cssi/SciSciGPT-SciSciNet huggingface dataset:
MEANING OF ‘MORE RISK AVERSE’ WHEN PREFERENCES ARE OVER MEAN AND VARIANCE

Building upon the intuition of Ross, a definition of more risk averse is proposed for situations in which preferences are over mean and variance. If agents can be compared by this definition, the more risk averse agent will choose a less risky alternative. If this definition cannot be applied, it is not clear which agent will choose a riskier alternative. The definition applies whenever agents are ordered according to Ross's notion of more risk averse. The definition may or may not be consistent with the Arrow–Pratt notion of more risk averse (and therefore may apply when Ross's notion does not).
The Effect of Choice in Physical Education on Time on Task for Students with Emotional and Behavioral Disorders

Research indicates that exercise has a positive effect on the behavior of individuals with emotional and behavioral disorders (EBDs). In order to realize this benefit, the students must be engaged in the physical activity, maintaining high time on task (TOT). Management of students with EBDs, especially large groups of these students, maintaining high TOT and engagement, can be challenging in the physical education (PE) environment. Given the research indicating the positive influence choice in PE has on student engagement, a study examining the effect of choice in PE on TOT among students with emotional and behavioral disorders was conducted. The research question examined was the following: Is there a difference in mean TOT scores for students with EBDs in a PE class in a choice setting compared to a no-choice setting? Results of the independent samples t-test indicated a statistically significant difference in mean TOT scores among students with EBDs in a choice PE setting compared to a no-choice PE setting. Examples of ways choice can be incorporated in PE are provided.
To blame or not to blame: Analysts’ reactions to external explanations for poor financial performance

Abstract Managers often provide self-serving disclosures that blame poor financial performance on temporary external factors. Results of an experiment conducted with 124 financial analysts suggest that when analysts perceive such disclosures as plausible, they provide higher earnings forecasts and stock valuations than if the explanation had not been provided. However, we also show that these disclosures can backfire if analysts find them implausible. Specifically, implausible explanations that blame poor performance on temporary external factors lead analysts to provide lower earnings forecasts and assess a higher cost of capital than if the explanation had not been provided.
Federalism From Federal Statutes: Health Reform, Medicaid, and the Old-Fashioned Federalists’ Gamble

How can the states retain relevance in an era of federal statutory law? The persistence of the states and our enduring attachment to “federalism” in an increasingly national and global regulatory environment has occupied the minds of many scholars.1 For the most part, however, the U.S. Supreme Court, because of its role as the final expositor of constitutional meaning, has been viewed as the primary arbiter of what federalism is and what is required to protect it. Less often explored has been Congress’s role in giving meaning to federalism in the modern administrative state.2 Specifically, the possibility to which this Essay wishes to draw attention is that federal statutes may now be the primary way in which state power is created and protected. To be clear, the claim is not about federal statutes that are modest in ambition and leave most areas exclusively to state regulation. Rather, the claim is about major federal statutes that, even as they extend federal power, entrust to the states much of their implementation and elaboration. The 2010 health reform legislation—The Patient Protection and Affordable Care Act3 (ACA)—is the most prominent recent example of
"True Threats" and the Issue of Intent

What mens rea, if any, is required for threatening speech to be constitutionally criminalized? Must the speaker have intended for his communication to be threatening? In Virginia v. Black (2003), the Supreme Court for the first time provided a definition of "true threats," a categorical exception to the First Amendment. However, the Court failed to clearly answer the above questions. As a result, lower courts have interpreted Black's language to reach contradictory results regarding the issue of intent. Consequently, speech that is constitutionally protected in one jurisdiction may be criminalized in another. Such controversy and confusion is not new to the jurisprudence. Since the Court's decision in Watts v. United States (1969), which created the "true threats" exception, lower courts have disagreed over the proper intent standard for threatening speech. Instead of clarifying the doctrine, the Court's decision in Black has only served to further muddy the jurisprudence. When Black was decided, the overwhelming majority of courts had settled on an objective intent standard; however, the Court's language in Black has cast some doubt on the propriety of that approach. This Note will examine the issue of intent and its disputed place in the true threats jurisprudence from the debate's inception to the present day. Moreover, the impact of Black will be elucidated and explored. Finally, this Note suggests that when the Court addresses the issue of intent again, it should adopt a standard that requires the government to prove that the speaker intended for his communication to be threatening.
Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance

In this paper, we propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties for safer navigation through cluttered environments. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates through each step of our MPC’s finite time horizon. The RNN model is trained on a dataset that comprises of robot and landmark poses generated from camera images and inertial measurement unit (IMU) readings via a state-of-the-art visualinertial odometry framework. To detect and extract object locations for avoidance, we use a custom-trained convolutional neural network model in conjunction with a feature extractor to retrieve 3D centroid and radii boundaries of nearby obstacles. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms, demonstrating autonomous behaviors that can plan fast and collision-free paths towards a goal point.
The Chow cohomology of affine toric varieties

