cssi/SciSciGPT-SciSciNet huggingface dataset:
The Power of a Single Qubit: Two-Way Quantum Finite Automata and the Word Problem

The two-way finite automaton with quantum and classical states (2QCFA), defined by Ambainis and Watrous, is a model of quantum computation whose quantum part is extremely limited; however, as they showed, 2QCFA are surprisingly powerful: a 2QCFA, with a single qubit, can recognize, with bounded error, the language L_{eq} = {a^m b^m :m ∈ ℕ} in expected polynomial time and the language L_{pal} = {w ∈ {a,b}^*:w is a palindrome} in expected exponential time. We further demonstrate the power of 2QCFA by showing that they can recognize the word problems of many groups. In particular 2QCFA, with a single qubit and algebraic number transition amplitudes, can recognize, with bounded error, the word problem of any finitely generated virtually abelian group in expected polynomial time, as well as the word problems of a large class of linear groups in expected exponential time. This latter class (properly) includes all groups with context-free word problem. We also exhibit results for 2QCFA with any constant number of qubits. As a corollary, we obtain a direct improvement on the original Ambainis and Watrous result by showing that L_{eq} can be recognized by a 2QCFA with better parameters. As a further corollary, we show that 2QCFA can recognize certain non-context-free languages in expected polynomial time. In a companion paper, we prove matching lower bounds, thereby showing that the class of languages recognizable with bounded error by a 2QCFA in expected subexponential time is properly contained in the class of languages recognizable with bounded error by a 2QCFA in expected exponential time.
Fluid MPC: Secure Multiparty Computation with Dynamic Participants

Existing approaches to secure multiparty computation (MPC) require all participants to commit to the entire duration of the protocol. As interest in MPC continues to grow, it is inevitable that there will be a desire to use it to evaluate increasingly complex functionalities, resulting in computations spanning several hours or days.
Optimal control of a production-inventory system with both backorders and lost sales

We consider the optimal control of a production inventory-system with a single product and two customer classes where items are produced one unit at a time. Upon arrival, customer orders can be fulfilled from existing inventory, if there is any, backordered, or rejected. The two classes are differentiated by their backorder and lost sales costs. At each decision epoch, we must determine whether or not to produce an item and if so, whether to use this item to increase inventory or to reduce backlog. At each decision epoch, we must also determine whether or not to satisfy demand from a particular class (should one arise), backorder it, or reject it. In doing so, we must balance inventory holding costs against the costs of backordering and lost sales. We formulate the problem as a Markov decision process and use it to characterize the structure of the optimal policy. We show that the optimal policy can be described by three state-dependent thresholds: a production base-stock level and two order-admission levels, one for each class. The production base-stock level determines when production takes place and how to allocate items that are produced. This base-stock level also determines when orders from the class with the lower shortage costs (Class 2) are backordered and not fulfilled from inventory. The order-admission levels determine when orders should be rejected. We show that the threshold levels are monotonic (either nonincreasing or nondecreasing) in the backorder level of Class 2. We also characterize analytically the sensitivity of these thresholds to the various cost parameters. Using numerical results, we compare the performance of the optimal policy against several heuristics and show that those that do not allow for the possibility of both backordering and rejecting orders can perform poorly. © 2010 Wiley Periodicals, Inc. Naval Research Logistics 57: 252-265, 2010
Incidence of Venous Thromboembolism in Critically Ill Coronavirus Disease 2019 Patients Receiving Prophylactic Anticoagulation.

OBJECTIVES: One of the defining features of the novel coronavirus disease 2019 infection has been high rates of venous thromboses. The present study aimed to describe the prevalence of venous thromboembolism in critically ill patients receiving different regimens of prophylactic anticoagulation. DESIGN: Single-center retrospective review using data from patients with confirmed severe acute respiratory syndrome coronavirus 2 requiring intubation. SETTING: Tertiary-care center in Indianapolis, IN, United States. PATIENTS: Patients hospitalized at international units Health Methodist Hospital with severe acute respiratory syndrome coronavirus 2 requiring intubation between March 23, 2020, and April 8, 2020, who underwent ultrasound evaluation for venous thrombosis. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 45 patients were included. Nineteen of 45 patients (42.2%) were found to have deep venous thrombosis. Patients found to have deep venous thrombosis had no difference in time to intubation (p = 0.97) but underwent ultrasound earlier in their hospital course (p = 0.02). Sequential Organ Failure Assessment scores were similar between the groups on day of intubation and day of ultrasound (p = 0.44 and p = 0.07, respectively). D-dimers were markedly higher in patients with deep venous thrombosis, both for maximum value and value on day of ultrasound (p < 0.01 for both). Choice of prophylactic regimen was not related to presence of deep venous thrombosis (p = 0.35). Ultrasound evaluation is recommended if D-dimer is greater than 2,000 ng/mL (sensitivity 95%, specificity 46%) and empiric anticoagulation considered if D-dimer is greater than 5,500 ng/mL (sensitivity 53%, specificity 88%). CONCLUSIONS: Deep venous thrombosis is very common in critically ill patients with coronavirus disease 2019. There was no difference in incidence of deep venous thrombosis among different pharmacologic prophylaxis regimens, although our analysis is limited by small sample size. D-dimer values are elevated in the majority of these patients, but there may be thresholds at which screening ultrasound or even empiric systemic anticoagulation is indicated.
Functional anatomy of the full-length CXCR4-CXCL12 complex systematically dissected by quantitative model-guided mutagenesis.

