cssi/SciSciGPT-SciSciNet huggingface dataset:
A new theory of fractional differential calculus

This paper presents a self-contained new theory of weak fractional differential calculus in one-dimension. The crux of this new theory is the introduction of a weak fractional derivative notion whi...
A coupled model of episodic warming, oxidation and geochemical transitions on early Mars

Reconciling the geology of Mars with models of atmospheric evolution remains a major challenge. Martian geology is characterized by past evidence for episodic surface liquid water, and geochemistry indicating a slow and intermittent transition from wetter to drier and more oxidizing surface conditions. Here we present a model that incorporates randomized injection of reducing greenhouse gases and oxidation due to hydrogen escape to investigate the conditions responsible for these diverse observations. We find that Mars could have transitioned repeatedly from reducing (hydrogen-rich) to oxidizing (oxygen-rich) atmospheric conditions in its early history. Our model predicts a generally cold early Mars, with mean annual temperatures below 240 K. If peak reducing-gas release rates and background carbon dioxide levels are high enough, it nonetheless exhibits episodic warm intervals sufficient to degrade crater walls, form valley networks and create other fluvial/lacustrine features. Our model also predicts transient build-up of atmospheric oxygen, which can help explain the occurrence of oxidized mineral species such as manganese oxides at Gale Crater. We suggest that the apparent Noachian–Hesperian transition from phyllosilicate deposition to sulfate deposition around 3.5 billion years ago can be explained as a combined outcome of increasing planetary oxidation, decreasing groundwater availability and a waning bolide impactor flux, which dramatically slowed the remobilization and thermochemical destruction of surface sulfates. Ultimately, rapid and repeated variations in Mars’s early climate and surface chemistry would have presented both challenges and opportunities for any emergent microbial life. Mars’s early climate and surface chemistry varied between a generally cold, oxidizing environment and warmer, more reducing conditions, according to a model of atmospheric evolution driven by stochastic, random injection of greenhouse gases.
Dynamical Sampling with Additive Random Noise

Dynamical sampling deals with signals that evolve in time under the action of a linear operator. The purpose of the present paper is to analyze the performance of the basic dynamical sampling algorithms in the finite dimensional case and study the impact of additive noise. The algorithms are implemented and tested on synthetic and real data sets, and denoising techniques are integrated to mitigate the effect of the noise. We also develop theoretical and numerical results that validate the algorithm for recovering the driving operators, which are defined via a real symmetric convolution.
Evaluating Thin Flat Surfaces

We consider recognizable evaluations for a suitable category of oriented two-dimensional cobordisms with corners between finite unions of intervals. We call such cobordisms thin flat surfaces. An evaluation is given by a power series in two variables. Recognizable evaluations correspond to series that are ratios of a two-variable polynomial by the product of two one-variable polynomials, one for each variable. They are also in a bijection with isomorphism classes of commutative Frobenius algebras on two generators with a nondegenerate trace fixed. The latter algebras of dimension n correspond to points on the dual tautological bundle on the Hilbert scheme of n points on the affine plane, with a certain divisor removed from the bundle. A recognizable evaluation gives rise to a functor from the above cobordism category of thin flat surfaces to the category of finite-dimensional vector spaces. These functors may be non-monoidal in interesting cases. To a recognizable evaluation we also assign an analogue of the Deligne category and of its quotient by the ideal of negligible morphisms.
Cut-off for sandpiles on tiling graphs

Sandpile dynamics are considered on graphs constructed from periodic plane and space tilings by assigning a growing piece of the tiling, either torus or open boundary conditions. A general method of obtaining the Green’s function of the tiling is given, and a total variation cut-off phenomenon is demonstrated under general conditions. It is shown that the boundary condition does not affect the mixing time for planar tilings. In a companion paper, computational methods are used to demonstrate that an open boundary condition alters the mixing time for the D4 lattice in dimension 4, while an asymptotic evaluation shows that it does not change the asymptotic mixing time for the cubic lattice Zd for all sufficiently large d.
Extrapolation Bias and Robust Dynamic Liquidity Management

We consider the optimal dynamic liquidity management of a financially constrained firm when its existing shareholders are risk neutral but ambiguity averse with respect to the firm’s future cash fl...
LuxT controls specific quorum-sensing-regulated behaviors in Vibrionaceae spp. via repression of qrr1, encoding a small regulatory RNA