We study the Fulton-Macpherson Chow cohomology of affine toric varieties. In particular, we prove that the Chow cohomology vanishes in positive degree. We prove an analogous result for the operational $K$-theory defined by Anderson and Payne.
Microscopic conservation laws for integrable lattice models

We consider two discrete completely integrable evolutions: the Toda Lattice and the Ablowitz–Ladik system. The principal thrust of the paper is the development of microscopic conservation laws that witness the conservation of the perturbation determinant under these dynamics. In this way, we obtain discrete analogues of objects that we found essential in our recent analyses of KdV, NLS, and mKdV. In concert with this, we revisit the classical topic of microscopic conservation laws attendant to the (renormalized) trace of the Green’s function.
Polynomial-Time Trace Reconstruction in the Low Deletion Rate Regime

In the trace reconstruction problem, an unknown source string x ∈ {0,1}ⁿ is transmitted through a probabilistic deletion channel which independently deletes each bit with some fixed probability δ and concatenates the surviving bits, resulting in a trace of x. The problem is to reconstruct x given access to independent traces. Trace reconstruction of arbitrary (worst-case) strings is a challenging problem, with the current state of the art for poly(n)-time algorithms being the 2004 algorithm of Batu et al. [T. Batu et al., 2004]. This algorithm can reconstruct an arbitrary source string x ∈ {0,1}ⁿ in poly(n) time provided that the deletion rate δ satisfies δ ≤ n^{-(1/2 + e)} for some e > 0. In this work we improve on the result of [T. Batu et al., 2004] by giving a poly(n)-time algorithm for trace reconstruction for any deletion rate δ ≤ n^{-(1/3 + e)}. Our algorithm works by alternating an alignment-based procedure, which we show effectively reconstructs portions of the source string that are not "highly repetitive", with a novel procedure that efficiently determines the length of highly repetitive subwords of the source string.
Censorship of Online Encyclopedias: Implications for NLP Models

While artificial intelligence provides the backbone for many tools people use around the world, recent work has brought to attention that the algorithms powering AI are not free of politics, stereotypes, and bias. While most work in this area has focused on the ways in which AI can exacerbate existing inequalities and discrimination, very little work has studied how governments actively shape training data. We describe how censorship has affected the development of Wikipedia corpuses, text data which are regularly used for pre-trained inputs into NLP algorithms. We show that word embeddings trained on Baidu Baike, an online Chinese encyclopedia, have very different associations between adjectives and a range of concepts about democracy, freedom, collective action, equality, and people and historical events in China than its regularly blocked but uncensored counterpart - Chinese language Wikipedia. We examine the implications of these discrepancies by studying their use in downstream AI applications. Our paper shows how government repression, censorship, and self-censorship may impact training data and the applications that draw from them.
The RED scheme: Rate-constant estimation from pre-steady state weighted ensemble simulations.

We present the Rate from Event Durations (RED) scheme, a new scheme that more efficiently calculates rate constants using the weighted ensemble path sampling strategy. This scheme enables rate-constant estimation from shorter trajectories by incorporating the probability distribution of event durations, or barrier-crossing times, from a simulation. We have applied the RED scheme to weighted ensemble simulations of a variety of rare-event processes that range in complexity: residue-level simulations of protein conformational switching, atomistic simulations of Na+/Cl− association in explicit solvent, and atomistic simulations of protein–protein association in explicit solvent. Rate constants were estimated with up to 50% greater efficiency than the original weighted ensemble scheme. Importantly, our scheme accounts for the systematic error that results from statistical bias toward the observation of events with short durations and reweights the event duration distribution accordingly. The RED scheme is relevant to any simulation strategy that involves unbiased trajectories of similar length to the most probable event duration, including weighted ensemble, milestoning, and standard simulations as well as the construction of Markov state models.
Interfacial area transport for bubbly-to-slug transition flows in small diameter pipes

Abstract This study aims to experimentally investigate the two-group interfacial area transport in small diameter pipes. Experimental data focusing on the bubbly to slug transition regime, namely one-group to two-group transport region, are collected in a 12.7 mm vertical pipe under adiabatic, air-water conditions. The result shows the intergroup transfer in the small diameter pipe can be drastic, especially under low superficial liquid velocities. The cause of this phenomenon is mainly due to the large relative bubble size comparing to the pipe cross-sectional area. The wake entrainment effect could be enhanced by the small spherical bubbles that are acting like cap or slug bubbles in a medium-size pipe. Based on the experimental observation, a throughout analysis of the dependence of the drastic intergroup transfer is provided in this study. The models predicting the initiation of drastic intergroup transfer in small diameter pipes in terms of the bubble diameter and the void fraction are developed. These models are compared with the two-phase data among the different pipe sizes and the results show a good agreement. These newly developed models are applied to the IATE wake entrainment model by developing a transition function analogous to the sigmoid function. With the transition function, the revised IATE model is given the new ability on predicting the drastic intergroup transfer phenomenon.
Light pollution affects West Nile virus exposure risk across Florida.

