cssi/SciSciGPT-SciSciNet huggingface dataset:
Standardized Tests and Affirmative Action: The Role of Bias and Variance

The University of California suspended through 2024 the requirement that applicants from California submit SAT scores, upending the major role standardized testing has played in college admissions. We study the impact of such decisions and its interplay with other policies---such as affirmative action---on admitted class composition. This paper considers a theoretical framework to study the effect of requiring test scores on academic merit and diversity in college admissions. The model has a college and set of potential students. Each student has observed application components and group membership, as well as an unobserved noisy skill level generated from an observed distribution. The college is Bayesian and maximizes an objective that depends on both diversity and merit. It estimates each applicant's true skill level using the observed features and potentially their group membership, and then admits students with or without affirmative action. We characterize the trade-off between the (potentially positive) informational role of standardized testing in college admissions and its (negative) exclusionary nature. Dropping test scores may exacerbate disparities by decreasing the amount of information available for each applicant, especially those from non-traditional backgrounds. However, if there are substantial barriers to testing, removing the test improves both academic merit and diversity by increasing the size of the applicant pool. Finally, using application and transcript data from the University of Texas at Austin, we demonstrate how an admissions committee could measure the trade-off in practice to better decide whether to drop their test scores requirement. The full paper can be found at https://arxiv.org/abs/2010.04396.
miTAR: a hybrid deep learning-based approach for predicting miRNA targets

microRNAs (miRNAs) have been shown to play essential roles in a wide range of biological processes. Many computational methods have been developed to identify targets of miRNAs. However, the majority of these methods depend on pre-defined features that require considerable efforts and resources to compute and often prove suboptimal at predicting miRNA targets. We developed a novel hybrid deep learning-based (DL-based) approach that is capable of predicting miRNA targets at a higher accuracy. This approach integrates convolutional neural networks (CNNs) that excel in learning spatial features and recurrent neural networks (RNNs) that discern sequential features. Therefore, our approach has the advantages of learning both the intrinsic spatial and sequential features of miRNA:target. The inputs for our approach are raw sequences of miRNAs and genes that can be obtained effortlessly. We applied our approach on two human datasets from recently miRNA target prediction studies and trained two models. We demonstrated that the two models consistently outperform the previous methods according to evaluation metrics on test datasets. Comparing our approach with currently available alternatives on independent datasets shows that our approach delivers substantial improvements in performance. We also show with multiple evidences that our approach is more robust than other methods on small datasets. Our study is the first study to perform comparisons across multiple existing DL-based approaches on miRNA target prediction. Furthermore, we examined the contribution of a Max pooling layer in between the CNN and RNN and demonstrated that it improves the performance of all our models. Finally, a unified model was developed that is robust on fitting different input datasets. We present a new DL-based approach for predicting miRNA targets and demonstrate that our approach outperforms the current alternatives. We supplied an easy-to-use tool, miTAR, at https://github.com/tjgu/miTAR . Furthermore, our analysis results support that Max Pooling generally benefits the hybrid models and potentially prevents overfitting for hybrid models.
Transformed central quantile subspace

Quantile regression (QR) is a well-established method of tail analysis. Application of QR can become very challenging when dealing with high-dimensional data, thus requiring dimension reduction tec...
Void formation in operator growth, entanglement, and unitarity

The structure of the Heisenberg evolution of operators plays a key role in explaining diverse processes in quantum many-body systems. In this paper, we discuss a new universal feature of operator evolution: an operator can develop a void during its evolution, where its nontrivial parts become separated by a region of identity operators. Such processes are present in both integrable and chaotic systems, and are required by unitarity. We show that void formation has important implications for unitarity of entanglement growth and generation of mutual information and multipartite entanglement. We study explicitly the probability distributions of void formation in a number of unitary circuit models, and conjecture that in a quantum chaotic system the distribution is given by the one we find in random unitary circuits, which we refer to as the random void distribution. We also show that random unitary circuits lead to the same pattern of entanglement growth for multiple intervals as in (1 + 1)-dimensional holographic CFTs after a global quench, which can be used to argue that the random void distribution leads to maximal entanglement growth.
Nonlinear saturation of thermal instabilities

Recent low-frequency simulations of a one-layer model with lateral buoyancy inhomogeneity have revealed circulatory motions resembling quite closely submesoscale features on the ocean surface often visible in satellite observations. This model is known to lack a high-wavenumber instability cutoff and, thus, to possibly undergo ultraviolet catastrophe. However, the numerically observed instabilities, referred to as “thermal” due the ability of the above inhomogeneous-layer model to incorporate thermodynamic processes, are not seen to grow indefinitely. In this note, I show that the presence of a convex pseudo-energy–momentum integral of motion for the inviscid, unforced dynamics can arrest their nonlinear grow in the zonally symmetric case. Our result is an application of Arnold and Shepherd's methods.
Dual linear programming bounds for sphere packing via modular forms

