cssi/SciSciGPT-SciSciNet huggingface dataset:
MF-Net: Compute-In-Memory SRAM for Multibit Precision Inference Using Memory-Immersed Data Conversion and Multiplication-Free Operators

We propose a co-design approach for compute-in-memory inference for deep neural networks (DNN). We use multiplication-free function approximators based on $\ell _{1}$ norm along with a co-adapted processing array and compute flow. Using the approach, we overcame many deficiencies in the current art of in-SRAM DNN processing such as the need for digital-to-analog converters (DACs) at each operating SRAM row/column, the need for high precision analog-to-digital converters (ADCs), limited support for multi-bit precision weights, and limited vector-scale parallelism. Our co-adapted implementation seamlessly extends to multi-bit precision weights, it doesn’t require DACs, and it easily extends to higher vector-scale parallelism. We also propose an SRAM-immersed successive approximation ADC (SA-ADC), where we exploit the parasitic capacitance of bit lines of SRAM array as a capacitive DAC. Since the dominant area overhead in SA-ADC comes due to its capacitive DAC, by exploiting the intrinsic parasitic of SRAM array, our approach allows low area implementation of within-SRAM SA-ADC. Our $8\times 62$ SRAM macro, which requires a 5-bit ADC, achieves ~105 tera operations per second per Watt (TOPS/W) with 8-bit input/weight processing at 45 nm CMOS. Our $8\times 30$ SRAM macro, which requires a 4-bit ADC, achieves ~84 TOPS/W. SRAM macros that require lower ADC precision are more tolerant of process variability, however, have lower TOPS/W as well. We evaluated the accuracy and performance of our proposed network for MNIST, CIFAR10, and CIFAR100 datasets. We chose a network configuration which adaptively mixes multiplication-free and regular operators. The network configurations utilize the multiplication-free operator for more than 85% operations from the total. The selected configurations are 98.6% accurate for MNIST, 90.2% for CIFAR10, and 66.9% for CIFAR100. Since most of the operations in the considered configurations are based on proposed SRAM macros, our compute-in-memory’s efficiency benefits broadly translate to the system-level.
Progressive Property In Action: The Land Reform (Scotland) Act 2003

This article responds to a material deficit at the heart of American property law scholarship. For years, property scholars have debated whether the right to exclude deserves to be the centerpiece of our property regime in the United States. This article seeks to transform that debate by introducing to an American audience a remarkable piece of property legislation recently enacted in Scotland. Part I of the Land Reform (Scotland) Act 2003 creates a right of responsible, non-motorized access across almost all land and in-land water in Scotland, private as well as publicly owned, for purposes of recreation, education and passage. This legislation thus reverses the traditionally robust, ex ante presumption in favor of a landowner’s right to exclude and replaces it with an equally robust, ex ante presumption in favor of the public’s right of responsible access. By introducing this new property right in Scotland and creating an entire property regime to contextualize the right, a regime that is much bolder, in fact, than has been established in England and Wales under the better known Countryside and Rights of Way Act 2000, Scotland has provided property scholars with a case study in property law institutional design that is unique in modern legal systems. This article will demonstrate how the LRSA reveals that it is possible for a property regime to promote the ends of human flourishing without necessarily sacrificing all of the efficiency gains and coordination benefits that flow from the common law’s traditional preference for rules of exclusion.
Market Evidence of Misperceived Prices and Mistaken Mortality Risks

We construct and implement a test of rational consumer behavior in a highstakes financial market. In particular, we test whether consumers make systematic mistakes in perceiving their mortality risks. We implement this test using data from secondary life insurance markets where consumers with a lifethreatening illness sell their life insurance policies to firms in return for an up-front payment. We compare predictions from two models: one with consumers who correctly perceive their mortality risk, and one with consumers who are misguided about their life expectancy, and find that our data are most consistent with the predictions made by the second model.
A Novel Key Generation Scheme Using Quaternary PUF Responses and Wiretap Polar Coding

Physical unclonable functions (PUFs) are widely considered in secret key generation for resource constrained devices. However, PUFs require additional hardware overhead. In this letter, we focus on developing a PUF-efficient, robust, and secure key generation scheme. First, a novel method for extracting quaternary PUF responses is proposed to increase the entropy of a PUF response, in which a 2-bit response is extracted from evaluating a single PUF cell multiple times. The probability masses of the responses can be adjusted by setting parameters appropriately. Then, a chosen secret model based fuzzy extractor (FE) is designed to extract secret keys from the quaternary PUF responses. To improve the security of this FE, it is modeled as a wiretap channel system, and wiretap polar coding is adopted to reduce secrecy leakage. An upper bound of secrecy leakage is also given in this letter, and it suggests that an arbitrarily small (even zero) leakage can be achieved by properly choosing parameters of the quaternary PUF responses generation. Comparison results show that the required number of PUF cells to achieve the same level of secrecy in our scheme is as low as half that of the state-of-the-art schemes.
Political beliefs affect compliance with government mandates