Emerging infectious diseases (EIDs) present global health threats, and their emergences are often linked to anthropogenic change. Artificial light at night (ALAN) is one form of anthropogenic chang...
Morphometrical Brain Markers of Sex Difference.

Many major neuropsychiatric pathologies, some of which appear in adolescence, show differentiated prevalence, onset, and symptomatology across the biological sexes. Therefore, mapping differences in brain structure between males and females during this critical developmental period may provide information about the neural mechanisms underlying the dimorphism of these pathologies. Utilizing a large dataset collected through the Adolescent Brain Cognitive Development study, we investigated the differences of adolescent (9-10 years old) male and female brains (n = 8325) by using a linear Support-Vector Machine Classifier to predict sex based on morphometry and image intensity values of structural brain imaging data. The classifier correctly classified the sex of 86% individuals with the insula, the precentral and postcentral gyri, and the pericallosal sulcus as the most discernable features. These results demonstrate the existence of complex, yet robustly measurable morphometrical brain markers of sex difference.
Semidefinite Programming Relaxations of the Traveling Salesman Problem and Their Integrality Gaps

The traveling salesman problem (TSP) is a fundamental problem in combinatorial optimization. Several semidefinite programming relaxations have been proposed recently that exploit a variety of mathe...
Cell Phone Access and Election Fraud: Evidence from a Spatial Regression Discontinuity Design in Afghanistan

This paper examines the impact of cell phone access on election fraud. I combine cell phone coverage maps with the location of polling centers during the 2009 Afghan presidential election to pinpoint which centers were exposed to coverage. Results from a spatial regression discontinuity design along the two-dimensional coverage boundary suggest that coverage deters corrupt behavior. Polling centers just inside coverage report a drop in the share of fraudulent votes of 4 percentage points, while the likelihood of a fraudulent station decreases by 8 percentage points. Analyses of the effect of coverage on citizen participation in election monitoring, election-related insurgent violence, and the tribal composition of villages suggest that the observed declines in fraud are likely attributed to cell phone access strengthening social monitoring capacity.
Vorticity and particle polarization in heavy ion collisions (experimental perspective)

The recent measurements of the global polarization and vector meson spin alignment along the system orbital momentum in heavy ion collisions are briefly reviewed. A possible connection between the global polarization and the chiral anomalous effects is discussed along with possible experimental checks. Future directions, in particular those aimed on the detailed mapping of the vorticity fields, are outlined. The Blast Wave model is used for an estimate of the anisotropic flow effect on the vorticity component along the beam direction. We also point to a possibility of a circular pattern in the vorticity field in asymmetric, e.g. Cu+Au, central collisions.
Positive Configuration Space

We define and study the totally nonnegative part of the Chow quotient of the Grassmannian, or more simply the nonnegative configuration space. This space has a natural stratification by positive Chow cells, and we show that nonnegative configuration space is homeomorphic to a polytope as a stratified space. We establish bijections between positive Chow cells and the following sets: (a) regular subdivisions of the hypersimplex into positroid polytopes, (b) the set of cones in the positive tropical Grassmannian, and (c) the set of cones in the positive Dressian. Our work is motivated by connections to super Yang–Mills scattering amplitudes, which will be discussed in a sequel.
Cascade Attribute Network: Decomposing Reinforcement Learning Control Policies using Hierarchical Neural Networks

Abstract Reinforcement learning methods have been developed to achieve great success in training control policies in various automation tasks. However, a main challenge of the wider application of reinforcement learning in practical automation is that the training process is hard and the pretrained policy networks are hardly reusable in other similar cases. To address this problem, we propose the cascade attribute network (CAN), which utilizes its hierarchical structure to decompose a complicated control policy in terms of the requirement constraints, which we call attributes, encoded in the control tasks. We validated the effectiveness of our proposed method on two robot control scenarios with various add-on attributes. For some control tasks with more than one add-on attribute attribute, by directly assembling the attribute modules in cascade, the CAN can provide ideal control policies in a zero-shot manner.
Comparing workflow application designs for high resolution satellite image analysis