Because of their prominent roles in development, cancer, and HIV, the chemokine receptor CXCR4 and its ligand CXCL12 have been the subject of numerous structural and functional studies, but the determinants of ligand binding, selectivity, and signaling are still poorly understood. Here, building on our latest structural model, we used a systematic mutagenesis strategy to dissect the functional anatomy of the CXCR4-CXCL12 complex. Key charge swap mutagenesis experiments provided evidence for pairwise interactions between oppositely charged residues in the receptor and chemokine, confirming the accuracy of the predicted orientation of the chemokine relative to the receptor and providing insight into ligand selectivity. Progressive deletion of N-terminal residues revealed an unexpected contribution of the receptor N terminus to chemokine signaling. This finding challenges a longstanding “two-site” hypothesis about the essential features of the receptor-chemokine interaction in which the N terminus contributes only to binding affinity. Our results suggest that although the interaction of the chemokine N terminus with the receptor-binding pocket is the key driver of signaling, the signaling amplitude depends on the extent to which the receptor N terminus binds the chemokine. Together with systematic characterization of other epitopes, these data enable us to propose an experimentally consistent structural model for how CXCL12 binds CXCR4 and initiates signal transmission through the receptor transmembrane domain.
A class of distributed event-triggered average consensus algorithms for multi-agent systems

This paper proposes a class of distributed event-triggered algorithms that solve the average consensus problem in multi-agent systems. By designing events such that a specifically chosen Lyapunov f...
STAGdb: a 30K SNP genotyping array and Science Gateway for Acropora corals and their dinoflagellate symbionts.

Standardized identification of genotypes is necessary in animals that reproduce asexually and form large clonal populations such as coral. We developed a high-resolution hybridization-based genotype array coupled with an analysis workflow and database for the most speciose genus of coral, Acropora, and their symbionts. We designed the array to co-analyze host and symbionts based on bi-allelic single nucleotide polymorphisms (SNP) markers identified from genomic data of the two Caribbean Acropora species as well as their dominant dinoflagellate symbiont, Symbiodinium ‘fitti’. SNPs were selected to resolve multi-locus genotypes of host (called genets) and symbionts (called strains), distinguish host populations and determine ancestry of coral hybrids between Caribbean acroporids. Pacific acroporids can also be genotyped using a subset of the SNP loci and additional markers enable the detection of symbionts belonging to the genera Breviolum, Cladocopium, and Durusdinium. Analytic tools to produce multi-locus genotypes of hosts based on these SNP markers were combined in a workflow called the Standard Tools for Acroporid Genotyping (STAG). The STAG workflow and database are contained within a customized Galaxy environment (https://coralsnp.science.psu.edu/galaxy/), which allows for consistent identification of host genet and symbiont strains and serves as a template for the development of arrays for additional coral genera. STAG data can be used to track temporal and spatial changes of sampled genets necessary for restoration planning and can be applied to downstream genomic analyses. Using STAG, we uncover bi-directional hybridization between and population structure within Caribbean acroporids and detect a cryptic Acroporid species in the Pacific.
Association between DNA methylation levels in brain tissue and late-life depression in community-based participants.

Major depressive disorder (MDD) arises from a combination of genetic and environmental risk factors and DNA methylation is one of the molecular mechanisms through which these factors can manifest. However, little is known about the epigenetic signature of MDD in brain tissue. This study aimed to investigate associations between brain tissue-based DNA methylation and late-life MDD. We performed a brain epigenome-wide association study (EWAS) of late-life MDD in 608 participants from the Religious Order Study and the Rush Memory and Aging Project (ROS/MAP) using DNA methylation profiles of the dorsal lateral prefrontal cortex generated using the Illumina HumanMethylation450 Beadchip array. We also conducted an EWAS of MDD in each sex separately. We found epigenome-wide significant associations between brain tissue-based DNA methylation and late-life MDD. The most significant and robust association was found with altered methylation levels in the YOD1 locus (cg25594636, p value = 2.55 × 10−11; cg03899372, p value = 3.12 × 10−09; cg12796440, p value = 1.51 × 10−08, cg23982678, p value = 7.94 × 10−08). Analysis of differentially methylated regions (p value = 5.06 × 10−10) further confirmed this locus. Other significant loci include UGT8 (cg18921206, p value = 1.75 × 10−08), FNDC3B (cg20367479, p value = 4.97 × 10−08) and SLIT2 (cg10946669, p value = 8.01 × 10−08). Notably, brain tissue-based methylation levels were strongly associated with late-life MDD in men more than in women. We identified altered methylation in the YOD1, UGT8, FNDC3B, and SLIT2 loci as new epigenetic factors associated with late-life MDD. Furthermore, our study highlights the sex-specific molecular heterogeneity of MDD.
Transmission of sound through nonuniform circular ducts with compressible mean flows