We obtain new restrictions on the linear programming bound for sphere packing, by optimizing over spaces of modular forms to produce feasible points in the dual linear program. In contrast to the situation in dimensions 8 and 24, where the linear programming bound is sharp, we show that it comes nowhere near the best packing densities known in dimensions 12, 16, 20, 28, and 32. More generally, we provide a systematic technique for proving separations of this sort.
On the Approximability of Related Machine Scheduling Under Arbitrary Precedence

Distributed computing systems often need to consider the scheduling problem involving a collection of highly dependent data-processing tasks that must work in concert to achieve mission-critical objectives. This paper considers the unrelated machine scheduling problem for minimizing weighted sum completion time under arbitrary precedence constraints and on heterogeneous machines with different processing speeds. The problem is known to be strongly NP-hard even in the single machine setting. By making use of Queyranne’s constraint set and constructing a novel Linear Programming relaxation for the scheduling problem under arbitrary precedence constraints, our results in this paper advance the state of the art. We develop a 2(1 + ( ${m}$ –1)/ ${D}$ )-approximation algorithm (and 2(1 + ( ${m}$ –1)/ ${D}$ ) + 1-approximation) for the scheduling problem with zero release time (and arbitrary release time), where ${m}$ is the number of servers and ${D}$ is the task-skewness product. The algorithm can be efficiently computed in polynomial time using the Ellipsoid method and achieves nearly optimal performance in practice as ${D} > {O}$ ( ${m}$ ) when the number of tasks per job to schedule is sufficiently larger than the number of machines available. Our implementation and evaluation using a heterogeneous testbed and real-world benchmarks confirms significant improvement in weighted sum completion time for dependent computing tasks.
Unsupervised Machine Learning Via Transfer Learning and k-Means Clustering to Classify Materials Image Data

Unsupervised machine learning offers significant opportunities for extracting knowledge from unlabeled datasets and for achieving maximum machine learning performance. This paper demonstrates how to construct, use, and evaluate a high-performance unsupervised machine learning system for classifying images in a popular microstructural dataset. The Northeastern University Steel Surface Defects Database includes micrographs of six different defects observed on hot-rolled steel in a format that is convenient for training and evaluating models for image classification. We use the VGG16 convolutional neural network pre-trained on the ImageNet dataset of natural images to extract feature representations for each micrograph. After applying principal component analysis to extract signal from the feature descriptors, we use k-means clustering to classify the images without needing labeled training data. The approach achieves 99.4% ± 0.16% accuracy, and the resulting model can be used to classify new images without retraining. This approach demonstrates an improvement in both performance and utility compared to a previous study. A sensitivity analysis is conducted to better understand the influence of each step on the classification performance. The results provide insight toward applying unsupervised machine learning techniques to problems of interest in materials science.
An arithmetic count of the lines on a smooth cubic surface

We give an arithmetic count of the lines on a smooth cubic surface over an arbitrary field , \[ \sum_{\text{lines}} \operatorname{Tr}_{L/k} \langle \alpha \rangle = 15 \cdot \langle 1 \rangle + 12 \cdot \langle -1 \rangle, \] where -homotopy theory for algebraic vector bundles. We expect that further arithmetic counts generalizing enumerative results in complex and real algebraic geometry can be obtained with similar methods.
Flattening the Curve: Pandemic-Induced Revaluation of Urban Real Estate

Abstract We show that the COVID-19 pandemic brought house price and rent declines in city centers, and price and rent increases away from the center, thereby flattening the bid-rent curve in most U.S. metropolitan areas. Across MSAs, the flattening of the bid-rent curve is larger when working from home is more prevalent, housing markets are more regulated, and supply is less elastic. Housing markets predict an urban revival with urban rent growth exceeding suburban rent growth for the foreseeable future, as working from home recedes.
Real-time causal message ordering in multimedia systems

Messages that are sent to and received by multiple sites need to have a consistent order imposed by all sites. Causal ordering allows the cause and effect relations of messages to be maintained. This paper presents an algorithm that ensures that multimedia data with real?time deadlines are delivered to the application layer in causal order. The algorithm is designed to ensure that any message that arrives at a destination site before its deadline will be delivered to the application before the message expires. In addition, by focusing on a form of causal ordering violations caused by "the triangle inequality", this algorithm has a low overhead with respect to the amount of information that must be appended to each message.
Decentralized Multi-agent Reinforcement Learning with Shared Actions

