cssi/SciSciGPT-SciSciNet huggingface dataset:
Cerebral Organoids Derived from a Parkinson’s Patient Exhibit Unique Pathogenesis from Chikungunya Virus Infection when Compared to a Non-Parkinson’s Patient

(1) Background: Arboviruses of medical and veterinary significance have been identified on all seven continents, with every human and animal population at risk for exposure. Like arboviruses, chronic neurodegenerative diseases, like Alzheimer's and Parkinson's disease, are found wherever there are humans. Significant differences in baseline gene and protein expression have been determined between human-induced pluripotent stem cell lines derived from non-Parkinson's disease individuals and from individuals with Parkinson's disease. It was hypothesized that these inherent differences could impact cerebral organoid responses to viral infection. (2) Methods: In this study, cerebral organoids from a non-Parkinson's and Parkinson's patient were infected with Chikungunya virus and observed for two weeks. (3) Results: Parkinson's organoids lost mass and exhibited a differential antiviral response different from non-Parkinson's organoids. Neurotransmission data from both infected non-Parkinson's and Parkinson's organoids had dysregulation of IL-1, IL-10, and IL-6. These cytokines are associated with mood and could be contributing to persistent depression seen in patients following CHIKV infection. Both organoid types had increased expression of CXCL10, which is linked to demyelination. (4) Conclusions: The differential antiviral response of Parkinson's organoids compared with non-Parkinson's organoids highlights the need for more research in neurotropic infections in a neurologically compromised host.
Cavity Optomechanical Sensing in the Nonlinear Saturation Limit

Photonic sensors based upon high-quality optical microcavities have found a wide variety of applications ranging from inertial sensing, electro- and magnetometry to chemical and biological sensing. These sensors have a dynamic range limited by the linewidth of the cavity mode transducing the input. This dynamic range not only determines the range of the signal strength that can be detected, but also affects the resilience of the sensor to large deteriorating external perturbations and shocks in a practical environment. Unfortunately, there is a general trade-off between the detection sensitivity and the dynamic range, which undermines the performance of all microcavity-based sensors. Here we propose an approach to extend the dynamic range significantly beyond the cavity linewidth limit, in the nonlinear modulation regime, without degrading the detection sensitivity for weak signals. With a cavity optomechanical system, we experimentally demonstrate a dynamic range six times larger than the cavity linewidth, far beyond the conventional linear region of operation for such a sensor. The approach demonstrated here will help design microcavity-based sensors to achieve high detection sensitivity and a large dynamic range at the same time, a crucial property for their use in a practical environment.
Exposure to concentrated animal feeding operations (CAFOs) and risk of mortality in North Carolina, USA

Concentrated animal feeding operations (CAFOs) have emerged as an environmental justice issue due to disproportionate siting in low-income and minority communities. However, CAFOs' impact on health is not fully understood. We examined risk of cause-specific mortality associated with CAFOs in North Carolina (NC) for 2000-2017 and health disparities. We obtained data on individual-level cause-specific mortality and on permitted animal facilities. We estimated associations between exposure to CAFOs and cause-specific mortality using logistic regression, controlling for demographics (e.g., age) and area-level covariates. To estimate exposure to CAFOs, we considered (1) a binary indicator (presence or absence) of CAFOs within a buffer around individual residence based on several buffer sizes, and (2) four levels of exposure (no, low, medium, and high) based on the number of CAFOs within 15 km around each residence. We considered individual-level (sex, race/ethnicity, age, education) and community-level (median household income, urbanicity, and region) factors. Under all buffer sizes used to estimate CAFOs exposure, people living near CAFOs had significantly higher risk of cardiovascular mortality than other persons. Comparing those living near CAFOs to the no exposure group, odds ratios (ORs) for cardiovascular mortality were 1.01 (95% confidence interval (CI) 1.00, 1.03), 1.04 (1.03, 1.06), and 1.06 (1.05, 1.07) for low, medium, and high CAFOs exposure, respectively, indicating a trend of higher risk with higher exposure. Those in the high CAFOs exposure group had significantly higher risk of anemia and kidney disease mortality than those with no exposure. Results suggest higher mortality risk from CAFOs for some subpopulations, however differences were not statistically significant. Findings provide evidence of excess mortality risk from CAFOs in NC. These results have implications for future studies of environmental justice and CAFOs.
Physics-Informed Learning for High Impedance Faults Detection

High impedance faults (HIFs) in distribution grids may cause wildfires and threaten human lives. Conventional protection relays at substations fail to detect more than 10% HIFs since over-currents are low and the signatures of HIFs are local. With more µPMU being installed in the distribution system, high- resolution µPMU datasets provide the opportunity of detecting HIFs from multiple points. Still, the main obstacle in applying the µPMU datasets is the lack of labels. To address this issue, we construct a physics-informed convolutional auto-encoder (PICAE) to detect HIFs without labeled HIFs for training. The significance of our PICAE is a physical regularization, derived from the elliptical trajectory of voltages-current characteristics, to distinguish HIFs from other abnormal events even in highly noisy situations. We formulate a system-wide detection framework that merges multiple nodes’ local detection results to improve the detection accuracy and reliability. The proposed approaches are validated in the IEEE 34-node test feeder simulated through PSCAD/EMTDC. Our PICAE outperforms the existing works in various scenarios and is robust to different observability and noise.
Go with the FLOW: visualizing spatiotemporal dynamics in optical widefield calcium imaging

Widefield calcium imaging has recently emerged as a powerful experimental technique to record coordinated large-scale brain activity. These measurements present a unique opportunity to characterize...
Variance misperception under skewed empirical noise statistics explains overconfidence in the visual periphery.

