cssi/SciSciGPT-SciSciNet huggingface dataset:
Rapid, Full-Scale Change to Virtual PCIT During the COVID-19 Pandemic: Implementation and Clinical Implications.

Health agencies call for the immediate mobilization of existing interventions in response to numerous child and family mental health concerns that have arisen as result of the COVID-19 pandemic. Answering this call, this pilot study describes the rapid, full-scale change from a primarily clinic-based Parent-Child Interaction Therapy (PCIT) model to a virtual service model (i.e., I-PCIT) in an academic and community-based program in Miami, Florida. First, we describe the virtual service training model our program developed and its implementation with 17 therapists (MAge = 32.35, 88.2% female, 47.1% Hispanic) to enable our clinic to shift from providing virtual services to a small portion of the families served (29.1%) to all of the families served. Second, we examine the effect of I-PCIT on child and caregiver outcomes during the 2-month stay-at-home period between March 16, 2020, and May 16, 2020, in 86 families (MChildAge = 4.75, 71% Hispanic). Due to the rapid nature of the current study, all active participants were transferred to virtual services, and therefore there was no comparison or control group, and outcomes represent the most recently available scores and not treatment completion. Results reveal that I-PCIT reduced child externalizing and internalizing problems and caregiver stress, and increased parenting skills and child compliance with medium to large effects even in the midst of the COVID-19 pandemic. Finally, the study examined components of our virtual service training model associated with the greatest improvements in child and caregiver outcomes. Preliminary findings revealed that locally and collaboratively developed strategies (e.g., online communities of practice, training videos and guides) had the strongest association with child and caregiver outcomes. Implications for virtual service delivery, implementation, and practice in the midst of the COVID-19 pandemic are discussed.
PyLog: An Algorithm-Centric Python-Based FPGA Programming and Synthesis Flow

The exploding complexity and computation efficiency requirements of applications are stimulating a strong demand for hardware acceleration with heterogeneous platforms such as FPGAs. However, a high-quality FPGA design is very hard to create as it requires FPGA expertise and a long design iteration time. In contrast, software applications are typically developed in a short development cycle, in high-level languages like Python, which is at a much higher level of abstraction than all existing hardware design flows. To close this gap between hardware design flows and software applications, and simplify FPGA programming, we create PyLog, a high-level, algorithm-centric Python-based programming and synthesis flow for FPGA. PyLog is powered by a set of compiler optimization passes and a type inference system to generate high-quality design. It abstracts away the implementation details and allows designers to focus on algorithm specification. PyLog captures more high-level computation patterns for better optimization than traditional HLS systems. PyLog also has a runtime for running PyLog code directly on FPGA platform without any extra code development. Evaluation shows that PyLog significantly improves FPGA design productivity and generates highly efficient FPGA designs that outperform highly optimized CPU and FPGA version by 3.17× and 1.24× on average.
Incorporating Symmetry into Deep Dynamics Models for Improved Generalization

Recent work has shown deep learning can accelerate the prediction of physical dynamics relative to numerical solvers. However, limited physical accuracy and an inability to generalize under distributional shift limit its applicability to the real world. We propose to improve accuracy and generalization by incorporating symmetries into convolutional neural networks. Specifically, we employ a variety of methods each tailored to enforce a different symmetry. Our models are both theoretically and experimentally robust to distributional shift by symmetry group transformations and enjoy favorable sample complexity. We demonstrate the advantage of our approach on a variety of physical dynamics including Rayleigh–Benard convection and real-world ocean currents and temperatures. Compare with image or text applications, our work is a significant step towards applying equivariant neural networks to high-dimensional systems with complex dynamics.
SANSCrypt: A Sporadic-Authentication-Based Sequential Logic Encryption Scheme

We propose SANSCrypt, a novel sequential logic encryption scheme to protect integrated circuits against reverse engineering. Previous sequential encryption methods focus on modifying the circuit state machine such that the correct functionality can be accessed by applying the correct key sequence only once. Considering the risk associated with one-time authentication, SANSCrypt adopts a new temporal dimension to logic encryption, by requiring the user to sporadically perform multiple authentications according to a protocol based on pseudorandom number generation. Analysis and validation results on a set of benchmark circuits show that SANSCrypt offers a substantial output corruptibility if the key sequences are applied incorrectly. Moreover, it exhibits an exponential resilience to existing attacks, including SAT-based attacks, while maintaining a reasonably low overhead.
Chinese public’s support for covid-19 surveillance in relation to the west