In this paper, we consider a multi-agent system with $N$ cooperative agents where each agent privately observes its own private type and publicly observes each others' actions. We propose a novel model-free reinforcement learning algorithm to compute the optimal policies for the agents that maximizes their collective reward. This setting belongs to the broad class of decentralized control problems with partial information. We use the common agent approach [1], wherein some fictitious common agent chooses the best policy based on a belief on the current states of the agents. These beliefs are updated individually for each agent from their current belief and action histories without the knowledge of the system dynamics. In this paper, we employ particle filters, called the bootstrap filters, to update the belief of all agents in a distributed manner. We illustrate our results with the help of a smart-grid application, where the users strive to reduce collective cost of power for all the agents in the grid. Finally, we compare the performances for model and model-free implementation of the reinforcement learning (RL) algorithm establishing the effectiveness of particle filter (PF) method.
Regression Discontinuity Design under Self-selection

In Regression Discontinuity (RD) design, self-selection leads to different distributions of covariates on two sides of the policy intervention, which essentially violates the continuity of potential outcome assumption. The standard RD estimand becomes difficult to interpret due to the existence of some indirect effect, i.e. the effect due to self selection. We show that the direct causal effect of interest can still be recovered under a class of estimands. Specifically, we consider a class of weighted average treatment effects tailored for potentially different target populations. We show that a special case of our estimands can recover the average treatment effect under the conditional independence assumption per Angrist and Rokkanen (2015), and another example is the estimand recently proposed in Fr\"olich and Huber (2018). We propose a set of estimators through a weighted local linear regression framework and prove the consistency and asymptotic normality of the estimators. Our approach can be further extended to the fuzzy RD case. In simulation exercises, we compare the performance of our estimator with the standard RD estimator. Finally, we apply our method to two empirical data sets: the U.S. House elections data in Lee (2008) and a novel data set from Microsoft Bing on Generalized Second Price (GSP) auction.
FIX-DES: A computer-aided modular fixture configuration design system

This paper presents a computer-aided modular fixture configuration design system: FIX-DES. When fixturing requirements are specified as locating/clamping surfaces and points, a fixture configuration will be automatically generated by 1. Selecting fixture elements from a fixture element database to form fixture units based on fixture element assembly relationships. 2. Placing the fixture units and elements into position on a baseplate while the fixturing requirements and assembly relationships (e.g. hole alignments) are maintained. The fixture element assembly relationships can be established automatically when the geometric models and fixturing functions of the fixture elements are specified so that the FIX-DES system can be applied easily to different fixture systems. The system also provides the interactive design and design modification functions for human expert involvement. The system was developed with core programs in C and C++ and interface programs in a specific CAD environment. The system has been successfully applied in industry and can be transferred easily to other CAD systems. The overall structure of the FIX-DES system is introduced in the paper. The development of the automated fixture configuration design (AFCD) functions are presented in detail. Finally a fixture configuration design example is given to illustrate the application of the system.
Non-equivalence of stochastic optimal control problems with open and closed loop controls

Abstract For an optimal control problem of an Ito’s type stochastic differential equation, the control process could be taken in open-loop or closed-loop forms. In the standard literature, provided appropriate regularity, the value functions under these two types of controls are equal and are the unique (viscosity) solution to the corresponding (path-dependent) HJB equation. In this short note, we provide a counterexample in the path dependent setting showing that these value functions can be different in general.
Computational structure prediction provides a plausible mechanism for electron transfer by the outer membrane protein Cyc2 from Acidithiobacillus ferrooxidans.

Cyc2 is the key protein in the outer membrane of Acidithiobacillus ferrooxidans that mediates electron transfer between extracellular inorganic iron and the intracellular central metabolism. This cytochrome c is specific for iron and interacts with periplasmic proteins to complete a reversible electron transport chain. A structure of Cyc2 has not yet been characterized experimentally. Here we describe a structural model of Cyc2, and associated proteins, to highlight a plausible mechanism for the ferrous iron electron transfer chain. A comparative modeling protocol specific for trans membrane beta barrel (TMBB) proteins in acidophilic conditions (pH ~ 2) was applied to the primary sequence of Cyc2. The proposed structure has three main regimes: Extracellular loops exposed to low-pH conditions, a TMBB, and an N-terminal cytochrome-like region within the periplasmic space. The Cyc2 model was further refined by identifying likely iron and heme docking sites. This represents the first computational model of Cyc2 that accounts for the membrane microenvironment and the acidity in the extracellular matrix. This approach can be used to model other TMBBs which can be critical for chemolithotrophic microbial growth.
Bayes-Optimal Methods for Finding the Source of a Cascade

We study the problem of estimating the source of a network cascade. The cascade initially starts from a single vertex and spreads deterministically over time, but only a noisy version of the propagation is observable. The goal is then to design a stopping time and estimator that will estimate the source well while ensuring the number of affected vertices is not too large. We rigorously formulate a Bayesian approach to the problem. If vertices can be labelled by vectors in Euclidean space (which is natural in spatial networks), the optimal estimator is the conditional mean estimator, and we derive an explicit form for the optimal stopping time under minimal assumptions on the cascade dynamics. We study the performance of the optimal stopping time on lattices, and show that a computationally efficient but suboptimal stopping time which compares the posterior variance to a threshold has near-optimal performance.
Pressure-Induced Insulator–Metal Transition in Silicon Telluride from First-Principles Calculations