Abstract We use the state-mandated stay-at-home orders during the coronavirus pandemic as a setting to study whether political beliefs inhibit compliance with government orders. Using geolocation data sourced from smartphones, we find residents in Republican counties are less likely to completely stay at home after a state order has been implemented relative to those in Democratic counties. Debit card transaction data shows that Democrats are more likely to switch to remote spending after state orders are implemented. Heterogeneity in factors such as Covid-19 risk exposure, geography, and county characteristics do not completely rule out our findings, suggesting political beliefs are an important determinant in the effectiveness of government mandates. Political alignment with officials giving orders may partially explain these partisan differences.
Bi-atomic classes of positive semirings

A subsemiring S of $$\mathbb {R}$$ is called a positive semiring provided that S consists of nonnegative numbers and $$1 \in S$$ . Here we study factorizations in both the additive monoid $$(S,+)$$ and the multiplicative monoid $$(S\backslash \{0\}, \cdot )$$ . In particular, we investigate when, for a positive semiring S, both $$(S,+)$$ and $$(S\backslash \{0\}, \cdot )$$ have the following properties: atomicity, the ACCP, the bounded factorization property (BFP), the finite factorization property (FFP), and the half-factorial property (HFP). It is well known that in the context of cancellative and commutative monoids, the chain of implications HFP $$\Rightarrow $$ BFP and FFP $$\Rightarrow $$ BFP $$\Rightarrow $$ ACCP $$\Rightarrow $$ atomicity holds. Here we construct classes of positive semirings wherein both the additive and multiplicative structures satisfy each of these properties, and we also give examples to show that, in general, none of the implications in the previous chain is reversible.
Oxygen False Positives on Habitable Zone Planets Around Sun-Like Stars

Oxygen is a promising exoplanet biosignature due to the evolutionary advantage conferred by harnessing starlight for photosynthesis, and the apparent low likelihood of maintaining oxygen-rich atmospheres without life. Hypothetical scenarios have been proposed for non-biological oxygen accumulation on planets around late M-dwarfs, where the extended pre-main sequence may favor abiotic O$_2$ accumulation. In contrast, abiotic oxygen accumulation on planets around F, G, and K-type stars is seemingly less likely, provided they possess substantial non-condensable gas inventories. The comparative robustness of oxygen biosignatures around larger stars has motivated plans for next-generation telescopes capable of oxygen detection on planets around sun-like stars. However, the general tendency of terrestrial planets to develop oxygen-rich atmospheres across a broad range of initial conditions and evolutionary scenarios has not been explored. Here, we use a coupled thermal-geochemical-climate model of terrestrial planet evolution to illustrate three scenarios whereby significant abiotic oxygen can accumulate around sun-like stars, even when significant non-condensable gas inventories are present. For Earth-mass planets, we find abiotic oxygen can accumulate to modern levels if (1) the CO$_2$:H$_2$O ratio of the initial volatile inventory is high, (2) the initial water inventory exceeds ~50 Earth oceans, or (3) the initial water inventory is very low. Fortunately, these three abiotic oxygen scenarios could be distinguished from biological oxygen with observations of other atmospheric constituents or characterizing the planetary surface. This highlights the need for broadly capable next-generation telescopes that are equipped to constrain surface water inventories via time-resolved photometry and search for temporal biosignatures or disequilibrium biosignatures to assess whether oxygen is biogenic.
Hairy magnetic and dyonic black holes in the Standard Model

Spherically symmetric magnetic and dyonic black holes with a magnetic charge Q = 2 are studied in the Standard Model and general relativity. A magnetically charged black hole with mass below 9.3 × 1035 GeV has a “hairy” cloud of electroweak gauge and Higgs fields outside the event horizon with 1/mW in size. An extremal magnetic black hole has a hair mass of 3.6 TeV, while an extremal dyonic black hole has an additional mass of q2 × 1.6 GeV for a small electric charge q ≪ 2π/e2. A hairy dyonic black hole with an integer charge is not stable and can decay into a magnetic one plus charged fermions. On the other hand, a hairy magnetic black hole can evolve via Hawking radiation into a nearly extremal one that is cosmologically stable and an interesting object to be searched for.
Quantifying the Persistence of Vaccine-Related T Cell Epitopes in Circulating Swine Influenza A Strains from 2013-2017.

