is a long-standing research problem. Here, we introduce the task of hospital-course summarization. Given the documentation authored throughout a patient’s hospitalization, generate a paragraph that tells the story of the patient admission. We construct an English, text-to-text dataset of 109,000 hospitalizations (2M source notes) and their corresponding summary proxy: the clinician-authored “Brief Hospital Course” paragraph written as part of a discharge note. Exploratory analyses reveal that the BHC paragraphs are highly abstractive with some long extracted fragments; are concise yet comprehensive; differ in style and content organization from the source notes; exhibit minimal lexical cohesion; and represent silver-standard references. Our analysis identifies multiple implications for modeling this complex, multi-document summarization task.
Causal Understanding of Fake News Dissemination on Social Media

Recent years have witnessed remarkable progress towards computational fake news detection. To mitigate its negative impact, we argue that it is critical to understand what user attributes potentially cause users to share fake news. The key to this causal-inference problem is to identify confounders -- variables that cause spurious associations between treatments (e.g., user attributes) and outcome (e.g., user susceptibility). In fake news dissemination, confounders can be characterized by fake news sharing behavior that inherently relates to user attributes and online activities. Learning such user behavior is typically subject to selection bias in users who are susceptible to share news on social media. Drawing on causal inference theories, we first propose a principled approach to alleviating selection bias in fake news dissemination. We then consider the learned unbiased fake news sharing behavior as the surrogate confounder that can fully capture the causal links between user attributes and user susceptibility. We theoretically and empirically characterize the effectiveness of the proposed approach and find that it could be useful in protecting society from the perils of fake news.
Faithfully Explainable Recommendation via Neural Logic Reasoning.

Knowledge graphs (KG) have become increasingly important to endow modern recommender systems with the ability to generate traceable reasoning paths to explain the recommendation process. However, prior research rarely considers the faithfulness of the derived explanations to justify the decision-making process. To the best of our knowledge, this is the first work that models and evaluates faithfully explainable recommendation under the framework of KG reasoning. Specifically, we propose neural logic reasoning for explainable recommendation (LOGER) by drawing on interpretable logical rules to guide the path-reasoning process for explanation generation. We experiment on three large-scale datasets in the e-commerce domain, demonstrating the effectiveness of our method in delivering high-quality recommendations as well as ascertaining the faithfulness of the derived explanation.
A Simplified Dynamic Model of the T700 Turboshaft Engine

A simplified open-loop dynamic model of the T700 turboshaft engine, valid within the normal operating range of the engine, is developed. This model is obtained by linking linear state space models obtained at different engine operating points. Each linear model is developed from a detailed nonlinear engine simulation using a multivariable system identification and realization method. The simplified model may be used with a model-based real time diagnostic scheme for fault detection and diagnostics, as well as for open loop engine dynamics studies and closed loop control analysis utilizing a user generated control law.
Fuzzy Logic Controller Stability Analysis Using a Satisfiability Modulo Theories Approach

While many widely accepted methods and techniques exist for validation and verification of traditional controllers, at this time no solutions have been accepted for Fuzzy Logic Controllers (FLCs). Due to the highly nonlinear nature of such systems, and the fact that developing a valid FLC does not require a mathematical model of the system, it is quite difficult to use conventional techniques to prove controller stability. Since safety-critical systems must be tested and verified to work as expected for all possible circumstances, the fact that FLC controllers cannot be tested to achieve such requirements poses limitations on the applications for such technology. Therefore, alternative methods for verification and validation of FLCs needs to be explored. In this study, a novel approach using formal verification methods to ensure the stability of a FLC is proposed. Main research challenges include specification of requirements for a complex system, conversion of a traditional FLC to a piecewise polynomial representation, and using a formal verification tool in a nonlinear solution space. Using the proposed architecture, the Fuzzy Logic Controller was found to always generate negative feedback, but inconclusive for Lyapunov stability.
Automatically Selecting Striking Images for Social Cards