Silicon telluride (Si2Te3) is a two-dimensional semiconductor with unique structural properties due to the size contrast between Si and Te atoms. A recent experiment shows that the material turns metallic under hydrostatic pressure, while the lattice structure of the metallic phase remains to be identified. In this paper, we propose two metallic phases, M1 and M2, of Si2Te3 using the evolution algorithm and first-principles density functional theory (DFT) calculations. Unlike the presence of Si-Si dimers in the semiconducting (SC) phase, both M1 and M2 phases have individual Si atoms, which play important roles in the metallicity. Analysis of structural properties, electronic properties, dynamical as well as thermal stability is performed. The energies of these new structures are compared with the SC phase under the subsequent hydrostatic pressure up to 12 GPa. The results show that M1 and M2 phases have lower energies under high pressure, thus elucidating the appearance of the metallic phase of Si2Te3. In addition, the external pressure causes the SC phase to have an indirect-direct-indirect bandgap transition. Analysis of Raman spectra of the SC phase at a different pressure shows the shifting of the major Raman peaks, and finally disappearing confirms the phase transition. The results are in good agreement with the experimental observations. The understanding of the insulator-metal phase transition increases the potential usefulness of the material system.
Ask what's missing and what's useful: Improving Clarification Question Generation using Global Knowledge.

The ability to generate clarification questions i.e., questions that identify useful missing information in a given context, is important in reducing ambiguity. Humans use previous experience with similar contexts to form a global view and compare it to the given context to ascertain what is missing and what is useful in the context. Inspired by this, we propose a model for clarification question generation where we first identify what is missing by taking a difference between the global and the local view and then train a model to identify what is useful and generate a question about it. Our model outperforms several baselines as judged by both automatic metrics and humans.
A diagrammatic approach for determining the braid index of alternating links

This paper concerns the braid index of an alternating link. It is well known that the braid index of any link equals the minimum number of Seifert circles among all link diagrams representing it. F...
Defending Web Servers Against Flash Crowd Attacks

A flash crowd attack (FCA) floods a service, such as a Web server, with well-formed requests, generated by numerous bots. FCA traffic is difficult to filter, since individual attack and legitimate service requests look identical. We propose robust and reliable models of human interaction with server, which can identify and block a wide variety of bots. We implement the models in a system called FRADE, and evaluate them on three Web servers with different server applications and content. Our results show that FRADE detects both naive and sophisticated bots within seconds, and successfully filters out attack traffic. FRADE significantly raises the bar for a successful attack, by forcing attackers to deploy at least three orders of magnitude larger botnets than today.
INO80 promotes H2A.Z occupancy to regulate cell fate transition in pluripotent stem cells.

The INO80 chromatin remodeler is involved in many chromatin-dependent cellular functions. However, its role in pluripotency and cell fate transition is not fully defined. We examined the impact of Ino80 deletion in the naive and primed pluripotent stem cells. We found that Ino80 deletion had minimal effect on self-renewal and gene expression in the naive state, but led to cellular differentiation and de-repression of developmental genes in the transition toward and maintenance of the primed state. In the naive state, INO80 pre-marked gene promoters that would adopt bivalent histone modifications by H3K4me3 and H3K27me3 upon transition into the primed state. In the primed state, in contrast to its known role in H2A.Z exchange, INO80 promoted H2A.Z occupancy at these bivalent promoters and facilitated H3K27me3 installation and maintenance as well as downstream gene repression. Together, our results identified an unexpected function of INO80 in H2A.Z deposition and gene regulation. We showed that INO80-dependent H2A.Z occupancy is a critical licensing step for the bivalent domains, and thereby uncovered an epigenetic mechanism by which chromatin remodeling, histone variant deposition and histone modification coordinately control cell fate.
Rocky Mountain Spotted Fever

Information pertinent to the history, etiology, clinical presentation, diagnosis, treatment, and prevention of Rocky Mountain spotted fever is provided. Emphasis is placed on the early diagnosis and appropriate specific treatment of this disease.
Exploration and Discovery of the COVID-19 Literature through Semantic Visualization

We propose semantic visualization as a linguistic visual analytic method. It can enable exploration and discovery over large datasets of complex networks by exploiting the semantics of the relations in them. This involves extracting information, applying parameter reduction operations, building hierarchical data representation and designing visualization. We also present the accompanying COVID-SemViz a searchable and interactive visualization system for knowledge exploration of COVID-19 data to demonstrate the application of our proposed method. In the user studies, users found that semantic visualization-powered COVID-SemViz is helpful in terms of finding relevant information and discovering unknown associations.
Assurance monitoring of learning-enabled cyber-physical systems using inductive conformal prediction based on distance learning

