cssi/SciSciGPT-SciSciNet huggingface dataset:
Trends in homelessness and injection practices among young urban and suburban people who inject drugs: 1997-2017

Abstract Background Among young people who inject drugs (PWID) homelessness is associated with numerous adverse psychosocial and health consequences, including risk of relapse and overdose, psychological distress and suicidality, limited treatment access, and injection practices that increase the risk of HIV and hepatitis C (HCV) transmission. Homeless PWID may also be less likely to access sterile syringes through pharmacies or syringe service programs. Methods This study applied random-effects meta-regression to examine trends over time in injection risk behaviors and homelessness among young PWID in Chicago and surrounding suburban and rural areas using data from 11 studies collected between 1997 and 2017. In addition, subject-level data were pooled to evaluate the effect of homelessness on risk behaviors across all studies using mixed effects logistic and negative binomial regression with random study effects. Results There was a significant increase in homelessness among young PWID over time, consistent with the general population trend of increasing youth homelessness. In mixed-effects regression, homelessness was associated with injection risk behaviors (receptive syringe sharing, syringe mediated sharing, equipment sharing) and exchange sex, though we detected no overall changes in risk behavior over time. Conclusions Increases over time in homelessness among young PWID highlight a need for research to understand factors contributing to youth homelessness to inform HIV/STI, HCV, and overdose prevention and intervention services for this population.
Fast margin maximization via dual acceleration

We present and analyze a momentum-based gradient method for training linear classifiers with an exponentially-tailed loss (e.g., the exponential or logistic loss), which maximizes the classification margin on separable data at a rate of $\widetilde{\mathcal{O}}(1/t^2)$. This contrasts with a rate of $\mathcal{O}(1/\log(t))$ for standard gradient descent, and $\mathcal{O}(1/t)$ for normalized gradient descent. This momentum-based method is derived via the convex dual of the maximum-margin problem, and specifically by applying Nesterov acceleration to this dual, which manages to result in a simple and intuitive method in the primal. This dual view can also be used to derive a stochastic variant, which performs adaptive non-uniform sampling via the dual variables.
Role of BMP signaling during early development of the annelid Capitella teleta.

Abstract The mechanisms regulating nervous system development are still unknown for a wide variety of taxa. In insects and vertebrates, bone morphogenetic protein (BMP) signaling plays a key role in establishing the dorsal-ventral (D-V) axis and limiting the neuroectoderm to one side of that axis, leading to speculation about the conserved evolution of centralized nervous systems. Studies outside of insects and vertebrates show a more diverse picture of what, if any role, BMP signaling plays in neural development across Bilateria. This is especially true in the morphologically diverse Spiralia (≈Lophotrochozoa). Despite several studies of D-V axis formation and neural induction in spiralians, there is no consensus for how these two processes are related, or whether BMP signaling may have played an ancestral role in either process. To determine the function of BMP signaling during early development of the spiralian annelid Capitella teleta, we incubated embryos and larvae in BMP4 protein for different amounts of time. Adding exogenous BMP protein to early-cleaving C. teleta embryos had a striking effect on formation of the brain, eyes, foregut, and ventral midline in a time-dependent manner. However, adding BMP did not block brain or VNC formation or majorly disrupt the D-V axis. We identified three key time windows of BMP activity. 1) BMP treatment around birth of the 3rd-quartet micromeres caused the loss of the eyes, radialization of the brain, and a reduction of the foregut, which we interpret as a loss of A- and C-quadrant identities with a possible trans-fate switch to a D-quadrant identity. 2) Treatment after the birth of micromere 4d induced formation of a third ectopic brain lobe, eye, and foregut lobe, which we interpret as a trans-fate switch of B-quadrant micromeres to a C-quadrant identity. 3) Continuous BMP treatment from late cleavage (4d ​+ ​12 ​h) through mid-larval stages resulted in a modest expansion of Ct-chrdl expression in the dorsal ectoderm and a concomitant loss of the ventral midline (neurotroch ciliary band). Loss of the ventral midline was accompanied by a collapse of the bilaterally-symmetric ventral nerve cord, although the total amount of neural tissue was not greatly affected. Our results compared with those from other annelids and molluscs suggest that BMP signaling was not ancestrally involved in delimiting neural tissue to one region of the D-V axis. However, the effects of ectopic BMP on quadrant-identity during cleavage stages may represent a non-axial organizing signal that was present in the last common ancestor of annelids and mollusks. Furthermore, in the last common ancestor of annelids, BMP signaling may have functioned in patterning ectodermal fates along the D-V axis in the trunk. Ultimately, studies on a wider range of spiralian taxa are needed to determine the role of BMP signaling during neural induction and neural patterning in the last common ancestor of this group. Ultimately, these comparisons will give us insight into the evolutionary origins of centralized nervous systems and body plans.
A Feynman–Kac formula for magnetic monopoles

We consider the quantum mechanics of a charged particle in the presence of Dirac’s magnetic monopole. Wave functions are sections of a complex line bundle and the magnetic potential is a connection...
FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding.