During the first two months after the first COVID-19 case was identified in Wuhan, Chinese authorities suppressed information, causing delayed disease control. When the outbreak became too severe to be kept from the public in January 2020, Chinese authorities radically changed their stance and started to battle COVID-19 with intensive efforts, facing the double challenge of a legitimacy crisis and the uncertain virus. Expanded surveillance protocols were established while intensive surveillance practices were introduced and mobilized. Many locations reported that people who had been wanted by the police for years were turning themselves in, as expanded and routinized surveillance made it more difficult for them to hide. Here, Liu discusses the Chinese residents' perceptions of surveillance during the pandemic.
Exploiting Natural Language for Efficient Risk-Aware Multi-Robot SaR Planning

The ability to develop a high-level understanding of a scene, such as perceiving danger levels, can prove valuable in planning multi-robot search and rescue (SaR) missions. In this work, we propose to uniquely leverage natural language descriptions from the mission commander in chief and image data captured by robots to estimate scene danger. Given a description and an image, a state-of-the-art deep neural network is used to assess a corresponding similarity score, which is then converted into a probabilistic distribution of danger levels. Because commonly used visio-linguistic datasets do not represent SaR missions well, we collect a large-scale image-description dataset from synthetic images taken from realistic disaster scenes and use it to train our machine learning model. A risk-aware variant of the Multi-robot Efficient Search Path Planning (MESPP) problem is then formulated to use the danger estimates in order to account for high-risk locations in the environment when planning the searchers’ paths. The problem is solved via a distributed approach based on Mixed-Integer Linear Programming. Our experiments demonstrate that our framework allows to plan safer yet highly successful search missions, abiding to the two most important aspects of SaR missions: to ensure both searchers’ and victim safety.
Church-State Relationships in America.

Acknowledgments Introduction Everson's History: The One (and Only?) Justification of No Aid The Sacred Canopy: Law and Lexicon of Church-State in the Founding Era Ratification of the Constitution: The "Whale's" Demands In Congress: Throwing a Tub, or Tubs, to the "Whale" Ratification: The "Whale" Satisfied The Founder's Worldview: The Sacred Canopy Explained The Meaning of the Words: Yesterday and Today Appendixes Bibliographical Essay Index
Batch Exploration With Examples for Scalable Robotic Reinforcement Learning

Learning from diverse offline datasets is a promising path towards learning general purpose robotic agents. However, a core challenge in this paradigm lies in collecting large amounts of meaningful data, while not depending on a human in the loop for data collection. One way to address this challenge is through task-agnostic exploration , where an agent attempts to explore without a task-specific reward function, and collect data that can be useful for any subsequent task. While these approaches have shown some promise in simple domains, they often struggle to explore the relevant regions of the state space in more challenging settings, such as vision-based robotic manipulation. This challenge stems from an objective that encourages exploring everything in a potentially vast state space. To mitigate this challenge, we propose to focus exploration on the important parts of the state space using weak human supervision . Concretely, we propose an exploration technique, Batch Exploration with Examples (BEE), that explores relevant regions of the state-space, guided by a modest number of human-provided images of important states. These human-provided images only need to be provided once at the beginning of data collection and can be acquired in a matter of minutes, allowing us to scalably collect diverse datasets, which can then be combined with any batch RL algorithm. We find that BEE is able to tackle challenging vision-based manipulation tasks both in simulation and on a real Franka Emika Panda robot, and observe that compared to task-agnostic and weakly-supervised exploration techniques, it (1) interacts more than twice as often with relevant objects, and (2) improves subsequent task performance when used in conjunction with offline RL.
Stimuli-Sensitive Hawkes Processes for Personalized Student Procrastination Modeling

Student procrastination and cramming for deadlines are major challenges in online learning environments, with negative educational and well-being side effects. Modeling student activities in continuous time and predicting their next study time are important problems that can help in creating personalized timely interventions to mitigate these challenges. However, previous attempts on dynamic modeling of student procrastination suffer from major issues: they are unable to predict the next activity times, cannot deal with missing activity history, are not personalized, and disregard important course properties, such as assignment deadlines, that are essential in explaining the cramming behavior. To resolve these problems, we introduce a new personalized stimuli-sensitive Hawkes process model (SSHP), by jointly modeling all student-assignment pairs and utilizing their similarities, to predict students’ next activity times even when there are no historical observations. Unlike regular point processes that assume a constant external triggering effect from the environment, we model three dynamic types of external stimuli, according to assignment availabilities, assignment deadlines, and each student’s time management habits. Our experiments on two synthetic datasets and two real-world datasets show a superior performance of future activity prediction, comparing with state-of-the-art models. Moreover, we show that our model achieves a flexible and accurate parameterization of activity intensities in students.
Game Theoretical Approach for Non-Overlapping Community Detection

