cssi/SciSciGPT-SciSciNet huggingface dataset:
Copyright Class War

Developments in digital technology are dramatically reordering the content distribution business. The industry's traditional business model, based on controlling the channels of distribution for tangible copies, is threatened by digital distribution, with its low barriers to entry, low marginal costs and worldwide reach. An industry preoccupied with marketing, distributing and selling containers for content-books, CD's, DVD's, and so on - is discovering that the ability to control distribution channels for such containers is increasingly irrelevant in a world where the content formerly anchored to physical containers now flows freely through fiber-optic cable. The threat to the content industry has resulted in a kind of class war. The combatants' positions, and the changes in position that occur over time, can be understood in terms of a struggle between those that claim to possess a valuable resource and those that cannot, or cannot yet, make such a claim. Today, the Copyright Act, which was drafted by special-interest groups and is essentially incomprehensible to the uninitiated, is being used against the public. It has become critical for the public to engage with copyright law, and re-assert the primacy of the public interest. The author maintains that a nation gets the copyright law it deserves. A public that is passive in the face of expanding property rights in information will pay a heavy price in the form of homogenized culture and loss of intellectual liberty.
Credit Supply and Housing Speculation

Speculation is a critical channel through which credit supply expansion affects the housing cycle. The surge in private label mortgage securitization in 2003 fueled a large expansion in mortgage credit supply by lenders financed with non-core deposits. Areas more exposed to these lenders experienced a large relative rise in transaction volume driven by a small group of speculators, and these areas simultaneously witnessed an amplified housing boom and bust. Consistent with the importance of belief heterogeneity, house price growth expectations of marginal buyers rose during the boom, while housing market pessimism among the general population increased.
Foundation of Hydrodynamics of Strongly Interacting Systems

Hydrodynamics and quantum mechanics have many elements in common, as the density field and velocity fields are common variables that can be constructed in both descriptions. Starting with the Schrodinger equation and the Klein-Gordon for a single particle in hydrodynamical form, we examine the basic assumptions under which a quantum system of particles interacting through their mean fields can be described by hydrodynamics.
Stability Analysis using Quadratic Constraints for Systems with Neural Network Controllers

A method is presented to analyze the stability of feedback systems with neural network controllers. Two stability theorems are given to prove asymptotic stability and to compute an ellipsoidal inner-approximation to the region of attraction (ROA). The first theorem addresses linear time-invariant systems, and merges Lyapunov theory with local (sector) quadratic constraints to bound the nonlinear activation functions in the neural network. The second theorem allows the system to include perturbations such as unmodeled dynamics, slope-restricted nonlinearities, and time delay, using integral quadratic constraint (IQCs) to capture their input/output behavior. This in turn allows for off-by-one IQCs to refine the description of activation functions by capturing their slope restrictions. Both results rely on semidefinite programming to approximate the ROA. The method is illustrated on systems with neural networks trained to stabilize a nonlinear inverted pendulum as well as vehicle lateral dynamics with actuator uncertainty.
The nuclear to cytoplasmic ratio directly regulates zygotic transcription in Drosophila through multiple modalities.

Early embryos must rapidly generate large numbers of cells to form an organism. Many species accomplish this through a series of rapid, reductive, and transcriptionally silent cleavage divisions. Previous work has demonstrated that the number of divisions before both cell cycle elongation and zygotic genome activation (ZGA) is regulated by the ratio of nuclear content to cytoplasm (N/C). To understand how the N/C ratio affects the timing of ZGA, we directly assayed the behavior of several previously identified N/C ratio-dependent genes using the MS2-MCP reporter system in living Drosophila embryos with altered ploidy and cell cycle durations. For every gene that we examined, we found that nascent RNA output per cycle is delayed in haploid embryos. Moreover, we found that the N/C ratio influences transcription through three overlapping modes of action. For some genes (knirps, fushi tarazu, and snail), the effect of ploidy can be primarily attributed to changes in cell cycle duration. However, additional N/C ratio-mediated mechanisms contribute significantly to transcription delays for other genes. For giant and bottleneck, the kinetics of transcription activation are significantly disrupted in haploids, while for fruhstart and Kruppel, the N/C ratio controls the probability of transcription initiation. Our data demonstrate that the regulatory elements of N/C ratio-dependent genes respond directly to the N/C ratio through multiple modes of regulation.
Time-optimal frictionless atom cooling in harmonic traps

Frictionless atom cooling in harmonic traps is formulated as a time-optimal control problem and a synthesis of optimal controlled trajectories is obtained. This work has already been used to determine the minimum time for transition between two thermal states and to show the emergence of the third law of classical thermodynamics from quantum thermodynamics. It can also find application in the fast adiabatic-like expansion of Bose-Einstein condensates, with possible applications in atom interferometry. This paper is based on our recently published article in SIAM J. Control Optim. [1].
Effects of Peripheral Haptic Feedback on Intracortical Brain-Computer Interface Control and Associated Sensory Responses in Motor Cortex.