Large-scale cross-lingual language models (LM), such as mBERT, Unicoder and XLM, have achieved great success in cross-lingual representation learning. However, when applied to zero-shot cross-lingual transfer tasks, most existing methods use only single-language input for LM finetuning, without leveraging the intrinsic cross-lingual alignment between different languages that proves essential for multilingual tasks. In this paper, we propose FILTER, an enhanced fusion method that takes cross-lingual data as input for XLM finetuning. Specifically, FILTER first encodes text input in the source language and its translation in the target language independently in the shallow layers, then performs cross-language fusion to extract multilingual knowledge in the intermediate layers, and finally performs further language-specific encoding. During inference, the model makes predictions based on the text input in the target language and its translation in the source language. For simple tasks such as classification, translated text in the target language shares the same label as the source language. However, this shared label becomes less accurate or even unavailable for more complex tasks such as question answering, NER and POS tagging. To tackle this issue, we further propose an additional KL-divergence self-teaching loss for model training, based on auto-generated soft pseudo-labels for translated text in the target language. Extensive experiments demonstrate that FILTER achieves new state of the art on two challenging multilingual multi-task benchmarks, XTREME and XGLUE.
How Big Should Your Data Really Be? Data-Driven Newsvendor and the Transient of Learning

We study the classical newsvendor problem in which the decision-maker must trade-off underage and overage costs. In contrast to the typical setting, we assume that the decision-maker does not know the underlying distribution driving uncertainty but has only access to historical data. In turn, the key questions are how to map existing data to a decision and what type of performance to expect as a function of the data size. We analyze the classical setting with access to past samples drawn from the distribution (e.g., past demand), focusing not only on asymptotic performance but also on what we call the transient of learning, i.e., performance for arbitrary data sizes. We evaluate the performance of any algorithm through its worst-case relative expected regret, compared to an oracle with knowledge of the distribution. We provide the first finite sample exact analysis of the classical Sample Average Approximation (SAA) algorithm for this class of problems across all data sizes. This allows to uncover novel fundamental insights on the value of data: it reveals that tens of samples are sufficient to perform very efficiently but also that more data can lead to worse out-of-sample performance for SAA. We then focus on the general class of mappings from data to decisions without any restriction on the set of policies and derive an optimal algorithm as well as characterize its associated performance. This leads to significant improvements for limited data sizes, and allows to exactly quantify the value of historical information.
Retinotopic organization of visual cortex in human infants

Summary Vision develops rapidly during infancy, yet how visual cortex is organized during this period is unclear. In particular, it is unknown whether functional maps that organize the mature adult visual cortex are present in the infant striate and extrastriate cortex. Here, we test the functional maturity of infant visual cortex by performing retinotopic mapping with functional magnetic resonance imaging (fMRI). Infants aged 5–23 months had retinotopic maps, with alternating preferences for vertical and horizontal meridians indicating the boundaries of visual areas V1 to V4 and an orthogonal gradient of preferences from high to low spatial frequencies. The presence of multiple visual maps throughout visual cortex in infants indicates a greater maturity of extrastriate cortex than previously appreciated. The areas showed subtle age-related fine-tuning, suggesting that early maturation undergoes continued refinement. This early maturation of area boundaries and tuning may scaffold subsequent developmental changes.
Textual Features for Programming by Example

In Programming by Example, a system attempts to infer a program from input and output examples, generally by searching for a composition of certain base functions. Performing a naїve brute force search is infeasible for even mildly involved tasks. We note that the examples themselves often present clues as to which functions to compose, and how to rank the resulting programs. In text processing, which is our domain of interest, clues arise from simple textual features: for example, if parts of the input and output strings are permutations of one another, this suggests that sorting may be useful. We describe a system that learns the reliability of such clues, allowing for faster search and a principled ranking over programs. Experiments on a prototype of this system show that this learning scheme facilitates efficient inference on a range of text processing tasks.
Reidemeister moves in Gauss diagrams

Recognizing and performing Reidemeister moves in Gauss diagrams is a useful technique for manipulating virtual knots. For R1 and R2 moves, this process is well understood. We review these cases and then provide a new, elementary algorithm for recognizing R3 moves.
Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer

We sought to exploit the heterogeneity afforded by patient-derived tumor xenografts (PDX) to first, optimize and identify robust radiomic features to predict response to therapy in subtype-matched triple negative breast cancer (TNBC) PDX, and second, to implement PDX-optimized image features in a TNBC co-clinical study to predict response to therapy using machine learning (ML) algorithms. TNBC patients and subtype-matched PDX were recruited into a co-clinical FDG-PET imaging trial to predict response to therapy. One hundred thirty-one imaging features were extracted from PDX and human-segmented tumors. Robust image features were identified based on reproducibility, cross-correlation, and volume independence. A rank importance of predictors using ReliefF was used to identify predictive radiomic features in the preclinical PDX trial in conjunction with ML algorithms: classification and regression tree (CART), Naive Bayes (NB), and support vector machines (SVM). The top four PDX-optimized image features, defined as radiomic signatures (RadSig), from each task were then used to predict or assess response to therapy. Performance of RadSig in predicting/assessing response was compared to SUVmean, SUVmax, and lean body mass-normalized SULpeak measures. Sixty-four out of 131 preclinical imaging features were identified as robust. NB-RadSig performed highest in predicting and assessing response to therapy in the preclinical PDX trial. In the clinical study, the performance of SVM-RadSig and NB-RadSig to predict and assess response was practically identical and superior to SUVmean, SUVmax, and SULpeak measures. We optimized robust FDG-PET radiomic signatures (RadSig) to predict and assess response to therapy in the context of a co-clinical imaging trial.
Skyrmion Control of Majorana States in Planar Josephson Junctions