Quorum sensing (QS) is a process of chemical communication bacteria use to transition between individual and collective behaviors. QS depends on the production, release, and synchronous response to signaling molecules called autoinducers (AIs). The marine bacterium Vibrio harveyi monitors AIs using a signal transduction pathway that relies on five small regulatory RNAs (called Qrr1-5) that post-transcriptionally control target genes. Curiously, the small RNAs largely function redundantly making it difficult to understand the necessity for five of them. Here, we identify LuxT as a transcriptional repressor of qrr1. LuxT does not regulate qrr2-5, demonstrating that qrr genes can be independently controlled to drive unique downstream QS gene expression patterns. LuxT reinforces its control over the same genes it regulates indirectly via repression of qrr1, through a second transcriptional control mechanism. Genes dually regulated by LuxT specify public goods including an aerolysin-type pore-forming toxin. Phylogenetic analyses reveal that LuxT is conserved among Vibrionaceae and sequence comparisons predict that LuxT represses qrr1 in additional species. The present findings reveal that the QS regulatory RNAs can carry out both shared and unique functions to endow bacteria with plasticity in their output behaviors.
Trajectory Prediction in Autonomous Driving With a Lane Heading Auxiliary Loss

Predicting a vehicle's trajectory is an essential ability for autonomous vehicles navigating through complex urban traffic scenes. Bird's-eye-view roadmap information provides valuable information for making trajectory predictions, and while state-of-the-art models extract this information via image convolution, auxiliary loss functions can augment patterns inferred from deep learning by further encoding common knowledge of social and legal driving behaviors. Since human driving behavior is inherently multimodal, models which allow for multimodal output tend to outperform single-prediction models on standard metrics. We propose a loss function which enhances such models by enforcing expected driving rules on all predicted modes. Our contribution to trajectory prediction is twofold; we propose a new metric which addresses failure cases of the off-road rate metric by penalizing trajectories that oppose the ascribed heading (flow direction) of a driving lane, and we show this metric to be differentiable and therefore suitable as an auxiliary loss function. We then use this auxiliary loss to extend the the standard multiple trajectory prediction (MTP) and MultiPath models, achieving improved results on the nuScenes prediction benchmark by predicting trajectories which better conform to the lane-following rules of the road.
B Production In p-p and A-A Collisions

This is an extension of our recent work on $D^{+}(c\bar {d}),D^{o}(c\bar {u})$ production from p-p and d-Au collisions to $B^{+}(b\bar {d}),B^{o}(b\bar {u})$ production from p-p and A-A collisions. The rapidity cross sections for $B^{+}(c\bar {d}),B^{o}(b\bar {u})$ production from both p-p and A-A collisions are estimated. Our present work makes use of previous work on J/Ψ, Ψ′(2S), ϒ(n S) production in p-p and A-A collisions, with the main new aspect being the fragmentation probability, $D_{b \rightarrow b\bar {q}}$ , which turns out to be similar to the fragmentation probability $D_{c \rightarrow c\bar {q}}$ used in our recent work.
The Importance of Prioritizing Exoplanet Experimental Facilities

Continuous improvements of observations and modeling efforts have led to tremendous strides in exoplanetary science. However, as instruments and techniques advance laboratory data becomes more important to interpret exoplanet observations and verify theoretical modeling. Though experimental studies are often deferred due to their high costs and long timelines, it is imperative that laboratory investigations are prioritized to ensure steady advances in the field of exoplanetary science. This White Paper discusses the importance of prioritizing exoplanetary laboratory efforts, and discusses several experimental facilities currently performing exoplanetary research.
The Hall algebras of annuli

We refine and prove the central conjecture of our first paper for annuli with at least two marked intervals on each boundary component by computing the derived Hall algebras of their Fukaya categories.
Predicting Goal-directed Attention Control Using Inverse-Reinforcement Learning.

Understanding how goals control behavior is a question ripe for interrogation by new methods from machine learning. These methods require large and labeled datasets to train models. To annotate a large-scale image dataset with observed search fixations, we collected 16,184 fixations from people searching for either microwaves or clocks in a dataset of 4,366 images (MS-COCO). We then used this behaviorally-annotated dataset and the machine learning method of inverse-reinforcement learning (IRL) to learn target-specific reward functions and policies for these two target goals. Finally, we used these learned policies to predict the fixations of 60 new behavioral searchers (clock = 30, microwave = 30) in a disjoint test dataset of kitchen scenes depicting both a microwave and a clock (thus controlling for differences in low-level image contrast). We found that the IRL model predicted behavioral search efficiency and fixation-density maps using multiple metrics. Moreover, reward maps from the IRL model revealed target-specific patterns that suggest, not just attention guidance by target features, but also guidance by scene context (e.g., fixations along walls in the search of clocks). Using machine learning and the psychologically-meaningful principle of reward, it is possible to learn the visual features used in goal-directed attention control.
MM for penalized estimation