Perceptual confidence typically corresponds to accuracy. However, observers can be overconfident relative to accuracy, termed “subjective inflation.” Inflation is stronger in the visual periphery relative to central vision, especially under conditions of peripheral inattention. Previous literature suggests inflation stems from errors in estimating noise (i.e., “variance misperception”). However, despite previous Bayesian hypotheses about metacognitive noise estimation, no work has systematically explored how noise estimation may critically depend on empirical noise statistics, which may differ across the visual field, with central noise distributed symmetrically but peripheral noise positively skewed. Here, we examined central and peripheral vision predictions from five Bayesian-inspired noise-estimation algorithms under varying usage of noise priors, including effects of attention. Models that failed to optimally estimate noise exhibited peripheral inflation, but only models that explicitly used peripheral noise priors—but used them incorrectly—showed increasing peripheral inflation under increasing peripheral inattention. Further, only one model successfully captured previous empirical results, which showed a selective increase in confidence in incorrect responses under performance reductions due to inattention accompanied by no change in confidence in correct responses; this was the model that implemented Bayesian estimation of peripheral noise, but using an (incorrect) symmetric rather than the correct positively skewed peripheral noise prior. Our findings explain peripheral inflation, especially under inattention, and suggest future experiments that might reveal the noise expectations used by the visual metacognitive system.
Quantum Annealing for Automated Feature Selection in Stress Detection

We present a novel methodology for automated feature subset selection from a pool of physiological signals using Quantum Annealing (QA). As a case study, we will investigate the effectiveness of QA-based feature selection techniques in selecting the optimal feature subset for stress detection. Features are extracted from four signal sources: foot EDA, hand EDA, ECG, and respiration. The proposed method embeds the feature variables extracted from the physiological signals in a binary quadratic model. The bias of the feature variable is calculated using the Pearson correlation coefficient between the feature variable and the target variable. The weight of the edge connecting the two feature variables is calculated using the Pearson correlation coefficient between two feature variables in the binary quadratic model. Subsequently, D-Wave’s clique sampler is used to sample cliques from the binary quadratic model. The underlying solution is then re-sampled to obtain multiple good solutions and the clique with the lowest energy is returned as the optimal solution. The proposed method is compared with commonly used feature selection techniques for stress detection. Results indicate that QA-based feature subset selection performed equally as that of classical techniques. However, under data uncertainty conditions such as limited training data, the performance of quantum annealing for selecting optimum features remained unaffected, whereas a significant decrease in performance is observed with classical feature selection techniques. Preliminary results show the promise of quantum annealing in optimizing the training phase of a machine learning classifier, especially under data uncertainty conditions.
The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective

Image-based navigation is widely considered the next frontier of minimally invasive surgery. It is believed that image-based navigation will increase the access to reproducible, safe, and high-precision surgery as it may then be performed at acceptable costs and effort. This is because image-based techniques avoid the need of specialized equipment and seamlessly integrate with contemporary workflows. Furthermore, it is expected that image-based navigation techniques will play a major role in enabling mixed reality environments, as well as autonomous and robot-assisted workflows. A critical component of image guidance is 2D/3D registration, a technique to estimate the spatial relationships between 3D structures, e.\,g., preoperative volumetric imagery or models of surgical instruments, and 2D images thereof, such as intraoperative X-ray fluoroscopy or endoscopy. While image-based 2D/3D registration is a mature technique, its transition from the bench to the bedside has been restrained by well-known challenges, including brittleness with respect to optimization objective, hyperparameter selection, and initialization, difficulties in dealing with inconsistencies or multiple objects, and limited single-view performance. One reason these challenges persist today is that analytical solutions are likely inadequate considering the complexity, variability, and high-dimensionality of generic 2D/3D registration problems. The recent advent of machine learning-based approaches to imaging problems that, rather than specifying the desired functional mapping, approximate it using highly expressive parametric models holds promise for solving some of the notorious challenges in 2D/3D registration. In this manuscript, we review the impact of machine learning on 2D/3D registration to systematically summarize the recent advances made by introduction of this novel technology. Grounded in these insights, we then offer our perspective on the most pressing needs, significant open problems, and possible next steps.
Presynaptic FMRP and Local Protein Synthesis Support Structural and Functional Plasticity of Glutamatergic Axon Terminals