Planar Josephson junctions provide a versatile platform, alternative to the nanowire-based geometry, for the generation of the Majorana bound states, due to the additional phase tunability of the topological superconductivity. The proximity induction of chiral magnetism and superconductivity in a two-dimensional electron gas showed remarkable promise to manipulate topological superconductivity. Here, we consider a Josephson junction involving a skyrmion crystal and show that the chiral magnetism of the skyrmions can create and control the Majorana bound states without the requirement of an intrinsic Rashba spin-orbit coupling. Interestingly, the Majorana bound states in our geometry are realized robustly at zero phase difference at the junction. The skyrmion radius, being externally tunable by a magnetic field or a magnetic anisotropy, brings a unique control feature for the Majorana bound states. Majorana fermions are elusive particles which have proven extremely tricky to observe experimentally, with current efforts focused on hybrid superconducting devices. Here, the authors theoretically propose a set up combining a Josephson junction and a skyrmion crystal to create and control the Majorana bound states.
Versa: A Dataflow-Centric Multiprocessor with 36 Systolic ARM Cortex-M4F Cores and a Reconfigurable Crossbar-Memory Hierarchy in 28nm

We present Versa, an energy-efficient processor with 36 systolic ARM Cortex-M4F cores and a runtime-reconfigurable memory hierarchy. Versa exploits algorithm-specific characteristics in order to optimize bandwidth, access latency, and data reuse. Measured on a set of kernels with diverse data access, control, and synchronization characteristics, reconfiguration between different Versa modes yields median energy-efficiency improvements of 11.6× and 37.2× over mobile CPU and GPU baselines, respectively.
On the singularity method of subsonic lifting- surface theory

Subsonic lifting surface theory including leading edge, discussing singularities in solution of integral equation for determination of aerodynamic properties
Entire theta operators at unramified primes

Starting with work of Serre, Katz, and Swinnerton-Dyer, theta operators have played a key role in the study of $p$-adic and $\bmod p$ modular forms and Galois representations. This paper achieves two main results for theta operators on automorphic forms on PEL-type Shimura varieties: 1) the analytic continuation at unramified primes $p$ to the whole Shimura variety of the $\bmod p$ reduction of $p$-adic Maass--Shimura operators {\it a priori} defined only over the $\mu$-ordinary locus, and 2) the construction of new $\bmod p$ theta operators that do not arise as the $\bmod p$ reduction of Maass--Shimura operators. While the main accomplishments of this paper concern the geometry of Shimura varieties and consequences for differential operators, we conclude with applications to Galois representations. Our approach involves a careful analysis of the behavior of Shimura varieties and enables us to obtain more general results than allowed by prior techniques, including for arbitrary signature, vector weights, and unramified primes in CM fields of arbitrary degree.
Uranyl Speciation in the Presence of Specific Ion Gradients at the Electrolyte/Organic Interface

Uranyl (UO 22+) speciation at the liquid/liquid interface is an essential aspect of the mechanism that underlies its extraction as part of spent nuclear fuel reprocessing schemes and environmental ...
Ultrahigh thermal conductivity in hexagonal BC6N- An efficient material for nanoscale thermal management- A first principles study

Abstract Engineering materials with high thermal conductivity are of fundamental interest for efficiently dissipating heat in micro/nanoelectronics. Using first principles computations we report an ultra-high thermal conductivity of 2090 Wm−1K−1 (1395 Wm−1K−1) for hexagonal pure (natural) BC6N(h-BC6N). This value is among the highest thermal conductivities known after diamond and cubic boron arsenide. This ultra-high lattice thermal conductivity (k) is mainly attributed with high phonon group velocities of both acoustic and optical phonons arising from strong C C and B N bonds as well as the light atomic mass of the constituent elements such as boron (B), carbon (C) and nitrogen (N). We also report size dependent thermal conductivity of h-BC6N nanostructures by including boundary scattering. At room temperature (300 K) and at nanoscale length (L) of 100 nm, a high k value of 175 Wm−1K−1 is observed (higher than the bulk k value of silicon). Optical phonons with large group velocities are mainly responsible for this high thermal conductivity in h-BC6N nanostructures. High thermal conductivity of h-BC6N makes it a candidate material for heat dissipation in micro/nano thermal management applications.
Retail Shareholder Participation in the Proxy Process: Monitoring, Engagement, and Voting