An acoustic theory is developed to determine the sound transmission and attenuation through an infinite, hard-walled or lined, circular duct carrying compressible, sheared, mean flows and having a variable cross section. The theory is applicable to large as well as small axial variations, as long as the mean flow does not separate. Although the theory is described for circular ducts, it is applicable to other duct configurations - annular, two dimensional, and rectangular. The theory is described for the linear problem, but the technique is general and has the advantage of being applicable to the nonlinear case as well as the linear case. The technique is based on solving for the envelopes of the quasi-parallel acoustic modes that exist in the duct instead of solving for the actual wave. A computer program was developed. The mean flow model consists of a one dimensional flow in the core and a quarter-sine profile in the boundary layer. Results are presented for the reflection and transmission coefficients in ducts with varying slopes and carrying different mean flows.
Development of a Novel Class of Self-Assembling dsRNA Cancer Therapeutics: A Proof-of-Concept Investigation.

Cancer has proven to be an extremely difficult challenge to treat. Several fundamental issues currently underlie cancer treatment, including differentiating self from nonself, functional coupling of the recognition and therapeutic components of various therapies, and the propensity of cancerous cells to develop resistance to common treatment modalities via evolutionary pressure. Given these limitations, there is an increasing need to develop an all-encompassing therapeutic that can uniquely target malignant cells, decouple recognition from treatment, and overcome evolutionarily driven cancer resistance. We describe herein a new class of programmable self-assembling double-stranded RNA (dsRNA)-based cancer therapeutics that uniquely targets aberrant genetic sequences and in a functionally decoupled manner, undergoes oncogenic RNA-activated displacement (ORAD), initiating a therapeutic cascade that induces apoptosis and immune activation. As a proof of concept, we show that RNA strands targeting the EWS/Fli1 fusion gene in Ewing sarcoma cells that are end blocked with phosphorothioate bonds and additionally sealed with a 2′-deoxyuridine (2′-U)-modified DNA protector can be used to induce specific and potent killing of cells containing the target oncogenic sequence but not wild type.
Development and assessment of adjoint sensitivity analysis method for transient two-phase flow simulations

Abstract The adjoint method is efficient for calculating sensitivities of a few responses to a large number of parameters. The cost of solving an adjoint equation is comparable to the cost of solving the forward governing equation. Once the adjoint solution is available, the sensitivities to any number of parameters can be calculated with little effort. There are two methods to develop the adjoint equations: continuous method and discrete method. In the continuous method, the control theory is applied to the forward governing equation and produces an analytical partial differential equation for solving the adjoint variable; in the discrete method, the control theory is applied to the discrete form of the forward governing equation and produces a linear system of equations for solving the adjoint variable. In this article, an adjoint sensitivity analysis framework is developed using both the continuous and discrete methods. These two methods are assessed with one steady-state and one transient test cases. Adjoint sensitivities from both methods are verified by sensitivities calculated with a perturbation method. Adjoint sensitivities from both methods are physically reasonable and match each other. Though continuous method is computationally more efficient than the discrete method, difficulties are observed in solving the continuous adjoint equation for cases where the adjoint equation contains sharp discontinuities in the source terms. In such cases, the continuous method is not as robust as the discrete adjoint method.
Pathogen Infection Recovery Probability (PIRP) Versus Proinflammatory Anti-Pathogen Species (PIAPS) Levels: Modelling and Therapeutic Strategies

CoVID-19 pandemic due to SARS-CoV-2 virus has been spreading rapidly worldwide since late 2019, and it may become one of the largest pandemic events in modern human history if out of control. It appears most of the CoVID-19 infection resulted deaths are mainly due to severe hypoxia from dysfunction of the lung, and that could be attributed to host’s immunodysfunctions particularly hyperinflammatory type disorders or allergic reaction. In this brief review and study, a mathematical model is proposed to correlate the Pathogen Infection Recovery Probability (PIRP) versus Proinflammatory Anti-Pathogen Species (PIAPS) levels, where a maximum PIRP is expected when the PIAPS levels are equal to or around PIAPS equilibrium levels at the pathogen elimination or clearance onset. Based on this model, rational or effective therapeutic strategies at right stages or timing, with right type of agents (immuno-stimulators or immuno-suppressors), and right dosages, could be designed and implemented that are expected to effectively achieve maximum PIRP or reduce the mortality.
Near-surface characterization using a roadside distributed acoustic sensing array

Abstract Due to the broadband nature of distributed acoustic sensing (DAS) measurement, a roadside section of the Stanford DAS-2 array can record seismic signals from various sources. For example, ...
Bilaterally Mirrored Movements Improve the Accuracy and Precision of Training Data for Supervised Learning of Neural or Myoelectric Prosthetic Control