Learning and memory rely on long-lasting, synapse-specific modifications. While postsynaptic forms of plasticity typically require local protein synthesis, whether and how local protein synthesis contributes to presynaptic changes remains unclear. Here, we examined the hippocampal mossy fiber (MF)-CA3 synapse which expresses both structural and functional presynaptic plasticity and contains presynaptic Fragile X mental retardation protein (FMRP), an RNA-binding protein previously implicated in local postsynaptic protein synthesis-dependent plasticity. We report that MF boutons contain ribosomes and synthesize protein locally. Long-term potentiation of MF-CA3 synaptic transmission (MF-LTP) was associated with translation-dependent enlargement of MF boutons. Remarkably, increasing in vitro or in vivo MF activity enhanced protein synthesis in MFs. Moreover, deletion of presynaptic Fragile X mental retardation protein (FMRP) blocked structural and functional MF-LTP, suggesting that FMRP is a critical regulator of presynaptic MF plasticity. Thus, presynaptic FMRP and protein synthesis dynamically control presynaptic structure and function in the mature mammalian brain.
On Artin’s conjecture for pairs of diagonal forms

Let p be an odd prime and d = pτ(p − 1). In the spirit of Artin’s conjecture, consider the system of two diagonal forms of degree d in s variables given by a1x1d + ⋯ + a sxsd = 0, b1x1d + ⋯ + b sxs...
Hamiltonian Assisted Metropolis Sampling

Various Markov chain Monte Carlo (MCMC) methods are studied to improve upon random walk Metropolis sampling, for simulation from complex distributions. Examples include Metropolis-adjusted Langevin...
Panopticonism, Pines and POWs: Applying Conflict Landscape Tools to the Archaeology of Internment

The military terrain analysis system KOCOA (Key Terrain, Observation, Cover/concealment, Obstacles, and Avenues of approach; also OAKOC or OCOKA) was developed as part of the burgeoning discipline ...
Reproducibility in the UK biobank of genome-wide significant signals discovered in earlier genome-wide association studies.

With the establishment of large biobanks, discovery of single nucleotide variants (SNVs, also known as single nucleotide polymorphisms (SNVs)) associated with various phenotypes has accelerated. An open question is whether genome-wide significant SNVs identified in earlier genome-wide association studies (GWAS) are replicated in later GWAS conducted in biobanks. To address this, we examined a publicly available GWAS database and identified two, independent GWAS on the same phenotype (an earlier, "discovery" GWAS and a later, "replication" GWAS done in the UK biobank). The analysis evaluated 136,318,924 SNVs (of which 6289 reached P < 5e-8 in the discovery GWAS) from 4,397,962 participants across nine phenotypes. The overall replication rate was 85.0%; although lower for binary than quantitative phenotypes (58.1% versus 94.8% respectively). There was a 18.0% decrease in SNV effect size for binary phenotypes, but a 12.0% increase for quantitative phenotypes. Using the discovery SNV effect size, phenotype trait (binary or quantitative), and discovery P value, we built and validated a model that predicted SNV replication with area under the Receiver Operator Curve = 0.90. While non-replication may reflect lack of power rather than genuine false-positives, these results provide insights about which discovered associations are likely to be replicated across subsequent GWAS.
FLASH: Fast Neural Architecture Search with Hardware Optimization

Neural architecture search (NAS) is a promising technique to design efficient and high-performance deep neural networks (DNNs). As the performance requirements of ML applications grow continuously, the hardware accelerators start playing a central role in DNN design. This trend makes NAS even more complicated and time-consuming for most real applications. This paper proposes FLASH, a very fast NAS methodology that co-optimizes the DNN accuracy and performance on a real hardware platform. As the main theoretical contribution, we first propose the NN-Degree, an analytical metric to quantify the topological characteristics of DNNs with skip connections (e.g., DenseNets, ResNets, Wide-ResNets, and MobileNets). The newly proposed NN-Degree allows us to do training-free NAS within one second and build an accuracy predictor by training as few as 25 samples out of a vast search space with more than 63 billion configurations. Second, by performing inference on the target hardware, we fine-tune and validate our analytical models to estimate the latency, area, and energy consumption of various DNN architectures while executing standard ML datasets. Third, we construct a hierarchical algorithm based on simplicial homology global optimization (SHGO) to optimize the model-architecture co-design process, while considering the area, latency, and energy consumption of the target hardware. We demonstrate that, compared to the state-of-the-art NAS approaches, our proposed hierarchical SHGO-based algorithm enables more than four orders of magnitude speedup (specifically, the execution time of the proposed algorithm is about 0.1 seconds). Finally, our experimental evaluations show that FLASH is easily transferable to different hardware architectures, thus enabling us to do NAS on a Raspberry Pi-3B processor in less than 3 seconds.
Parametric Interaction of VLF and ELF Waves in the Ionosphere