To allow previewing a web page, social media platforms have developed social cards: visualizations consisting of vital information about the underlying resource. At a minimum, social cards often include features such as the web resource’s title, text summary, striking image, and domain name. News and scholarly articles on the web are frequently subject to social card creation when being shared on social media. However, we noticed that not all web resources offer sufficient metadata elements to enable appealing social cards. For example, the COVID-19 emergency has made it clear that scholarly articles, in particular, are at an aesthetic disadvantage in social media platforms when compared to their often more flashy disinformation rivals. Also, social cards are often not generated correctly for archived web resources, including pages that lack or predate standards for specifying striking images. With these observations, we are motivated to quantify the levels of inclusion of required metadata in web resources, its evolution over time for archived resources, and create and evaluate an algorithm to automatically select a striking image for social cards. We find that more than 40% of archived news articles sampled from the NEWSROOM dataset and 22% of scholarly articles sampled from the PubMed Central dataset fail to supply striking images. We demonstrate that we can automatically predict the striking image with a Precision@1 of 0.83 for news articles from NEWSROOM and 0.78 for scholarly articles from the open access journal PLOS ONE.
DARCNN: Domain Adaptive Region-based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Images

In the biomedical domain, there is an abundance of dense, complex data where objects of interest may be challenging to detect or constrained by limits of human knowledge. Labelled domain specific datasets for supervised tasks are often expensive to obtain, and furthermore discovery of novel distinct objects may be desirable for unbiased scientific discovery. Therefore, we propose leveraging the wealth of annotations in benchmark computer vision datasets to conduct unsupervised instance segmentation for diverse biomedical datasets. The key obstacle is thus overcoming the large domain shift from common to biomedical images. We propose a Domain Adaptive Region-based Convolutional Neural Network (DARCNN), that adapts knowledge of object definition from COCO, a large labelled vision dataset, to multiple biomedical datasets. We introduce a domain separation module, a self-supervised representation consistency loss, and an augmented pseudo-labelling stage within DARCNN to effectively perform domain adaptation across such large domain shifts. We showcase DARCNN’s performance for unsupervised instance segmentation on numerous biomedical datasets.
Millimeter-Wave Angle Estimation of Multiple Targets Using Space–Time Modulation and Interferometric Antenna Arrays

A new method of angle estimation of multiple targets using a distributed interferometric antenna array and wideband space–time modulation generating a dynamic array pattern is presented in this work. Interferometric array measurements of angle of arrival are generally ambiguous in the presence of two or more targets. We propose a new method of mitigating ambiguities in interferometric measurements by multiplying the angle pseudo-spectra from multiple antenna baselines, resulting in detections at only the angles of the targets. Using a single linear frequency-modulated transmitter and a receive interferometric array with $N$ elements, we show a simple and computationally efficient technique to estimate the angle of up to $\mathcal {O}(N^{2})$ targets. We describe the theory behind the technique, present detailed simulations, and provide an experimental verification using a three-element millimeter-wave measurement system.
A unifying framework for quantifying and comparing n-dimensional hypervolumes

1. The quantification of Hutchison9s n-dimensional hypervolume has enabled substantial progress in community ecology, species niche analysis and beyond. While non-parametric methods for quantifying and comparing hypervolumes are popular, they do not support a partitioning of the different components and drivers of hypervolume variation. Here, we propose as alternative the use of multivariate normal distributions for the assessment and comparison of niche hypervolumes and introduce this as the multivariate-normal hypervolume (MVNH) framework. 2. The framework provides parametric measures of the size and dissimilarity of niche hypervolumes, each of which can be partitioned into biologically interpretable components. Specifically, We use 1) the determinant of the covariance matrix (i.e. the generalized variance) of a MVNH as a measure of total niche size, which can be partitioned into the components of univariate niche variances and a correlation component; and 2) the Bhattacharyya distance between two MVNHs as a measure of niche dissimilarity, which can be partitioned into the components of Mahalanobis distance between hypervolume centroids and the determinant ratio which measures hypervolume size difference. 3. We use empirical examples of community- and species-level analysis to demonstrate the new insights provided by these metrics. We show that the newly proposed framework enables us to quantify the relative contributions of different hypervolume components and to identify the drivers of functional diversity and environmental niche variation. 4. Our approach overcomes several operational and computational limitations of non-parametric methods and provides a framework that offers both unification and granularity in the assessment of niche volumes and differences, which has wide implications for understanding niche evolution, niche shifts and expansion during biotic invasions etc.
Transport Information Hessian Distances