Abstract We study retail shareholder voting using a nearly comprehensive sample of U.S. ownership and voting records. Analyzing turnout within a rational-choice framework, we find participation increases with ownership and expected benefits from winning and decreases with higher costs of participation. Even shareholders with a negligible likelihood of affecting the outcome have non zero turnout, consistent with consumption benefits from voting. Conditional on participation, retail shareholders punish the management of poorly performing firms. Overall, our evidence provides support for the idea that retail shareholders utilize their voting power to monitor firms and communicate with incumbent boards and management.
The existence of linear selection and the quotient lifting property

Lifting properties for Banach spaces are studied. An alternate version of the lifting property due to Lindenstrass and Tzafriri leads to a new geometric property called the quotient lifting property for pairs of Banach spaces (X, J), with J proximinal in X. Several necessary and sufficient conditions for the property to hold are given.
Clonal evolution in serially passaged Cryptococcus neoformans x deneoformans hybrids reveals a heterogenous landscape of genomic change.

Cryptococcus neoformans x deneoformans hybrids (also known as serotype AD hybrids) are basidiomycete yeasts that are common in a clinical setting. Like many hybrids, the AD hybrids are largely locked at the F1 stage and are mostly unable to undergo normal meiotic reproduction. However, these F1 hybrids, which display a high (∼10%) sequence divergence are known to genetically diversify through mitotic recombination and aneuploidy, and this diversification may be adaptive. In this study, we evolved a single AD hybrid genotype in six diverse environments by serial passaging and then used genome resequencing of evolved clones to determine evolutionary mechanisms of adaptation. The evolved clones generally increased fitness after passaging, accompanied by an average of 3.3 point mutations, 2.9 loss of heterozygosity (LOH) events, and 0.7 trisomic chromosomes per clone. LOH occurred through nondisjunction of chromosomes, crossing over consistent with break-induced replication, and gene conversion, in that order of prevalence. The breakpoints of these recombination events were significantly associated with regions of the genome with lower sequence divergence between the parents and clustered in subtelomeric regions, notably in regions that had undergone introgression between the two parental species. Parallel evolution was observed, particularly through repeated homozygosity via nondisjunction, yet there was little evidence of environment-specific parallel change for either LOH, aneuploidy, or mutations. These data show that AD hybrids have both a remarkable genomic plasticity and yet are challenged in the ability to recombine through sequence divergence and chromosomal rearrangements, a scenario likely limiting the precision of adaptive evolution to novel environments.
A new take on voice: the influence of BlackRock’s ‘Dear CEO’ letters

Abstract We examine whether broad-based public engagement by institutional investors influences the behavior of portfolio firms. We investigate this question in the context of BlackRock’s annual Dear CEO letter, which in recent years has called for portfolio firms to acknowledge and quantify the impact of environmental and regulatory factors on their firms. We find that portfolio firms’ disclosures during the post-letter period reflect topics similar to those discussed in the letters, controlling for a variety of firm and disclosure characteristics and the occurrence of private engagements. Moreover, BlackRock appears to value these additional disclosures, as it more often votes with management on shareholder proposals during subsequent annual shareholder meetings. Finally, motivated by BlackRock’s attempts to mobilize firms toward its specific policy recommendations, we also provide some evidence that firms’ lobbying efforts during the post-letter period become more aligned with the issues highlighted in the letter, especially when firms’ share BlackRock’s policy preferences ex ante. Taken together, our evidence suggests that portfolio firms are responsive to BlackRock’s public engagement efforts.
TED-Net: Convolution-Free T2T Vision Transformer-Based Encoder-Decoder Dilation Network for Low-Dose CT Denoising

Low dose computed tomography (CT) is a mainstream for clinical applications. However, compared to normal dose CT, in the low dose CT (LDCT) images, there are stronger noise and more artifacts which are obstacles for practical applications. In the last few years, convolution-based end-to-end deep learning methods have been widely used for LDCT image denoising. Recently, transformer has shown superior performance over convolution with more feature interactions. Yet its applications in LDCT denoising have not been fully cultivated. Here, we propose a convolution-free T2T vision transformer-based Encoder-decoder Dilation Network (TED-Net) to enrich the family of LDCT denoising algorithms. The model is free of convolution blocks and consists of a symmetric encoder-decoder block with sole transformer. Our model (Codes are available at https://github.com/wdayang/TED-Net) is evaluated on the AAPM-Mayo clinic LDCT Grand Challenge dataset, and results show outperformance over the state-of-the-art denoising methods.
Second-order Stein: SURE for SURE and other applications in high-dimensional inference