Generation of Very Low Frequency (VLF) electromagnetic whistler waves due to the parametric interaction of quasi-electrostatic VLF waves known as Lower Oblique Resonance (LOR) waves and Extremely Low Frequency (ELF) waves are analyzed in the frame of two possible scenarios. In the first scenario, quasi-electrostatic LOR waves and ELF waves are excited by conventional loop and dipole antennas. In the second one, they are naturally excited by plasma instabilities in the plasmasphere boundary layer. We present the results of numerical simulations of nonlinear coupling between LOR and Fast Magnetosonic (MS) waves, which was suggested to explain the observations of VLF electromagnetic emissions at frequencies well above the lower hybrid resonance frequency in the Turbulent Plasmasphere Boundary Layer (TPBL). These emissions represent a distinctive subset of the substorm/storm-related VLF whistler activity contributing to the alteration of the outer radiation belt boundary. As the TPBL is interior to the plasma sheet inner boundary and thus devoid of substorm-injected kiloelectronvolt electrons, the standard whistler generation mechanism must be excluded. Numerical solution of the derived nonlinear equations show that parametric coupling of LOR and ELF waves creates VLF electromagnetic emissions with the spectral characteristics consistent with the observations.
Deep Open Snake Tracker for Vessel Tracing

Vessel tracing by modeling vascular structures in 3D medical images with centerlines and radii can provide useful information for vascular health. Existing algorithms have been developed but there are certain persistent problems such as incomplete or inaccurate vessel tracing, especially in complicated vascular beds like the intracranial arteries. We propose here a deep learning based open curve active contour model (DOST) to trace vessels in 3D images. Initial curves were proposed from a centerline segmentation neural network. Then data-driven machine knowledge was used to predict the stretching direction and vessel radius of the initial curve, while the active contour model (as human knowledge) maintained smoothness and intensity fitness of curves. Finally, considering the non-loop topology of most vasculatures, individually traced vessels were connected into a tree topology by applying a minimum spanning tree algorithm on a global connection graph. We evaluated DOST on a Time-of-Flight (TOF) MRA intracranial artery dataset and demonstrated its superior performance over existing segmentation-based and tracking-based vessel tracing methods. In addition, DOST showed strong adaptability on different imaging modalities (CTA, MR T1 SPACE) and vascular beds (coronary arteries).
On Additive Spanners in Weighted Graphs with Local Error

An additive \(+\beta \) spanner of a graph G is a subgraph which preserves distances up to an additive \(+\beta \) error. Additive spanners are well-studied in unweighted graphs but have only recently received attention in weighted graphs [Elkin et al. 2019 and 2020, Ahmed et al. 2020]. This paper makes two new contributions to the theory of weighted additive spanners.
A Cross-Sectional Survey of the Workplace Factors Contributing to Symptoms of Anxiety and Depression Among Nurses and Physicians During the First Wave of COVID-19 Pandemic in Two US Healthcare Systems.

Background Anxiety and depression among physicians and nurses during the COVID-19 pandemic in the USA are not well described and their modifiable causes are poorly understood. Methods We conducted a cross-sectional survey of symptoms of anxiety and depression (Hospital Anxiety and Depression Scale) among physicians and nurses in two US healthcare systems in June through September 2020; participation rate was 5-10%. We described features of work as well as their perceptions and associated concerns in relation to the risk of anxiety and depression, while controlling for health history via regression and path analyses. Results About a third of 684 nurses and 185 physicians surveyed showed symptoms of anxiety or depression, and the excess of symptoms of mood disorders was particularly prominent in nurses. The belief that one was infected was a dominant correlate of both anxiety and depression. This belief was more associated with history of symptoms of pneumonia than the contact with COVID-19 diagnosed patients. Factors found to be associated with reduced anxiety and depression in this working environment were having confidence in the competent use of and access to personal protective equipment, maintaining usual working hours, being surrounded by colleagues who were both sufficient in numbers and not stressed, and the support of immediate family and religious communities. Involvement in aerosol-generating procedures with infected patients was linked with lower depression in nurses but higher among physicians. Likewise, the setting of recent patient encounters affected risk of anxiety and depression differently for physicians and nurses. Conclusions Our findings may help develop mitigation measures and underscore the need to help nurses and physicians bear the psychological burden of the COVID-19 pandemic and similar events in the future.
Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image

We present a method that takes as input a single dual-pixel image, and simultaneously estimates the image's defocus map -- the amount of defocus blur at each pixel -- and recovers an all-in-focus image. Our method is inspired from recent works that leverage the dual-pixel sensors available in many consumer cameras to assist with autofocus, and use them for recovery of defocus maps or all-in-focus images. These prior works have solved the two recovery problems independently of each other, and often require large labeled datasets for supervised training. By contrast, we show that it is beneficial to treat these two closely-connected problems simultaneously. To this end, we set up an optimization problem that, by carefully modeling the optics of dual-pixel images, jointly solves both problems. We use data captured with a consumer smartphone camera to demonstrate that, after a one-time calibration step, our approach improves upon prior works for both defocus map estimation and blur removal, despite being entirely unsupervised.
On the Adversarial Robustness of LASSO Based Feature Selection