Graph clustering, i.e., partitioning nodes or data points into non-overlapping clusters, can be beneficial in a large varieties of computer vision and machine learning applications. However, main graph clustering schemes such as spectral clustering cannot be applied to a large network due to prohibitive computational complexity required. While there exist methods applicable to large networks, these methods do not offer convincing comparisons against known ground truth. For the first time, this work conducts clustering algorithm performance evaluations on large networks (consisting of one million nodes) with ground truth information. Ideas and concepts from game theory are applied towards graph clustering to formulate a new proposed algorithm, Game Theoretical Approach for Clustering (GTAC). This theoretical framework is shown to be a generalization of both the Label Propagation and Louvain methods, offering an additional means of derivation and analysis. GTAC introduces a tuning parameter which allows variable algorithm performance in accordance with application needs. Experimentation shows that these GTAC algorithms offer scalability and tunability towards big data applications.
The Effect of Investor Attention on Fraud Discovery and Value Loss in Securities Class Action Litigation

We examine the effect of investor attention on value loss due to securities class action lawsuits and litigation‐based fraud discovery. We find that investor attention is positively associated with damage to corporate reputation and the magnitude of the value losses suffered by defendant firms. The reputational damage to defendant firms with higher investor attention is evident from poor operational performance and lower institutional ownership after filing. Investor attention is positively associated with the diffusion of information regarding fraud and it accelerates lawsuit filing. The effects of investor attention, however, are not subsumed by the severity of the fraud. Our results are robust to a battery of tests that addresses selection and endogeneity concerns.
Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent

We present a primal only derivation of Mirror Descent as a "partial" discretization of gradient flow on a Riemannian manifold where the metric tensor is the Hessian of the Mirror Descent potential. We contrast this discretization to Natural Gradient Descent, which is obtained by a "full" forward Euler discretization. This view helps shed light on the relationship between the methods and allows generalizing Mirror Descent to general Riemannian geometries, even when the metric tensor is {\em not} a Hessian, and thus there is no "dual."
Visualizing Multiwavelength Properties of Classified X-Ray Sources from Chandra Source Catalog

We present a simple but informative online tool to visualize the multiwavelength properties of about 2,700 X-ray sources from Chandra Source Catalog version 2.0 with literature verified classifications. Here we describe the catalogs that we used to collect the classifications and extract the MW properties of these sources, and the properties themselves. We also describe the design and functionality of the tool.
A specialization inequality for tropical complexes

We prove a specialization inequality relating the dimension of the complete linear series on a variety to the tropical complex of a regular semistable degeneration. Our result extends Baker's specialization inequality to arbitrary dimension.
Functional dynamic genetic effects on gene regulation are specific to particular cell types and environmental conditions.

The activity of the genes in a cell depends on the type of cell they are in, the interactions with other genes, the environment and genetics. Active genes produce a greater number of mRNA molecules, which act as messenger molecules to instruct the cell to produce proteins. The amount of mRNA molecules in cells can be measured to assess the levels of gene activity. Genes produce mRNAs through a process called transcription, and the collection of all the mRNA molecules in a cell is called the transcriptome. Cells obtained from human samples can be grown in the lab under different conditions, and this can be used to transform them into different types of cells. These cells can then be exposed to different treatments – such as specific chemicals – to understand how the environment affects them. Cells derived from different people may respond differently to the same treatment based on their unique genetics. Exposing different types of cells from many people to different treatments can help explain how genetics, the environment and cell type affect gene activity. Findley et al. grew three different types of cells from six different people in the lab. The cells were exposed to 28 different treatments, which reflect different environmental changes. Studying all these different factors together allowed Findley et al. to understand how genetics, cell type and environment affect the activity of over 53,000 genes. Around half of the effects due to an interaction between genetics and the environment and had not been seen in other larger studies of the transcriptome. Many of these newly observed changes are in genes that have connections to different diseases, including heart disease. The results of Findley et al. provide evidence indicating to which extent lifestyle and the environment can interact with an individual’s genetic makeup to impact gene activity and long-term health. The more researchers can understand these factors, the more useful they can be in helping to predict, detect and treat illnesses. The findings also show how genes and the environment interact, which may be relevant to understanding disease development. There is more work to be done to understand a wider range of environmental factors across more cell types. It will also be important to establish how this work on cells grown in the lab translates to human health.
Soliciting Stakeholders’ Fairness Notions in Child Maltreatment Predictive Systems

Recent work in fair machine learning has proposed dozens of technical definitions of algorithmic fairness and methods for enforcing these definitions. However, we still lack an understanding of how to develop machine learning systems with fairness criteria that reflect relevant stakeholders’ nuanced viewpoints in real-world contexts. To address this gap, we propose a framework for eliciting stakeholders’ subjective fairness notions. Combining a user interface that allows stakeholders to examine the data and the algorithm’s predictions with an interview protocol to probe stakeholders’ thoughts while they are interacting with the interface, we can identify stakeholders’ fairness beliefs and principles. We conduct a user study to evaluate our framework in the setting of a child maltreatment predictive system. Our evaluations show that the framework allows stakeholders to comprehensively convey their fairness viewpoints. We also discuss how our results can inform the design of predictive systems.
Inhalable Nanobody (PiN-21) prevents and treats SARS-CoV-2 infections in Syrian hamsters at ultra-low doses