Intracortical brain-computer interfaces (iBCIs) provide people with paralysis a means to control devices with signals decoded from brain activity. Despite recent impressive advances, these devices still cannot approach able-bodied levels of control. To achieve naturalistic control and improved performance of neural prostheses, iBCIs will likely need to include proprioceptive feedback. With the goal of providing proprioceptive feedback via mechanical haptic stimulation, we aim to understand how haptic stimulation affects motor cortical neurons and ultimately, iBCI control. We provided skin shear haptic stimulation as a substitute for proprioception to the back of the neck of a person with tetraplegia. The neck location was determined via assessment of touch sensitivity using a monofilament test kit. The participant was able to correctly report skin shear at the back of the neck in 8 unique directions with 65% accuracy. We found motor cortical units that exhibited sensory responses to shear stimuli, some of which were strongly tuned to the stimuli and well modeled by cosine-shaped functions. We also demonstrated online iBCI cursor control with continuous skin-shear feedback driven by decoded command signals. Cursor control performance increased slightly but significantly when the participant was given haptic feedback, compared to the purely visual feedback condition.
The maintenance of polygenic sex determination depends on the dominance of fitness effects which are predictive of the role of sexual antagonism

In species with polygenic sex determination, multiple male- and female-determining loci on different proto-sex chromosomes segregate as polymorphisms within populations. The extent to which these polymorphisms are at stable equilibria is not yet resolved. Previous work demonstrated that polygenic sex determination is most likely to be maintained as a stable polymorphism when the proto-sex chromosomes have opposite (sexually antagonistic) fitness effects in males and females. However, these models usually consider polygenic sex determination systems with only two proto-sex chromosomes, or they do not broadly consider the dominance of the alleles under selection. To address these shortcomings, I used forward population genetic simulations to identify selection pressures that can maintain polygenic sex determination under different dominance scenarios in a system with more than two proto-sex chromosomes (modeled after the house fly). I found that overdominant fitness effects of male-determining proto-Y chromosomes are more likely to maintain polygenic sex determination than dominant, recessive, or additive fitness effects. The overdominant fitness effects that maintain polygenic sex determination tend to have proto-Y chromosomes with sexually antagonistic effects (male-beneficial and female-detrimental). In contrast, dominant fitness effects that maintain polygenic sex determination tend to have sexually antagonistic multi-chromosomal genotypes, but the individual proto-sex chromosomes do not have sexually antagonistic effects. These results demonstrate that sexual antagonism can be an emergent property of the multi-chromosome genotype without individual sexually antagonistic chromosomes. My results further illustrate how the dominance of fitness effects has consequences for both the likelihood that polygenic sex determination will be maintained as well as the role sexually antagonistic selection is expected to play in maintaining the polymorphism.
RNN Training along Locally Optimal Trajectories via Frank-Wolfe Algorithm

We propose a novel and efficient training method for RNNs by iteratively seeking a local minima on the loss surface within a small region, and leverage this directional vector for the update, in an outer-loop. We propose to utilize the Frank-Wolfe (FW) algorithm in this context. Although, FW implicitly involves normalized gradients, which can lead to a slow convergence rate, we develop a novel RNN training method that, surprisingly, even with the additional cost, the overall training cost is empirically observed to be lower than backpropagation. Our method leads to a new Frank-Wolfe method, that is in essence an SGD algorithm with a restart scheme. We prove that under certain conditions our algorithm has a sublinear convergence rate of $O$ (1/∊) for ∊ error. We then conduct empirical experiments on several benchmark datasets including those that exhibit long-term dependencies, and show significant performance improvement. We also experiment with deep RNN architectures and show efficient training performance. Finally, we demonstrate that our training method is robust to noisy data.
Two-curve Green’s function for 2-SLE: the boundary case

We prove that for κ∈(0,8), if (η1,η2) is a 2-SLEκpair in a simply connected domain D with an analytic boundary point z0, then as r→0+, P[dist(z0,ηj) 0, which is called the two-curve Green’s function. The exponent α equals 12 κ−1 or 2(12 κ−1) depending on whether z0 is one of the endpoints of η1 or η2. We also find the convergence rate and the exact formula for the Green’s function up to a multiplicative constant. To derive these results, we construct two-dimensional diffusion processes and use orthogonal polynomials to obtain their transition density.
Higher order functions and Brouwer’s thesis