Penalized estimation can conduct variable selection and parameter estimation simultaneously. The general framework is to minimize a loss function subject to a penalty designed to generate sparse variable selection. The majorization–minimization (MM) algorithm is a computational scheme for stability and simplicity, and the MM algorithm has been widely applied in penalized estimation. Much of the previous work has focused on convex loss functions such as generalized linear models. When data are contaminated with outliers, robust loss functions can generate more reliable estimates. Recent literature has witnessed a growing impact of nonconvex loss-based methods, which can generate robust estimation for data contaminated with outliers. This article investigates MM algorithm for penalized estimation, provides innovative optimality conditions and establishes convergence theory with both convex and nonconvex loss functions. With respect to applications, we focus on several nonconvex loss functions, which were formerly studied in machine learning for regression and classification problems. Performance of the proposed algorithms is evaluated on simulated and real data including cancer clinical status. Efficient implementations of the algorithms are available in the R package mpath in CRAN.
Thermogenic Fat: Development, Physiological Function, and Therapeutic Potential

The concerning worldwide increase of obesity and chronic metabolic diseases, such as T2D, dyslipidemia, and cardiovascular disease, motivates further investigations into preventive and alternative therapeutic approaches. Over the past decade, there has been growing evidence that the formation and activation of thermogenic adipocytes (brown and beige) may serve as therapy to treat obesity and its associated diseases owing to its capacity to increase energy expenditure and to modulate circulating lipids and glucose levels. Thus, understanding the molecular mechanism of brown and beige adipocytes formation and activation will facilitate the development of strategies to combat metabolic disorders. Here, we provide a comprehensive overview of pathways and players involved in the development of brown and beige fat, as well as the role of thermogenic adipocytes in energy homeostasis and metabolism. Furthermore, we discuss the alterations in brown and beige adipose tissue function during obesity and explore the therapeutic potential of thermogenic activation to treat metabolic syndrome.
On-Request Wireless Charging and Partial Computation Offloading In Multi-Access Edge Computing Systems

Wireless charging coupled with computation offloading in edge networks offers a promising solution for realizing power-hungry and computation intensive applications on user-devices. We consider a multi-access edge computing (MEC) system with collocated MEC server and base-station/access point (AP), each equipped with a massive MIMO antenna array, supporting multiple users requesting data computation and wireless charging. The goal is to minimize the energy consumption for computation offloading and maximize the received energy at the user from wireless charging. The proposed solution is a novel two-stage algorithm employing a nested descent algorithm, primal-dual subgradient and linear programming techniques to perform data partitioning and time allocation for computation offloading and design the optimal energy beamforming for wireless charging, all within MEC-AP transmit power and latency constraints. Algorithm results show that optimal energy beamforming significantly outperforms other schemes such as isotropic or directed charging without beam power allocation. Compared to binary offloading, data partition in partial offloading leads to lower energy consumption and more charging time, resulting in better wireless charging performance. The charged energy over an extended period of multiple time-slots both with and without computation offloading can be substantial. Wireless charging from MEC-AP thus offers a viable untethered approach for supplying energy to user-devices.
Remote Learning Barriers and Opportunities for Graduate Student and Postdoctoral Learners in Career and Professional Skill Development: A Case Study

Career and professional development competencies are critical for biomedical PhD and postdoctoral training. In the current educational landscape, programs that meet these competencies are offered and attended in an ad hoc manner. During the COVID-19 pandemic and the accompanying switch to virtual learning, our team observed a surge in interest for our weekly nonsequential programs. In this study, we surveyed our learners to better understand motivators for attending these programs during the pandemic and to identify barriers for participating in such events before and during Work-from-Home. Our data indicate that conflict with research responsibilities, time spent to get to the event location, and planning time to attend are all significant barriers to engagement. Notably, feelings of being overwhelmed, which increased slightly during the pandemic, stood out as an identified barrier. Per our results, the virtual format was an attraction. While 58% of respondents would prefer to access professional development programs virtually in the future, almost 42% indicated a preference for in-person events when normalcy resumes, as the physical presence of an instructor and of peers result in a deeper engagement. Our collective analysis here suggests that learners will benefit from a hybrid or combination of synchronous and asynchronous career and professional development programming in the future, even postpandemic, to reduce identified barriers. Alongside hybrid learning engagements, we strongly recommend structured time for learners to enhance their professional competencies, enabled by a commitment from departments and faculty mentors to bring equity in professional skill building and foster a life-long growth mindset.
Aspects of income inequality in a creative region

We analyze a stylized creative region’s economy. There are two goods (consumption and creative capital) and creative class members differ in their income generating abilities. The distribution of abilities in the creative class population is such that the median ability is less than the average ability. There is a proportional income tax rate and all tax revenues are redistributed to the creative class members by the regional authority (RA) with a uniform, lump-sum transfer. In this setting, we perform four tasks. First, we determine the tax rate that maximizes the lump-sum transfer. Second, we show that income equality requires the tax rate to equal unity and that the poorest creative class member is better off with a lower tax rate and hence more inequality. Third, given the $$ith$$ creative class member’s preference over the tax rate and the transfer, we ascertain the tax rate that will be chosen by majority voting. Finally, we discuss the connection between increasing inequality and the majority chosen tax rate.
Shooting from the 'Scrip: Scope of Practice Laws and Access to Immunizations in the Pharmacy Setting.