Machine learning components such as deep neural networks are used extensively in cyber-physical systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance monitoring of learning-enabled CPS based on the conformal prediction framework. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. By leveraging conformal prediction, the approach provides well-calibrated confidence and ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. We demonstrate the approach using three datasets of mobile robot following a wall, speaker recognition, and traffic sign recognition. The experimental results demonstrate that the error rates are well-calibrated while the number of alarms is very small. Furthermore, the method is computationally efficient and allows real-time assurance monitoring of CPS.
Photometric Observations and Reductions of Lightcurves of Asteroids

The brightness-time variation (the lightcurve) of an asteroid is observed to obtain the rate of rotation, some indication of the shape, and the orientation of the rotation axis in space. The brightness-phase relations are observed for the study of surface texture. This paper deals specifically with observing routines and reductions, including discussions of lightcurves, rotation periods, absolute magnitudes, phase coefficients, opposition effects, and pole determinations. This paper supplements the review chapter by Gehrels (1970). Only photoelectric techniques are considered because the visual and photographic ones are, nearly without exception, not precise enough.
Confirmation of Interpersonal Expectations is Intrinsically Rewarding.

People want to interact successfully with other individuals, and they invest significant efforts in attempting to do so. Decades of research have demonstrated that to simplify the dauntingly complex task of interpersonal communication, perceivers predict the responses of individuals in their environment using stereotypes and other sources of prior knowledge. Here, we show that these top-down expectations can also shape the subjective value of expectation-consistent and expectation-violating targets. Specifically, in two neuroimaging experiments (n = 58), we observed increased activation in brain regions associated with reward processing-including the nucleus accumbens-when perceivers observed information consistent with their social expectations. In two additional behavioral experiments (n = 704), we observed that perceivers were willing to forgo money to encounter an expectation-consistent target and avoid an expectation-violating target. Together, these findings suggest that perceivers value having their social expectations confirmed, much like food or monetary rewards.
Legal pluralism across the global South: colonial origins and contemporary consequences

This essay conveys past and present legally plural situations across the Global South, highlighting critical issues. It provide readers a deep sense of legal pluralism and an appreciation of its c...
Clinically-Identified C-terminal Mutations in Fibulin-3 are Prone to Misfolding and Destabilization

Distinct mutations in the secreted extracellular matrix protein, fibulin-3 (F3), have been associated with a number of ocular diseases ranging from primary open angle glaucoma to cuticular age-related macular degeneration to a rare macular dystrophy, Malattia Leventinese (ML). The R345W F3 mutation that causes ML leads to F3 misfolding, inefficient secretion and accumulation at higher intracellular steady state levels in cultured cells. Herein, we determined whether fifteen other clinically-identified F3 mutations also led to similar levels of misfolding and secretion defects, which might provide insight into their potential pathogenicity. Surprisingly, we found that only a single F3 variant, L451F, presented with a significant secretion defect (69.5 ± 2.4% of wild-type (WT) F3 levels) and a corresponding increase in intracellular levels (226.8 ± 25.4% of WT F3 levels). Upon follow-up studies, when this conserved residue (L451) was mutated to a charged (Asp or Arg) or bulky (Pro, Trp, Tyr) residue, F3 secretion was also compromised, indicating the importance of small side chains (Leu, Ala, or Gly) at this residue. To uncover potential inherent F3 instability not easily observed under typical culture conditions, we genetically eliminated the sole stabilizing N-linked glycosylation site (N249) from select clinically-identified F3 mutants. This removal exacerbated R345W and L451F secretion defects (19.8 ± 3.0% and 12.4 ± 1.2% of WT F3 levels, respectively), but also revealed a previously undiscovered secretion defect in another C-terminal variant, Y397H (42.0 ± 10.1% of WT F3 levels). Yet, glycan removal did not change the relative secretion of the N-terminal mutants tested (D49A, R140W, I220F). These results highlight the uniqueness and molecular similarities between the R345W and L451F variants and also suggest that previously identified disease-associated mutations (e.g., R140W) are indistinguishable from WT with respect to secretion, hinting that they may lead to disease by an alternative mechanism.
One-Step Synthesis of Dithiocarbamates from Metal Powders