Stein’s formula states that a random variable of the form z⊤f(z)−divf(z) is mean-zero for all functions f with integrable gradient. Here, divf is the divergence of the function f and z is a standard normal vector. This paper aims to propose a second-order Stein formula to characterize the variance of such random variables for all functions f(z) with square integrable gradient, and to demonstrate the usefulness of this second-order Stein formula in various applications. In the Gaussian sequence model, a remarkable consequence of Stein’s formula is Stein’s Unbiased Risk Estimate (SURE), an unbiased estimate of the mean squared risk for almost any given estimator μˆ of the unknown mean vector. A first application of the second-order Stein formula is an Unbiased Risk Estimate for SURE itself (SURE for SURE): an unbiased estimate providing information about the squared distance between SURE and the squared estimation error of μˆ. SURE for SURE has a simple form as a function of the data and is applicable to all μˆ with square integrable gradient, for example, the Lasso and the Elastic Net. In addition to SURE for SURE, the following statistical applications are developed: (1) upper bounds on the risk of SURE when the estimation target is the mean squared error; (2) confidence regions based on SURE and using the second-order Stein formula; (3) oracle inequalities satisfied by SURE-tuned estimates under a mild Lipschtiz assumption; (4) an upper bound on the variance of the size of the model selected by the Lasso, and more generally an upper bound on the variance of the empirical degrees-of-freedom of convex penalized estimators; (5) explicit expressions of SURE for SURE for the Lasso and the Elastic Net; (6) in the linear model, a general semiparametric scheme to de-bias a differentiable initial estimator for the statistical inference of a low-dimensional projection of the unknown regression coefficient vector, with a characterization of the variance after debiasing; and (7) an accuracy analysis of a Gaussian Monte Carlo scheme to approximate the divergence of functions f:Rn→Rn.
Programmable FPGA-based Memory Controller

Even with generational improvements in DRAM technology, memory access latency still remains the major bottleneck for application accelerators, primarily due to limitations in memory interface IPs which cannot fully account for variations in target applications, the algorithms used, and accelerator architectures. Since developing memory controllers for different applications is time-consuming, this paper introduces a modular and programmable memory controller that can be configured for different target applications on available hardware resources. The proposed memory controller efficiently supports cache-line accesses along with bulk memory transfers. The user can configure the controller depending on the available logic resources on the FPGA, memory access pattern, and external memory specifications. The modular design supports various memory access optimization techniques including, request scheduling, internal caching, and direct memory access. These techniques contribute to reducing the overall latency while maintaining high sustained bandwidth. We implement the system on a state-of-the-art FPGA and evaluate its performance using two widely studied domains: graph analytics and deep learning workloads. We show improved overall memory access time up to 58% on CNN and GCN workloads compared with commercial memory controller IPs.
A multi-plank generalization of the Bang and Kadets inequalities

If a convex body K ⊂ ℝn is covered by the union of convex bodies C1, …, CN, multiple subadditivity questions can be asked. Two classical results regard the subadditivity of the width (the smallest distance between two parallel hyperplanes that sandwich K) and the inradius (the largest radius of a ball contained in K): the sum of the widths of the Ci is at least the width of K (this is the plank theorem of Thoger Bang), and the sum of the inradii of the Ci is at least the inradius of K (this is due to Vladimir Kadets). We adapt the existing proofs of these results to prove a theorem on coverings by certain generalized non-convex “multi-planks”. One corollary of this approach is a family of inequalities interpolating between Bang’s theorem and Kadets’s theorem. Other corollaries include results reminiscent of the Davenport-Alexander problem, such as the following: if an m-slice pizza cutter (that is, the union of m equiangular rays in the plane with the same endpoint) in applied N times to the unit disk, then there will be a piece of the partition of inradius at least $${{\sin \pi /m} \over {N + \sin \pi /m}}$$ .
AdaPool: A Diurnal-Adaptive Fleet Management Framework Using Model-Free Deep Reinforcement Learning and Change Point Detection

This paper introduces an adaptive model-free deep reinforcement approach that can recognize and adapt to the diurnal patterns in the ride-sharing environment with car-pooling. Deep Reinforcement Learning (RL) suffers from catastrophic forgetting due to being agnostic to the timescale of changes in the distribution of experiences. Although RL algorithms are guaranteed to converge to optimal policies in Markov decision processes (MDPs), this only holds in the presence of static environments. However, this assumption is very restrictive. In many real-world problems like ride-sharing, traffic control, etc., we are dealing with highly dynamic environments, where RL methods yield only sub-optimal decisions. To mitigate this problem in highly dynamic environments, we (1) adopt an online Dirichlet change point detection (ODCP) algorithm to detect the changes in the distribution of experiences, (2) develop a Deep Q Network (DQN) agent that is capable of recognizing diurnal patterns and making informed dispatching decisions according to the changes in the underlying environment. Rather than fixing patterns by time of week, the proposed approach automatically detects that the MDP has changed, and uses the results of the new model. In addition to the adaptation logic in dispatching, this paper also proposes a dynamic, demand aware vehicle-passenger matching and route planning framework that dynamically generates optimal routes for each vehicle based on online demand, vehicle capacities, and locations. Evaluation on New York City Taxi public dataset shows the effectiveness of our approach in improving the fleet utilization, where less than 50% of the fleet are utilized to serve the demand of up to 90% of the requests, while maximizing profits and minimizing idle times.
Can weather help explain 'why now?': The potential role of hourly temperature as a stroke trigger.