In the past two decades, most states in the United States have added authorization for pharmacists to administer some vaccinations. Expansions of this authority have also come with prescription requirements or other regulatory burdens. The objective of this study was to evaluate the impact of these expansions on influenza immunization rates in adults age 65 and over. A panel data, differences-in-differences regression framework to control for state-level unobserved confounders and shocks at the national level was used on a combination of a dataset of state-level statute and regulatory changes and influenza immunization data from the Behavioral Risk Factor Surveillance System. Giving pharmacists permission to vaccinate had a positive impact on adult influenza immunization rates of 1.4 percentage points for adults age 65 and over. This effect was diminished by the presence of laws requiring pharmacists to obtain patient-specific prescriptions. There was no evidence that allowing pharmacists to administer vaccinations led patients to have fewer annual check-ups with physicians or not have a usual source of health care. Expanding pharmacists’ scope of practice laws to include administering the influenza vaccine had a positive impact on influenza shot uptake. This may have implications for relaxing restrictions on other forms of care that could be provided by pharmacists.
VGAI: End-to-End Learning of Vision-Based Decentralized Controllers for Robot Swarms

Decentralized coordination of a robot swarm requires addressing the tension between local perceptions and actions, and the accomplishment of a global objective. In this work, we propose to learn decentralized controllers based solely on raw visual inputs. For the first time, this integrates the learning of two key components: communication and visual perception, in one end-to-end framework. More specifically, we consider that each robot has access to a visual perception of the immediate surroundings, and communication capabilities to transmit and receive messages from other neighboring robots. Our proposed learning framework combines a convolutional neural network (CNN) for each robot to extract messages from the visual inputs, and a graph neural network (GNN) over the entire swarm to transmit, receive and process these messages in order to decide on actions. The use of a GNN and locally-run CNNs results naturally in a decentralized controller. We jointly train the CNNs and the GNN so that each robot learns to extract messages from the images that are adequate for the team as a whole. Our experiments demonstrate the proposed architecture in the problem of drone flocking and show its promising performance and scalability, e.g., achieving successful decentralized flocking for large-sized swarms.
Fundamental Frequency Feature Normalization and Data Augmentation for Child Speech Recognition

Automatic speech recognition (ASR) systems for young children are needed due to the importance of age-appropriate educational technology. Because of the lack of publicly available young child speech data, feature extraction strategies such as feature normalization and data augmentation must be considered to successfully train child ASR systems. This study proposes a novel technique for child ASR using both feature normalization and data augmentation methods based on the relationship between formants and fundamental frequency (f o ). Both the f o feature normalization and data augmentation techniques are implemented as a frequency shift in the Mel domain. These techniques are evaluated on a child read speech ASR task. Child ASR systems are trained by adapting a BLSTM-based acoustic model trained on adult speech. Using both f o normalization and data augmentation results in a relative word error rate (WER) improvement of 19.3% over the baseline when tested on the OGI Kids’ Speech Corpus, and the resulting child ASR system achieves the best WER currently reported on this corpus.
The Chicxulub impactor: comet or asteroid?

A recent paper by Siraj & Loeb (2021) entitled "Breakup of a long-period comet as the origin of the dinosaur extinction" attempts to revive the perennial debate about what type of body hit the Earth 66 million years ago, triggering the end-Cretaceous extinction. Here we critique the paper and assess the evidence it presents. To consider a comet more likely than an asteroid requires extreme assumptions about how comets fragment, conflation of carbonaceous chondrites with specific types of carbonaceous chondrites, and a blind eye to the evidence of the iridium layer.
Gyrator-Capacitor Modeling of A Continuously Variable Series Reactor in Different Operating Modes

Continuously Variable Series Reactor (CVSR) can regulate its reactance on the ac side using the nonlinear ferromagnetic core, shared by an ac and a dc winding for applications like power flow control, oscillation damping, load balancing and others. In order to use a CVSR in the power grid, it is essential to know its operating characteristics. The Gyrator-Capacitor approach has been applied to model the magnetic circuit coupled with the electrical circuit. In the paper, we investigate the CVSR behavior in terms of induced voltages across the dc winding, flux densities (B) through the core legs, and power exchange between the ac controlled and the dc control circuit.
Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence.