We formulate closed-form Hessian distances of information entropies in one-dimensional probability density space embedded with the \(L^2\)-Wasserstein metric. Some analytical examples are provided.
A Trustworthy, Responsible and Interpretable System to Handle Chit-Chat in Conversational Bots

Most often, chat-bots are built to solve the purpose of a search engine or a human assistant: Their primary goal is to provide information to the user or help them complete a task. However, these chat-bots are incapable of responding to unscripted queries like "Hi, what's up", "What's your favourite food". Human evaluation judgments show that 4 humans come to a consensus on the intent of a given query which is from chat domain only 77% of the time, thus making it evident how non-trivial this task is. In our work, we show why it is difficult to break the chitchat space into clearly defined intents. We propose a system to handle this task in chat-bots, keeping in mind scalability, interpretability, appropriateness, trustworthiness, relevance and coverage. Our work introduces a pipeline for query understanding in chitchat using hierarchical intents as well as a way to use seq-seq auto-generation models in professional bots. We explore an interpretable model for chat domain detection and also show how various components such as adult/offensive classification, grammars/regex patterns, curated personality based responses, generic guided evasive responses and response generation models can be combined in a scalable way to solve this problem.
Integer Programming-based Error-Correcting Output Code Design for Robust Classification

Error-Correcting Output Codes (ECOCs) offer a principled approach for combining binary classifiers into multiclass classifiers. In this paper, we study the problem of designing optimal ECOCs to achieve both nominal and adversarial accuracy using Support Vector Machines (SVMs) and binary deep neural networks. We develop a scalable Integer Programming (IP) formulation to design minimal codebooks with desirable error correcting properties. Our work leverages the advances in IP solution techniques to generate codebooks with optimality guarantees. To achieve tractability, we exploit the underlying graph-theoretic structure of the constraint set. Particularly, the size of the constraint set can be significantly reduced using edge clique covers. Using this reduction technique along with Plotkin's bound in coding theory, we demonstrate that our approach is scalable to a large number of classes. The resulting codebooks achieve a high nominal accuracy relative to standard codebooks (e.g., one-vs-all, one-vs-one, and dense/sparse codes). Interestingly, our codebooks provide non-trivial robustness to white-box attacks without any adversarial training.
How much for a haircut? Illiquidity, secondary markets, and the value of private equity

Limited partners (LPs) of private equity funds commit to invest with significant uncertainty regarding the timing of capital calls and payoffs and extreme restrictions on liquidity. Secondary markets have emerged which alleviate some of the associated cost. This paper develops a subjective valuation model that incorporates these institutional features. For a risk-averse LP private equity values are highly sensitive to the time-varying discount observed in secondary market transactions. Model-implied breakeven returns generally exceed empirically observed returns. Only for highly risk-tolerant LPs who can access above-average funds and an efficient secondary market are the costs of private equity justified by performance. Preliminary; please do not circulate *Corresponding author. Contact information: Nicolas P.B. Bollen, The Owen Graduate School of Management, Vanderbilt University, 401 21 Avenue South, Nashville, TN 37203, Telephone: 615-343-5029, Email: nick.bollen@owen.vanderbilt.edu. Berk A. Sensoy, Department of Finance, Fisher College of Business, Ohio State University, Columbus, OH 43210, Telephone: 614-292-3217, email: sensoy.4@osu.edu. For helpful comments and discussions, we thank Nick Crain and seminar participants at Boston College, University of Michigan, University of Utah, and Vanderbilt University. This research is supported by the Financial Markets Research Center at Vanderbilt University and The Risk Institute at the Fisher College of Business, Ohio State University.
Matched Illumination Waveforms Using Multi-Tone Sinusoidal Frequency Modulation