Intuitive control of prostheses relies on training algorithms to correlate biological recordings to motor intent. The quality of the training dataset is critical to run-time performance, but it is difficult to label hand kinematics accurately after the hand has been amputated. We quantified the accuracy and precision of labeling hand kinematics for two different training approaches: 1) assuming a participant is perfectly mimicking predetermined motions of a prosthesis (mimicked training), and 2) assuming a participant is perfectly mirroring their contralateral hand during identical bilateral movements (mirrored training). We compared these approaches in non-amputee individuals, using an infrared camera to track eight different joint angles of the hands in real-time. Aggregate data revealed that mimicked training does not account for biomechanical coupling or temporal changes in hand posture. Mirrored training was significantly more accurate and precise at labeling hand kinematics. However, when training a modified Kalman filter to estimate motor intent, the mimicked and mirrored training approaches were not significantly different. The results suggest that the mirrored training approach creates a more faithful but more complex dataset. Advanced algorithms, more capable of learning the complex mirrored training dataset, may yield better run-time prosthetic control.
Autonomous Control of a Tendon-driven Robotic Limb with Elastic Elements Reveals that Added Elasticity can Enhance Learning

Passive elastic elements can contribute to stability, energetic efficiency, and impact absorption in both biological and robotic systems. They also add dynamical complexity which makes them more challenging to model and control. The impact of this added complexity to autonomous learning has not been thoroughly explored. This is especially relevant to tendon-driven limbs whose cables and tendons are inevitably elastic. Here, we explored the efficacy of autonomous learning and control on a simulated bio-plausible tendon-driven leg across different tendon stiffness values. We demonstrate that increasing stiffness of the simulated muscles can require more iterations for the inverse map to converge but can then perform more accurately, especially in discrete tasks. Moreover, the system is robust to subsequent changes in muscle stiffnesses and can adapt on-the-go within 5 attempts. Lastly, we test the system for the functional task of locomotion and found similar effects of muscle stiffness to learning and performance. Given that a range of stiffness values led to improved learning and maximized performance, we conclude the robot bodies and autonomous controllers—at least for tendon-driven systems—can be co-developed to take advantage of elastic elements. Importantly, this opens also the door to development efforts that recapitulate the beneficial aspects of the co-evolution of brains and bodies in vertebrates.
Identifying signaling genes in spatial single cell expression data.

MOTIVATION Recent technological advances enable the profiling of spatial single-cell expression data. Such data present a unique opportunity to study cell-cell interactions and the signaling genes that mediate them. However, most current methods for the analysis of these data focus on unsupervised descriptive modeling, making it hard to identify key signaling genes and quantitatively assess their impact. RESULTS We developed a Mixture of Experts for Spatial Signaling genes Identification (MESSI) method to identify active signaling genes within and between cells. The mixture of experts strategy enables MESSI to subdivide cells into subtypes. MESSI relies on multi-task learning using information from neighboring cells to improve the prediction of response genes within a cell. Applying the methods to three spatial single-cell expression datasets, we show that MESSI accurately predicts the levels of response genes, improving upon prior methods and provides useful biological insights about key signaling genes and subtypes of excitatory neuron cells. AVAILABILITY AND IMPLEMENTATION MESSI is available at: https://github.com/doraadong/MESSI. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Motivic volumes of fibers of tropicalization

Let $T$ be an algebraic torus over an algebraically closed field, let $X$ be a smooth closed subvariety of a $T$-toric variety such that $U = X \cap T$ is not empty, and let $\mathscr{L}(X)$ be the arc scheme of $X$. We define a tropicalization map on $\mathscr{L}(X) \setminus \mathscr{L}(X \setminus U)$, the set of arcs of $X$ that do not factor through $X \setminus U$. We show that each fiber of this tropicalization map is a constructible subset of $\mathscr{L}(X)$ and therefore has a motivic volume. We prove that if $U$ has a compactification with simple normal crossing boundary, then the generating function for these motivic volumes is rational, and we express this rational function in terms of certain lattice maps constructed in Hacking, Keel, and Tevelev's theory of geometric tropicalization. We explain how this result, in particular, gives a formula for Denef and Loeser's motivic zeta function of a polynomial. To further understand this formula, we also determine precisely which lattice maps arise in the construction of geometric tropicalization.
Root Cause Analysis and Its Effect on Auditors’ Judgments and Decisions in an Integrated Audit