Abstract Very High Resolution satellite and aerial imagery are used to monitor and conduct large scale surveys of ecological systems. Convolutional Neural Networks have successfully been employed to analyze such imagery to detect large animals and salient features. As the datasets increase in volume and number of images, utilizing High Performance Computing resources becomes necessary. In this paper, we investigate three task-parallel, data-driven workflow designs to support imagery analysis pipelines with heterogeneous tasks on high performance computing platforms. We analyze the capabilities of each design when processing 3097 and 1575 images for two distinct use cases, for a total of 4,672 satellite and aerial images and 8.35 TB of data. We experimentally model the execution time of the tasks of the image processing pipelines. We perform experiments to characterize resource utilization, total time to completion and overheads of each design. Our analysis shows which design is best suited to scientific pipelines with similar characteristics.
A tale of "second chances": an experimental examination of popular support for early release mechanisms that reconsider long-term prison sentences.

This study examines US popular support for mechanisms that provide early release and “second chances” for individuals serving long-term prison sentences. An experiment using a national sample of US adults (N=836). Data showed moderate, consistent levels of general support for using a range of commonly available “second chance” mechanisms that also extended to offenders convicted of both violent and non-violent offenses. Levels of support significantly varied by race, gender, and age. There was significantly more support for using certain mechanisms in response to the trafficking of serious drugs, which was fully mediated by participants’ views on the importance of the cost of incarceration. Members of the public appear open and supportive to utilizing “second chance” mechanisms in a variety of contexts. Yet the cost of incarceration to taxpayers appears to particularly motivate increased public interest in using such mechanisms for offenders convicted of the trafficking of serious drugs.
In Vivo Brain Glutathione is Higher in Older Age and Correlates with Mobility.

Brain markers of oxidative damage increase with advancing age. In response, brain antioxidant levels may also increase with age, although this has not been well investigated. Here, we used edited magnetic resonance spectroscopy to quantify endogenous levels of glutathione (GSH, one of the most abundant brain antioxidants) in 37 young [mean: 21.8 (2.5) years; 19 female] and 23 older adults [mean: 72.8 (8.9) years; 19 female]. Accounting for age-related atrophy, we identified higher frontal and sensorimotor GSH levels for the older compared with the younger adults. For the older adults only, higher sensorimotor (but not frontal) GSH was correlated with poorer balance and gait. This suggests a regionally specific relationship between higher brain oxidative stress levels and motor performance declines with age. We suggest these findings reflect an upregulation of GSH in response to increasing brain oxidative stress with normal aging. Together, these results provide insight into age differences in brain antioxidant levels and implications for motor function.
Exact transcript quantification over splice graphs.

The probability of sequencing a set of RNA-seq reads can be directly modeled using the abundances of splice junctions in splice graphs instead of the abundances of a list of transcripts. We call this model graph quantification, which was first proposed by Bernard et al. (Bioinformatics 30:2447–55, 2014). The model can be viewed as a generalization of transcript expression quantification where every full path in the splice graph is a possible transcript. However, the previous graph quantification model assumes the length of single-end reads or paired-end fragments is fixed. We provide an improvement of this model to handle variable-length reads or fragments and incorporate bias correction. We prove that our model is equivalent to running a transcript quantifier with exactly the set of all compatible transcripts. The key to our method is constructing an extension of the splice graph based on Aho-Corasick automata. The proof of equivalence is based on a novel reparameterization of the read generation model of a state-of-art transcript quantification method. We propose a new approach for graph quantification, which is useful for modeling scenarios where reference transcriptome is incomplete or not available and can be further used in transcriptome assembly or alternative splicing analysis.
Mortgage Contracts and Underwater Default

We analyze recently proposed mortgage contracts that aim to eliminate selective borrower default when the loan balance exceeds the house price (the ``underwater'' effect). We show contracts that automatically reduce the outstanding balance in the event of house price decline remove the default incentive, but may induce prepayment in low price states. However, low state prepayments vanish if the benefit from home ownership is sufficiently high. We also show that capital gain sharing features, such as prepayment penalties in high house price states, are ineffective as they virtually eliminate prepayment. For observed foreclosure costs, we find that contracts with automatic balance adjustments become preferable to the traditional fixed-rate contracts at mortgage rate spreads between 50-100 basis points. We obtain these results for perpetual versions of the contracts using American options pricing methodology, in a continuous-time model with diffusive home prices. The contracts' values and optimal decision rules are associated with free boundary problems, which admit semi-explicit solutions.
Environmental DNA Metabarcoding for Simultaneous Monitoring and Ecological Assessment of Many Harmful Algae