Globally, there is an urgency to develop effective, low-cost therapeutic interventions for coronavirus disease 2019 (COVID-19). We previously generated the stable and ultrapotent homotrimeric Pittsburgh inhalable Nanobody 21 (PiN-21). Using Syrian hamsters that model moderate to severe COVID-19 disease, we demonstrate the high efficacy of PiN-21 to prevent and treat SARS-CoV-2 infection. Intranasal delivery of PiN-21 at 0.6 mg/kg protects infected animals from weight loss and substantially reduces viral burdens in both lower and upper airways compared to control. Aerosol delivery of PiN-21 facilitates deposition throughout the respiratory tract and dose minimization to 0.2 mg/kg. Inhalation treatment quickly reverses animals' weight loss after infection, decreases lung viral titers by 6 logs leading to drastically mitigated lung pathology, and prevents viral pneumonia. Combined with the marked stability and low production cost, this innovative therapy may provide a convenient and cost-effective option to mitigate the ongoing pandemic.
Contingency and chance erase necessity in the experimental evolution of ancestral proteins.

One of the most fundamental and unresolved questions in evolutionary biology is whether the outcomes of evolution are predictable. Is the diversity of life we see today the expected result of organisms adapting to their environment throughout history (also known as natural selection) or the product of random chance? Or did chance events early in history shape the paths that evolution could take next, determining the biological forms that emerged under natural selection much later? These questions are hard to study because evolution happened only once, long ago. To overcome this barrier, Xie, Pu, Metzger et al. developed an experimental approach that can evolve reconstructed ancestral proteins that existed deep in the past. Using this method, it is possible to replay evolution multiple times, from various historical starting points, under conditions similar to those that existed long ago. The end products of the evolutionary trajectories can then be compared to determine how predictable evolution actually is. Xie, Pu, Metzger et al. studied proteins belonging to the BCL-2 family, which originated some 800 million years ago. These proteins have diversified greatly over time in both their genetic sequences and their ability to bind to specific partner proteins called co-regulators. Xie, Pu, Metzger et al. synthesized BCL-2 proteins that existed at various times in the past. Each ancestral protein was then allowed to evolve repeatedly under natural selection to acquire the same co-regulator binding functions that evolved during history. At the end of each evolutionary trajectory, the genetic sequence of the resulting BCL-2 proteins was recorded. This revealed that the outcomes of evolution were almost completely unpredictable: trajectories initiated from the same ancestral protein produced proteins with very different sequences, and proteins launched from different ancestral starting points were even more dissimilar. Further experiments identified the mutations in each trajectory that caused changes in coregulator binding. When these mutations were introduced into other ancestral proteins, they did not yield the same change in function. This suggests that early chance events influenced each protein’s evolution in an unpredictable way by opening and closing the paths available to it in the future. This research expands our understanding of evolution on a molecular level whilst providing a new experimental approach for studying evolutionary drivers in more detail. The results suggest that BCL-2 proteins, in all their various forms, are unique products of a particular, unpredictable course of history set in motion by ancient chance events.
Safe Controller Synthesis With Tunable Input-to-State Safe Control Barrier Functions

To bring complex systems into real world environments in a safe manner, they will have to be robust to uncertainties—both in the environment and the system. This letter investigates the safety of control systems under input disturbances, wherein the disturbances can capture uncertainties in the system. Safety, framed as forward invariance of sets in the state space, is ensured with the framework of control barrier functions (CBFs). Concretely, the definition of input-to-state safety (ISSf) is generalized to allow the synthesis of non-conservative, tunable controllers that are provably safe under varying disturbances. This is achieved by formulating the concept of tunable input-to-state safe control barrier functions (TISSf-CBFs), which guarantee safety for disturbances that vary with state and, therefore, provide less conservative means of accommodating uncertainty. The theoretical results are demonstrated with a simple control system with input disturbance and also applied to design a safe connected cruise controller for a heavy duty truck.
Risk-Taking Incentives and Earnings Management: New Evidence†

We reexamine the positive association between stock option vega and earnings management previously documented in Armstrong et al. (2013; ALOT). In contrast to ALOT, prior empirical research and practitioner literature emphasizes earnings management’s goals of increasing stock price and reducing volatility. Specifically, we assess whether the association is robust to (i) employing discretionary accruals that are less prone to misspecification, (ii) focusing on a more recent time period, and (iii) including additional controls for period-specific factors. Our main findings are as follows. First, we fail to find a positive association between vega and earnings management after controlling for performance-related misspecification in discretionary accruals. Second, we find no association between vega and earnings management in a more recent time period, suggesting the results of ALOT may be sensitive to period-specific factors. Lastly, the positive association vanishes when we control for year fixed effects, growth opportunities, or monitoring, suggesting the original ALOT’s results may be sensitive to correlated, omitted variables. Overall, our results question the extent to which vega incentivizes earnings management. Our results may be of interest to boards of directors in designing executive compensation contracts; to regulators in crafting policies that maintain high levels of financial reporting quality; and to researchers seeking to identify settings where earnings management incentives are most salient.
Ribosome profiling elucidates differential gene expression in bundle sheath and mesophyll cells in maize