Abstract Background While evidence suggests that daily ambient temperature exposure influences stroke risk, little is known about the potential triggering role of ultra short-term temperature. Methods We examined the association between hourly temperature and ischemic and hemorrhagic stroke, separately, and identified any relevant lags of exposure among adult New York State residents from 2000 to 2015. Cases were identified via ICD-9 codes from the New York Department of Health Statewide Planning and Reearch Cooperative System. We estimated ambient temperature up to 36 h prior to estimated stroke onset based on patient residential ZIP Code. We applied a time-stratified case-crossover study design; control periods were matched to case periods by year, month, day of week, and hour of day. Additionally, we assessed effect modification by leading stroke risk factors hypertension and atrial fibrillation. Results We observed 578,181 ischemic and 164,755 hemorrhagic strokes. Among ischemic and hemorrhagic strokes respectively, the mean (standard deviation; SD) patient age was 71.8 (14.6) and 66.8 (17.4) years, with 55% and 49% female. Temperature ranged from −29.5 °C to 39.2 °C, with mean (SD) 10.9 °C (10.3 °C). We found linear relationships for both stroke types. Higher temperature was associated with ischemic stroke over the 7 h following exposure; a 10 °C increase over 7 h was associated with 5.1% (95% Confidence Interval [CI]: 3.8, 6.4%) increase in hourly stroke rate. In contrast, temperature was negatively associated with hemorrhagic stroke over 5 h, with a 5-h cumulative association of −6.2% (95% CI: 8.6, −3.7%). We observed suggestive evidence of a larger association with hemorrhagic stroke among patients with hypertension and a smaller association with ischemic stroke among those with atrial fibrillation. Conclusion Hourly temperature was positively associated with ischemic stroke and negatively associated with hemorrhagic stroke. Our results suggest that ultra short-term weather influences stroke risk and hypertension may confer vulnerability.
A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection

This paper proposes a Fully Spiking Hybrid Neural Network (FSHNN) for energy-efficient and robust object detection in resource-constrained platforms. The network architecture is based on a Spiking Convolutional Neural Network using leaky-integrate-fire neuron models. The model combines unsupervised Spike Time-Dependent Plasticity (STDP) learning with back-propagation (STBP) learning methods and also uses Monte Carlo Dropout to get an estimate of the uncertainty error. FSHNN provides better accuracy compared to DNN based object detectors while being more energy-efficient. It also outperforms these object detectors, when subjected to noisy input data and less labeled training data with a lower uncertainty error.
Capital and Labor Income Pareto Exponents Across Time and Space

We estimate capital and labor income Pareto exponents across 475 country-year observations that span 52 countries over half a century (1967-2018). We document two stylized facts: (i) capital income is more unequally distributed than labor income in the tail; namely, the capital exponent (1-3, median 1.46) is smaller than labor (2-5, median 3.35), and (ii) capital and labor exponents are nearly uncorrelated. To explain these findings, we build an incomplete market model with job ladders and capital income risk that gives rise to a capital income Pareto exponent smaller than but nearly unrelated to the labor exponent. Our results suggest the importance of distinguishing income and wealth inequality.
Verification of the Abbott Alinity m Resp-4-Plex Assay for detection of SARS-CoV-2, influenza A/B, and respiratory syncytial virus

COVID-19 symptomology may overlap with other circulating respiratory viruses that may also cause severe disease and for which there are specific and potentially life-saving treatments. The Abbott Alinity m Resp-4-Plex assay is a multiplex PCR assay that simultaneously detects and differentiates infection with SARS-CoV-2, influenza A, influenza B, and respiratory syncytial virus (RSV). We characterized its accuracy, precision, and analytical sensitivity. All were found to be robust for measures examined. In the context of sample-to-answer, near random access automation on the Alinity m platform, we believe that the Resp-4-Plex assay offers significant utility in addressing the current needs of the SARS-CoV-2 pandemic and future needs during anticipated endemic circulation of SARS-CoV-2 with other respiratory viruses.
Do large losses loom larger than gains? Salience, holding periods, and the disposition effect

Individual investors are more likely to sell stocks with nominal gains and losses that are large relative to their brokerage portfolio value. The salience of nominal gains and losses affects stock sales in both taxable and tax-deferred accounts and across investor groups, but the effect of nominal losses is weaker for stocks with high valuation uncertainty. The effect has a time dimension: at short holding periods, individuals are more likely to sell stocks with large nominal losses than gains of the same size, mitigating the disposition effect. Investors may be compelled to revisit their beliefs after incurring large losses quickly.
How Fine-Tuning Allows for Effective Meta-Learning

Representation learning has been widely studied in the context of meta-learning, enabling rapid learning of new tasks through shared representations. Recent works such as MAML have explored using fine-tuning-based metrics, which measure the ease by which fine-tuning can achieve good performance, as proxies for obtaining representations. We present a theoretical framework for analyzing representations derived from a MAML-like algorithm, assuming the available tasks use approximately the same underlying representation. We then provide risk bounds on the best predictor found by fine-tuning via gradient descent, demonstrating that the algorithm can provably leverage the shared structure. The upper bound applies to general function classes, which we demonstrate by instantiating the guarantees of our framework in the logistic regression and neural network settings. In contrast, we establish the existence of settings where any algorithm, using a representation trained with no consideration for task-specific fine-tuning, performs as well as a learner with no access to source tasks in the worst case. This separation result underscores the benefit of fine-tuning-based methods, such as MAML, over methods with "frozen representation" objectives in few-shot learning.
Reduced order models for Lagrangian hydrodynamics