Harmful algae can have profound economic, environmental, and social consequences. As the timing, frequency, and severity of harmful algal blooms (HABs) change alongside global climate, efficient tools to monitor and understand the current ecological context of these taxa are increasingly important. Here we employ environmental DNA metabarcoding to identify patterns in a wide variety of potentially harmful algae and associated ecological communities in the Hood Canal of Puget Sound in Washington State, USA. Tracking trends of occurrence in a series of water samples over a period of 19 months, we find algal sequences from genera with harmful members in a majority of samples, suggesting that these groups are routinely present in local waters. We report patterns in variants of the economically important genus Pseudo-nitzschia (of which some members produce domoic acid; family Bacillariaceae), as well as multiple potentially harmful algal taxa previously unknown or poorly documented in the region, including a cold-water variant from the genus Alexandrium (of which some members produce saxitoxin; family Gonyaulacaceae), two variants from the genus Karlodinium (of which some members produce karlotoxins; family Kareniaceae), and one variant from the parasitic genus Hematodinium (family Syndiniaceae). We then use data on environmental variables and the biological community surrounding each algal taxon to illustrate the ecological context in which they are commonly found. Environmental DNA metabarcoding thus simultaneously (1) alerts us to potential new or cryptic occurrences of algae from harmful genera, (2) expands our knowledge of the co-occurring conditions and species associated with the growth of these organisms in changing marine environments, and (3) suggests a pathway for multispecies monitoring and management moving forward.
Subchondral Bone Length in Knee Osteoarthritis: A Deep Learning-Derived Imaging Measure and Its Association With Radiographic and Clinical Outcomes.

OBJECTIVE To develop a bone shape measure that reflects the extent of cartilage loss and bone flattening in knee osteoarthritis (OA) and test it against estimates of disease severity. METHODS A fast region-based convolutional neural network was trained to crop the knee joints in sagittal dual-echo steady-state magnetic resonance imaging sequences obtained from the Osteoarthritis Initiative (OAI). Publicly available annotations of the cartilage and menisci were used as references to annotate the tibia and the femur in 61 knees. Another deep neural network (U-Net) was developed to learn these annotations. Model predictions were compared to radiologist-driven annotations on an independent test set (27 knees). The U-Net was applied to automatically extract the knee joint structures on the larger OAI data set (n = 9,434 knees). We defined subchondral bone length (SBL), a novel shape measure characterizing the extent of overlying cartilage and bone flattening, and examined its relationship with radiographic joint space narrowing (JSN), concurrent pain and disability (according to the Western Ontario and McMaster Universities Osteoarthritis Index), as well as subsequent partial or total knee replacement. Odds ratios (ORs) and 95% confidence intervals (95% CIs) for each outcome were estimated using relative changes in SBL from the OAI data set stratified into quartiles. RESULTS The mean SBL values for knees with JSN were consistently different from knees without JSN. Greater changes of SBL from baseline were associated with greater pain and disability. For knees with medial or lateral JSN, the ORs for future knee replacement between the lowest and highest quartiles corresponding to SBL changes were 5.68 (95% CI 3.90-8.27) and 7.19 (95% CI 3.71-13.95), respectively. CONCLUSION SBL quantified OA status based on JSN severity and shows promise as an imaging marker in predicting clinical and structural OA outcomes.
Generalization of the Tavis–Cummings model for multi-level anharmonic systems

The interaction between anharmonic quantum emitters (e.g., molecular vibrations) and confined electromagnetic fields gives rise to quantum states with optical and chemical properties that are different from those of their precursors. The exploration of these properties has been typically constrained to the first excitation manifold, the harmonic approximation, ensembles of two-level systems [Tavis-Cummings (TC) model], or the anharmonic single-molecule case. The present work studies, for the first time, a collective ensemble of identical multi-level anharmonic emitters and their dipolar interaction with a photonic cavity mode. The permutational properties of the system allow identifying symmetry classified submanifolds in the energy spectrum. Notably, in this approach, the number of particles, typically in the order of several millions, becomes only a parameter from the operational standpoint, and the size of the dimension of the matrices to diagonalize is independent of it. The formalism capabilities are illustrated by showing the energy spectrum structure, up to the third excitation manifold, and the calculation of the photon contents as a permutationally invariant quantity. Emphasis is placed on (a) the collective (superradiant) scalings of light-matter couplings and the various submanifolds of dark (subradiant) states with no counterpart in the single-molecule case, as well as (b) the delocalized modes containing more than one excitation per molecule with no equivalent in the TC model. We expect these findings to be applicable in the study of non-linear spectroscopy and chemistry of polaritons.
Integrative learning for population of dynamic networks with covariates.