The efficiencies offered by C4 photosynthesis have motivated efforts to understand its biochemical, genetic, and developmental basis. Reactions underlying C4 traits in most C4 plants are partitioned between two cell types, bundle sheath (BS), and mesophyll (M) cells. RNA-seq has been used to catalog differential gene expression in BS and M cells in maize (Zea mays) and several other C4 species. However, the contribution of translational control to maintaining the distinct proteomes of BS and M cells has not been addressed. In this study, we used ribosome profiling and RNA-seq to describe translatomes, translational efficiencies, and microRNA abundance in BS- and M-enriched fractions of maize seedling leaves. A conservative interpretation of our data revealed 182 genes exhibiting cell type-dependent differences in translational efficiency, 31 of which encode proteins with core roles in C4 photosynthesis. Our results suggest that non-AUG start codons are used preferentially in upstream open reading frames of BS cells, revealed mRNA sequence motifs that correlate with cell type-dependent translation, and identified potential translational regulators that are differentially expressed. In addition, our data expand the set of genes known to be differentially expressed in BS and M cells, including genes encoding transcription factors and microRNAs. These data add to the resources for understanding the evolutionary and developmental basis of C4 photosynthesis and for its engineering into C3 crops.
Spectral analysis of the acoustic near field of a solid propellant rocket

Recently, Horne et al. (“Measurements of Unsteady Pressure Fluctuations in the Near-Field of a Solid Rocket Motor Plume,” International Journal of Aeroacoustics,Vol. 15, Nos. 4–5, July 2016, pp. 55...
FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks

We develop a fused matrix multiplication kernel that unifies sampled dense-dense matrix multiplication and sparsedense matrix multiplication under a single operation called FusedMM. By using user-defined functions, FusedMM can capture almost all computational patterns needed by popular graph embedding and GNN approaches.FusedMM is an order of magnitude faster than its equivalent kernels in Deep Graph Library. The superior performance of FusedMM comes from the low-level vectorized kernels, a suitable load balancing scheme and an efficient utilization of the memory bandwidth. FusedMM can tune its performance using a code generator and perform equally well on Intel, AMD and ARM processors. FusedMM speeds up an end-to-end graph embedding algorithm by up to $28 \times$ on different processors. The source code is available at https://github.com/HipGraph/FusedMM.
Communication-Efficient Tracking of Unknown, Spatially Correlated Signals in Ad-Hoc Wireless Sensor Networks: Two Machine Learning Approaches

A low-cost machine learning (ML) algorithm is proposed and discussed for spatial tracking of unknown, correlated signals in localized, ad-hoc wireless sensor networks. Each sensor is modeled as one neuron and a selected subset of these neurons are called to identify the spatial signal. The algorithm is implemented in two phases of spatial modeling and spatial tracking. The spatial signal is modeled using its M iso-contour lines at levels {lj}j=1M and those sensors that their sensor observations are in Δ margin of any of these levels report their sensor observations to the fusion center (FC) for spatial signal reconstruction. In spatial modeling phase, the number of these contour lines, their levels and a proper Δ are identified. In this phase, the algorithm may either use adaptive-weight stochastic gradient or scaled stochastic gradient method to select a proper Δ. Additive white Gaussian noise (AWGN) with zero mean is assumed along with the sensor observations. To reduce the observation noise’s effect, each sensor applies moving average filter on its observation to drastically reduce the effect of noise. The modeling performance, the cost and the convergence of the algorithm are discussed based on extensive computer simulations and reasoning. The algorithm is proposed for climate and environmental monitoring. In this paper, the percentage of wireless sensors that initiate a communication attempt is assumed as cost. The performance evaluation results show that the proposed spatial tracking approach is low-cost and can model the spatial signal over time with the same performance as that of spatial modeling.
Counterexamples to Hochschild-Kostant-Rosenberg in characteristic p

We give counterexamples to the degeneration of the Hochschild-Kostant-Rosenberg spectral sequence in characteristic p, both in the untwisted and twisted settings. We also prove that the de Rham-HP and crystalline-TP spectral sequences need not degenerate.
Neural Clinical Event Sequence Prediction through Personalized Online Adaptive Learning.