In this paper, we investigate the adversarial robustness of feature selection based on the $\ell _1$ regularized linear regression model, namely LASSO. In the considered model, there is a malicious adversary who can observe the whole dataset, and then will carefully modify the response values or the feature matrix in order to manipulate the selected features. We formulate the modification strategy of the adversary as a bi-level optimization problem. Due to the difficulty of the non-differentiability of the $\ell _1$ norm at the zero point, we reformulate the $\ell _1$ norm regularizer as linear inequality constraints. We employ the interior-point method to solve this reformulated LASSO problem and obtain the gradient information. Then we use the projected gradient descent method to design the modification strategy. In addition, we demonstrate that this method can be extended to other $\ell _1$ based feature selection methods, such as group LASSO and sparse group LASSO. Numerical examples with synthetic and real data illustrate that our method is efficient and effective.
Computational Modeling: Human Dynamic Model

Improvements in quantitative measurements of human physical activity are proving extraordinarily useful for studying the underlying musculoskeletal system. Dynamic models of human movement support clinical efforts to analyze, rehabilitate injuries. They are also used in biomechanics to understand and diagnose motor pathologies, find new motor strategies that decrease the risk of injury, and predict potential problems from a particular procedure. In addition, they provide valuable constraints for understanding neural circuits. This paper describes a physics-based movement analysis method for analyzing and simulating bipedal humanoid movements. The model includes the major body segments and joints to report human movements' energetic components. Its 48 degrees of freedom strike a balance between very detailed models that include muscle models and straightforward two-dimensional models. It has sufficient accuracy to analyze and synthesize movements captured in real-time interactive applications, such as psychophysics experiments using virtual reality or human-in-the-loop teleoperation of a simulated robotic system. The dynamic model is fast and robust while still providing results sufficiently accurate to be used to animate a humanoid character. It can also estimate internal joint forces used during a movement to create effort-contingent stimuli and support controlled experiments to measure the dynamics generating human behaviors systematically. The paper describes the innovative features that allow the model to integrate its dynamic equations accurately and illustrates its performance and accuracy with demonstrations. The model has a two-foot stance ability, capable of generating results comparable with an experiment done with subjects, and illustrates the uncontrolled manifold concept. Additionally, the model's facility to capture large energetic databases opens new possibilities for theorizing as to human movement function. The model is freely available.
A simple numerical method for evaluating heat dissipation from curved wires with periodic applied heating

In many situations, the dual-purpose heater/thermometer wires used in the three-omega method—one of the most precise and sensitive techniques for measuring the thermal conductivity of thin films and interfaces—must include bends and curves to avoid obstructions on the surface of a sample. Although the three-omega analysis assumes that the heating wire is infinitely long and straight, recent experimental work has demonstrated that, in some cases, curved-wire geometries can be used without introducing detectable systematic error. We describe a general numerical method that can be used to calculate the temperature of three-omega heating wires with arbitrary wire geometries. This method provides experimentalists with a simple quantitative procedure for calculating how large the systematic error caused by a particular wire asymmetry will be. We show calculations of two useful cases: a straight wire with a single bend of arbitrary angle and a wire that forms a circle. We find that the amplitude of the in-phase temperature oscillations near a wire that forms a circle differs from the prediction using the analytic straight-line source solution by <12%, provided that the thermal penetration depth is less than ten times the radius of curvature of the wire path. The in-phase temperature amplitude 1.5 wire widths away from a 90° bend in a wire is within 11% of the straight-line source prediction for all penetration depths greater than the wire width. Our calculations indicate that the straight-line source solution breaks down significantly when the wire bend angle is less than 45°.
Measurement of Hybrid Rocket Solid Fuel Regression Rate for a Slab Burner using Deep Learning

Abstract This study presents an imaging-based deep learning tool to measure the fuel regression rate in a 2D slab burner experiment for hybrid rocket fuels. The slab burner experiment is designed to verify mechanistic models of reacting boundary layer combustion in hybrid rockets by the measurement of fuel regression rates. A DSLR camera with a high intensity flash is used to capture images throughout the burn and the images are then used to find the fuel boundary to calculate the regression rate. A U-net convolutional neural network architecture is explored to segment the fuel from the experimental images. A Monte-Carlo Dropout process is used to quantify the regression rate uncertainty produced from the network. The U-net computed regression rates are compared with values from other techniques from literature and show error less than 10%. An oxidizer flux dependency study is performed and shows the U-net predictions of regression rates are accurate and independent of the oxidizer flux, when the images in the training set are not over-saturated. Training with monochrome images is explored and is not successful at predicting the fuel regression rate from images with high noise. The network is superior at filtering out noise introduced by soot, pitting, and wax deposition on the chamber glass as well as the flame when compared to traditional image processing techniques, such as threshold binary conversion and spatial filtering. U-net consistently provides low error image segmentations to allow accurate computation of the regression rate of the fuel.
A Dataset for Answering Time-Sensitive Questions