This paper explores the design of constant modulus Matched-Illumination (MI) waveforms using the Multi-Tone Sinusoidal Frequency Modulation (MTSFM) waveform model. MI waveforms are optimized for detecting targets in known noise and clutter Power Spectral Densities (PSDs). There exist well-defined information theoretic methods that describe the design of MI waveforms for a myriad of target/noise/clutter models. However, these methods generally only produce the magnitude square of the MI waveform’s spectrum. Additionally, the waveform’s time-series is not guaranteed to be constant modulus. The MTSFM is a constant modulus waveform model with a discrete set of design coefficients. The coefficients are adjusted to synthesize constant modulus waveforms that approximate the ideal MI waveform’s spectrum. Simulations demonstrate that the MTSFM’s detection performance closely approximates an ideal MI waveform spectrum and generally outperforms flat spectrum waveforms across a range of transmit energies when the noise and clutter PSDs vary greatly across the operational band.
Keratin 19 interacts with GSK3β to regulate its nuclear accumulation and degradation of cyclin D3

Cyclin D3 regulates the G1/S transition and is frequently overexpressed in several cancer types including breast cancer, where it promotes tumor progression. Here, we show that a cytoskeletal prote...
Multi-modal Program Inference: a Marriage of Pre-trained Language Models and Component-based Synthesis

Multi-modal program synthesis refers to the task of synthesizing programs (code) from their specification given in different forms, such as a combination of natural language and examples. Examples provide a precise but incomplete specification, and natural language provides an ambiguous but more "complete" task description. Machine-learned pre-trained models (PTMs) are adept at handling ambiguous natural language, but struggle with generating syntactically and semantically precise code. Program synthesis techniques can generate correct code, often even from incomplete but precise specifications, such as examples, but they are unable to work with the ambiguity of natural languages. We present an approach that combines PTMs with component-based synthesis (CBS): PTMs are used to generate candidates programs from the natural language description of the task, which are then used to guide the CBS procedure to find the program that matches the precise examples-based specification. We use our combination approach to instantiate multi-modal synthesis systems for two programming domains: the domain of regular expressions and the domain of CSS selectors. Our evaluation demonstrates the effectiveness of our domain-agnostic approach in comparison to a state-of-the-art specialized system, and the generality of our approach in providing multi-modal program synthesis from natural language and examples in different programming domains.
QubiC: An Open-Source FPGA-Based Control and Measurement System for Superconducting Quantum Information Processors

As quantum information processors grow in quantum bit (qubit) count and functionality, the control and measurement system becomes a limiting factor to large-scale extensibility. To tackle this challenge and keep pace with rapidly evolving classical control requirements, full control stack access is essential to system-level optimization. We design a modular field-programmable gate array (FPGA)-based system called QubiC to control and measure a superconducting quantum processing unit. The system includes room temperature electronics hardware, FPGA gateware, and engineering software. A prototype hardware module is assembled from several commercial off-the-shelf evaluation boards and in-house-developed circuit boards. Gateware and software are designed to implement basic qubit control and measurement protocols. System functionality and performance are demonstrated by performing qubit chip characterization, gate optimization, and randomized benchmarking sequences on a superconducting quantum processor operating at the Advanced Quantum Testbed at the Lawrence Berkeley National Laboratory. The single-qubit and two-qubit process fidelities are measured to be 0.9980 $\pm$ 0.0001 and 0.948 $\pm$ 0.004, respectively, by randomized benchmarking. With fast circuit sequence loading capability, the QubiC performs randomized compiling experiments efficiently and improves the feasibility of executing more complex algorithms.
Multidimensional data organization and random access in large-scale DNA storage systems