Extending Martin Escardo’s effectful forcing technique, we give a new proof of a well-known result: Brouwer’s monotone bar theorem holds for any bar that can be realized by a functional of type (ℕ→ℕ)→ℕ in Godel’s System T. Effectful forcing is an elementary alternative to standard sheaf-theoretic forcing arguments, using ideas from programming languages, including computational effects, monads, the algebra interpretation of call-by-name λ-calculus, and logical relations. Our argument proceeds by interpreting System T programs as well-founded dialogue trees whose nodes branch on a query to an oracle of type ℕ→ℕ, lifted to higher type along a call-by-name translation. To connect this interpretation to the bar theorem, we then show that Brouwer’s famous “mental constructions” of barhood constitute an invariant form of these dialogue trees in which queries to the oracle are made maximally and in order.
Temporal State Machines: Using Temporal Memory to Stitch Time-based Graph Computations

Race logic, an arrival-time-coded logic family, has demonstrated energy and performance improvements for applications ranging from dynamic programming to machine learning. However, the various ad h...
CyAnno: A semi-automated approach for cell type annotation of mass cytometry datasets.

Motivation For immune system monitoring in large-scale studies at the single-cell resolution using CyTOF, (semi-)automated computational methods are applied for annotating live cells of mixed cell types. Here, we show that the live cell pool can be highly enriched with undefined heterogeneous cells, i.e., 'ungated' cells, and that current semi-automated approaches ignore their modeling resulting in misclassified annotations. Result We introduce 'CyAnno', a novel semi-automated approach for deconvoluting the unlabeled cytometry dataset based on a machine learning framework utilizing manually gated training data that allows the integrative modeling of 'gated' cell types and the 'ungated' cells. By applying this framework on several CyTOF datasets, we demonstrated that including the 'ungated' cells can lead to a significant increase in the precision of the 'gated' cell types prediction. CyAnno can be used to identify even a single cell type, including rare cells, with higher efficacy than current state-of-the-art semi-automated approaches. Availability The CyAnno is available as a python script with a user-manual and sample dataset at https://github.com/abbioinfo/CyAnno. Supplementary information Supplementary data are available at Bioinformatics online.
Optimization of High-Entropy Alloy Catalyst for Ammonia Decomposition and Ammonia Synthesis.

The successful synthesis of high-entropy alloy (HEA) nanoparticles, a long-sought goal in materials science, opens a new frontier in materials science with applications across catalysis, structural alloys, and energetic materials. Recently, a Co25Mo45Fe10Ni10Cu10 HEA made of earth-abundant elements was shown to have a high catalytic activity for ammonia decomposition, which rivals that of state-of-the-art, but prohibitively expensive, ruthenium catalysts. Using a computational approach based on first-principles calculations in conjunction with data analytics and machine learning, we build a model to rapidly compute the adsorption energy of H, N, and NHx (x = 1, 2, 3) species on CoMoFeNiCu alloy surfaces with varied alloy compositions and atomic arrangements. We show that the 25/45 Co/Mo ratio identified experimentally as the most active composition for ammonia decomposition increases the likelihood that the surface adsorbs nitrogen equivalently to that of ruthenium while at the same time interacting moderately strongly with intermediates. Our study underscores the importance of computational modeling and machine learning to identify and optimize HEA alloys across their near-infinite materials design space.
Women at work in the United States since 1860: An analysis of unreported family workers

Abstract Estimated labor force participation rates among free women in the pre-Civil War period were exceedingly low. This is due, in part, to cultural or societal expectations of the role of women and the lack of thorough enumeration by Census takers. This paper develops an augmented labor force participation rate for free women in 1860 and compares it with the augmented rate for 1920 and today. Our methodology identifies women who are likely providing informal and unenumerated labor for market production in support of a family business, that is, unreported family workers. These individuals are not coded in the original data as formally working, but are likely to be engaged in the labor force on the basis of the self-employment of other relatives in their household. Unreported family workers are classified into four categories: farm, merchant, craft, and boardinghouse keepers. Using microdata, the inclusion of these workers more than triples the free female labor force participation rate in the 1860 Census from 16 percent to 57 percent, more than doubles the participation rate in the 1920 Census from 24 percent to 50 percent, and has a small effect on the currently measured rate of 56 percent (2015-2019 American Community Survey). This suggests that rather than a steep rise from a very low level in the female labor force participation rate since 1860, it has in fact always been high and fairly stable over time. In contrast, the effect of including unreported family workers in the male augmented labor force participation rate is relatively small.
Neuro-Reachability of Networked Microgrids

A neural ordinary differential equations network(ODE-Net)-enabled reachability method (Neuro-Reachability) is devised for the dynamic verification of networked microgrids (NMs) with unidentified subsystems and heterogeneous uncertainties. Three new contributions are presented: 1) An ODE-Net-enabled dynamic model discovery approach is devised to construct the data-driven state-space model which preserves the nonlinear differential structure of the NMs system; 2) A physics-data-integrated (PDI) NMs model is established, which empowers various NM analytics; and 3) A conformance-empowered reach-ability analysis is developed to enhance the reliability of the PDI-driven dynamic verification. Extensive case studies demonstrate the efficacy and robustness of the ODE-Net-enabled method in microgrid dynamic model discovery, and the effectiveness of the Neuro-Reachability approach in verifying the NMs dynamics under multiple uncertainties and various operational scenarios.
A multi-sensor evaluation of precipitation uncertainty for landslide-triggering storm events.