Typical fact verification models use retrieved written evidence to verify claims. Evidence sources, however, often change over time as more information is gathered and revised. In order to adapt, models must be sensitive to subtle differences in supporting evidence. We present VitaminC, a benchmark infused with challenging cases that require fact verification models to discern and adjust to slight factual changes. We collect over 100,000 Wikipedia revisions that modify an underlying fact, and leverage these revisions, together with additional synthetically constructed ones, to create a total of over 400,000 claim-evidence pairs. Unlike previous resources, the examples in VitaminC are contrastive, i.e., they contain evidence pairs that are nearly identical in language and content, with the exception that one supports a given claim while the other does not. We show that training using this design increases robustness—improving accuracy by 10% on adversarial fact verification and 6% on adversarial natural language inference (NLI). Moreover, the structure of VitaminC leads us to define additional tasks for fact-checking resources: tagging relevant words in the evidence for verifying the claim, identifying factual revisions, and providing automatic edits via factually consistent text generation.
Polynomially Isometric Matrices in Low Dimensions

Given two d × d matrices, say A and B, when do p(A) and p(B) have the same “size” for every polynomial p? In this article, we provide definitive results in the cases d = 2 and d = 3 when the notion...
Kinetic Monte Carlo Simulations of Oxygen Diffusion in Environmental Barrier Coating Materials

Ceramic Matrix Composite (CMC) materials are of interest for use in next-generation turbine engine components, offering a number of significant advantages, including reduced weight and high operating temperatures. However, in the hot environment in which such components operate, the presence of water vapor can lead to corrosion and recession, limiting the useful life of the components. Such degradation can be reduced through the use of Environmental Barrier Coatings (EBCs) that limit the amount of oxygen and water vapor reaching the component. Candidate EBC materials include Yttrium and Ytterbium silicates. In this work we present results of kinetic Monte Carlo (kMC) simulations of oxygen diffusion, via the vacancy mechanism, in Yttrium and Ytterbium disilicates, along with a brief discussion of interstitial diffusion. An EBC system typically includes a bond coat located between the EBC and the component surface. Bond coat materials are generally chosen for properties other than low oxygen diffusivity, but low oxygen diffusivity is nevertheless a desirable characteristic, as the bond coat could provide some additional component protection, particularly in the case where cracks in the coating system provide a direct path from the environment to the bond coat interface. We have therefore performed similar kMC simulations of oxygen diffusion in this material.
Plasma Switch-Based Technology for High-Speed and High-Power Impedance Tuning

This paper introduces a new technology for a high-speed, high-power mobile form-factor tuner utilizing gas discharge tube plasma cells as switching components. To the best of our knowledge, this represents the first plasma-enabled RF matching network. Technology development is reviewed, the fabrication and measurement of a proof-of-concept switched stub impedance tuner are presented, and techniques for improvement are discussed. The proof-of-concept impedance tuner functions with a 27% bandwidth from 3 to almost 4 GHz and shows a power gain better than -2.5dB across all switching state-frequency combinations at a 50 W input power level with spread coverage of the Smith chart. State change transient timing is measured to be on the order of 500 ns. This technology demonstration highlights the potential of miniaturized, rapidly-tunable, high-power, plasma-based RF devices.
Numerical Computations of Turbulence Amplification in Shock-Wave Interactions

Numerical computations are presented which illustrate and test various effects pertinent to the amplification and generation of turbulence in shock wave-turbulent boundary layer interactions. Several fundamental physical mechanisms are identified. Idealizations of these processes are examined by nonlinear numerical calculations. The results enable some limits to be placed on the range of validity of existing linear theories. Additional results are given which are of a fundamentally nonlinear nature.
What is Islamic Law? How Should We Study It?

“When did pious speculation by Muslim individuals become Islamic law?,” asks Professor Joseph Lowry in his essay. He suggests that formal institutions applying legal norms historically may not have been necessary for the formation of Islamic law, especially if we understand that term to mean a collection of “juristic discourses.” Although we should not assume that the Qur’an and the prophetic sayings inevitably culminated in a legal tradition, we can certainly see these sources as contributing to a “distinctively Islamic legal hermeneutics.” Read more to see how, and why scholars should clarify their own working definitions of “Islamic law” in their own discourse and use of the early sources.
Timely Transmissions Using Optimized Variable Length Coding

A status updating system is considered in which a variable length code is used to transmit messages to a receiver over a noisy channel. The goal is to optimize the codewords lengths such that successfully-decoded messages are timely. That is, such that the age-of-information (AoI) at the receiver is minimized. A hybrid ARQ (HARQ) scheme is employed, in which variable-length incremental redundancy (IR) bits are added to the originally-transmitted codeword until decoding is successful. With each decoding attempt, a non-zero processing delay is incurred. The optimal codewords lengths are analytically derived utilizing a sequential differential optimization (SDO) framework. The framework is general in that it only requires knowledge of an analytical expression of the positive feedback (ACK) probability as a function of the codeword length.
TryOnGAN: body-aware try-on via layered interpolation