Time is an important dimension in our physical world. Lots of facts can evolve with respect to time. For example, the U.S. President might change every four years. Therefore, it is important to consider the time dimension and empower the existing QA models to reason over time. However, the existing QA datasets contain rather few time-sensitive questions, hence not suitable for diagnosing or benchmarking the model's temporal reasoning capability. In order to promote research in this direction, we propose to construct a time-sensitive QA dataset. The dataset is constructed by 1) mining time-evolving facts from WikiData and align them to their corresponding Wikipedia page, 2) employing crowd workers to verify and calibrate these noisy facts, 3) generating question-answer pairs based on the annotated time-sensitive facts. Our dataset poses two novel challenges: 1) the model needs to understand both explicit and implicit mention of time information in the long document, 2) the model needs to perform temporal reasoning like comparison, addition, subtraction. We evaluate different SoTA long-document QA systems like BigBird and FiD on our dataset. The best-performing model FiD can only achieve 46\% accuracy, still far behind the human performance of 87\%. We demonstrate that these models are still lacking the ability to perform robust temporal understanding and reasoning. Therefore, we believe that our dataset could serve as a benchmark to empower future studies in temporal reasoning. The dataset and code are released in~\url{this https URL}.
Learning-Based Strategy Design for Robot-Assisted Reminiscence Therapy Based on a Developed Model for People with Dementia.

In this paper, the robot-assisted Reminiscence Therapy (RT) is studied as a psychosocial intervention to persons with dementia (PwDs). We aim at a conversation strategy for the robot by reinforcement learning to stimulate the PwD to talk. Specifically, to characterize the stochastic reactions of a PwD to the robot’s actions, a simulation model of a PwD is developed which features the transition probabilities among different PwD states consisting of the response relevance, emotion levels and confusion conditions. A Q-learning (QL) algorithm is then designed to achieve the best conversation strategy for the robot. The objective is to stimulate the PwD to talk as much as possible while keeping the PwD’s states as positive as possible. In certain conditions, the achieved strategy gives the PwD choices to continue or change the topic, or stop the conversation, so that the PwD has a sense of control to mitigate the conversation stress. To achieve this, the standard QL algorithm is revised to deliberately integrate the impact of PwD’s choices into the Q-value updates. Finally, the simulation results demonstrate the learning convergence and validate the efficacy of the achieved strategy. Tests show that the strategy is capable to duly adjust the difficulty level of prompt according to the PwD’s states, take actions (e.g., repeat or explain the prompt, or comfort) to help the PwD out of bad states, and allow the PwD to control the conversation tendency when bad states continue.
On the infrared polarization of the Orion Nebula.

The implications of the 10-micron polarization of the Orion Nebula are reexamined, and it is concluded that the polarization is produced by preferential extinction in one of the well-studied regions of the Kleinmann-Low nebula and/or its surroundings. In particular, the need for abnormally low temperatures (about 6 K) in the polarizing medium is obviated. The most likely candidate for the polarizing medium is the extended region surrounding the Kleinmann-Low nebula. The degree of polarization produced by emission in the far-infrared (around 100 microns) is estimated to be as large as 8%, although other factors could reduce the observable value.
Genomic Prediction of Strawberry Resistance to Postharvest Fruit Decay Caused by the Fungal Pathogen Botrytis cinerea.

Gray mold, a disease of strawberry (Fragaria ×ananassa) caused by the ubiquitous necrotroph Botrytis cinerea, renders fruit unmarketable and causes economic losses in the postharvest supply chain. To explore the feasibility of selecting for increased resistance to gray mold, we undertook genetic and genomic prediction studies in strawberry populations segregating for fruit quality and shelf life traits hypothesized to pleiotropically affect susceptibility. As predicted, resistance to gray mold was heritable but quantitative and genetically complex. While every individual was susceptible, the speed of symptom progression and severity differed. Narrow-sense heritability ranged from 0.38-0.71 for lesion diameter (LD) and 0.39-0.44 for speed of emergence of external mycelium (EM). Even though significant additive genetic variation was observed for LD and EM, the phenotypic ranges were comparatively narrow and genome-wide analyses did not identify any large effect loci. Genomic selection accuracy ranged from 0.28-0.59 for LD and 0.37-0.47 for EM. Additive genetic correlations between fruit quality and gray mold resistance traits were consistent with prevailing hypotheses: LD decreased as titratable acidity increased, whereas EM increased as soluble solid content decreased and firmness increased. We concluded that phenotypic and genomic selection could be effective for reducing LD and increasing EM, especially in long shelf life populations, but that a significant fraction of the genetic variation for resistance to gray mold was caused by the pleiotropic effects of fruit quality traits that differ among market and shelf life classes.
Effective Sequence-to-Sequence Dialogue State Tracking