Extreme precipitation can have profound consequences for communities, resulting in natural hazards such as rainfall-triggered landslides that cause casualties and extensive property damage. A key challenge to understanding and predicting rainfall-triggered landslides comes from observational uncertainties in the depth and intensity of precipitation preceding the event. Practitioners and researchers must select from a wide range of precipitation products, often with little guidance. Here we evaluate the degree of precipitation uncertainty across multiple precipitation products for a large set of landslide-triggering storm events and investigate the impact of these uncertainties on predicted landslide probability using published intensity-duration thresholds. The average intensity, peak intensity, duration, and NOAA-Atlas return periods are compared ahead of 177 reported landslides across the continental United States and Canada. Precipitation data are taken from four products that cover disparate measurement methods: near real-time and post-processed satellite (IMERG), radar (MRMS), and gauge-based (NLDAS-2). Landslide-triggering precipitation was found to vary widely across precipitation products with the depth of individual storm events diverging by as much as 296 mm with an average range of 51 mm. Peak intensity measurements, which are typically influential in triggering landslides, were also highly variable with an average range of 7.8 mm/h and as much as 57 mm/h. The two products more reliant upon ground-based observations (MRMS and NLDAS-2) performed better at identifying landslides according to published intensity-duration storm thresholds, but all products exhibited hit ratios of greater than 0.56. A greater proportion of landslides were predicted when including only manually verified landslide locations. We recommend practitioners consider low-latency products like MRMS for investigating landslides, given their near-real time data availability and good performance in detecting landslides. Practitioners would be well-served considering more than one product as a way to confirm intense storm signals and minimize the influence of noise and false alarms.
Understanding police decisions to arrest: The impact of situational, officer, and neighborhood characteristics on police discretion

Abstract Purpose Promote the use of multilevel modeling to refine theories of police discretion. Specifically, examine the simultaneous influence of situational factors, responding officers, and neighborhood context on arrests guided by a social-ecological framework. Methods Cross-classified models were used to assess the outcomes of 835,381 incidents responded to by 835 Phoenix Police officers in 388 census tracts. Results Situational, officer, and neighborhood factors all significantly influence arrests. Incident-level factors account for most of the variation, followed by officers, and neighborhoods. Incidents that resulted in arrest were more likely to involve victims, a greater number of responding officers, female officers, and college-educated officers. Arrests were more likely to occur in Hispanic and Black neighborhoods, even when controlling for situational and officer-level characteristics. Arrests were less likely to occur when officers were assigned to high crime precincts. Conclusions Multilevel models enable a comprehensive examination of police use of arrest using administrative data that are already being collected by police agencies. Through accounting for both officer and neighborhood-level influences, this strategy improves on prior theory testing in policing research. The results support elements of some theories, but conflict with others. These findings have important implications for police policies surrounding arrest and resource deployment.
The Sample Complexity of Robust Covariance Testing

We study the problem of testing the covariance matrix of a high-dimensional Gaussian in a robust setting, where the input distribution has been corrupted in Huber's contamination model. Specifically, we are given i.i.d. samples from a distribution of the form $Z = (1-\eps) X + \eps B$, where $X$ is a zero-mean and unknown covariance Gaussian $\mathcal{N}(0, \Sigma)$, $B$ is a fixed but unknown noise distribution, and $\eps>0$ is an arbitrarily small constant representing the proportion of contamination. We want to distinguish between the cases that $\Sigma$ is the identity matrix versus $\gamma$-far from the identity in Frobenius norm. In the absence of contamination, prior work gave a simple tester for this hypothesis testing task that uses $O(d)$ samples. Moreover, this sample upper bound was shown to be best possible, within constant factors. Our main result is that the sample complexity of covariance testing dramatically increases in the contaminated setting. In particular, we prove a sample complexity lower bound of $\Omega(d^2)$ for $\eps$ an arbitrarily small constant and $\gamma = 1/2$. This lower bound is best possible, as $O(d^2)$ samples suffice to even robustly {\em learn} the covariance. The conceptual implication of our result is that, for the natural setting we consider, robust hypothesis testing is at least as hard as robust estimation.
Computational thermo-fluid dynamics contributions to advanced gas turbine engine design

The design practices for the gas turbine are traced throughout history with particular emphasis on the calculational or analytical methods. Three principal components of the gas turbine engine will be considered: namely, the compressor, the combustor and the turbine.
Combined Neural Network-based Intra Prediction and Transform Selection