Clinical event sequences consist of thousands of clinical events that represent records of patient care in time. Developing accurate prediction models for such sequences is of a great importance for defining representations of a patient state and for improving patient care. One important challenge of learning a good predictive model of clinical sequences is patient-specific variability. Based on underlying clinical complications, each patient’s sequence may consist of different sets of clinical events. However, population-based models learned from such sequences may not accurately predict patient-specific dynamics of event sequences. To address the problem, we develop a new adaptive event sequence prediction framework that learns to adjust its prediction for individual patients through an online model update.
Trajectory Design Employing Convex Optimization for Landing on Irregularly Shaped Asteroids

The problem under investigation is how to rapidly design a propellant optimal powered descent trajectory to land a spacecraft on an arbitrarily shaped asteroid, subject to realistic constraints on ...
A synthetic defective interfering SARS-CoV-2

Viruses thrive by exploiting the cells they infect, but in order to replicate and infect other cells they must produce viral proteins. As a result, viruses are also susceptible to exploitation by defective versions of themselves that do not produce such proteins. A defective viral genome with deletions in protein-coding genes could still replicate in cells coinfected with full-length viruses. Such a defective genome could even replicate faster due to its shorter size, interfering with the replication of the virus. We have created a synthetic defective interfering version of SARS-CoV-2, the virus causing the Covid-19 pandemic, assembling parts of the viral genome that do not code for any functional protein but enable the genome to be replicated and packaged. This synthetic defective genome replicates three times faster than SARS-CoV-2 in coinfected cells, and interferes with it, reducing the viral load of infected cells by half in 24 hours. The synthetic genome is transmitted as efficiently as the full-length genome, suggesting the location of the putative packaging signal of SARS-CoV-2. A version of such a synthetic construct could be used as a self-promoting antiviral therapy: by enabling replication of the synthetic genome, the virus would promote its own demise.
Neutron stars with a crossover equation of state

The question of whether quark matter exists in neutron stars is a long standing one. Generally one finds that a first order phase transition from baryons to quarks softens the equation of state so much that the star would collapse into a black hole. We consider a crossover equation of state, similar to the crossover that is found in lattice QCD studies at finite temperature and zero or small baryon chemical potentials. We find that with reasonable parameters it may be possible to support neutron stars up to about 2.2 solar masses. In that case 1% to 10% of the pressure would be contributed by quark matter in the central core of the highest mass stars.
Entropy admissibility of the limit solution for a nonlocal model of traffic flow

We consider a conservation law model of traffic flow, where the velocity of each car depends on a weighted average of the traffic density $\rho$ ahead. The averaging kernel is of exponential type: $w_\varepsilon(s)=\varepsilon^{-1} e^{-s/\varepsilon}$. For any decreasing velocity function $v$, we prove that, as $\varepsilon\to 0$, the limit of solutions to the nonlocal equation coincides with the unique entropy-admissible solution to the scalar conservation law $\rho_t + (\rho v(\rho))_x=0$.
Constitutive modeling of dilute wormlike micelle solutions: Shear-induced structure and transient dynamics

Abstract We present a reformulation of the ‘reactive rod model’ (RRM) of Dutta and Graham (2018), a constitutive model for describing the behavior of dilute wormlike micelle solutions. The RRM treats wormlike micelle solutions as dilute suspensions of rigid Brownian rods undergoing reversible scission and growth in flow. Evolution equations for micelle orientation and stress contribution are coupled to a kinetic reaction equation for a collective micelle length, producing dynamic variations in the length and rotational diffusivity of the rods. This model has previously shown success in capturing many critical steady-state rheological features of dilute wormlike micelle solutions, particularly shear-thickening and -thinning, non-zero normal stress differences, and a reentrant shear stress–shear rate curve, and could fit a variety of steady state experimental data. The present work improves on this framework, which showed difficulty in capturing transient dynamics and high-shear behavior, by reformulating the kinetic equation for micelle growth on a more microstructural (though still highly idealized) basis. In particular, we allow for micelle growth associated with strong alignment of rods and breakage due to tensile stresses along the micelles. This new formulation captures both steady and transient shear rheology in good agreement with experiments. We also find good agreement with available steady state extensional rheology.
State Constrained Stochastic Optimal Control Using LSTMs

In this paper, we propose a new methodology for state constrained stochastic optimal control (SOC) problems. The solution is based on past work in solving SOC problems using forward-backward stochastic differential equations (FB-SDE). Our approach in solving the FBSDE utilizes a deep neural network (DNN), specifically Long Short-Term Memory (LSTM) networks. LSTMs are chosen to solve the FBSDE to address the curse of dimensionality, non-linearities, and long time horizons. In addition, the state constraints are incorporated using a hard penalty function, resulting in a controller that respects the constraint boundaries. Numerical instability that would be introduced by the penalty function is dealt with through an adaptive update scheme. The control design methodology is applicable to a large class of control problems. The performance and scalability of our proposed algorithm are demonstrated by numerical simulations.
Aircraft Fault Detection Using Real-Time Frequency Response Estimation