This study evaluates whether auditor use of root cause analysis (RCA) for an identified client misstatement affects auditors’ assessments of underlying control issues and materiality in an integrated audit setting. We also test whether auditor cognitive style moderates these effects given prior findings that a misfit between task structure and cognitive style undermines performance. This research is motivated by concerns about integrated audit quality and auditors failing to consider control deficiencies indicated by client misstatements. We randomly assigned 147 auditors to four RCA treatments (No RCA, Unstructured RCA, Structured “5 Whys” RCA, Structured “Fishbone” RCA). As predicted, the results suggest that auditors using (not using) structured RCA are more (less) likely to identify control-related root causes of a financial misstatement and judge the misstatement to be more (less) material. Also consistent with our prediction, we find that the assessed severity of identified control deficiencies mediates RCA’s effect on materiality judgments. Finally, the materiality judgment results reveal the expected significant interaction between structured RCA method and auditor cognitive style, suggesting the importance of allowing flexibility in the specific RCA method applied in practice. Overall, the study’s results provide evidence that auditor use of RCA as a judgment framework prompts deeper evaluation of audit findings and stronger consideration of critical links between financial reporting and related internal controls in integrated audit settings.
Active nonlocal metasurfaces

Actively tunable and reconfigurable wavefront shaping by optical metasurfaces poses a significant technical challenge often requiring unconventional materials engineering and nanofabrication. Most wavefront-shaping metasurfaces can be considered 'local' in that their operation depends on the responses of individual meta-units. In contrast, 'nonlocal' metasurfaces function based on the modes supported by many adjacent meta-units, resulting in sharp spectral features but typically no spatial control of the outgoing wavefront. Recently, nonlocal metasurfaces based on quasi-bound states in the continuum have been shown to produce designer wavefronts only across the narrow bandwidth of the supported Fano resonance. Here, we leverage the enhanced light-matter interactions associated with sharp Fano resonances to explore the active modulation of optical spectra and wavefronts by refractive index tuning and mechanical stretching. We experimentally demonstrate proof-of-principle thermo-optically tuned nonlocal metasurfaces made of silicon, and numerically demonstrate nonlocal metasurfaces that thermo-optically switch between distinct wavefront shapes. This meta-optics platform for thermally reconfigurable wavefront-shaping requires neither unusual materials and fabrication nor active control of individual meta-units.
Data-driven prediction and analysis of chaotic origami dynamics

Advances in machine learning have revolutionized capabilities in applications ranging from natural language processing to marketing to health care. Recently, machine learning techniques have also been employed to learn physics, but one of the formidable challenges is to predict complex dynamics, particularly chaos. Here, we demonstrate the efficacy of quasi-recurrent neural networks in predicting extremely chaotic behavior in multistable origami structures. While machine learning is often viewed as a “black box”, we conduct hidden layer analysis to understand how the neural network can process not only periodic, but also chaotic data in an accurate manner. Our approach shows its effectiveness in characterizing and predicting chaotic dynamics in a noisy environment of vibrations without relying on a mathematical model of origami systems. Therefore, our method is fully data-driven and has the potential to be used for complex scenarios, such as the nonlinear dynamics of thin-walled structures and biological membrane systems. Predicting chaotic behaviour is challenging due to the sensitivity to initial conditions, noise, the environment, and unknown factors. Here, the authors apply quasi-recurrent neural networks to predict both periodic and chaotic dynamics of the triangulated cylindrical origami cells, and provide an analysis of the hidden units’ distinctive responses.
Robust, Occlusion-aware Pose Estimation for Objects Grasped by Adaptive Hands

Many manipulation tasks, such as placement or within-hand manipulation, require the object's pose relative to a robot hand. The task is difficult when the hand significantly occludes the object. It is especially hard for adaptive hands, for which it is not easy to detect the finger's configuration. In addition, RGB-only approaches face issues with texture-less objects or when the hand and the object look similar. This paper presents a depth-based framework, which aims for robust pose estimation and short response times. The approach detects the adaptive hand's state via efficient parallel search given the highest overlap between the hand's model and the point cloud. The hand's point cloud is pruned and robust global registration is performed to generate object pose hypotheses, which are clustered. False hypotheses are pruned via physical reasoning. The remaining poses' quality is evaluated given agreement with observed data. Extensive evaluation on synthetic and real data demonstrates the accuracy and computational efficiency of the framework when applied on challenging, highly-occluded scenarios for different object types. An ablation study identifies how the framework's components help in performance. This work also provides a dataset for in-hand 6D object pose estimation. Code and dataset are available at: https://github.com/wenbowen123/icra20-hand-object-pose
Importance of van der Waals interactions for ab initio studies of topological insulators.

We investigate the lattice and electronic structures of the bulk and surface of the prototypical layered topological insulators Bi2Se3and Bi2Te3using ab initio density functional methods, and systematically compare the results of different methods of including van der Waals (vdW) interactions. We show that the methods utilizing semi-empirical energy corrections yield accurate descriptions of these materials, with the most precise results obtained by properly accounting for the long-range tail of the vdW interactions. The bulk lattice constants, distances between quintuple layers and the Dirac velocity of the topological surface states (TSS) are all in excellent agreement with experiment. In Bi2Te3, hexagonal warping of the energy dispersion leads to complex spin textures of the TSS at moderate energies, while in Bi2Se3these states remain almost perfectly helical away from the Dirac point, showing appreciable signs of hexagonal warping at much higher energies, above the minimum of the bulk conduction band. Our results establish a framework for unified and systematic self-consistent first principles calculations of topological insulators in bulk, slab and interface geometries, and provides the necessary first step towards ab initio modeling of topological heterostructures.
Home Alone: Implications of COVID-19 for Mental Health