When swine flu vaccines and circulating influenza A virus (IAV) strains are poorly matched, vaccine-induced antibodies may not protect from infection. Highly conserved T cell epitopes may, however, have a disease-mitigating effect. The degree of T cell epitope conservation among circulating strains and vaccine strains can vary, which may also explain differences in vaccine efficacy. Here, we evaluate a previously developed conserved T cell epitope-based vaccine and determine the persistence of T cell epitope conservation over time. We used a pair-wise homology score to define the conservation between the vaccine’s swine leukocyte antigen (SLA) class I and II-restricted epitopes and T cell epitopes found in 1272 swine IAV strains sequenced between 2013 and 2017. Twenty-four of the 48 total T cell epitopes included in the epitope-based vaccine were highly conserved and found in >1000 circulating swine IAV strains over the 5-year period. In contrast, commercial swine IAV vaccines developed in 2013 exhibited a declining conservation with the circulating IAV strains over the same 5-year period. Conserved T cell epitope vaccines may be a useful adjunct for commercial swine flu vaccines and to improve protection against influenza when antibodies are not cross-reactive.
Reactive astrocytes facilitate vascular repair and remodeling after stroke

Summary Brain injury causes astrocytes to assume a reactive state that is essential for early tissue protection, but how reactive astrocytes affect later reparative processes is incompletely understood. In this study, we show that reactive astrocytes are crucial for vascular repair and remodeling after ischemic stroke in mice. Analysis of astrocytic gene expression data reveals substantial activation of transcriptional programs related to vascular remodeling after stroke. In vivo two-photon imaging provides evidence of astrocytes contacting newly formed vessels in cortex surrounding photothrombotic infarcts. Chemogenetic ablation of a subset of reactive astrocytes after stroke dramatically impairs vascular and extracellular matrix remodeling. This disruption of vascular repair is accompanied by prolonged blood flow deficits, exacerbated vascular permeability, ongoing cell death, and worsened motor recovery. In contrast, vascular structure in the non-ischemic brain is unaffected by focal astrocyte ablation. These findings position reactive astrocytes as critical cellular mediators of functionally important vascular remodeling during neural repair.
Problematic Machine Behavior: A Systematic Literature Review of Algorithm Audits

While algorithm audits are growing rapidly in commonality and public importance, relatively little scholarly work has gone toward synthesizing prior work and strategizing future research in the area. This systematic literature review aims to do just that, following PRISMA guidelines in a review of over 500 English articles that yielded 62 algorithm audit studies. The studies are synthesized and organized primarily by behavior (discrimination, distortion, exploitation, and misjudgement), with codes also provided for domain (e.g. search, vision, advertising, etc.), organization (e.g. Google, Facebook, Amazon, etc.), and audit method (e.g. sock puppet, direct scrape, crowdsourcing, etc.). The review shows how previous audit studies have exposed public-facing algorithms exhibiting problematic behavior, such as search algorithms culpable of distortion and advertising algorithms culpable of discrimination. Based on the studies reviewed, it also suggests some behaviors (e.g. discrimination on the basis of intersectional identities), domains (e.g. advertising algorithms), methods (e.g. code auditing), and organizations (e.g. Twitter, TikTok, LinkedIn) that call for future audit attention. The paper concludes by offering the common ingredients of successful audits, and discussing algorithm auditing in the context of broader research working toward algorithmic justice.
Rapid adaptation of endocytosis, exocytosis and eisosomes after an acute increase in membrane tension in yeast cells.

During clathrin-mediated endocytosis in eukaryotes, actin assembly is required to overcome large membrane tension and turgor pressure. However, the molecular mechanisms by which the actin machinery adapts to varying membrane tension remain unknown. In addition, how cells reduce their membrane tension when they are challenged by hypotonic shocks remains unclear. We used quantitative microscopy to demonstrate that cells rapidly reduce their membrane tension using three parallel mechanisms. In addition to using their cell wall for mechanical protection, yeast cells disassemble eisosomes to buffer moderate changes in membrane tension on a minute time scale. Meanwhile, a temporary reduction of the rate of endocytosis for 2 to 6 minutes, and an increase in the rate of exocytosis for at least 5 minutes allow cells to add large pools of membrane to the plasma membrane. We built on these results to submit the cells to abrupt increases in membrane tension and determine that the endocytic actin machinery of fission yeast cells rapidly adapts to perform clathrin-mediated endocytosis. Our study sheds light on the tight connection between membrane tension regulation, endocytosis and exocytosis.
On induction of class functions

Let G be a connected reductive group defined over a finite field F_q and let L be the Levi subgroup (defined over F_q) of a parabolic subgroup P of G. We define a linear map from class functions on L(F_q) to class functions on G(F_q). This map is independent of the choice of P. We show that for large q this map coincides with the known cohomological induction (whose definition involves a choice of P).
Physics-Informed Deep Learning for Computational Elastodynamics without Labeled Data

AbstractNumerical methods such as finite element have been flourishing in the past decades for modeling solid mechanics problems via solving governing partial differential equations (PDEs). A salie...
Event Prediction in the Big Data Era: A Systematic Survey

Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as earthquakes, civil unrest, system failures, pandemics, and crimes. It is highly desirable to be able to anticipate the occurrence of such events in advance to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth, also thanks to advances in high performance computers and new Artificial Intelligence techniques. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex (e.g., spatial, temporal, and semantic) dependencies, and streaming data feeds. Due to the strong interdisciplinary nature of event prediction problems, most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This article aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts’ searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided to introduce wider applications to model developers to help them expand the impacts of their research. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions are discussed. Additional resources related to event prediction are included in the paper website: http://cs.emory.edu/∼lzhao41/projects/event_prediction_site.html.
Estimation of the Mean Function of Functional Data via Deep Neural Networks

In this work, we propose a deep neural network method to perform nonparametric regression for functional data. The proposed estimators are based on sparsely connected deep neural networks with ReLU activation function. By properly choosing network architecture, our estimator achieves the optimal nonparametric convergence rate in empirical norm. Under certain circumstances such as trigonometric polynomial kernel and a sufficiently large sampling frequency, the convergence rate is even faster than root-$n$ rate. Through Monte Carlo simulation studies we examine the finite-sample performance of the proposed method. Finally, the proposed method is applied to analyze positron emission tomography images of patients with Alzheimer disease obtained from the Alzheimer Disease Neuroimaging Initiative database.
Integrative Use of Computer Vision and Unmanned Aircraft Technologies in Public Inspection: Foreign Object Debris Image Collection

Unmanned Aircraft Systems (UAS) have become an important resource for public service providers and smart cities. The purpose of this study is to expand this research area by integrating computer vision and UAS technology to automate public inspection. As an initial case study for this work, a dataset of common foreign object debris (FOD) is developed to assess the potential of light-weight automated detection. This paper presents the rationale and creation of this dataset. Future iterations of our work will include further technical details analyzing experimental implementation. At a local airport, UAS and portable cameras are used to collect the data contained in the initial version of this dataset. After collecting these videos of FOD, they were split into individual frames and stored as several thousand images. These frames are then annotated following standard computer vision format and stored in a folder-structure that reflects our creation method. The dataset annotations are validated using a custom tool that could be abstracted to fit future applications. Initial detection models were successfully created using the famous You Only Look Once algorithm, which indicates the practicality of the proposed data. Finally, several potential scenarios that could utilize either this dataset or similar methods for other public service are presented.
Modeling Fine-Grained Entity Types with Box Embeddings

Neural entity typing models typically represent fine-grained entity types as vectors in a high-dimensional space, but such spaces are not well-suited to modeling these types' complex interdependencies. We study the ability of box embeddings, which embed concepts as d-dimensional hyperrectangles, to capture hierarchies of types even when these relationships are not defined explicitly in the ontology. Our model represents both types and entity mentions as boxes. Each mention and its context are fed into a BERT-based model to embed that mention in our box space; essentially, this model leverages typological clues present in the surface text to hypothesize a type representation for the mention. Box containment can then be used to derive both the posterior probability of a mention exhibiting a given type and the conditional probability relations between types themselves. We compare our approach with a vector-based typing model and observe state-of-the-art performance on several entity typing benchmarks. In addition to competitive typing performance, our box-based model shows better performance in prediction consistency (predicting a supertype and a subtype together) and confidence (i.e., calibration), demonstrating that the box-based model captures the latent type hierarchies better than the vector-based model does.
Low-redshift Lyman-alpha absorption lines and the dark matter halos of disk galaxies

Ultraviolet observations of the low-redshift quasar 3C 273 using the Hubble Space Telescope have revealed many more Lyman-alpha absorption lines than would be expected from extrapolation of the absorption systems seen toward QSOs at z about 2. It is shown here that these absorption lines can plausibly be produced by gas at large radii in the disks of spiral and irregular galaxies; the gas is confined by the dark matter halos and ionized and heated by the extragalactic radiation field. This scenario does not require the extragalactic ionizing radiation field to decline as rapidly with decreasing z as the QSO emissivity. Observations of Ly-alpha absorption through the halos of known galaxies at low redshift will constrain both the extragalactic background and the properties of galactic halos.
Graph Cuts Always Find a Global Optimum for Potts Models (With a Catch)

We prove that the $\alpha$-expansion algorithm for MAP inference always returns a globally optimal assignment for Markov Random Fields with Potts pairwise potentials, with a catch: the returned assignment is only guaranteed to be optimal for an instance within a small perturbation of the original problem instance. In other words, all local minima with respect to expansion moves are global minima to slightly perturbed versions of the problem. On "real-world" instances, MAP assignments of small perturbations of the problem should be very similar to the MAP assignment(s) of the original problem instance. We design an algorithm that can certify whether this is the case in practice. On several MAP inference problem instances from computer vision, this algorithm certifies that MAP solutions to all of these perturbations are very close to solutions of the original instance. These results taken together give a cohesive explanation for the good performance of "graph cuts" algorithms in practice. Every local expansion minimum is a global minimum in a small perturbation of the problem, and all of these global minima are close to the original solution.
Tensile buckling of advanced turboprops