Given a pair of images---target person and garment on another person---we automatically generate the target person in the given garment. Previous methods mostly focused on texture transfer via paired data training, while overlooking body shape deformations, skin color, and seamless blending of garment with the person. This work focuses on those three components, while also not requiring paired data training. We designed a pose conditioned StyleGAN2 architecture with a clothing segmentation branch that is trained on images of people wearing garments. Once trained, we propose a new layered latent space interpolation method that allows us to preserve and synthesize skin color and target body shape while transferring the garment from a different person. We demonstrate results on high resolution 512 × 512 images, and extensively compare to state of the art in try-on on both latent space generated and real images.
Retail Distribution Strategy with Outlet Stores

Traditionally, outlet stores were situated away from main stores in order to provide older, less desirable products at discounted prices. More recently, some firms have featured an outlet-within-a-store concept and offer consumers the experience of outlet shopping at the same location. With an interest in examining the effect of location differentiation on a firm’s distribution strategy, we model a firm with an existing main store and study its motivation on whether or not to open an outlet store, and where to locate it. When consumers are homogeneous in their travel sensitivity, our analysis shows that as consumers’ willingness to travel decreases, it becomes more likely for the firm to offer outlet stores at or near main store locations. This observation is consistent with the anecdotal evidence of an outlet-within-a-store strategy adopted by department stores such as Macy’s, who in contrast to single brand firms (such as Coach) have more uncertainty in consumers’ taste for product offerings leading to a lower willingness to travel. We also show that the introduction of outlet stores leads to higher quality offerings at the main store. When there is fit uncertainty in product offering at the outlet store, in contrast to intuition, as product fit increases, the firm builds the outlet store closer to the main store but increases both product quality and price to take advantage of their increased attraction. We also extend the model to consider consumer heterogeneity in travel sensitivity and the growing trend of urban sprawl.
A Decentralized Policy Gradient Approach to Multi-Task Reinforcement Learning

We develop a mathematical framework for solving multi-task reinforcement learning (MTRL) problems based on a type of policy gradient method. The goal in MTRL is to learn a common policy that operates effectively in different environments; these environments have similar (or overlapping) state spaces, but have different rewards and dynamics. We highlight two fundamental challenges in MTRL that are not present in its single task counterpart, and illustrate them with simple examples. We then develop a decentralized entropy-regularized policy gradient method for solving the MTRL problem, and study its finite-time convergence rate. We demonstrate the effectiveness of the proposed method using a series of numerical experiments. These experiments range from small-scale "GridWorld" problems that readily demonstrate the trade-offs involved in multi-task learning to large-scale problems, where common policies are learned to navigate an airborne drone in multiple (simulated) environments.
Chance-Constrained Optimal Covariance Steering with Iterative Risk Allocation

This paper extends the optimal covariance steering problem for linear stochastic systems subject to chance constraints to account for optimal risk allocation. Previous works have assumed a uniform risk allocation to cast the optimal control problem as a semi-definite program (SDP). We adopt the Iterative Risk Allocation (IRA) formalism from [1], which uses a two-stage approach to solve the optimal risk allocation problem for covariance steering. The upper-stage of IRA optimizes the risk, which is proved to be a convex problem, while the lower-stage optimizes the controller with the new constraints. The proposed framework results in solutions that tend to maximize the terminal covariance, while still satisfying the chance constraints, thus leading to less conservative solutions than previous methodologies.
Cooking or Clicking: The Impact of Online Food Delivery Platforms on Domestic Food Preparation

One of the most notable developments of the sharing economy is the unfettered proliferation of on-demand services. In this work, we examine how a subset of such platforms, online food delivery (e.g., Grubhub, UberEats, Doordash), has affected a particularly important household activity, meal preparation, and the dining behaviors of American households. To do so, we exploit the phased entry of the platform Grubhub into US counties from 2005 to 2019 using a difference in difference approach. Results indicate that the availability of an increasingly diversified delivery service leads to a significant reduction in time spent preparing meals within the home. Further, and alarmingly, we find that this impact is associated with an increase in the average BMI of affected persons, suggesting an unfavorable dietary shift from healthier home cooking to unhealthier food away from home consumption. We discuss implications for research, consumers, platforms, and public policymakers.
Experimental signatures of a new dark matter WIMP

The WIMP proposed here yields the observed abundance of dark matter, and is consistent with the current limits from direct detection, indirect detection, and collider experiments, if its mass is GeV/c 2 . It is also consistent with analyses of the gamma rays observed by Fermi-LAT from the Galactic center (and other sources), and of the antiprotons observed by AMS-02, in which the excesses are attributed to dark matter annihilation. These successes are shared by the inert doublet model (IDM), but the phenomenology is very different: the dark matter candidate of the IDM has first-order gauge couplings to other new particles, whereas the present candidate does not. In addition to indirect detection through annihilation products, it appears that the present particle can be observed in the most sensitive direct-detection and collider experiments currently being planned.
The Effect of Increased Horizontal Resolution on GLA Fourth Order Model Forecasts