Abstract Rationale The outbreak of the 2019 novel coronavirus disease (COVID-19) has induced a considerable degree of fear, emotional stress and anxiety among individuals around the world. Objective The objective of this study is to evaluate the relationship between COVID-19 virus cases per 1000 residents and mental health outcomes of individuals across the globe. Methods Using plausibly exogenous variation in daily country-level reports of new COVID-19 cases across the world, this study employs an individual-by-day global data set to assess the association between virus outbreak intensity and short-term measures of mental health outcomes. Results Results indicate that females are 20.02% (95 % CI [6.65 %, 33.39 %]) more likely than males to find life depressing, suggesting that they may bear a much larger mental health burden than males during the COVID-19 pandemic. The association between the pandemic and mental health is more pronounced among individuals staying at home for the past week, who are 14.81 % (95 % CI [3.46 %, 26.16 %]) more likely to feel anxious and 11.17 % (95 % CI [2.13 %, 20.21 %]) more likely to experience emotional instability than their counterparts. The association between virus outbreak intensity and the likelihood of anxiety among individuals staying at home increases with household size, ranging from 11.73 % (95 % CI [-4.65 %, 28.11 %]) among individuals with 0–1 members in the household to 21.02 % (95 % CI [5.73 %, 36.31 %]) among those with 4–8 members in the household. Conclusion These short-run estimates of mental health damages associated with COVID-19 imply that welfare losses from pandemics among individuals are enormous across the globe.
Razor: Provenance and Exploration in Model-Finding

Razor is a model-finder for first-order theories presented geometric form; geometric logic is a variant of first-order logic that focuses on “observable” properties. An important guiding principle of Razor is that it be accessible to users who are not necessarily expert in formal methods; application areas include software design, analysis of security protocols and policies, and configuration management. A core functionality of the tool is that it supports exploration of the space of models of a given input theory, as well as presentation of provenance information about the elements and facts of a model. The crucial mathematical tool is the ordering relation on models determined by homomorphism, and Razor prefers models that are minimal with respect to this homomorphismordering.
Connectivity of joins, cohomological quantifier elimination, and an algebraic Toda’s theorem

In this article, we use cohomological techniques to obtain an algebraic version of Toda’s theorem in complexity theory valid over algebraically closed fields of arbitrary characteristic. This result follows from a general ‘connectivity’ result in cohomology. More precisely, given a closed subvariety $$X \subset {\mathbb {P}}^{n}$$ over an algebraically closed field k, and denoting by $$\mathrm{J}^{[p]}(X) = \mathrm{J}(X,\mathrm{J}(X,\ldots ,\mathrm{J}(X,X)\cdots )$$ the p-fold iterated join of X with itself, we prove that the restriction homomorphism on (singular or $$\ell $$ -adic etale) cohomology $$\mathrm{H}^{i}({\mathbb {P}}^{N}) \rightarrow \mathrm{H}^{i}(\mathrm{J}^{[p]}(X))$$ , with $$N = (p+1)(n+1) - 1$$ , is an isomorphism for $$0 \le i < p$$ , and injective for $$i=p$$ . We also prove this result in the more general setting of relative joins for X over a base scheme S, where S is of finite type over k. We give several other applications of this connectivity result including a cohomological version of classical quantifier elimination in the first order theory of algebraically closed fields of arbitrary characteristic, and to obtain effective bounds on the Betti numbers of images of projective varieties under projection maps.
Essential support of Green biset functors via morphisms

In this note, we introduce a criterion to detect vanishing of essential algebras of a Green biset functor by means of morphisms. We introduce the inflation morphism and restriction morphism to prove that the essential supports of a Green biset functor and its shifted functors are the same. We use the kernel of the restriction to give a characterization of the seeds of the shifted functors.
The Dynamics, Destruction, and Survival of Supernova-formed Dust Grains

Observations have demonstrated that supernovae efficiently produce dust. This is consistent with the hypothesis that supernovae and asymptotic giant branch stars are the primary producers of dust in the Universe. However, there has been a longstanding question of how much of the dust detected in the interiors of young supernova remnants can escape into the interstellar medium. We present new hydrodynamical calculations of the evolution of dust grains that were formed in dense ejecta clumps within a Cas A-like remnant. We follow the dynamics of the grains as they decouple from the gas after their clump is hit by the reverse shock. They are subsequently subject to destruction by thermal and kinetic sputtering as they traverse the remnant. Grains that are large enough ($\sim 0.25\,\mu$m for silicates and $\sim 0.1\,\mu$m for carbonaceous grains) escape into the interstellar medium while smaller grains get trapped and destroyed. However, grains that reach the interstellar medium still have high velocities, and are subject to further destruction as they are slowed down. We find that for initial grain size distributions that include large ($\sim 0.25 - 0.5\,\mu$m) grains, 10--20\% of silicate grains can survive, while 30--50\% of carbonaceous grains survive even when the initial size distribution cuts off at smaller ($0.25\,\mu$m) sizes. For a 19 M$_{\odot}$ star similar to the progenitor of Cas A, up to 0.1 M$_{\odot}$ of dust can survive if the dust grains formed are large. Thus we show that supernovae under the right conditions can be significant sources of interstellar dust.
Multiscale dynamics of colloidal deposition and erosion in porous media.