Theoretical studies were conducted to determine analytically the tensile buckling of advanced propeller blades (turboprops) in centrifugal fields, as well as the effects of tensile buckling on other types of structural behavior, such as resonant frequencies and flutter. Theoretical studies were also conducted to establish the advantages of using high performance composite turboprops as compared to titanium. Results show that the vibration frequencies are not affected appreciably prior to 80 percent of the tensile speed. Some frequencies approach zero as the tensile buckling speed is approached. Composites provide a substantial advantage over titanium on a buckling speed to weight basis. Vibration modes change as the rotor speed is increased and substantial geometric coupling is present.
Bilinear Classes: A Structural Framework for Provable Generalization in RL

This work introduces Bilinear Classes, a new structural framework, which permit generalization in reinforcement learning in a wide variety of settings through the use of function approximation. The framework incorporates nearly all existing models in which a polynomial sample complexity is achievable, and, notably, also includes new models, such as the Linear $Q^*/V^*$ model in which both the optimal $Q$-function and the optimal $V$-function are linear in some known feature space. Our main result provides an RL algorithm which has polynomial sample complexity for Bilinear Classes; notably, this sample complexity is stated in terms of a reduction to the generalization error of an underlying supervised learning sub-problem. These bounds nearly match the best known sample complexity bounds for existing models. Furthermore, this framework also extends to the infinite dimensional (RKHS) setting: for the the Linear $Q^*/V^*$ model, linear MDPs, and linear mixture MDPs, we provide sample complexities that have no explicit dependence on the explicit feature dimension (which could be infinite), but instead depends only on information theoretic quantities.
Decremental all-pairs shortest paths in deterministic near-linear time

We study the decremental All-Pairs Shortest Paths (APSP) problem in undirected edge-weighted graphs. The input to the problem is an undirected n-vertex m-edge graph G with non-negative lengths on edges, that undergoes an online sequence of edge deletions. The goal is to support approximate shortest-paths queries: given a pair x,y of vertices of G, return a path P connecting x to y, whose length is within factor α of the length of the shortest x-y path, in time O(|E(P)|), where α is the approximation factor of the algorithm. APSP is one of the most basic and extensively studied dynamic graph problems. A long line of work culminated in the algorithm of [Chechik, FOCS 2018] with near optimal guarantees: for any constant 0k≥ 1, the algorithm achieves approximation factor (2+є)k−1, and total update time O(mn1/k+o(1)log(nL)), where L is the ratio of longest to shortest edge lengths. Unfortunately, as much of prior work, the algorithm is randomized and needs to assume an oblivious adversary; that is, the input edge-deletion sequence is fixed in advance and may not depend on the algorithm’s behavior. In many real-world scenarios, and in applications of APSP to static graph problems, it is crucial that the algorithm works against an adaptive adversary, where the edge deletion sequence may depend on the algorithm’s past behavior arbitrarily; ideally, such an algorithm should be deterministic. Unfortunately, unlike the oblivious-adversary setting, its adaptive-adversary counterpart is still poorly understood. For unweighted graphs, the algorithm of [Henzinger, Krinninger and Nanongkai, FOCS ’13, SICOMP ’16] achieves a (1+є)-approximation with total update time O(mn/є); the best current total update time guarantee of n2.5+O(є) is achieved by the recent deterministic algorithm of [Chuzhoy, Saranurak, SODA’21], with 2O(1/є)-multiplicative and 2O(log3/4n/є)-additive approximation. To the best of our knowledge, for arbitrary non-negative edge weights, the fastest current adaptive-update algorithm has total update time O(n3logL/є), achieving a (1+є)-approximation. Even if we are willing to settle for any o(n)-approximation factor, no currently known algorithm has a better than Θ(n3) total update time in weighted graphs and better than Θ(n2.5) total update time in unweighted graphs. Several conditional lower bounds suggest that no algorithm with a sufficiently small approximation factor can achieve an o(n3) total update time. Our main result is a deterministic algorithm for decremental APSP in undirected edge-weighted graphs, that, for any Ω(1/loglogm)≤ єm)2O(1/є), with total update time O(m1+O(є)· (logm)O(1/є2)· logL). In particular, we obtain a (polylogm)-approximation in time O(m1+є) for any constant є, and, for any slowly growing function f(m), we obtain (logm)f(m)-approximation in time m1+o(1). We also provide an algorithm with similar guarantees for decremental Sparse Neighborhood Covers.
Multi-facet Contextual Bandits: A Neural Network Perspective