Abstract With impressive physical density and molecular-scale coding capacity, DNA is a promising substrate for building long-lasting data archival storage systems. To retrieve data from DNA storage, recent implementations typically rely on large libraries of meticulously designed orthogonal PCR primers, which fundamentally limit the capacity and scalability of practical DNA storage. This work combines nested and semi-nested PCR to enable multidimensional data organization and random access in large DNA storage. Our strategy effectively pushes the limit of DNA storage capacity and dramatically reduces the number of orthogonal primers needed for efficient PCR random access. Our design uses only k ⁎ n primers to uniquely address n k data-encoding oligos. The architecture inherently supports various well-defined PCR random-access patterns that can be tailored to organize and preserve the underlying DNA-encoded data structures and relations in simple database-like formats such as rows, columns, tables, and blocks of data entries. We design in silico PCR experiments of a four-dimensional DNA storage to illustrate the mechanisms of sixteen different random-access patterns each requiring no more than two PCR reactions to selectively amplify a target dataset of various sizes. To better approximate the physical system, we formulate mathematical models based on empirical distributions to analyze the effect of pipetting, PCR bias, and PCR stochasticity on the performance of multidimensional data queries from large DNA storage.
Contextual Recommendations and Low-Regret Cutting-Plane Algorithms

We consider the following variant of contextual linear bandits motivated by routing applications in navigational engines and recommendation systems. We wish to learn a hidden $d$-dimensional value $w^*$. Every round, we are presented with a subset $\mathcal{X}_t \subseteq \mathbb{R}^d$ of possible actions. If we choose (i.e. recommend to the user) action $x_t$, we obtain utility $\langle x_t, w^* \rangle$ but only learn the identity of the best action $\arg\max_{x \in \mathcal{X}_t} \langle x, w^* \rangle$. We design algorithms for this problem which achieve regret $O(d\log T)$ and $\exp(O(d \log d))$. To accomplish this, we design novel cutting-plane algorithms with low "regret" -- the total distance between the true point $w^*$ and the hyperplanes the separation oracle returns. We also consider the variant where we are allowed to provide a list of several recommendations. In this variant, we give an algorithm with $O(d^2 \log d)$ regret and list size $\mathrm{poly}(d)$. Finally, we construct nearly tight algorithms for a weaker variant of this problem where the learner only learns the identity of an action that is better than the recommendation. Our results rely on new algorithmic techniques in convex geometry (including a variant of Steiner's formula for the centroid of a convex set) which may be of independent interest.
MindCraft: Theory of Mind Modeling for Situated Dialogue in Collaborative Tasks

An ideal integration of autonomous agents in a human world implies that they are able to collaborate on human terms. In particular, theory of mind plays an important role in maintaining common ground during human collaboration and communication. To enable theory of mind modeling in situated interactions, we introduce a fine-grained dataset of collaborative tasks performed by pairs of human subjects in the 3D virtual blocks world of Minecraft. It provides information that captures partners’ beliefs of the world and of each other as an interaction unfolds, bringing abundant opportunities to study human collaborative behaviors in situated language communication. As a first step towards our goal of developing embodied AI agents able to infer belief states of collaborative partners in situ, we build and present results on computational models for several theory of mind tasks.
Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression

Models like LASSO and ridge regression are extensively used in practice due to their interpretability, ease of use, and strong theoretical guarantees. Cross-validation (CV) is widely used for hyperparameter tuning in these models, but do practical optimization methods minimize the true out-of-sample loss? A recent line of research promises to show that the optimum of the CV loss matches the optimum of the out-of-sample loss (possibly after simple corrections). It remains to show how tractable it is to minimize the CV loss. In the present paper, we show that, in the case of ridge regression, the CV loss may fail to be quasiconvex and thus may have multiple local optima. We can guarantee that the CV loss is quasiconvex in at least one case: when the spectrum of the covariate matrix is nearly flat and the noise in the observed responses is not too high. More generally, we show that quasiconvexity status is independent of many properties of the observed data (response norm, covariate-matrix right singular vectors and singular-value scaling) and has a complex dependence on the few that remain. We empirically confirm our theory using simulated experiments.
Affinity Mixup for Weakly Supervised Sound Event Detection