Neutral metal dithiocarbamate complexes (M(NR2CS2)x) are well-known precursors to metal sulfides, a class of materials with numerous technological applications. We are involved in a research effort to prepare new precursors to metal sulfides using simple, reproducible synthetic procedures. We describe the results of our synthetic and characterization studies for M = Fe, Co, Ni, Cu, and In. For example, treatment of metallic indium with tetramethylthiuramdisulfide (tmtd) in 4-methylpyridine (4-Mepy) at 25°C produces a new homoleptic indium (III) dithiocarbamate, In(N(CH3)2CS2)3 (I), in yields of over 60%. The indium (III) dithiocarbamate was characterized by X-ray crystallography; (I) exists in the solid state as discrete distorted-octahedral molecules. Compound (I) crystallizes in the Plbar (No. 2) space group with lattice parameters: a = 9.282(1) A, b = 10.08 1(1) A, c = 12.502 A, ∝ = 73.9 1(1)°, β = 70.21(1)°, γ = 85.84(1)°, and Z = 2. X-ray diffraction and mass spectral data were used to characterize the products of the analogous reactions with Fe, Co, Ni, and Cu. We discuss both use of dithiocarbamates as precursors and our approach to their preparation.
Converting translation operators into plane polar and spherical coordinates and their use in determining quantum-mechanical wavefunctions in a representation-independent fashion

Quantum mechanics is often developed in the position representation, but this is not necessary, and one can perform calculations in a representation-independent fashion, even for wavefunctions. In this work, we illustrate how one can determine wavefunctions, aside from normalization, using only operators and how those operators act on state vectors. To do this in plane polar and spherical coordinates requires one to convert the translation operator into those coordinates. As examples of this approach, we illustrate the solution of the Coulomb problem in two and three dimensions without needing to express any operators in position space.
Algorithms, Reductions and Equivalences for Small Weight Variants of All-Pairs Shortest Paths

All-Pairs Shortest Paths (APSP) is one of the most well studied problems in graph algorithms. This paper studies several variants of APSP in unweighted graphs or graphs with small integer weights. APSP with small integer weights in undirected graphs [Seidel'95, Galil and Margalit'97] has an O(n^ω) time algorithm, where ω < 2.373 is the matrix multiplication exponent. APSP in directed graphs with small weights however, has a much slower running time that would be Ω(n^{2.5}) even if ω = 2 [Zwick'02]. To understand this n^{2.5} bottleneck, we build a web of reductions around directed unweighted APSP . We show that it is fine-grained equivalent to computing a rectangular Min-Plus product for matrices with integer entries; the dimensions and entry size of the matrices depend on the value of ω. As a consequence, we establish an equivalence between APSP in directed unweighted graphs, APSP in directed graphs with small (O(1)) integer weights, All-Pairs Longest Paths in DAGs with small weights, cRed-APSP in undirected graphs with small weights, for any c ≥ 2 (computing all-pairs shortest path distances among paths that use at most c red edges), #_{≤ c}APSP in directed graphs with small weights (counting the number of shortest paths for each vertex pair, up to c), and approximate APSP with additive error c in directed graphs with small weights, for c ≤ O(1). We also provide fine-grained reductions from directed unweighted APSP to All-Pairs Shortest Lightest Paths (APSLP) in undirected graphs with {0,1} weights and #_{mod c}APSP in directed unweighted graphs (computing counts mod c), thus showing that unless the current algorithms for APSP in directed unweighted graphs can be improved substantially, these problems need at least Ω(n^{2.528}) time. We complement our hardness results with new algorithms. We improve the known algorithms for APSLP in directed graphs with small integer weights (previously studied by Zwick [STOC'99]) and for approximate APSP with sublinear additive error in directed unweighted graphs (previously studied by Roditty and Shapira [ICALP'08]). Our algorithm for approximate APSP with sublinear additive error is optimal, when viewed as a reduction to Min-Plus product. We also give new algorithms for variants of #APSP (such as #_{≤ U}APSP and #_{mod U}APSP for U ≤ n^{O(1)}) in unweighted graphs, as well as a near-optimal O(n³)-time algorithm for the original #APSP problem in unweighted graphs (when counts may be exponentially large). This also implies an O(n³)-time algorithm for Betweenness Centrality, improving on the previous O(n⁴) running time for the problem. Our techniques also lead to a simpler alternative to Shoshan and Zwick’s algorithm [FOCS'99] for the original APSP problem in undirected graphs with small integer weights.
Deterministic Privacy Preservation in Static Average Consensus Problem

In this paper we consider the problem of privacy preservation in the static average consensus problem. This problem normally is solved by proposing privacy preservation augmentations for the popular first order Laplacian-based algorithm. These mechanisms however come with computational overhead, may need coordination among the agents to choose their parameters and also alter the transient response of the algorithm. In this paper we show that an alternative iterative algorithm that is proposed in the literature in the context of dynamic average consensus problem has intrinsic privacy preservation and can be used as a privacy preserving algorithm that yields the same performance behavior as the well-known Laplacian consensus algorithm but without the overheads that come with the existing privacy preservation methods.
Exact coherent structures and shadowing in turbulent Taylor–Couette flow

We investigate a theoretical framework for modelling fluid turbulence based on the formalism of exact coherent structures (ECSs). Although highly promising, existing evidence for the role of ECSs in turbulent flows is largely circumstantial and comes primarily from idealized numerical simulations. In particular, it remains unclear whether three-dimensional turbulent flows in experiment shadow any ECSs. In order to conclusively answer this question, a hierarchy of ECSs should be computed on a domain and with boundary conditions exactly matching experiment. The present study makes the first step in this direction by investigating a small-aspect-ratio Taylor–Couette flow with naturally periodic boundary conditions in the azimuthal direction. We describe the structure of the chaotic set underlying turbulent flow driven by counter-rotating cylinders and present direct numerical evidence for shadowing of a collection of unstable relative periodic orbits and a travelling wave, setting the stage for further experimental tests of the framework.
High-Girth Near-Ramanujan Graphs with Lossy Vertex Expansion.