Contextual multi-armed bandit has shown to be an effective tool in recommender systems. In this paper, we study a novel problem of multi-facet bandits involving a group of bandits, each characterizing the users' needs from one unique aspect. In each round, for the given user, we need to select one arm from each bandit, such that the combination of all arms maximizes the final reward. This problem can find immediate applications in E-commerce, healthcare, etc. To address this problem, we propose a novel algorithm, named MuFasa, which utilizes an assembled neural network to jointly learn the underlying reward functions of multiple bandits. It estimates an Upper Confidence Bound (UCB) linked with the expected reward to balance between exploitation and exploration. Under mild assumptions, we provide the regret analysis of MuFasa. It can achieve the near-optimal O((K + 1) √T) regret bound where K is the number of bandits and T is the number of played rounds. Furthermore, we conduct extensive experiments to show that MuFasa outperforms strong baselines on real-world data sets.
An mRNA SARS-CoV-2 Vaccine Employing Charge-Altering Releasable Transporters with a TLR-9 Agonist Induces Neutralizing Antibodies and T Cell Memory

The SARS-CoV-2 pandemic has necessitated the rapid development of prophylactic vaccines. Two mRNA vaccines have been approved for emergency use by the FDA and have demonstrated extraordinary effectiveness. The success of these mRNA vaccines establishes the speed of development and therapeutic potential of mRNA. These authorized vaccines encode full-length versions of the SARS-CoV-2 spike protein. They are formulated with lipid nanoparticle (LNP) delivery vehicles that have inherent immunostimulatory properties. Different vaccination strategies and alternative mRNA delivery vehicles would be desirable to ensure flexibility of future generations of SARS-CoV-2 vaccines and the development of mRNA vaccines in general. Here, we report on the development of an alternative mRNA vaccine approach using a delivery vehicle called charge-altering releasable transporters (CARTs). Using these inherently nonimmunogenic vehicles, we can tailor the vaccine immunogenicity by inclusion of coformulated adjuvants such as oligodeoxynucleotides with CpG motifs (CpG-ODN). Mice vaccinated with the mRNA-CART vaccine developed therapeutically relevant levels of receptor binding domain (RBD)-specific neutralizing antibodies in both the circulation and in the lung bronchial fluids. In addition, vaccination elicited strong and long-lasting RBD-specific TH1 T cell responses including CD4+ and CD8+ T cell memory.
Training ELECTRA Augmented with Multi-word Selection

Pre-trained text encoders such as BERT and its variants have recently achieved state-of-the-art performances on many NLP tasks. While being effective, these pre-training methods typically demand massive computation resources. To accelerate pre-training, ELECTRA trains a discriminator that predicts whether each input token is replaced by a generator. However, this new task, as a binary classification, is less semantically informative. In this study, we present a new text encoder pre-training method that improves ELECTRA based on multi-task learning. Specifically, we train the discriminator to simultaneously detect replaced tokens and select original tokens from candidate sets. We further develop two techniques to effectively combine all pre-training tasks: (1) using attention-based networks for task-specific heads, and (2) sharing bottom layers of the generator and the discriminator. Extensive experiments on GLUE and SQuAD datasets demonstrate both the effectiveness and the efficiency of our proposed method.
To Point or Not to Point: Understanding How Abstractive Summarizers Paraphrase Text

Abstractive neural summarization models have seen great improvements in recent years, as shown by ROUGE scores of the generated summaries. But despite these improved metrics, there is limited understanding of the strategies different models employ, and how those strategies relate their understanding of language. To understand this better, we run several experiments to characterize how one popular abstractive model, the pointer-generator model of See et al. (2017), uses its explicit copy/generation switch to control its level of abstraction (generation) vs extraction (copying). On an extractive-biased dataset, the model utilizes syntactic boundaries to truncate sentences that are otherwise often copied verbatim. When we modify the copy/generation switch and force the model to generate, only simple paraphrasing abilities are revealed alongside factual inaccuracies and hallucinations. On an abstractive-biased dataset, the model copies infrequently but shows similarly limited abstractive abilities. In line with previous research, these results suggest that abstractive summarization models lack the semantic understanding necessary to generate paraphrases that are both abstractive and faithful to the source document.
RetroGAN: A Cyclic Post-Specialization System for Improving Out-of-Knowledge and Rare Word Representations

Retrofitting is a technique used to move word vectors closer together or further apart in their space to reflect their relationships in a Knowledge Base (KB). However, retrofitting only works on concepts that are present in that KB. RetroGAN uses a pair of Generative Adversarial Networks (GANs) to learn a one-to-one mapping between concepts and their retrofitted counterparts. It applies that mapping (post-specializes) to handle concepts that do not appear in the original KB in a manner similar to how some natural language systems handle out-of-vocabulary entries. We test our system on three word-similarity benchmarks and a downstream sentence simplification task and achieve the state of the art (CARD-660). Altogether, our results demonstrate our system's effectiveness for out-of-knowledge and rare word generalization.
Fraternity Membership and Negative Racial Attitudes among U.S. College Students