The interactions between different tools added successively to a block-based video codec are critical to its ratedistortion efficiency. In particular, when deep neural network-based intra prediction modes are inserted into a block-based video codec, as the neural network-based prediction function cannot be easily characterized, the adaptation of the transform selection process to the new modes can hardly be performed manually. That is why this paper presents a combined neural network-based intra prediction and transform selection for a block-based video codec. When putting a single neural network-based intra prediction mode and the learned prediction of the selected LFNST pair index into VTM-8.0, -3.71%, -3.17%, and -3.37% of mean BD-rate reduction in all-intra is obtained.
Colloidal structure and proton conductivity of the gel within the electrosensory organs of cartilaginous fishes

Cartilaginous fishes possess gel-filled tubular sensory organs called Ampullae of Lorenzini (AoL) that are used to detect electric fields. Although recent studies have identified various components of AoL gel, it has remained unclear how the molecules are structurally arranged and how their structure influences the function of the organs. Here we describe the structure of AoL gel by microscopy and small-angle X-ray scattering and infer that the material is colloidal in nature. To assess the relative function of the gel's protein constituents, we compared the microscopic structure, X-ray scattering, and proton conductivity properties of the gel before and after enzymatic digestion with a protease. We discovered that while proteins were largely responsible for conferring the viscous nature of the gel, their removal did not diminish proton conductivity. The findings lay the groundwork for more detailed studies into the specific interactions of molecules inside AoL gel at the nanoscale.
Principal component analysis of the Chandra ACIS gain

Up to 2020, the Chandra ACIS gain has been calibrated using the External Calibration Source (ECS). The ECS consists of an 55Fe radioactive source and is placed in the ACIS housing such that all chips are fully illuminated. Since the radioactive source decays over time with a half-life of 2.7 years, count rates are becoming too low for gain calibration. Instead, astrophysical calibration sources will be needed, which do not fill and illuminate the entire field of view. Here, we determine the dominant spatial components of the gain maps through principal component analysis (PCA). We find that, given the noise levels observed today, all ACIS gain maps can be sufficiently described by just a few (often only one) spatial components. We conclude that illuminating a small area is sufficient for gain calibration. We apply this to observations of the astrophysical source Cassiopeia A. The resulting calibration is found to be accurate to 0.6% in at least 68% of the chip area, following the same definition for the calibration accuracy that has been used since launch.
Improving Object Detection and Attribute Recognition by Feature Entanglement Reduction

We explore object detection with two attributes: color and material. The task aims to simultaneously detect objects and infer their color and material. A straight-forward approach is to add attribute heads at the very end of a usual object detection pipeline. However, we observe that the two goals are in conflict: Object detection should be attribute-independent and attributes be largely object-independent. Features computed by a standard detection network entangle the category and attribute features; we disentangle them by the use of a two-stream model where the category and attribute features are computed independently but the classification heads share Regions of Interest (RoIs). Compared with a traditional single-stream model, our model shows significant improvements over VG-20, a subset of Visual Genome, on both supervised and attribute transfer tasks.
Walking the Tightrope: Navigating Faculty Status as a Mandatory Reporter in the #MeToo Era

One of the goals of the #MeToo movement is to elevate the voices of survivors. Course content related to sexual victimization often stresses the importance of understanding the experiences of victi...
Angular Velocity Estimation Using Non-Coplanar Accelerometer Array

Over the last few decades, Gyro-Free Inertial Measurement Units (GF-IMUs) have been extensively researched to overcome the limitations of gyroscopes. This research presents a Non-coplanar Accelerometer Array (NAA) for estimating angular velocity with non-specific geometric arrangement of four or more triaxial accelerometers with non-coplanarity constraint. The presented proof of non-coplanar special arrangement also provides insights into propagation of the sensor noise and construction of the noise covariance matrices. The system noise depends on the singular values of the relative displacement matrix (between the sensors). A dynamical system model with uncorrelated process and measurement noise is proposed where the accelerometer readings are used simultaneously as process and measurement inputs. The angular velocity is estimated using an Extended Kalman Filter (EKF) that discretizes and linearizes the continuous-discrete time dynamical system. The simulations are performed on a Cube-NAA (Cu-NAA) comprising four accelerometers placed at different vertices of a cube. They analyze the estimation error for static and dynamic movement as the distance between the accelerometers (four accelerometers in cube-orientation) is varied. Here, the system noise is observed to decrease inversely with the length of the cube edge as the arrangement is kept identical. Consequently, the simulation results indicate asymptotic decrease in the standard error of estimation with edge length. The experiments are conducted on a Cu-NAA with five reflective optical markers. The reflective markers are visually tracked using ViconⓇ to construct the ground truth angular velocity. This unique experimental setup, apart from providing three degrees of rotational freedom of movement, also allows for three degrees of spacial translation (linear acceleration of the Cu-NAA in space). The simulation and experimental results indicate better performance of the proposed EKF as compared to one with correlated process and measurement noises.
Exosomes from adipose-derived stem cells alleviate myocardial infarction via microRNA-31/FIH1/HIF-1α pathway.