Kahale proved that linear sized sets in $d$-regular Ramanujan graphs have vertex expansion $\sim\frac{d}{2}$ and complemented this with construction of near-Ramanujan graphs with vertex expansion no better than $\frac{d}{2}$. However, the construction of Kahale encounters highly local obstructions to better vertex expansion. In particular, the poorly expanding sets are associated with short cycles in the graph. Thus, it is natural to ask whether high-girth Ramanujan graphs have improved vertex expansion. Our results are two-fold: 1. For every $d = p+1$ for prime $p$ and infinitely many $n$, we exhibit an $n$-vertex $d$-regular graph with girth $\Omega(\log_{d-1} n)$ and vertex expansion of sublinear sized sets bounded by $\frac{d+1}{2}$ whose nontrivial eigenvalues are bounded in magnitude by $2\sqrt{d-1}+O\left(\frac{1}{\log n}\right)$. 2. In any Ramanujan graph with girth $C\log n$, all sets of size bounded by $n^{0.99C/4}$ have vertex expansion $(1-o_d(1))d$. The tools in analyzing our construction include the nonbacktracking operator of an infinite graph, the Ihara--Bass formula, a trace moment method inspired by Bordenave's proof of Friedman's theorem, and a method of Kahale to study dispersion of eigenvalues of perturbed graphs.
Response of Mnbi-Bi Eutectic to Freezing Rate Changes

Previously we reported on a theoretical treatment of the influence on freezing rate of sudden changes in translation rate in the Bridgman-Stockbarger technique [1]. This has now been extended to consideration of a linear ramped translation rate and an oscillatory freezing rate. Oscillations above a few hertz are found to be highly damped in small diameter apparatus. An experimental test was made of the theoretical predictions for a sudden change of translation rate. MnBi-Bi eutectic was solidified with current induced interface demarcation. The experimental results correspond reasonably well with theory if the silica ampoule wall is assumed either (1) to contribute only a resistance to heat exchange of sample with the furnace wall, or (2) to transmit heat effectively in the axial direction by radiation. In an attempt to explain the fact that a finer micro-structure is obtained in space, MnBi-Bi microstructure is being determined when the freezing rate is rapidly increased or decreased. Preliminary results indicate that fiber branching does not occur as readily as does fiber termination.
Communities, Co-ops, and Clubs: Social Capital and Incentives in Large Collective Organizations

We study a continuous-time organization design problem. Each member's output is an imperfect signal of his underlying effort, and each member's utility from remaining in the organization is endogenous to other members' efforts. Monetary transfers are assumed infeasible. Incentives can be provided only through two channels: expulsion following poor performance and respite following good performance. We derive the steady state distribution of members' continuation utilities for arbitrary values of the initial and maximum continuation utilities and then optimize these values according to organizational objectives. An optimally designed organization can be implemented by associating continuation utilities with a performance-tracking reputation system.
Double-Site Substitution of Ce into (Ba, Sr)MnO3 Perovskites for Solar Thermochemical Hydrogen Production

Solar thermochemical hydrogen production (STCH) is a renewable alternative to hydrogen produced using fossil fuels. While serial bulk experimental methods can accurately measure STCH performance, screening chemically complex materials systems for new promising candidates is more challenging. Here we identify double-site Ce-substituted (Ba,Sr)MnO3 oxide perovskites as promising STCH candidates using a combination of bulk synthesis and high-throughput thin film experiments. The Ce substitution on the B-site in 10H-BaMnO3 and on the A-site in 4P-SrMnO3 lead to 2-3x higher hydrogen production compared to CeO2, but these bulk single-site substituted perovskites suffer from incomplete reoxidation. Double-site Ce substitution on both A- and B-site in (Ba,Sr)MnO3 thin films increases Ce solubility and extends the stability of 10H and 4P structures, which is promising for their thermochemical reversibility. This study demonstrates a high-throughput experimental method for screening complex oxide materials for STCH applications.
Abelian Complex Structures and Generalizations

After a review on the development of deformation theory of abelian complex structures from both the classical and generalized sense, we propose the concept of semi-abelian generalized complex structure. We present some observations on such structure and illustrate this new concept with a variety of examples.
Stealing Neural Network Structure Through Remote FPGA Side-Channel Analysis