Abstract Although there is a rapidly growing literature on dynamic connectivity methods, the primary focus has been on separate network estimation for each individual, which fails to leverage common patterns of information. We propose novel graph-theoretic approaches for estimating a population of dynamic networks that are able to borrow information across multiple heterogeneous samples in an unsupervised manner and guided by covariate information. Specifically, we develop a Bayesian product mixture model that imposes independent mixture priors at each time scan and uses covariates to model the mixture weights, which results in time-varying clusters of samples designed to pool information. The computation is carried out using an efficient Expectation-Maximization algorithm. Extensive simulation studies illustrate sharp gains in recovering the true dynamic network over existing dynamic connectivity methods. An analysis of fMRI block task data with behavioral interventions reveal sub-groups of individuals having similar dynamic connectivity, and identifies intervention-related dynamic network changes that are concentrated in biologically interpretable brain regions. In contrast, existing dynamic connectivity approaches are able to detect minimal or no changes in connectivity over time, which seems biologically unrealistic and highlights the challenges resulting from the inability to systematically borrow information across samples.
Delving into Deep Imbalanced Regression

Real-world data often exhibit imbalanced distributions, where certain target values have significantly fewer observations. Existing techniques for dealing with imbalanced data focus on targets with categorical indices, i.e., different classes. However, many tasks involve continuous targets, where hard boundaries between classes do not exist. We define Deep Imbalanced Regression (DIR) as learning from such imbalanced data with continuous targets, dealing with potential missing data for certain target values, and generalizing to the entire target range. Motivated by the intrinsic difference between categorical and continuous label space, we propose distribution smoothing for both labels and features, which explicitly acknowledges the effects of nearby targets, and calibrates both label and learned feature distributions. We curate and benchmark large-scale DIR datasets from common real-world tasks in computer vision, natural language processing, and healthcare domains. Extensive experiments verify the superior performance of our strategies. Our work fills the gap in benchmarks and techniques for practical imbalanced regression problems. Code and data are available at this https URL.
Incidence monoids: automorphisms and complexity

The algebraic monoid structure of an incidence algebra is investigated. We show that the multiplicative structure alone determines the algebra automorphisms of the incidence algebra. We present a formula that expresses the complexity of the incidence monoid with respect to the two sided action of its maximal torus in terms of the zeta polynomial of the poset. In addition, we characterize the finite (connected) posets whose incidence monoids have complexity $$\le 1$$ . Finally, we determine the covering relations of the adherence order on the incidence monoid of a star poset.
Active Covering

We analyze the problem of active covering, where the learner is given an unlabeled dataset and can sequentially label query examples. The objective is to label query all of the positive examples in the fewest number of total label queries. We show under standard non-parametric assumptions that a classical support estimator can be repurposed as an offline algorithm attaining an excess query cost of $\widetilde{\Theta}(n^{D/(D+1)})$ compared to the optimal learner, where $n$ is the number of datapoints and $D$ is the dimension. We then provide a simple active learning method that attains an improved excess query cost of $\widetilde{O}(n^{(D-1)/D})$. Furthermore, the proposed algorithms only require access to the positive labeled examples, which in certain settings provides additional computational and privacy benefits. Finally, we show that the active learning method consistently outperforms offline methods as well as a variety of baselines on a wide range of benchmark image-based datasets.
Implementing NEPA in the Age of Climate Change

The federal government has a crucial role to play in combating climate change, yet federal projects continue to constitute a major source of United States greenhouse gas emissions. Under the National Environmental Policy Act (“NEPA”), agencies must consider the environmental impacts of major federal actions before they can move forward. But agencies frequently downplay or ignore the climate change impacts of their projects in NEPA analyses, citing a slew of technical difficulties and uncertainties. This Article analyzes a suite of the most common analytical failures on the part of agencies with respect to climate change: failure to account for a project’s downstream and upstream greenhouse gas emissions; failure to acknowledge a project’s effect on the country’s energy mix; and failure to consider a reasonable social cost of carbon. After summarizing current regulatory practice and case law on each topic, this Article finds that despite protestations that accounting for such impacts is infeasible, agencies already possess many of the tools needed to assess such impacts, and indeed, some agencies already use these tools to do so. Furthermore, courts are increasingly holding agencies accountable for a full and fair assessment of climate change effects in NEPA analysis. This Article aims to highlight best practices so that agency offices can learn from one another, fulfill NEPA’s mandate, and begin to provide leadership in the fight against climate change.
What Context Features Can Transformer Language Models Use

Transformer-based language models benefit from conditioning on contexts of hundreds to thousands of previous tokens. What aspects of these contexts contribute to accurate model prediction? We describe a series of experiments that measure usable information by selectively ablating lexical and structural information in transformer language models trained on English Wikipedia. In both mid- and long-range contexts, we find that several extremely destructive context manipulations—including shuffling word order within sentences and deleting all words other than nouns—remove less than 15% of the usable information. Our results suggest that long contexts, but not their detailed syntactic and propositional content, are important for the low perplexity of current transformer language models.
Anonymous device authorization for cellular networks