The weakly supervised sound event detection (WSSED) problem is the task of predicting the presence of sound events and their corresponding starting and ending points in a weakly labeled dataset. A weak dataset associates each training sample (a short recording) to one or more present sources. Networks that solely rely on convolutional and recurrent layers cannot directly relate multiple frames in a recording. Motivated by attention and graph neural networks, we introduce the concept of an affinity mixup (AM) to incorporate time-level similarities and make a connection between frames. This regularization technique mixes up features in different layers using an adaptive affinity matrix. Our proposed affinity mixup network (AMN) improves over state-of-the-art techniques event-F1 scores by 8.2%.
Cleaning Dirty Books: Post-OCR Processing for Previously Scanned Texts

Substantial amounts of work are required to clean large collections of digitized books for NLP analysis, both because of the presence of errors in the scanned text and the presence of duplicate volumes in the corpora. In this paper, we consider the issue of deduplication in the presence of optical character recognition (OCR) errors. We present methods to handle these errors, evaluated on a collection of 19,347 texts from the Project Gutenberg dataset and 96,635 texts from the HathiTrust Library. We demonstrate that improvements in language models now enable the detection and correction of OCR errors without consideration of the scanning image itself. The inconsistencies found by aligning pairs of scans of the same underlying work provides training data to build models for detecting and correcting errors. We identify the canonical version for each of 17,136 repeatedly-scanned books from 58,808 scans. Finally, we investigate methods to detect and correct errors in single-copy texts. We show that on average, our method corrects over six times as many errors as it introduces. We also provide interesting analysis on the relation between scanning quality and other factors such as location and publication year.
A transcriptome atlas of the mouse iris at single-cell resolution defines cell types and the genomic response to pupil dilation

The iris controls the level of retinal illumination by controlling pupil diameter. It is a site of diverse ophthalmologic diseases and it is a potential source of cells for ocular auto-transplantation. The present study provides foundational data on the mouse iris based on single nucleus RNA sequencing. More specifically, this work has (1) defined all of the major cell types in the mouse iris and ciliary body, (2) led to the discovery of two types of iris stromal cells and two types of iris sphincter cells, (3) revealed the differences in cell type-specific transcriptomes in the resting vs. dilated states, and (4) identified and validated antibody and in situ hybridization probes that can be used to visualize the major iris cell types. By immunostaining for specific iris cell types, we have observed and quantified distortions in nuclear morphology associated with iris dilation and clarified the neural crest contribution to the iris by showing that Wnt1-Cre-expressing progenitors contribute to nearly all iris cell types, whereas Sox10-Cre-expressing progenitors contribute only to stromal cells. This work should be useful as a point of reference for investigations of iris development, disease, and pharmacology, for the isolation and propagation of defined iris cell types, and for iris cell engineering and transplantation.
A Separation Result Between Data-oblivious and Data-aware Poisoning Attacks

Poisoning attacks have emerged as a significant security threat to machine learning algorithms. It has been demonstrated that adversaries who make small changes to the training set, such as adding specially crafted data points, can hurt the performance of the output model. Some of the stronger poisoning attacks require the full knowledge of the training data. This leaves open the possibility of achieving the same attack results using poisoning attacks that do not have the full knowledge of the clean training set. In this work, we initiate a theoretical study of the problem above. Specifically, for the case of feature selection with LASSO, we show that full-information adversaries (that craft poisoning examples based on the rest of the training data) are provably stronger than the optimal attacker that is oblivious to the training set yet has access to the distribution of the data. Our separation result shows that the two setting of data-aware and data-oblivious are fundamentally different and we cannot hope to always achieve the same attack or defense results in these scenarios.
Locally isometric families of minimal surfaces