Sequence-to-sequence models have been applied to a wide variety of NLP tasks, but how to properly use them for dialogue state tracking has not been systematically investigated. In this paper, we study this problem from the perspectives of pre-training objectives as well as the formats of context representations. We demonstrate that the choice of pre-training objective makes a significant difference to the state tracking quality. In particular, we find that masked span prediction is more effective than auto-regressive language modeling. We also explore using Pegasus, a span prediction-based pre-training objective for text summarization, for the state tracking model. We found that pre-training for the seemingly distant summarization task works surprisingly well for dialogue state tracking. In addition, we found that while recurrent state context representation works also reasonably well, the model may have a hard time recovering from earlier mistakes. We conducted experiments on the MultiWOZ 2.1-2.4, WOZ 2.0, and DSTC2 datasets with consistent observations.
De novo deletions and duplications at recombination hotspots in mouse germlines

Numerous DNA double-strand breaks (DSBs) arise during meiosis to initiate homologous recombination. These DSBs are usually repaired faithfully, but here, we uncover a distinct type of mutational event in which deletions form via joining of ends from two closely spaced DSBs (double cuts) within a single hotspot or at adjacent hotspots on the same or different chromatids. Deletions occur in normal meiosis but are much more frequent when DSB formation is dysregulated in the absence of the ATM kinase. Events between chromosome homologs point to multi-chromatid damage and aborted gap repair. Some deletions contain DNA from other hotspots, indicating that double cutting at distant sites creates substrates for insertional mutagenesis. End joining at double cuts can also yield tandem duplications or extrachromosomal circles. Our findings highlight the importance of DSB regulation and reveal a previously hidden potential for meiotic mutagenesis that is likely to affect human health and genome evolution.
A comprehensive chemical abundance analysis of the extremely metal poor Leoncino Dwarf galaxy (AGC 198691)

We re-examine the extremely metal-poor (XMP) dwarf galaxy AGC 198691 using a high quality spectrum obtained by the LBT's MODS instrument. Previous spectral observations obtained from KOSMOS on the Mayall 4-m and the Blue Channel spectrograph on the MMT 6.5-m telescope did not allow for the determination of sulfur, argon, or helium abundances. We report an updated and full chemical abundance analysis for AGC 198691, including confirmation of the extremely low "direct" oxygen abundance with a value of 12 + log(O/H) = 7.06 $\pm$ 0.03. AGC 198691's low metallicity potentially makes it a high value target for helping determine the primordial helium abundance ($Y_p$). Though complicated by a Na I night sky line partially overlaying the He I $\lambda$5876 emission line, the LBT/MODS spectrum proved adequate for determining AGC 198691's helium abundance. We employ the recently expanded and improved model of Aver et al. (2021), incorporating higher Balmer and Paschen lines, augmented by the observation of the infrared helium emission line He I $\lambda$10830 obtained by Hsyu et al. (2020). Applying our full model produced a reliable helium abundance determination, consistent with the expectation for its metallicity. Although this is the lowest metallicity object with a detailed helium abundance, unfortunately, due to its faintness (EW(H$\beta$) $<$ 100 AA) and the compromised He I $\lambda$5876, the resultant uncertainty on the helium abundance is too large to allow a significant improvement on the measurement of $Y_p$.
Tightness of Bernoulli Gibbsian line ensembles

A Bernoulli Gibbsian line ensemble L=(L1,…,LN) is the law of the trajectories of N−1 independent Bernoulli random walkers L1,…,LN−1 with possibly random initial and terminal locations that are conditioned to never cross each other or a given random up-right path LN (i.e. L1≥⋯≥LN). In this paper we investigate the asymptotic behavior of sequences of Bernoulli Gibbsian line ensembles LN=(L1N,…,LNN) when the number of walkers tends to infinity. We prove that if one has mild but uniform control of the one-point marginals of the lowest-indexed (or top) curves L1N then the sequence LN is tight in the space of line ensembles. Furthermore, we show that if the top curves L1N converge in the finite dimensional sense to the parabolic Airy2 process then LN converge to the parabolic Airy line ensemble.
Managers, Shareholders, and the Corporate Double Tax

INTRODUCTION 518 I. HETEROGENEITY AND THE CORPORATE DOUBLE TAX 524 A. The Mechanics of the Double Tax 524 B. The Paradox of the Double Tax 528 C. Heterogeneity of Interests and Effects 535 1. Managers and Collateral Interests 536 2. Shareholders and Collateral Interests 542 3. Integration Models 547 a. Differential Effects at the Corporate Level 553 b. Differential Effects at the Shareholder Level 559 c. Differential Transition Effects 563 II. EVIDENCE FROM THE BUSH ADMINISTRATION’S DIVIDENDEXCLUSION PROPOSAL 565 A. The Divided-Exclusion Proposal 566 B. Lobbying Positions of Managers and Collateral Interests 568 C. Lobbying Positions of Shareholders and Collateral Interests 579
Stronger Data Poisoning Attacks Break Data Sanitization Defenses