A real-time method for estimating time-varying aircraft frequency responses from input and output measurements was demonstrated. The Bat-4 subscale airplane was used with NASA Langley Research Center's AirSTAR unmanned aerial flight test facility to conduct flight tests and collect data for dynamic modeling. Orthogonal phase-optimized multisine inputs, summed with pilot stick and pedal inputs, were used to excite the responses. The aircraft was tested in its normal configuration and with emulated failures, which included a stuck left ruddervator and an increased command path latency. No prior knowledge of a dynamic model was used or available for the estimation. The longitudinal short period dynamics were investigated in this work. Time-varying frequency responses and stability margins were tracked well using a 20 second sliding window of data, as compared to a post-flight analysis using output error parameter estimation and a low-order equivalent system model. This method could be used in a real-time fault detection system, or for other applications of dynamic modeling such as real-time verification of stability margins during envelope expansion tests.
Distributional Metareasoning for Heuristic Search.

Heuristic search methods are widely used in many real-world autonomous systems. Yet, people always want to solve search problems that are larger than time allows. To address these challenging problems, even suboptimally, a planning agent should be smart enough to intelligently allocate its computational resources, to think carefully about where in the state space it should spend time searching. For finding optimal solutions, we must examine every node that is not provably too expensive. In contrast, to find suboptimal solutions when under time pressure, we need to be very selective about which nodes to examine. In this work, we will demonstrate that estimates of uncertainty, represented as belief distributions, can be used to drive search effectively. This type of algorithmic approach is known as metareasoning, which refers to reasoning about which reasoning to do. We will provide examples of improved algorithms for real-time search, bounded-cost search, and situated planning.
Active rheumatoid arthritis in a mouse model is not an independent risk factor for periprosthetic joint infection.

Introduction Periprosthetic joint infection (PJI) represents a devastating complication of total joint arthroplasty associated with significant morbidity and mortality. Literature suggests a possible higher incidence of periprosthetic joint infection (PJI) in patients with rheumatoid arthritis (RA). There is, however, no consensus on this purported risk nor a well-defined mechanism. This study investigates how collagen-induced arthritis (CIA), a validated animal model of RA, impacts infectious burden in a well-established model of PJI. Methods Control mice were compared against CIA mice. Whole blood samples were collected to quantify systemic IgG levels via ELISA. Ex vivo respiratory burst function was measured via dihydrorhodamine assay. Ex vivo Staphylococcus aureus Xen36 burden was measured directly via colony forming unit (CFU) counts and crystal violet assay to assess biofilm formation. In vivo, surgical placement of a titanium implant through the knee joint and inoculation with S. aureus Xen36 was performed. Bacterial burden was then quantified by longitudinal bioluminescent imaging. Results Mice with CIA demonstrated significantly higher levels of systemic IgG compared with control mice (p = 0.003). Ex vivo, there was no significant difference in respiratory burst function (p = 0.89) or S. aureus bacterial burden as measured by CFU counts (p = 0.91) and crystal violet assay (p = 0.96). In vivo, no significant difference in bacterial bioluminescence between groups was found at all postoperative time points. CFU counts of both the implant and the peri-implant tissue were not significantly different between groups (p = 0.82 and 0.80, respectively). Conclusion This study demonstrated no significant difference in S. aureus infectious burden between mice with CIA and control mice. These results suggest that untreated, active RA may not represent a significant intrinsic risk factor for PJI, however further mechanistic translational and clinical studies are warranted.
Stochastic optimization approaches for elective surgery scheduling with downstream capacity constraints: Models, challenges, and opportunities

Abstract Elective surgery patients have surgery in the operating room (OR), and then recover in one or more downstream recovery units for several consecutive hours or days after surgery. Upstream scheduling that focuses on OR alone or a resource-constrained scheduling approach that fails to account for the inherent uncertainty in surgery durations and postoperative downstream recovery times yield sub-optimal or infeasible schedules and, consequently, higher cost and reduced quality of care. However, modeling such uncertainties at multiple levels is challenging, especially with limited reliable data on the random parameters in the models. Moreover, sequencing of surgical and recovery activities, and the multiple conflicting objectives of all parties involved (including management, clinicians, patients), lead to a class of complex combinatorial and multi-criteria stochastic optimization problems. In this review, we focus on stochastic optimization (SO) approaches for elective surgery scheduling and downstream capacity planning. We describe the art of formulating and solving such a class of stochastic resource-constrained scheduling problems, provide an analysis of existing SO approaches and their challenges, and highlight areas of opportunity for developing tractable, implementable, and data-driven approaches that might be applicable within and outside healthcare operations, particularly where multiple entities/jobs share the same downstream limited resources.
Optimally-secure Coin-tossing against a Byzantine Adversary