We consider a surface M immersed inR 3 with induced metric g = ˆ-2 where -2 is the two dimensional Euclidean metric. We then con- struct a system of partial difierential equations that constrain M to lift to a minimal surface via the Weierstrauss-Enneper representation, demand- ing the metric is of the above form. It is concluded that the associated surfaces connecting the prescribed minimal surface and its conjugate sur- face satisfy the system. Moreover, we flnd a non-trivial symmetry of the PDE which generates a one parameter family of surfaces isometric to a specifled minimal surface.
A Mathematical Description of Bacterial Chemotaxis in Response to Two Stimuli

Bacteria are often exposed to multiple stimuli in complex environments, and their efficient chemotactic decisions are critical to survive and grow in their native environments. Bacterial responses to the environmental stimuli depend on the ratio of their corresponding chemoreceptors. By incorporating the signaling machinery of individual cells, we analyze the collective motion of a population of Escherichia coli bacteria in response to two stimuli, mainly serine and methyl-aspartate (MeAsp), in a one-dimensional and a two-dimensional environment, which is inspired by experimental results in Y. Kalinin et al., J. Bacteriol. 192(7):1796–1800, 2010. Under suitable conditions, we show that if the ratio of the main chemoreceptors of individual cells, namely Tar/Tsr, is less than a specific threshold, the bacteria move to the gradient of serine, and if the ratio is greater than the threshold, the group of bacteria moves toward the gradient of MeAsp. Finally, we examine the theory with Monte Carlo agent-based simulations and verify that our results qualitatively agree well with the experimental results in Y. Kalinin et al. (2010).
Fixation and Creativity in Data Visualization Design: Experiences and Perspectives of Practitioners

Data visualization design often requires creativity, and research is needed to understand its nature and means for promoting it. The current visualization literature on creativity is not well developed, especially with respect to the experiences of professional data visualization designers. We conducted semi-structured interviews with 15 data visualization practitioners, focusing on a specific aspect of creativity known as design fixation. Fixation occurs when designers adhere blindly or prematurely to a set of ideas that limit creative outcomes. We present practitioners' experiences and perspectives from their own design practice, specifically focusing on their views of (i) the nature of fixation, (ii) factors encouraging fixation, and (iii) factors discouraging fixation. We identify opportunities for future research related to chart recommendations, inspiration, and perspective shifts in data visualization design.
Obama-Bush China WTO Cases (2001-2012)

U.S.-China trade relations, particularly as they played out in the WTO dispute resolution system, became a leading foreign policy and trade issue in the 2012 presidential campaign. However, this was an issue before the debates and will remain long after them.What does the WTO data show?Any conclusions? Hopefully, this short presentation and the data presented provide a bit stronger basis for a more serious debate beyond this presidential campaign.
Depression in the Preschool Child

Abstract There are no systematic studies which deal primarily with the investigation of depression among preschool age children. At the present time, there is controversy about the existence of depression as a clinical entity in this age group. DSM in uses diagnostic criteria for depression in preschool children which are modified from the criteria used in prepubertal, adolescents and adults. This paper discusses contributory factors, symptomatology, epidemiology, assessment and intervention in preschool children with depression and concludes with a description of the unique feature of depression in this age as contrasted with other age groups.
The Stability and Growth Pact: A Delicate Balance or an Albatross?