Diverse processes—e.g., environmental pollution, groundwater remediation, oil recovery, filtration, and drug delivery—involve the transport of colloidal particles in porous media. Using confocal microscopy, we directly visualize this process in situ and thereby identify the fundamental mechanisms by which particles are distributed throughout a medium. At high injection pressures, hydrodynamic stresses cause particles to be continually deposited on and eroded from the solid matrix—notably, forcing them to be distributed throughout the entire medium. By contrast, at low injection pressures, the relative influence of erosion is suppressed, causing particles to localize near the inlet of the medium. Unexpectedly, these macroscopic distribution behaviors depend on imposed pressure in similar ways for particles of different charges, although the pore-scale distribution of deposition is sensitive to particle charge. These results reveal how the multiscale interactions between fluid, particles, and the solid matrix control how colloids are distributed in a porous medium.
Perturbation theory, effective field theory, and oscillations in the power spectrum

We explore the relationship between the nonlinear matter power spectrum and the various Lagrangian and Standard Perturbation Theories (LPT and SPT). We first look at it in the context of one dimensional (1-d) dynamics, where 1LPT is exact at the perturbative level and one can exactly resum the SPT series into the 1LPT power spectrum. Shell crossings lead to non-perturbative effects, and the PT ignorance can be quantified in terms of their ratio, which is also the transfer function squared in the absence of stochasticity. At the order of PT we work, this parametrization is equivalent to the results of effective field theory (EFT), and can thus be expanded in terms of the same parameters. We find that its radius of convergence is larger than the SPT loop expansion. The same EFT parametrization applies to all SPT loop terms and if stochasticity can be ignored, to all N-point correlators. In 3-d, the LPT structure is considerably more complicated, and we find that LPT models with parametrization motivated by the EFT exhibit running with k and that SPT is generally a better choice. Since these transfer function expansions contain free parameters that change with cosmological model their usefulness for broadband power is unclear. For this reason we test the predictions of these models on baryonic acoustic oscillations (BAO) and other primordial oscillations, including string monodromy models, for which we ran a series of simulations with and without oscillations. Most models are successful in predicting oscillations beyond their corresponding PT versions, confirming the basic validity of the model. We show that if primordial oscillations are localized to a scale q, the wiggles in power spectrum are approximately suppressed as exp[−k2Σ2(q)/2], where Σ(q) is rms displacement of particles separated by q, which saturates on large scales, and decreases as q is reduced. No oscillatory features survive past k ~ 0.5h/Mpc at z = 0.
Mechanical Control of a Microrod-Resonator Optical Frequency Comb

``Microcombs,'' frequency combs based on optical microcavities, are small in size and low in power consumption. They are seen as a very attractive alternative to traditional optical frequency combs based on tabletop lasers. Now, experimentalists advance the development of microcombs in two important ways: simplifying and speeding up their fabrication to just under one minute and stabilizing their frequency spacing with a precision of $5\ifmmode\times\else\texttimes\fi{}{10}^{-15}$ by controlling cavity modes with mechanical strain.
NNBench-X: A Benchmarking Methodology for Neural Network Accelerator Designs

The tremendous impact of deep learning algorithms over a wide range of application domains has encouraged a surge of neural network (NN) accelerator research. Facilitating the NN accelerator design calls for guidance from an evolving benchmark suite that incorporates emerging NN models. Nevertheless, existing NN benchmarks are not suitable for guiding NN accelerator designs. These benchmarks are either selected for general-purpose processors without considering unique characteristics of NN accelerators or lack quantitative analysis to guarantee their completeness during the benchmark construction, update, and customization. In light of the shortcomings of prior benchmarks, we propose a novel benchmarking methodology for NN accelerators with a quantitative analysis of application performance features and a comprehensive awareness of software-hardware co-design. Specifically, we decouple the benchmarking process into three stages: First, we characterize the NN workloads with quantitative metrics and select the representative applications for the benchmark suite to ensure diversity and completeness. Second, we refine the selected applications according to the customized model compression techniques provided by specific software-hardware co-design. Finally, we evaluate a variety of accelerator designs on the generated benchmark suite. To demonstrate the effectiveness of our benchmarking methodology, we conduct a case study of composing an NN benchmark from the TensorFlow Model Zoo and compress these selected models with various model compression techniques. Finally, we evaluate compressed models on various architectures, including GPU, Neurocube, DianNao, and Cambricon-X.
Compressed sensing recovery via nonconvex shrinkage penalties