Over the years, race-related incidents involving Greek letter organizations (GLOs), especially fraternities, continue to arise despite the ensuing controversy and negative media attention that inevitably follow such incidents. The present study examines whether there is an association between GLO membership and negative racial attitudes given a range of relevant psychological, sociological, and political theories about racial attitudes. Building on insights from microclass research on attitudinal structuring and drawing on the National Longitudinal Study of Freshmen (N=2817), results of linear regression models indicate that GLO membership is associated with heightened negative racial attitudes, particularly among white male students. Moreover, a measure of racial group affiliation partially mediates this effect. This latter finding suggests that among the various racial attitude theories, group position theory may be better formulated to explain an association between GLO membership and negative racial attitudes, given its consideration of both in-group boundary formation and organizations. The results also support microclass processes and suggest future research to more clearly account for pre-occupational structuring that later feed into occupational processes.
The Limits of Multi-task Peer Prediction

Recent advances in multi-task peer prediction have greatly expanded our knowledge about the power of multi-task peer prediction mechanisms. Various mechanisms have been proposed in different settings to elicit different types of information. But we still lack understanding about when desirable mechanisms will exist for a multi-task peer prediction problem. In this work, we study the elicitability of multi-task peer prediction problems. We consider a designer who has certain knowledge about the underlying information structure and wants to elicit certain information from a group of participants. Our goal is to infer the possibility of having a desirable mechanism based on the primitives of the problem. Our contribution is twofold. First, we provide a characterization of the elicitable multi-task peer prediction problems, assuming that the designer only uses scoring mechanisms. Scoring mechanisms are the mechanisms that reward participants' reports for different tasks separately. The characterization uses a geometric approach based on the power diagram characterization in the single-task setting. For general mechanisms, we also give a necessary condition for a multi-task problem to be elicitable. Second, we consider the case when the designer aims to elicit some properties that are linear in the participant's posterior about the state of the world. We first show that in some cases, the designer basically can only elicit the posterior itself. We then look into the case when the designer aims to elicit the participants' posteriors. We give a necessary condition for the posterior to be elicitable. This condition implies that the mechanisms proposed by Kong and Schoenebeck are already the best we can hope for in their setting, in the sense that their mechanisms can solve any problem instance that can possibly be elicitable.
Computer modeling of thermoelectric generator performance

Features of the DEGRA 2 computer code for simulating the operations of a spacecraft thermoelectric generator are described. The code models the physical processes occurring during operation. Input variables include the thermoelectric couple geometry and composition, the thermoelectric materials' properties, interfaces and insulation in the thermopile, the heat source characteristics, mission trajectory, and generator electrical requirements. Time steps can be specified and sublimation of the leg and hot shoe is accounted for, as are shorts between legs. Calculations are performed for conduction, Peltier, Thomson, and Joule heating, the cold junction can be adjusted for solar radition, and the legs of the thermoelectric couple are segmented to enhance the approximation accuracy. A trial run covering 18 couple modules yielded data with 0.3% accuracy with regard to test data. The model has been successful with selenide materials, SiGe, and SiN4, with output of all critical operational variables.
Generation of pancreatic progenitors from human pluripotent stem cells by small molecules.

Summary Human pluripotent stem cell (hPSC)-derived pancreatic progenitors (PPs) provide promising cell therapies for type 1 diabetes. Current PP differentiation requires a high amount of Activin A during the definitive endoderm (DE) stage, making it economically difficult for commercial ventures. Here we identify a dose-dependent role for Wnt signaling in controlling DE differentiation without Activin A. While high-level Wnt activation induces mesodermal formation, low-level Wnt activation by a small-molecule inhibitor of glycogen synthase kinase 3 is sufficient for DE differentiation, yielding SOX17+FOXA2+ DE cells. BMP inhibition further enhances this DE differentiation, generating over 87% DE cells. These DE cells could be further differentiated into PPs and functional β cells. RNA-sequencing analysis of PP differentiation from hPSCs revealed expected transcriptome dynamics and new gene regulators during our small-molecule PP differentiation protocol. Overall, we established a robust growth-factor-free protocol for generating DE and PP cells, facilitating scalable production of pancreatic cells for regenerative applications.
A note on the positive mass theorem with boundary

In this short note we explain how one can use established results to prove various versions of the positive mass theorem for initial data sets with boundary, in dimensions less than 8.
Mimicking the Mad Printer: Legislating Illiberalism in Post-Soviet Eurasia

Illiberal policy innovation has grown in post-Soviet Eurasia over the 2010s, especially regarding controversial moral and cultural issues. These have often been developed by illiberal entrepreneur ...
Continuous evolution of electromagnetic field in heavy-ion collisions

Abstract In relativistic heavy-ion collisions the electromagnetic field exists before the hot nuclear matter emergence. Requiring the field continuity we compute it in the central rapidity region by taking into account the electromagnetic response of the Quark Gluon Plasma. We show that the electromagnetic field is nearly time-independent from about 1 fm/c after the collision until the freezeout. In particular, its value is about 10% of the initial magnitude of the magnetic field. Our result provides a theoretical basis for the common assumption that the electromagnetic field produced in relativistic heavy-ion collisions varies slowly with time.
Integrated random projection and dimensionality reduction by propagating light in photonic lattices.