Abstract As a mathematical model of high-speed flow and shock wave propagation in a complex multimaterial setting, Lagrangian hydrodynamics is characterized by moving meshes, advection-dominated solutions, and moving shock fronts with sharp gradients. These challenges hinder the existing projection-based model reduction schemes from being practical. We develop several variations of projection-based reduced order model techniques for Lagrangian hydrodynamics by introducing three different reduced bases for position, velocity, and energy fields. A time-windowing approach is also developed to address the challenge imposed by the advection-dominated solutions. Lagrangian hydrodynamics is formulated as a nonlinear problem, which requires a proper hyper-reduction technique. Therefore, we apply the over-sampling DEIM and SNS approaches to reduce the complexity due to the nonlinear terms. Finally, we also present both a posteriori and a priori error bounds associated with our reduced order model. We compare the performance of the spatial and time-windowing reduced order modeling approaches in terms of accuracy and speed-up with respect to the corresponding full order model for several numerical examples, namely Sedov blast, Gresho vortices, Taylor–Green vortices, and triple-point problems.
Learning Pareto-Frontier Resource Management Policies for Heterogeneous SoCs: An Information-Theoretic Approach

Mobile system-on-chips (SoCs) are growing in their complexity and heterogeneity (e.g., Arm’s Big-Little architecture) to meet the needs of emerging applications, including games and artificial intelligence. This makes it very challenging to optimally manage the resources (e.g., controlling the number and frequency of different types of cores) at runtime to meet the desired trade-offs among multiple objectives such as performance and energy. This paper proposes a novel information-theoretic framework referred to as PaRMIS to create Pareto-optimal resource management policies for given target applications and design objectives. PaRMIS specifies parametric policies to manage resources and learns statistical models from candidate policy evaluation data in the form of target design objective values. The key idea is to select a candidate policy for evaluation in each iteration guided by statistical models that maximize the information gain about the true Pareto front. Experiments on a commercial heterogeneous SoC show that PaRMIS achieves better Pareto fronts and is easily usable to optimize complex objectives (e.g., performance per Watt) when compared to prior methods
Toxoplasma bradyzoites exhibit physiological plasticity of calcium and energy stores controlling motility and egress.

Toxoplasma gondii has evolved different developmental stages for disseminating during acute infection (i.e. tachyzoites) and for establishing chronic infection (i.e. bradyzoites). Calcium ion (Ca2+) signaling tightly regulates the lytic cycle of tachyzoites by controlling microneme secretion and motility to drive egress and cell invasion. However, the roles of Ca2+ signaling pathways in bradyzoites remain largely unexplored. Here we show that Ca2+ responses are highly restricted in bradyzoites and that they fail to egress in response to agonists. Development of dual-reporter parasites revealed dampened Ca2+ responses and minimal microneme secretion by bradyzoites induced in vitro or harvested from infected mice and tested ex vivo. Ratiometric Ca2+ imaging demonstrated lower Ca2+ basal levels, reduced magnitude, and slower Ca2+ kinetics in bradyzoites compared with tachyzoites stimulated with agonists. Diminished responses in bradyzoites were associated with down-regulation of Ca2+-ATPases involved in intracellular Ca2+ storage in the endoplasmic reticulum (ER) and acidocalcisomes. Once liberated from cysts by trypsin digestion, bradyzoites incubated in glucose plus Ca2+ rapidly restored their intracellular Ca2+ and ATP stores leading to enhanced gliding. Collectively, our findings indicate that intracellular bradyzoites exhibit dampened Ca2+ signaling and lower energy levels that restrict egress, and yet upon release they rapidly respond to changes in the environment to regain motility.
Accelerated Information Gradient flow

We present a framework for Nesterov’s accelerated gradient flows in probability space to design efficient mean-field Markov chain Monte Carlo algorithms for Bayesian inverse problems. Here four examples of information metrics are considered, including Fisher-Rao metric, Wasserstein-2 metric, Kalman-Wasserstein metric and Stein metric. For both Fisher-Rao and Wasserstein-2 metrics, we prove convergence properties of accelerated gradient flows. In implementations, we propose a sampling-efficient discrete-time algorithm for Wasserstein-2, Kalman-Wasserstein and Stein accelerated gradient flows with a restart technique. We also formulate a kernel bandwidth selection method, which learns the gradient of logarithm of density from Brownian-motion samples. Numerical experiments, including Bayesian logistic regression and Bayesian neural network, show the strength of the proposed methods compared with state-of-the-art algorithms.
Estimation of the odds ratio in a proportional odds model with censored time-lagged outcome in a randomized clinical trial