Abstract Our previous study has revealed that exosomes from adipose-derived stem cells (ASCs) promote angiogenesis in subcutaneously transplanted gels by delivery of microRNA-31 (miR-31) which targets factor inhibiting hypoxia-inducible factor-1 (FIH1) in recipient cells. Here we hypothesized that ASC exosomes alleviate ischemic diseases through miR-31/FIH1/hypoxia-inducible factor-1α (HIF-1α) signaling pathway. Exosomes from ASCs were characterized with nanoparticle tracking analysis, transmission electron microscopy, and immunoblotting analysis for exosomal markers. Results from immunoblotting and laser imaging of ischemic mouse hindlimb revealed that miR-31 enriched ASC exosomes inhibited FIH1 expression and enhanced the blood perfusion, respectively. These effects were impaired when using miR-31-depleted exosomes. Immunohistochemistry analysis showed that administration of exosomes resulted in a higher arteriole density and larger CD31+ area in ischemic hindlimb than miR-31-delpleted exosomes. Similarly, knockdown of miR-31 in exosomes reduced the effects of the exosomes on increasing ventricular fraction shortening and CD31+ area, and on decreasing infarct size. Exosomes promoted endothelial cell migration and tube formation. These changes were attenuated when miR-31 was depleted in the exosomes or when FIH1 was overexpressed in the endothelial cells. Furthermore, the results from immunocytochemistry, co-immunoprecipitation, and luciferase reporter assay demonstrated that the effects of exosomes on nuclear translocation, binding with co-activator p300, and activation of HIF-1α were decreased when miR-31 was depleted in the exosomes or FIH1 was overexpressed. Our findings provide evidence that exosomes from ASCs promote angiogenesis in both mouse ischemic hindlimb and heart through transport of miR-31 which targets FIH1 and therefore triggers HIF-1α transcriptional activation.
Adding boundary terms to Anderson localized Hamiltonians leads to unbounded growth of entanglement

It is well known that in Anderson localized systems, starting from a random product state the entanglement entropy remains bounded at all times. However, we show that adding a single boundary term to an otherwise Anderson localized Hamiltonian leads to unbounded growth of entanglement. Our results imply that Anderson localization is not a local property. One cannot conclude that a subsystem has Anderson localized behavior without looking at the whole system, as a term that is arbitrarily far from the subsystem can affect the dynamics of the subsystem in such a way that the features of Anderson localization are lost.
Population growth, changing agricultural practices, and environmental degradation in Zaire

This paper examines the consequences for food production and the environment of changes in agricultural practices in Zaire. Boserup argues that population growth effects agriculture through increased use of extensification practices (reducing the fallow period increasing investment in land shifting to use of animals for plowing and maintaining fields and using manure). After increased density and exhaustion of extensification practices for meeting demand occurs other production techniques will be employed and technological change will occur such as in specialization of crops establishment of specific land rights and reduced per capita availability of common property resources. Lele and Stone postulate that environmental damage may be too great to be offset by new technology in Africa. Geores and Bilsborrow contend that movement onto marginal lands and increased fuel wood demand leads to soil degradation and deforestation. Jolly indicates that market mechanisms as controls do not always function effectively. In Zaire population growth has been about 3% annually over the past 30 years (5.4% in urban areas and 2.3% in rural areas). Between 1975 and 1984 urban areas grew at a rate of 7% compared to 1.6% in rural areas. The most densely populated areas were north and south Kivu (20% of total population) and southern Band (40% of total population) with 75 and 35 persons per sq. km respectively. The number of agricultural producers declined and the number of consumers increased. There is some evidence that forest lands and marginal savanna areas are used for food production and fallow periods are reduced. Crop rotations are less varied and less care goes into soil preparation. These changes in agricultural practices allow production to meet demand. However some of these practices will eventually lead to lower yields and increase degradation. During the 1980s 20000 hectares of forest land were lost and replaced as open savanna which lowered crop yields and increased travel time from villages. The prospects of a shift from a consideration of environmental degradation and lower food production as a remote problem to an immediate concern are considered quite good.
The democratization of investment research and the informativeness of retail investor trading

Abstract We study the effects of social media on the informativeness of retail trading. Our identification strategy exploits the editorial delay between report submission and publication on Seeking Alpha, a popular crowdsourced investment research platform. We find the ability of retail order imbalances to predict the cross-section of stock returns and cash-flow news increases sharply in the intraday post-publication window relative to the pre-publication window. The findings are robust to controlling for report tone and stronger for reports authored by more capable contributors. The evidence suggests that recent technology-enabled innovations in how individuals share information help retail investors become better informed.
Stability and Instability of Traveling Wave Solutions to Nonlinear Wave Equations