A benchmark series of ten-day weather forecasts has been run with the GLA Fourth Order GCM with both a 4 deg latitude by 5 deg longitude resolution and a 2 deg latitude by 2.5 deg longitude resolution. Ensemble statistics of forecast skill and maps of systematic error fields have been generated for both resolutions. The enhanced resolution added 24 hours of useful predictive skill to the sea level pressure forecasts and 6 hours to the 500 mb height forecasts, but 5 to 6 days into the forecasts the advantage of the finer resolution was lost. The systematic error fields showed that by 8 days the 'climate drift' of the 2 x 2.5 deg forecasts had become pronounced and had caused the loss of predictive skill relative to the 4 x 5 deg forecasts. Additional results indicate that a gravity wave drag parameterization scheme might alleviate the climate drift problem.
Loss of N1-methylation of G37 in tRNA induces ribosome stalling and reprograms gene expression.

N1-methylation of G37 is required for a subset of tRNAs to maintain the translational reading-frame. While loss of m1G37 increases ribosomal +1 frameshifting, whether it incurs additional translational defects is unknown. Here we address this question by applying ribosome profiling to gain a genome-wide view of the effects of m1G37 deficiency on protein synthesis. Using E. coli as a model, we show that m1G37 deficiency induces ribosome stalling at codons that are normally translated by m1G37-containing tRNAs. Stalling occurs during decoding of affected codons at the ribosomal A site, indicating a distinct mechanism than that of +1 frameshifting, which occurs after the affected codons leave the A site. Enzyme- and cell-based assays show that m1G37 deficiency reduces tRNA aminoacylation and in some cases peptide-bond formation. We observe changes of gene expression in m1G37 deficiency similar to those in the stringent response that is typically induced by deficiency of amino acids. This work demonstrates a previously unrecognized function of m1G37 that emphasizes its role throughout the entire elongation cycle of protein synthesis, providing new insight into its essentiality for bacterial growth and survival.
A Schatten-q low-rank matrix perturbation analysis via perturbation projection error bound

Abstract This paper studies the Schatten-q error of low-rank matrix estimation by singular value decomposition under perturbation. We specifically establish a perturbation bound on the low-rank matrix estimation via a perturbation projection error bound. Then, we establish lower bounds to justify the tightness of the upper bound on the low-rank matrix estimation error. We further develop a user-friendly sinΘ bound for singular subspace perturbation based on the matrix perturbation projection error bound. Finally, we demonstrate the advantage of our results over the ones in the literature by simulation.
Self-interaction-corrected Kohn-Sham effective potentials using the density-consistent effective potential method.

Density functional theory (DFT) and beyond-DFT methods are often used in combination with photoelectron spectroscopy to obtain physical insights into the electronic structure of molecules and solids. The Kohn–Sham eigenvalues are not electron removal energies except for the highest occupied orbital. The eigenvalues of the highest occupied molecular orbitals often underestimate the electron removal or ionization energies due to the self-interaction (SI) errors in approximate density functionals. In this work, we adapt and implement the density-consistent effective potential method of Kohut, Ryabinkin, and Staroverov [J. Chem. Phys. 140, 18A535 (2014)] to obtain SI-corrected local effective potentials from the SI-corrected Fermi–Lowdin orbitals and density in the Fermi–Lowdin orbital self-interaction correction scheme. The implementation is used to obtain the density of states (photoelectron spectra) and HOMO–LUMO gaps for a set of molecules and polyacenes. Good agreement with experimental values is obtained compared to a range of SI uncorrected density functional approximations.
Cross-beam energy transfer saturation by ion trapping-induced detuning

The performance of direct-drive inertial confinement fusion implosions relies critically on the coupling of laser energy to the target plasma. Cross-beam energy transfer (CBET), the resonant exchange of energy between intersecting laser beams mediated by ponderomotively driven ion-acoustic waves (IAWs), inhibits this coupling by scattering light into unwanted directions. The variety of beam intersection angles and varying plasma conditions in an implosion results in IAWs with a range of phase velocities. Here, we show that CBET saturates through a resonance detuning that depends on the IAW phase velocity and that results from trapping-induced modifications to the ion distribution functions. For smaller phase velocities, the modifications to the distribution functions can rapidly thermalize in the presence of mid-Z ions, leading to a blueshift in the resonant frequency. For larger phase velocities, the modifications can persist, leading to a redshift in the resonant frequency. Ultimately, these results may reveal pathways toward CBET mitigation and inform reduced models for radiation hydrodynamics codes to improve their predictive capability.
Characterizing Discourse about COVID-19 Vaccines: A Reddit Version of the Pandemic Story