Cellular networks connect nearly every human on the planet; they consequently have visibility into location data and voice, SMS, and data contacts and communications. Such near-universal visibility represents a significant threat to the privacy of mobile subscribers. In 5G networks, end-user mobile device manufacturers assign a Permanent Equipment Identifier (PEI) to every new device. Mobile operators legitimately use the PEI to blocklist stolen devices from the network to discourage device theft, but the static PEI also provides a mechanism to uniquely identify and track subscribers. Advertisers and data brokers have also historically abused the PEI for data fusion of location and analytics data, including private data sold by cellular providers. In this paper, we present a protocol that allows mobile devices to prove that they are not in the blocklist without revealing their PEI to any entity on the network. Thus, we maintain the primary purpose of the PEI while preventing potential privacy violations. We describe a provably-secure anonymous proof of blocklist non-membership for cellular network, based on the RSA accumulators and zero-knowledge proofs introduced by Camenisch and Lysyanskaya (Crypto'02) and expanded upon by Li, Li and Xue (ACNS'07). We show experimentally that this approach is viable for cellular networks: a phone can create a blocklist non-membership proof in only 3432 milliseconds of online computation, and the network can verify the proof in less than one second on average. In total this adds fewer than 4.5 seconds to the rare network attach process. This work shows that PEIs can be attested anonymously in 5G and future network generations, and it paves the way for additional advances toward a cellular network with guaranteed privacy.
An Empirical Study of OSS-Fuzz Bugs

Continuous fuzzing is an increasingly popular technique for automated quality and security assurance. Google maintains OSS-Fuzz: a continuous fuzzing service for open source software. We conduct the first empirical study of OSS-Fuzz, analyzing 23,907 bugs found in 316 projects. We examine the characteristics of fuzzer-found faults, the lifecycles of such faults, and the evolution of fuzzing campaigns over time. We find that OSS-Fuzz is often effective at quickly finding bugs, and developers are often quick to patch them. However, flaky bugs, timeouts, and out of memory errors are problematic, people rarely file CVEs for security vulnerabilities, and fuzzing campaigns often exhibit punctuated equilibria, where developers might be surprised by large spikes in bugs found. Our findings have implications on future fuzzing research and practice.
Parallel String Graph Construction and Transitive Reduction for De Novo Genome Assembly

One of the most computationally intensive tasks in computational biology is de novo genome assembly, the decoding of the sequence of an unknown genome from redundant and erroneous short sequences. A common assembly paradigm identifies overlapping sequences, simplifies their layout, and creates consensus. Despite many algorithms developed in the literature, the efficient assembly of large genomes is still an open problem. In this work, we introduce new distributed-memory parallel algorithms for overlap detection and layout simplification steps of de novo genome assembly, and implement them in the diBELLA 2D pipeline. Our distributed memory algorithms for both overlap detection and layout simplification are based on linear-algebra operations over semirings using 2D distributed sparse matrices. Our layout step consists of performing a transitive reduction from the overlap graph to a string graph. We provide a detailed communication analysis of the main stages of our new algorithms. diBELLA 2D achieves near linear scaling with over 80% parallel efficiency for the human genome, reducing the runtime for overlap detection by 1.2 – $1.3 \times$ for the human genome and 1.5 – $1.9 \times$ for C.elegans compared to the state-of-the-art. Our transitive reduction algorithm outperforms an existing distributed-memory implementation by 10.5 – $13.3 \times$ for the human genome and 18– $29 \times$ for the C. elegans. Our work paves the way for efficient de novo assembly of large genomes using long reads in distributed memory.
Variational Inference MPC using Tsallis Divergence

In this paper, we provide a generalized framework for Variational Inference-Stochastic Optimal Control by using the non-extensive Tsallis divergence. By incorporating the deformed exponential function into the optimality likelihood function, a novel Tsallis Variational Inference-Model Predictive Control algorithm is derived, which includes prior works such as Variational Inference-Model Predictive Control, Model Predictive Path Integral Control, Cross Entropy Method, and Stein Variational Inference Model Predictive Control as special cases. The proposed algorithm allows for effective control of the cost/reward transform and is characterized by superior performance in terms of mean and variance reduction of the associated cost. The aforementioned features are supported by a theoretical and numerical analysis on the level of risk sensitivity of the proposed algorithm as well as simulation experiments on 5 different robotic systems with 3 different policy parameterizations.
Group testing and local search: is there a computational-statistical gap?