The minimization of compressed sensing is often relaxed to , which yields easy computation using the shrinkage mapping known as soft thresholding, and can be shown to recover the original solution under certain hypotheses. Recent work has derived a general class of shrinkages and associated nonconvex penalties that better approximate the original penalty and empirically can recover the original solution from fewer measurements. We specifically examine p-shrinkage and firm thresholding. In this work, we prove that given data and a measurement matrix from a broad class of matrices, one can choose parameters for these classes of shrinkages to guarantee exact recovery of the sparsest solution. We further prove convergence of the algorithm iterative p-shrinkage (IPS) for solving one such relaxed problem.
On Generalized Monopole Spherical Harmonics and the Wave Equation of a Charged Massive Kerr Black Hole

We find linearly independent solutions of the Goncharov–Firsova equation in the case of a massive complex scalar field on a Kerr black hole. The solutions generalize, in some sense, the classical monopole spherical harmonic solutions previously studied in the massless cases.
Phase-locked patterns of the Kuramoto model on 3-regular graphs.

We consider the existence of non-synchronized fixed points to the Kuramoto model defined on sparse networks: specifically, networks where each vertex has degree exactly three. We show that “most” such networks support multiple attracting phase-locked solutions that are not synchronized and study the depth and width of the basins of attraction of these phase-locked solutions. We also show that it is common in “large enough” graphs to find phase-locked solutions where one or more of the links have angle difference greater than π/2.
Constraining the pitch angle of the galactic spiral arms in the Milky Way

Abstract We carry out analyses of some parameters of the galactic spiral arms, in the currently available samples. We present a catalog of the observed pitch angle for each spiral arm in the Milky Way disk. For each long spiral arm in the Milky Way, we investigate for each individual arm its pitch angle, as measured through different methods (parallaxes, twin-tangent arm, kinematical, etc), and assess their answers. Second, we catalog recent advances in the cartography of the Galaxy (global mean arm pitch, arm number, arm shape, interarm distance at the Sun). We statistically compare the results over a long time frame, from 1980 to 2017. Histograms of about 90 individual results published in recent years (since mid-2015) are compared to 66 earlier results (from 1980 to 2005), showing the ratio of primary to secondary peaks to have increased by almost a factor of 3. Similarly, many earlier discrepancies (expressed in r.m.s.) have been reduced by almost a factor 3.
On the first singularity for the Upsilon invariant of algebraic knots

We show that the location of the first singularity of the Upsilon function of an algebraic knot is determined by the first term of its Puiseux characteristic sequence. In many cases, this gives better bounds than the tau invariant on the genus of a cobordism between algebraic knots.
Signed Distance-based Deep Memory Recommender

Personalized recommendation algorithms learn a user's preference for an item by measuring a distance/similarity between them. However, some of the existing recommendation models (e.g., matrix factorization) assume a linear relationship between the user and item. This approach limits the capacity of recommender systems, since the interactions between users and items in real-world applications are much more complex than the linear relationship. To overcome this limitation, in this paper, we design and propose a deep learning framework called Signed Distance-based Deep Memory Recommender, which captures non-linear relationships between users and items explicitly and implicitly, and work well in both general recommendation task and shopping basket-based recommendation task. Through an extensive empirical study on six real-world datasets in the two recommendation tasks, our proposed approach achieved significant improvement over ten state-of-the-art recommendation models.
Sums of seven octahedral numbers

We show that for a large class of cubic polynomials $f$, every sufficiently large number can be written as a sum of seven positive values of $f$. As a special case, we show that every number greater than $e^{10^7}$ is a sum of seven positive octahedral numbers, where an octahedral number is a number of the form $\frac{2x^3+x}{3}$, reducing an open problem due to Pollock to a finite computation.
A reduced latency list decoding algorithm for polar codes

Long polar codes can achieve the capacity of arbitrary binary-input discrete memoryless channels under a low complexity successive cancelation (SC) decoding algorithm. But for polar codes with short and moderate code length, the decoding performance of the SC decoding algorithm is inferior. The cyclic redundancy check (CRC) aided successive cancelation list (SCL) decoding algorithm has better error performance than the SC decoding algorithm for short or moderate polar codes. However, the CRC aided SCL (CA-SCL) decoding algorithm still suffer from long decoding latency. In this paper, a reduced latency list decoding (RLLD) algorithm for polar codes is proposed. For the proposed RLLD algorithm, all rate-0 nodes and part of rate-1 nodes are decoded instantly without traversing the corresponding subtree. A list maximum-likelihood decoding (LMLD) algorithm is proposed to decode the maximum likelihood (ML) nodes and the remaining rate-1 nodes. Moreover, a simplified LMLD (SLMLD) algorithm is also proposed to reduce the computational complexity of the LMLD algorithm. Suppose a partial parallel list decoder architecture with list size L = 4 is used, for an (8192, 4096) polar code, the proposed RLLD algorithm can reduce the number of decoding clock cycles and decoding latency by 6.97 and 6.77 times, respectively.