Deep Neural Network (DNN) models have been extensively developed by companies for a wide range of applications. The development of a customized DNN model with great performance requires costly investments, and its structure (layers and hyper-parameters) is considered intellectual property and holds immense value. However, in this paper, we found the model secret is vulnerable when a cloud-based FPGA accelerator executes it. We demonstrate an end-to-end attack based on remote power side-channel analysis and machine-learning-based secret inference against different DNN models. The evaluation result shows that an attacker can reconstruct the layer and hyper-parameter sequence at over 90% accuracy using our method, which can significantly reduce her model development workload. We believe the threat presented by our attack is tangible, and new defense mechanisms should be developed against this threat.
Pushed into a Crowd: Repositioning Costs, Resources, and Competition in the RTE Cereal Industry

This paper exploits a natural experiment involving self-regulation in the ready-to-eat (RTE) breakfast cereal industry to evaluate how firms respond to entry in light of changes in competition and to estimate the value of brand equity as products are re-positioned. Self-regulation led to a crowding of the product space by forcing differentiated products to become more similar. Among other things we find that products constrained by regulation perform relatively worse than unconstrained products, brand equity specific to a product is tightly linked to existing product positions and does not transfer well to new positions, and that when competition increases, firms reposition to new segments and increase investments on product differentiation. The paper offers a performance based assessment of repositioning and to our knowledge offers the first empirical analysis in the strategy literature that measures in performance terms how repositioning changes the value of brand equity.
The necessity of periodic boundary conditions for the accurate calculation of crystalline terahertz spectra.

Terahertz vibrational spectroscopy has emerged as a powerful spectroscopic technique, providing valuable information regarding long-range interactions – and associated collective dynamics – occurring in solids. However, the terahertz sciences are relatively nascent, and there have been significant advances over the last several decades that have profoundly influenced the interpretation and assignment of experimental terahertz spectra. Specifically, because there do not exist any functional group or material-specific terahertz transitions, it is not possible to interpret experimental spectra without additional analysis, specifically, computational simulations. Over the years simulations utilizing periodic boundary conditions have proven to be most successful for reproducing experimental terahertz dynamics, due to the ability of the calculations to accurately take long-range forces into account. On the other hand, there are numerous reports in the literature that utilize gas phase cluster geometries, to varying levels of apparent success. This perspective will provide a concise introduction into the terahertz sciences, specifically terahertz spectroscopy, followed by an evaluation of gas phase and periodic simulations for the assignment of crystalline terahertz spectra, highlighting potential pitfalls and good practice for future endeavors.
A Morse theoretic approach to non-isolated singularities and applications to optimization

Abstract Let X be a complex affine variety in C N , and let f : C N → C be a polynomial function whose restriction to X is nonconstant. For g : C N → C a general linear function, we study the limiting behavior of the critical points of the one-parameter family f t : = f − t g as t → 0 . Our main result gives an expression of this limit in terms of critical sets of the restrictions of g to the singular strata of ( X , f ) . We apply this result in the context of optimization problems. For example, we consider nearest point problems (e.g., Euclidean distance degrees) for affine varieties and a possibly nongeneric data point.
Financial Assistance for Health Security: Effects of International Financial Assistance on Capacities for Preventing, Detecting, and Responding to Public Health Emergencies

Background Health security funding is intended to improve capacities for preventing, detecting, and responding to public health emergencies. Recent years have witnessed substantial increases in the amounts of donor financial assistance to health security from countries, philanthropies, and other development partners. To date, no work has examined the effects of assistance on health security capacity development over time. This paper presents an analysis of the time-lagged effects of assistance for health security on levels of capacity. Methods We collected publicly available health security assessment scores published between 2010 and 2019 and data relating to financial assistance for health security. Using validated methods, we rescaled assessment scores on analogous scales to enable comparison and binned them in quartiles. We then used a distributed lag model in a Bayesian ordinal regression framework to assess the effects of assistance for health security on capacity development over time. Results Strong evidence exists for associations between financial assistance and select capacities on a variety of lagged time intervals. Financial assistance had positive effects on zoonotic disease capacities in the year it was disbursed, and positive effects on legislation, laboratory, workforce, and risk communication capacities one year after disbursal. Financial assistance had negative effects on laboratory and emergency response capacities two years after it was disbursed. Financial assistance did not have measurable effects on coordination, antimicrobial resistance, food safety, biosafety, surveillance, or response preparedness capacities over the timeframe considered. Conclusions Financial assistance for health security is associated with positive effects for several core health security capacities. However, for the majority of capacities, levels of funding were not significantly associated with capacity level, though we cannot fully exclude endogeneity. Future work should continue to investigate these relationships in different contexts and examine other factors that may contribute to capacity development.