It has been one year since the outbreak of the COVID-19 pandemic. The good news is that vaccines developed by several manufacturers are being actively distributed worldwide. However, as more and more vaccines become available to the public, various concerns related to vaccines become the primary barriers that may hinder the public from getting vaccinated. Considering the complexities of these concerns and their potential hazards, this study is aimed at offering a clear understanding about different population groups’ underlying concerns when they talk about COVID-19 vaccines—particularly those active on Reddit. The goal is achieved by applying LDA and LIWC to characterize the pertaining discourse with insights generated through a combination of quantitative and qualitative comparisons. Findings include the following: (1) during the pandemic, the proportion of Reddit comments predominated by conspiracy theories outweighed that of any other topics; (2) each subreddit has its own user bases, so information posted in one subreddit may not reach that from other subreddits; and (3) since users’ concerns vary across time and subreddits, communication strategies must be adjusted according to specific needs. The results of this study manifest challenges as well as opportunities in the process of designing effective communication and immunization programs.
Spectrum of hidden-charm, open-strange exotics in the dynamical diquark model

The observation by BESIII and LHCb of states with hidden charm and open strangeness ($c\overline{c}q\overline{s}$) presents new opportunities for the development of a global model of heavy-quark exotics. Here we extend the dynamical diquark model to encompass such states, using the same values of Hamiltonian parameters previously obtained from the nonstrange and hidden-strange sectors. The large mass splitting between ${Z}_{cs}(4000)$ and ${Z}_{cs}(4220)$ suggests substantial ${\mathrm{SU}(3)}_{\text{flavor}}$ mixing between all ${J}^{P}={1}^{+}$ states, while their average mass compared to that of other sectors offers a direct probe of flavor octet-singlet mixing among exotics. We also explore the inclusion of $\ensuremath{\eta}$-like exchanges within the states, and find their effects to be quite limited. In addition, using the same diquark-mass parameters, we find ${P}_{c}(4312)$ and ${P}_{cs}(4459)$ to fit well as corresponding nonstrange and open-strange pentaquarks.
The unanticipated effect of COVID-19 on house apportionment

Abstract It is well understood thateven small differences in population can have a disproportionate impact on representation in the U.S. House of Representatives after a decennial census because of the peculiarities of rounding rules that require integer allocations. While the COVID-19 pandemic can be held responsible for accelerating the trend toward the increased use of mail-in balloting, and it affected the ability of the census to collect in-person information, here we call attention to an unanticipated effect of the pandemic on the electoral process that, as far as we are aware, has never previously been identified. By rerunning the apportionment numbers for all states under the assumption that deaths from COVID-19 prior to the start of the Census had not occurred, we show that New York's congressional delegation would not have lost a seat. New York was the only state whose House seat allocation was affected by disproportionate COVID-19 deaths.
Fairness-aware Agnostic Federated Learning.

Federated learning is an emerging framework that builds centralized machine learning models with training data distributed across multiple devices. Most of the previous works about federated learning focus on the privacy protection and communication cost reduction. However, how to achieve fairness in federated learning is under-explored and challenging especially when testing data distribution is different from training distribution or even unknown. Introducing simple fairness constraints on the centralized model cannot achieve model fairness on unknown testing data. In this paper, we develop a fairness-aware agnostic federated learning framework (AgnosticFair) to deal with the challenge of unknown testing distribution. We use kernel reweighing functions to assign a reweighing value on each training sample in both loss function and fairness constraint. Therefore, the centralized model built from AgnosticFair can achieve high accuracy and fairness guarantee on unknown testing data. Moreover, the built model can be directly applied to local sites as it guarantees fairness on local data distributions. To our best knowledge, this is the first work to achieve fairness in federated learning. Experimental results on two real datasets demonstrate the effectiveness in terms of both utility and fairness under data shift scenarios.
ChipQA: No-Reference Video Quality Prediction via Space-Time Chips

We propose a new model for no-reference video quality assessment (VQA). Our approach uses a new idea of highly-localized space-time (ST) slices called Space-Time Chips (ST Chips). ST Chips are localized cuts of video data along directions that implicitly capture motion. We use perceptually-motivated bandpass and normalization models to first process the video data, and then select oriented ST Chips based on how closely they fit parametric models of natural video statistics. We show that the parameters that describe these statistics can be used to reliably predict the quality of videos, without the need for a reference video. The proposed method implicitly models ST video naturalness, and deviations from naturalness. We train and test our model on several large VQA databases, and show that our model achieves state-of-the-art performance at reduced cost, without requiring motion computation.