The Stability and Growth Pact of 1997 tried to strike a delicate balance.1 On the one hand, it imposed the constraints on national fiscal policy that were deemed necessary to protect the new European Central Bank (ECB) from outside pressures, especially during the period in which it tried to establish credibility.2 At the same time, the Pact allowed some flexibility for a counter-cyclical fiscal policy. This too was deemed necessary in EMU: a stable ECB policy might be expected to create stable macroeconomic conditions for the Euro-zone as a whole, but it could not be expected to iron out regional cyclical imbalances. Did the Pact strike a workable balance? Or is the Pact an unnecessary albatross that could seriously hamper the whole EMU project?
Some observations based on simple models of MP scaling

The emergence of large shared memory multiprocessor systems offer the potential of accelerating the pace of ever increasing system performance. On the one hand, it seems simple: add more processors, get more performance. On the other hand, it is quite difficult, as efficient scaling of workloads to large numbers of processors is a nontrivial challenge. Nevertheless, the way we use these very large machines is intrinsically connected with our predictions of how well important applications scale. The author explores some of the consequences of simple mathematical models of MP scaling. He looks at the consequences of the projections these models give, both in terms of what performance gains we might expect to see, as well as the potential limits of scaling we may face.
Tuning the dynamic exchange interaction in ferromagnet/semiconductor heterostructures

We investigate the impact of tunnel barrier thickness on electron spin dynamics in Fe/MgO/GaAs heterostructures using spin-resolved optical pump-probe spectroscopy. Comparison of the Larmor frequency between thick and thin MgO barriers reveals a four-fold variation in exchange coupling strength, and investigation of the inhomogeneous dephasing time, T 2 * , argues that inhomogeneity in the local effective hyperfine field dominates free-carrier spin relaxation across the entire range of barrier thickness. These results provide additional evidence to support the theory of hyperfine-dominated spin relaxation in GaAs at low temperature and in the presence of an externally applied magnetic field. Further, this work lays the foundation for engineering both the exchange coupling and the free carrier spin dynamics in ferromagnet/semiconductor heterostructures, allowing for the exploration of dissipation and transport in the regime of dynamically-driven spin pumping.
Stress‐gradient hypothesis explains susceptibility to Bromus tectorum invasion and community stability in North America's semi‐arid Artemisia tridentata wyomingensis ecosystems

Questions (1) What combinations of overlapping water and heat stress and herbivory disturbance gradients are associated with shifts in interactions between Artemisia tridentata subsp. wyomingensis (Artemisia) and herbaceous beneficiary species? (2) Do interactions between Artemisia and beneficiaries shift from competition to facilitation with increasing stress-disturbance where facilitation and competition are most frequent and strongest at the highest and lowest levels, respectively? (3) Do such relationships differ for native and non-native beneficiaries? (4) What are the implications of any observed shifts in interactions between community compositional stability in space and susceptibility to invasion? Location North American Artemisia communities. Methods We tested the stress-gradient hypothesis (SGH) in an observational study consisting of 75 sites located along overlapping water and heat stress and disturbance gradients. We used spatial patterns of association among Artemisia and six native and two non-native beneficiary species; including the invasive annual grass Bromus tectorum, representing a diverse array of life history strategies, to infer whether the net outcome of interactions was facilitation or competition. We assessed implications for community stability by examining shifts in community composition in space and resistance to invasion. Results/Conclusions Cattle herbivory, a novel disturbance and selective force, was a significant component of two overlapping stress gradients most strongly associated with observed shifts in interactions. Facilitation and competition were strongest and most frequent at the highest and lowest stress levels along both gradients, respectively. Contrasting ecological optima among native and non-native beneficiaries led to strikingly different patterns of interactions. The four native bunchgrasses with the strongest competitive response abilities exhibited the strongest facilitation at their upper limits of stress tolerance, while the two non-natives exhibited the strongest competition at the highest stress levels, which coincided with their maximum abundance. Artemisia facilitation enhanced stability at intermediate stress levels by providing a refuge for native bunchgrasses, which in turn reduced the magnitude of B. tectorum invasion. However, facilitation was a destabilizing force at the highest stress levels when native bunchgrasses became obligate beneficiaries dependent on facilitation for their persistence. B. tectorum dominated these communities, and the next fire may convert them to annual grasslands.