In many randomized clinical trials of therapeutics for COVID-19, the primary outcome is an ordinal categorical variable, and interest focuses on the odds ratio (active agent vs. control) under the assumption of a proportional odds model. Although at the final analysis the outcome will be determined for all subjects, at an interim analysis, the status of some participants may not yet be determined, e.g., because ascertainment of the outcome may not be possible until some pre-specified follow-up time. Accordingly, the outcome from these subjects can be viewed as censored. A valid interim analysis can be based on data only from those subjects with full follow up; however, this approach is inefficient, as it does not exploit additional information that may be available on those for whom the outcome is not yet available at the time of the interim analysis. Appealing to the theory of semiparametrics, we propose an estimator for the odds ratio in a proportional odds model with censored, time-lagged categorical outcome that incorporates additional baseline and time-dependent covariate information and demonstrate that it can result in considerable gains in efficiency relative to simpler approaches. A byproduct of the approach is a covariate-adjusted estimator for the odds ratio based on the full data that would be available at a final analysis. This article is protected by copyright. All rights reserved.
Much Ado About Issue Statements

Issue statements are one of those things that legal writing “geeks” can debate endlessly. Which is better: the simple, clean “whether”-style question we all learned in law school, or a longer statement that includes some helpful factual background, and maybe even a rule? Many lawyers and judges strongly prefer one over the other.While a “whether” statement remains strongly suited to pure questions of law, a mixed question of law and fact is difficult to grasp without context. The critique of fuller issue statements usually lies in two places: (1) they are too long and inefficient; and (2) they tend to lapse into argument and thus lack objectivity. The critics make a good point. However, those valid concerns really have more to do with undisciplined writing, and not with the medium itself. Any style of well-executed issue statement can be both efficient and objective.The keys to an effective multi-sentence issue statement include (1) increasing objectivity by acknowledging and countering facts harmful to the clients position; (2) writing efficiently by limiting to 75 words, removing extraneous details, and using legally decisive facts rather than general background. The examples in this essay come from the newsworthy contraceptive health coverage cases in the Seventh Circuit. Both briefs employ an increasingly popular hybrid model: a paragraph of context followed by a series of short “whether” statements.
Engaging Boys in Treatment: Creative Approaches to the Therapy Process by Haen, C.

I like working with boys and am always a bit surprised about how many colleagues don't enjoy it. For those of us who do like working with them, Engaging Boys in Treatment edited by Craig Haen is a ...
Pitting corrosion inhibition of aluminum 2024 by Bacillus biofilms secreting polyaspartate or γ-polyglutamate

Pitting corrosion of aluminum 2024 in Luria Bertani medium was reduced by the secretion of anionic peptides by engineered and natural Bacillus biofilms and was studied in continuous reactors using electrochemical impedance spectroscopy. Compared to sterile controls, pitting was reduced dramatically by the presence of the biofilms. The secretion of a 20 amino acid polyaspartate peptide by an engineered Bacillus subtilis WB600/pBE92-Asp biofilm slightly reduced the corrosion rate of the passive aluminum alloy at pH 6.5; however, the secretion of γ-polyglutamate by a Bacillus licheniformis biofilm reduced the corrosion rate by 90% (compared to the B. subtilis WB600/pBE92 biofilm which did not secrete polyaspartate or γ-polyglutamate). The corrosion potential (Ecorr) of aluminum 2024 was increased by about 0.15–0.44 V due to the formation of B. subtilis and B. licheniformis biofilms as compared to sterile controls. The increase of Ecorr and the observed prevention of pitting indicate that the pitting potential (Epit) had increased. This result and the further decrease of corrosion rates for the passive aluminum alloy suggest that the rate of the anodic metal dissolution reaction was reduced by an inhibitor produced by the biofilms. Purified γ-polyglutamate also decreased the corrosion rates of aluminum 2024.
Identification and characterization of a new intron in Borna disease virus.

Borna disease virus (BDV) has a non-segmented, negative-strand (NNS) RNA genome. In contrast to all other known NNS RNA animal viruses, BDV replication and transcription occur in the nucleus of infected cells. Moreover, BDV uses RNA splicing for the regulation of its genome expression. Two introns (I and II), both present in two viral primary transcripts of 2·5 and 7·2 kb, have been reported in BDV. Here, evidence is provided of a new BDV intron, intron III, generated by alternative 3′ splice-site choice. Intron III-spliced mRNAs were detected at early times post-infection and found to be present in cells from different types and species. Intron III-spliced mRNAs have coding capability for two new viral proteins with predicted molecular masses of 8·4 and 165 (p165) kDa. p165 is a deleted form of the BDV L polymerase, containing three RGD motifs and a signal peptide signal that could target it into the secretory pathway. These findings underscore the proteomic complexity exhibited by BDV.
The 4th IJCAR Automated Theorem Proving System Competition - CASC-J4

The CADE ATP System Competition (CASC) is an annual evaluation of fully automatic, first order Automated Theorem Proving (ATP) systems. CASC-J4 was the thirteenth competition in the CASC series. Twenty-six ATP systems and system variants competed in the various competition and demonstration divisions. An outline of the competition design, and a commentated summary of the results, are presented.