Group testing is a fundamental problem in statistical inference with many real-world applications, including the need for massive group testing during the ongoing COVID-19 pandemic. In this paper we study the task of approximate recovery, in which we tolerate having a small number of incorrectly classified items. One of the most well-known, optimal, and easy to implement testing procedures is the non-adaptive Bernoulli group testing, where all tests are conducted in parallel, and each item is chosen to be part of any certain test independently with some fixed probability. In this setting, there is an observed gap between the number of tests above which recovery is information theoretically possible, and the number of tests required by the currently best known efficient algorithms to succeed. In this paper we seek to understand whether this computational-statistical gap can be closed. Our main contributions are the following: 1.Often times such gaps are explained by a phase transition in the landscape of the solution space of the problem (an Overlap Gap Property (OGP) phase transition). We provide first moment evidence that, perhaps surprisingly, such a phase transition does not take place throughout the regime for which recovery is information theoretically possible. This fact suggests that the model is in fact amenable to local search algorithms. 2. We prove the complete absence of “bad” local minima for a part of the “hard” regime, a fact which implies an improvement over known theoretical results on the performance of efficient algorithms for approximate recovery without false-negatives. 3. Finally, motivated by the evidence for the abscence for the OGP, we present extensive simulations that strongly suggest that a very simple local algorithm known as Glauber Dynamics does indeed succeed, and can be used to efficiently implement the well-known (theoretically optimal) Smallest Satisfying Set (SSS) estimator. Given that practical algorithms for this task utilize Branch and Bound and Linear Programming relaxation techniques, our finding could potentially be of practical interest.
On the Approximation Power of Two-Layer Networks of Random ReLUs

This paper considers the following question: how well can depth-two ReLU networks with randomly initialized bottom-level weights represent smooth functions? We give near-matching upper- and lower-bounds for L2-approximation in terms of the Lipschitz constant, the desired accuracy, and the dimension of the problem, as well as similar results in terms of Sobolev norms. Our positive results employ tools from harmonic analysis and ridgelet representation theory, while our lower-bounds are based on (robust versions of) dimensionality arguments.
The average size of a connected vertex set of a k-connected graph

Abstract The topic is the average order A ( G ) of a connected induced subgraph of a graph G. This generalizes, to graphs in general, the average order of a subtree of a tree. In 1983 Jamison proved that the average order, over all trees of order n, is minimized by the path P n , the average being A ( P n ) = ( n + 2 ) / 3 . In 2018, Kroeker, Mol, and Oellermann conjectured that P n minimizes the average order over all connected graphs G - a conjecture that was recently proved. In this short note we show that this lower bound can be improved if the connectivity of G is known. If G is k-connected, then A ( G ) > n 2 ( 1 − 1 2 k + 1 ) .
IS NEAR POINT OF CONVERGENCE ASSOCIATED WITH SYMPTOM PROFILES OR RECOVERY IN ADOLESCENTS AFTER CONCUSSION

Background:Visual impairments, including oculomotor impairments, are common following concussion. Accurate, efficient, and objective measurement of post-concussion visual impairments may help guide...
Silicon Photonic Micro-Ring Modulator-based 4 x 112 Gb/s O-band WDM Transmitter with Ring Photocurrent-based Thermal Control in 28nm CMOS

We present a 4λ×112 Gb/s/λ hybrid-integrated silicon photonic TX suitable for 400G Ethernet modules and co-packaged optics. The photonic IC (PIC) uses cascaded micro-ring modulators (MRMs) with integrated heaters for efficient wavelength division multiplexing (WDM). The 28nm CMOS electronic IC includes PAM4 MRM drivers with nonlinear FFE and control circuits to stabilize MRM performance against process and temperature variations. A thermal control scheme based on sensing MRM photocurrents is used to minimize monitoring hardware in the PIC. Measured results demonstrate 112 Gb/s PAM4 operation with <0.7 dB TDECQ from each of the 4 channels. To our best knowledge, this is the highest per-λ data rate reported for an O-band ring-based WDM transmitter.
Partition functions of Chern-Simons theory on handlebodies by radial quantization

We use radial quantization to compute Chern-Simons partition functions on handlebodies of arbitrary genus. The partition function is given by a particular transition amplitude between two states which are defined on the Riemann surfaces that define the (singular) foliation of the handlebody. The final state is a coherent state while on the initial state the holonomy operator has zero eigenvalue. The latter choice encodes the constraint that the gauge fields must be regular everywhere inside the handlebody. By requiring that the only singularities of the gauge field inside the handlebody must be compatible with Wilson loop insertions, we find that the Wilson loop shifts the holonomy of the initial state. Together with an appropriate choice of normalization, this procedure selects a unique state in the Hilbert space obtained from a Kahler quantization of the theory on the constant-radius Riemann surfaces. Radial quantization allows us to find the partition functions of Abelian Chern-Simons theories for handlebodies of arbitrary genus. For non-Abelian compact gauge groups, we show that our method reproduces the known partition function at genus one.