Machine learning models trained on data from the outside world can be corrupted by data poisoning attacks that inject malicious points into the models’ training sets. A common defense against these attacks is data sanitization: first filter out anomalous training points before training the model. In this paper, we develop three attacks that can bypass a broad range of common data sanitization defenses, including anomaly detectors based on nearest neighbors, training loss, and singular-value decomposition. By adding just 3% poisoned data, our attacks successfully increase test error on the Enron spam detection dataset from 3 to 24% and on the IMDB sentiment classification dataset from 12 to 29%. In contrast, existing attacks which do not explicitly account for these data sanitization defenses are defeated by them. Our attacks are based on two ideas: (i) we coordinate our attacks to place poisoned points near one another, and (ii) we formulate each attack as a constrained optimization problem, with constraints designed to ensure that the poisoned points evade detection. As this optimization involves solving an expensive bilevel problem, our three attacks correspond to different ways of approximating this problem, based on influence functions; minimax duality; and the Karush–Kuhn–Tucker (KKT) conditions. Our results underscore the need to develop more robust defenses against data poisoning attacks.
Emergency Medicine Residents Consistently Rate Themselves Higher than Attending Assessments on ACGME Milestones

In 2012 the Accreditation Council for Graduate Medical Education (ACGME) introduced the Next Accreditation System (NAS), which implemented milestones to assess the competency of residents and fellows. While attending evaluation and feedback is crucial for resident development, perhaps equally important is a resident's self-assessment. If a resident does not accurately self-assess, clinical and professional progress may be compromised. The objective of our study was to compare emergency medicine (EM) resident milestone evaluation by EM faculty with the same resident's self-assessment.This is an observational, cross-sectional study that was performed at an academic, four-year EM residency program. Twenty-five randomly chosen residents completed milestone self-assessment using eight ACGME sub-competencies deemed by residency leadership as representative of core EM principles. These residents were also evaluated by 20 faculty members. The milestone levels were evaluated on a nine-point scale. We calculated the average difference between resident self-ratings and faculty ratings, and used sample t-tests to determine statistical significance of the difference in scores.Eighteen residents evaluated themselves. Each resident was assessed by an average of 16 attendings (min=10, max=20). Residents gave themselves statistically significant higher milestone ratings than attendings did for each sub-competency examined (p<0.0001).Residents over-estimated their abilities in every sub-competency assessed. This underscores the importance of feedback and assessment transparency. More attention needs to be paid to methods by which residency leadership can make residents' self-perception of their clinical ability more congruent with that of their teachers and evaluators. The major limitation of our study is small sample size of both residents and attendings.
Hepatobiliary and Pancreatic Malignancies

A variety of benign and malignant tumors occur in the liver. The most common benign hepatic tumors are cysts followed by cavernous hemangiomas. Focal nodular hyperplasia and adenomas more often affect women on oral contraceptives, whereas fatty infiltration and regenerating nodules more commonly occur in patients with cirrhosis. Abscesses and angiomyolipomas are uncommon. Among malignant tumors, metastases to the liver from various primaries, often multifocal, occur 20 times more often than primary hepatocellular carcinoma (HCC). Although many tumors may metastasize to the liver, this occurs mainly in colorectal, gastric, pancreatic, lung, and breast carcinoma. Ninety percent of malignant primary hepatic tumors are of epithelial origin and include HCC and cholangiocarcinoma.
The Nuclear Factor-κB Transcription Factor Pathway

Nuclear factor (NF)-κB activating signals, which are discussed in this chapter, induce phosphorylation of I κB proteins, leading to their ubiquitination and proteasome-mediated degradation. Nuclear translocation of the subunits, however, may not be sufficient for transcriptional activation. NF-κB complexes induce expression of key tumor necrosis factor-α (TNF-α) target genes involved in the inflammatory responses, including leukocyte adhesion molecules and chemokines. Phosphorylation at a single key residue (Ser-276) by protein kinase A enhances RelA interaction with the coactivator CREB-binding protein (CBP), resulting in enhanced transcriptional activation potential. NF-κB is a key mediator of inflammatory cytokineinduced responses. The pathway through which TNF-α induces NF-κB activation is among the most well studied in higher eukaryotes. Absence of RelA results in massive hepatocyte apoptosis during embryogenesis, resulting in embryonic lethality. Studies of mice deficient in the NF-κB RelA subunit provided the first direct evidence of a role for NF-κB in inhibiting apoptosis. The NF-κB transcription factors play key functions in many aspects of the antiviral response. Respiratory syncytial virus (RSV) is a member of the paramyxovirus family that causes acute lower respiratory tract infections, which are characterized by high expression of proinflammatory cytokines and chemokines. Virus induction of the interferon (IFN)-β gene is one of the most studied transcriptional systems in mammals. In the heart, however, NF-κB is important for inducing IFN-β expression and therefore in limiting viral replication. Similarly disparate functions for NF-κB also come into play during induction of virus-induced inflammatory and adaptive immune responses.
Propagation of Geotrichum candidum in Acid Brine

Geotrichum candidum completely neutralized the acid brine and reduced its biochemical oxygen demand (BOD) by 88%. Yield of dry mycelium was 62 g per 100 g of BOD utilized.