In this paper, we study the stability and instability of plane wave solutions to semilinear systems of wave equations satisfying the null condition. We identify a condition which allows us to prove the global nonlinear asymptotic stability of the plane wave. The proof of global stability requires us to analyze the geometry of the interaction between the background plane wave and the perturbation. When this condition is not met, we are able to prove linear instability assuming an additional genericity condition. The linear instability is shown using a geometric optics ansatz.
Light Field Compression by Residual CNN-Assisted JPEG

Light field (LF) imaging has gained significant attention due to its recent success in 3-dimensional (3D) displaying and rendering as well as augmented and virtual reality usage. Because of the two extra dimensions, LFs are much larger than conventional images. We develop a JPEG-assisted learning-based technique to reconstruct an LF from a JPEG bitstream with a bit per pixel ratio of 0.0047 on average. For compression, we keep the LF's center view and use JPEG compression with 50% quality. Our reconstruction pipeline consists of a small JPEG enhancement network (JPEG-Hance), a depth estimation network (Depth-Net), followed by view synthesizing by warping the enhanced center view. Our pipeline is significantly faster than using video compression on pseudo-sequences extracted from an LF, both in compression and decompression, while maintaining effective performance. We show that with a 1% compression time cost and 18x speedup for decompression, our methods reconstructed LFs have better structural similarity index metric (SSIM) and comparable peak signal-to-noise ratio (PSNR) compared to the state-of-the-art video compression techniques used to compress LFs.
RetroCHMP3 blocks budding of enveloped viruses without blocking cytokinesis.

Summary Many enveloped viruses require the endosomal sorting complexes required for transport (ESCRT) pathway to exit infected cells. This highly conserved pathway mediates essential cellular membrane fission events, which restricts the acquisition of adaptive mutations to counteract viral co-option. Here, we describe duplicated and truncated copies of the ESCRT-III factor CHMP3 that block ESCRT-dependent virus budding and arose independently in New World monkeys and mice. When expressed in human cells, these retroCHMP3 proteins potently inhibit release of retroviruses, paramyxoviruses, and filoviruses. Remarkably, retroCHMP3 proteins have evolved to reduce interactions with other ESCRT-III factors and have little effect on cellular ESCRT processes, revealing routes for decoupling cellular ESCRT functions from viral exploitation. The repurposing of duplicated ESCRT-III proteins thus provides a mechanism to generate broad-spectrum viral budding inhibitors without blocking highly conserved essential cellular ESCRT functions.
Deep Q-network-based adaptive alert threshold selection policy for payment fraud systems in retail banking

Machine learning models have widely been used in fraud detection systems. Most of the research and development efforts have been concentrated on improving the performance of the fraud scoring models. Yet, the downstream fraud alert systems still have limited to no model adoption and rely on manual steps. Alert systems are pervasively used across all payment channels in retail banking and play an important role in the overall fraud detection process. Current fraud detection systems end up with large numbers of dropped alerts due to their inability to account for the alert processing capacity. Ideally, alert threshold selection enables the system to maximize the fraud detection while balancing the upstream fraud scores and the available bandwidth of the alert processing teams. However, in practice, fixed thresholds that are used for their simplicity do not have this ability. In this paper, we propose an enhanced threshold selection policy for fraud alert systems. The proposed approach formulates the threshold selection as a sequential decision making problem and uses Deep Q-Network based reinforcement learning. Experimental results show that this adaptive approach outperforms the current static solutions by reducing the fraud losses as well as improving the operational efficiency of the alert system.
Consumer Valuation of and Attitudes towards Novel Foods Produced with New Plant Engineering Techniques: A Review

We follow the PRISMA extension for scoping reviews to review the emerging international body of empirical evidence on consumers’ attitudes and willingness to pay (WTP) for novel foods produced with New Plant Engineering Techniques (NPETs). NPETs include genome/gene editing, cisgenesis, intragenesis, and RNA interference. These novel foods are often beneficial for the environment and human health and more sustainable under increasingly prevalent climate extremes. These techniques can also improve animal welfare and disease resistance when applied to animals. Despite these abilities of NPETs, evidence suggests that many, but not all, consumers discount these novel foods relative to conventional ones. Our review sorts out findings to identify conditioning factors that can increase the acceptance of and WTP for these novel foods in a significant segment of consumers. International patterns of acceptance are identified. We also analyze how information and knowledge interact with consumer acceptance of these novel foods and technologies. Heterogeneity of consumers—across cultures and borders and in attitudes towards science and innovation—emerges as a key determinant of acceptance and WTP. Acceptance and WTP tend to increase when socially beneficial attributes—as opposed to producer-oriented cost-saving attributes—are generated by NPETs. NPET-improved foods are systematically less discounted than transgenic foods. Most of the valuation estimates are based on hypothetical experiments and surveys and await validation through revealed preferences in actual purchases in food retailing environments.
Scalable adaptive PDE solvers in arbitrary domains