It is proposed that the propagation of light in disordered photonic lattices can be harnessed as a random projection that preserves distances between a set of projected vectors. This mapping is enabled by the complex evolution matrix of a photonic lattice with diagonal disorder, which turns out to be a random complex Gaussian matrix. Thus, by collecting the output light from a random subset of the waveguide channels, one can perform an embedding from a higher- to a lower-dimensional space that respects the Johnson–Lindenstrauss lemma and nearly preserves the Euclidean distances. The distance-preserving random projection through photonic lattices requires intermediate disorder levels that allow diffusive propagation of light. The proposed scheme can be utilized as a simple and powerful integrated dimension reduction stage that can greatly reduce the burden of a subsequent neural computation stage.
The Average-Case Time Complexity of Certifying the Restricted Isometry Property

In compressed sensing, the restricted isometry property (RIP) on $M \times N$ sensing matrices (where $M ) guarantees efficient reconstruction of sparse vectors. A matrix has the $(s,\delta)$ - $\mathsf {RIP}$ property if behaves as a $\delta $ -approximate isometry on $s$ -sparse vectors. It is well known that an $M\times N$ matrix with i.i.d. $\mathcal {N}(0,1/M)$ entries is $(s,\delta)$ - $\mathsf {RIP}$ with high probability as long as $s\lesssim \delta ^{2}~M/\log N$ . On the other hand, most prior works aiming to deterministically construct $(s,\delta)$ - $\mathsf {RIP}$ matrices have failed when $s \gg \sqrt {M}$ . An alternative way to find an RIP matrix could be to draw a random gaussian matrix and certify that it is indeed RIP. However, there is evidence that this certification task is computationally hard when $s \gg \sqrt {M}$ , both in the worst case and the average case. In this paper, we investigate the exact average-case time complexity of certifying the RIP property for $M\times N$ matrices with i.i.d. $\mathcal {N}(0,1/M)$ entries, in the “possible but hard” regime $\sqrt {M} \ll s\lesssim M/\log N$ . Based on analysis of the low-degree likelihood ratio, we give rigorous evidence that subexponential runtime $N^{\tilde \Omega (s^{2}/M)}$ is required, demonstrating a smooth tradeoff between the maximum tolerated sparsity and the required computational power. This lower bound is essentially tight, matching the runtime of an existing algorithm due to Koiran and Zouzias. Our hardness result allows $\delta $ to take any constant value in (0, 1), which captures the relevant regime for compressed sensing. This improves upon the existing average-case hardness result of Wang et al. , which is limited to $\delta = o(1)$ .
People-focused and Near-term Public Transit Performance Analysis

Public transit ridership forecasts have long played a role in understanding the potential success of a policy or investment, but their limitations have led researchers and practitioners to identify other performance analysis approaches. Accessibility, or the ease of reaching opportunities, has become very popular and widely used for this purpose. But commonly used accessibility measures also embody weaknesses that are seldom acknowledged; these limit their utility for truly understanding the benefits of transit investments. In this paper, we identify the pros and cons of these competing approaches and offer a third strategy. Specifically, we describe how revealed travel behavior data, potentially combined with near-term forecasts, can provide information about how current public transit users will be affected by a new project. While acknowledging the limitations of this approach, we demonstrate how accessibility can be misleading when applied without an understanding of ridership patterns.
Online Flocking Control of UAVs with Mean-Field Approximation

We present a novel approach to the formation controlling of aerial robot swarms that demonstrates the flocking behavior. The proposed method stems from the Unmanned Aerial Vehicle (UAV) dynamics; thus, it prevents any unattainable control inputs from being produced and subsequently leads to feasible trajectories. By modeling the inter-agent relationships using a pairwise energy function, we show that interacting robot swarms constitute a Markov Random Field. Our algorithm builds on the Mean-Field Approximation and incorporates the collective behavioral rules: cohesion, separation, and velocity alignment. We follow a distributed control scheme and show that our method can control a swarm of UAVs to a formation and velocity consensus with real-time collision avoidance. We validate the proposed method with physical and high-fidelity simulation experiments.
Machine learning for physics-informed generation of dispersed multiphase flow using generative adversarial networks

Fluid flow around a random distribution of stationary spherical particles is a problem of substantial importance in the study of dispersed multiphase flows. In this paper, we present a machine learning methodology using generative adversarial network framework and convolutional neural network architecture to recreate particle-resolved fluid flow around a random distribution of monodispersed particles. The model was applied to various Reynolds number and particle volume fraction combinations spanning over a range of [2.69, 172.96] and [0.11, 0.45], respectively. Test performance of the model for the studied cases is very promising.