Ben-Or and Linial (1985) introduced the full information model for coin-tossing protocols involving $n$ processors with unbounded computational power using a common broadcast channel for all their communications. For most adversarial settings, the characterization of the exact or asymptotically optimal protocols remains open. Furthermore, even for the settings where near-optimal asymptotic constructions are known, the exact constants or poly-logarithmic multiplicative factors involved are not entirely well-understood. This work studies $n$ -processor coin-tossing protocols where every processor broadcasts an arbitrary-length message once. An adaptive Byzantine adversary, based on the messages broadcast so far, can corrupt $k=1$ processor. A bias- $X$ coin-tossing protocol outputs 1 with probability $X$ ; otherwise, it outputs 0 with probability ( $1-X$ ). A coin-tossing protocol's insecurity is the maximum change in the output distribution (in the statistical distance) that a Byzantine adversary can cause. Our objective is to identify bias- $X$ coin-tossing protocols achieving near-optimal minimum insecurity for every $X\in[0,1]$ . Lichtenstein, Linial, and Saks (1989) studied bias- $X$ coin-tossing protocols in this adversarial model where each party broadcasts an independent and uniformly random bit. They proved that the elegant “threshold coin-tossing protocols” are optimal for all $n$ and $k$ . Furthermore, Goldwasser, Kalai, and Park (2015), Kalai, Komargodski, and Raz (2018), and Haitner and Karidi-Heller (2020) prove that $k=\mathcal{O}(\sqrt{n} \cdot \mathsf{polylog}(n)$ ) corruptions suffice to fix the output of any bias- $X$ coin-tossing protocol. These results encompass parties who send arbitrary-length messages, and each processor has multiple turns to reveal its entire message. We use an inductive approach to constructing coin-tossing protocols using a potential function as a proxy for measuring any bias- $X$ coin-tossing protocol's susceptibility to attacks in our adversarial model. Our technique is inherently constructive and yields protocols that minimize the potential function. It is incidentally the case that the threshold protocols minimize the potential function, even for arbitrary-length messages. We demonstrate that these coin-tossing protocols' insecurity is a 2-approximation of the optimal protocol in our adversarial model. For any other $X\in[0,1]$ that threshold protocols cannot realize, we prove that an appropriate (convex) combination of the threshold protocols is a 4-approximation of the optimal protocol. Finally, these results entail new (vertex) isoperimetric inequalities for density- $X$ subsets of product spaces of arbitrary-size alphabets.
TACo: Token-Aware Cascade Contrastive Learning for Video-Text Alignment

Contrastive learning has been widely used to train transformer-based vision-language models for video-text alignment and multi-modal representation learning. This paper presents a new algorithm called Token-Aware Cascade contrastive learning (TACo) that improves contrastive learning using two novel techniques. The first is the token-aware contrastive loss which is computed by taking into account the syntactic classes of words. This is motivated by the observation that for a video-text pair, the content words in the text, such as nouns and verbs, are more likely to be aligned with the visual contents in the video than the function words. Second, a cascade sampling method is applied to generate a small set of hard negative examples for efficient loss estimation for multi-modal fusion layers. To validate the effectiveness of TACo, in our experiments we finetune pretrained models for a set of downstream tasks including text-video retrieval (YouCook2, MSR-VTT and ActivityNet), video action step localization (CrossTask), video action segmentation (COIN). The results show that our models attain consistent improvements across different experimental settings over previous methods, setting new state-of-the-art on three public text-video retrieval benchmarks of YouCook2, MSR-VTT and ActivityNet.
Optimizing viral genome subsampling by genetic diversity and temporal distribution (TARDiS) for phylogenetics.

Summary TARDiS is a novel phylogenetic tool for optimal genetic subsampling. It optimizes both genetic diversity and temporal distribution through a genetic algorithm. Availability and implementation TARDiS, along with example data sets and a user manual, is available at https://github.com/smarini/tardis-phylogenetics.
Long-living excited states of a 2D diamagnetic exciton

Abstract Hydrogenic excited states of a 2D exciton are degenerate. In the presence of a weak magnetic field, the S -states with a zero momentum of the center of mass get coupled to the P -states with finite momentum of the center of mass. This field-induced coupling leads to a strong modification of the dispersion branches of the exciton spectrum. Namely, the lower branch acquires a shape of a “mexican hat” with a minimum at a finite momentum. At certain magnetic field, exciton branches exhibit a linear crossing, similarly to the spectrum of a 2D electron in the presence of spin–orbit coupling. While spin is not involved, degenerate S and P states play the role of the spin projections. Lifting of degeneracy due to diamagnetic shifts and deviation of electron–hole attraction from purely Coulomb suppresses the linear crossing.