Efficiently and accurately simulating partial differential equations (PDEs) in and around arbitrarily defined geometries, especially with high levels of adaptivity, has significant implications for different application domains. A key bottleneck in the above process is the fast construction of a `good' adaptively-refined mesh. In this work, we present an efficient novel octree-based adaptive discretization approach capable of carving out arbitrarily shaped void regions from the parent domain: an essential requirement for fluid simulations around complex objects. Carving out objects produces an incomplete octree. We develop efficient top-down and bottom-up traversal methods to perform finite element computations on incomplete octrees. We validate the framework by (a) showing appropriate convergence analysis and (b) computing the drag coefficient for flow past a sphere for a wide range of Reynolds numbers (O(1 - 106)) encompassing the drag crisis regime. Finally, we deploy the framework on a realistic geometry on a current project to evaluate COVID-19 transmission risk in classrooms.
Gold Panning: Automatic Extraction of Scientific Information from Publications

The millions of scientific papers published every year have made it well beyond any human’s ability to read and curate. As a result, valuable data and potentially ground-breaking insights remain buried deep in the mountain of publications. Unfortunately, while modern Information Extraction techniques show great promise, they are rarely developed for scientific text and often perform poorly in scientific scenarios. We present a Relation Extraction model that automates identification of relationships between entity pairs. We incorporate dependency parse information, relation-term attention, and specialized word embeddings in our model. Application to a benchmark dataset results in an F1 score of 0.755, outperforming the 0.682 by a more traditional model.
Code Status Transitions in Patients with High-Risk Acute Myeloid Leukemia (AML)

e19009Background: Patients with high-risk AML often experience aggressive medical care at the end of life (EOL) such as hospitalization and intensive care unit (ICU) admission. Despite this, studie...
Monoclonal Antibody Treatment of Breakthrough COVID-19 in Fully Vaccinated Individuals with High-Risk Comorbidities.

Breakthrough COVID-19 may occur in fully vaccinated persons. In this cohort of 1395 persons (mean age, 54.3 years; 60% female; median body mass index, 30.7) who developed breakthrough COVID-19, there were 107 (7.7%) who required hospitalization by day 28. Hospitalization was significantly associated with the number of medical comorbidities. Anti-spike monoclonal antibody treatment was significantly associated with a lower risk of hospitalization (Odds Ratio: 0.227; 95% confidence interval, 0.128 - 0.403; p<0.001). The number needed to treat (NNT) to prevent one hospitalization was 225 among the lowest-risk patient group compared to NNT of 4 among those with highest numbers of medical comorbidity.
Two-Way Spectrum Pursuit for CUR Decomposition and its Application in Joint Column/Row Subset Selection

The problem of simultaneous column and row subset selection is addressed in this paper. The column space and row space of a matrix are spanned by its left and right singular vectors, respectively. However, the singular vectors are not within actual columns/rows of the matrix. In this paper, an iterative approach is proposed to capture the most structural information of columns/rows via selecting a subset of actual columns/rows. This algorithm is referred to as two-way spectrum pursuit (TWSP) which provides us with an efficient solution for the CUR matrix decomposition. TWSP is applicable in a wide range of applications since it enjoys a linear complexity w.r.t. number of original columns/rows. We demonstrated the application of TWSP for joint channel and sensor selection in cognitive radio networks and efficient supervised data reduction.
Regulating algorithmic filtering on social media

Through the algorithmic filtering (AF) of content, social media platforms (SMPs) have the ability to influence users' perceptions and behaviors. Attempts to regulate externalities of AF are often difficult to pass or enforce due to critical social, legal, financial, and user related considerations. In this work, we explore this multifaceted problem by proposing a unifying framework that considers the key stakeholders of AF regulation (or self-regulation). We mathematically formalize this framework, using it to construct a data-driven, statistically sound regulatory procedure that satisfies several important criteria. First, by design, it moderates the effect of AF on user learning and decision-making. Second, it has desirable properties of online governance, including being normative and user-driven. Third, by illustrating the regulatory procedure in linear dynamical systems, we prove that it can align social and financial interests. Specifically, we identify conditions under which the regulation imposes a low cost on the SMP's reward (e.g., profits) and incentivizes the SMP to increase content diversity.
Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints

We study the problem of generating arithmetic math word problems (MWPs) given a math equation that specifies the mathematical computation and a context that specifies the problem scenario. Existing approaches are prone to generating MWPs that are either mathematically invalid or have unsatisfactory language quality. They also either ignore the context or require manual specification of a problem template, which compromises the diversity of the generated MWPs. In this paper, we develop a novel MWP generation approach that leverages i) pre-trained language models and a context keyword selection model to improve the language quality of generated MWPs and ii) an equation consistency constraint for math equations to improve the mathematical validity of the generated MWPs. Extensive quantitative and qualitative experiments on three real-world MWP datasets demonstrate the superior performance of our approach compared to various baselines.