cssi/SciSciGPT-SciSciNet huggingface dataset:
JOBS: Joint-Sparse Optimization from Bootstrap Samples

Classical sparse regression based on ${\ell _1}$ minimization solves the least squares problem with all available measurements via sparsity-promoting regularization. In challenging practical applications with high levels of noise and missing or adversarial samples, solving the problem using all measurements simultaneously may fail. In this paper, we propose a robust global sparse recovery strategy, named JOBS, which uses bootstrap samples of measurements to improve sparse regression in difficult cases. K measurement vectors are generated from the original pool of m measurements via bootstrapping, with each bootstrap sample containing L elements, and then a joint-sparse constraint is enforced to ensure support consistency among multiple predictors. The final estimate is obtained by averaging over K estimators.The performance limits associated with finite bootstrap sampling ratio L/m and number of estimates K is analyzed theoretically. Simulation results validate the theoretical analysis of proper choice of (L,K) and show that the proposed method yields stateof-the-art recovery performance, outperforming ${\ell _1}$ minimization and other existing bootstrap-based techniques, especially when the number of measurements are limited. With a proper choice of bootstrap sampling ratio (0.3−0.5) and a reasonably large number of estimates K ( ≥ 30), the SNR improvement over the baseline ${\ell _1}$-minimization algorithm can reach up to 336%.
Estimating the Probability that a Function Observed with Noise Is Convex

Consider a real-valued function that can be only observed with stochastic noise at a finite set of design points within a Euclidean space. We wish to determine whether there exists a convex functio...
Curl-free magnetic fields for stellarator optimization

This paper describes a new and efficient method of defining an annular region of a curl-free magnetic field with specific physics and coil properties that can be used in stellarator design. Three statements define the importance: (1) Codes can follow an optimized curl-free initial state to a final full-pressure equilibrium. The large size of the optimization space of stellarators, approximately 50 externally produced distributions of magnetic field, makes success in finding a global optimum largely determined by the starting point. (2) The design of a stellarator is actually improved when the central region of the plasma has rapid transport with the confinement provided by a surrounding annulus of magnetic surfaces with low transport. (3) The stellarator is unique among all fusion concepts, inertial as well as magnetic, in not using the plasma itself to provide an essential part of its confinement concept. This permits reliable computational design, which opens a path to faster, cheaper, and more certain achievement of fusion energy.
Non-equilibrium phonon transport in surface-roughness dominated nanowires

Experimental observation of highly reduced thermal conductivity in surface-roughness dominated silicon nanowires have generated renewed interest in low-dimensional thermoelectric devices. Using a previous work where the scattering of phonons from a rough surface is mapped to scattering from randomly situated localized phonons in the bulk of a smooth nanowire, we consider the thermal current across a nanowire for various strengths of surface disorder. We use non-equilibrium Green's function techniques that allow us to evaluate the thermal current beyond the linear response regime, for arbitrary cold and hot temperatures of the two semi-infinite connecting leads. We show how the surface-roughness affects the frequency dependence of the thermal current, eventually leading to a temperature dependent reduction of the net current at high temperatures. We use a universal disorder parameter to describe the surface-roughness as has been proposed, and show that the dependence of the net current on this parameter provides a natural explanation for the experimentally observed differences between smooth vs rough surfaces. We argue that a systematic study of the thermal current for different values of the temperature difference between the two sides of a surface-roughness dominated nanowire for various strengths of disorder would help in our understanding of how best to optimize the thermoelectric efficiency.
Leveraging upfront payments to curb employee misbehavior: Evidence from a natural field experiment

Abstract We use a natural field experiment in which we hired over 2000 workers from an online labor market to explore how upfront payment affects worker motivation and misbehavior on the job. We start with a simple theory that shows paying upfront can increase misbehavior through reducing the perceived costs of cheating, but it can decrease misbehavior through generating a gift-exchange effect. Motivated by the theory, we designed a task that provided workers with opportunities to reciprocate or misbehave. A unique aspect of our design is that we are permitted an opportunity to measure the curvature of the gift-exchange value of the upfront payment. Our results suggest paying workers upfront induces a gift-exchange effect that is concave in the share of total wage paid upfront. Moreover, the impact is strong enough to suggest that small upfront payments are a cost-effective means for an employer to curb employee misbehavior.
On free subsemigroups of associative algebras

In 1992, following earlier conjectures of Lichtman and Makar-Limanov, Klein conjectured that a noncommutative domain must contain a free, multiplicative, noncyclic subsemigroup. He verified the conjecture when the center is uncountable. In this note we consider the existence (or not) of free subsemigroups in associative $k$-algebras $R$, where $k$ is a field not algebraic over a finite subfield. We show that $R$ contains a free noncyclic subsemigroup in the following cases: (1) $R$ satisfies a polynomial identity and is noncommutative modulo its prime radical. (2) $R$ has at least one nonartinian primitive subquotient. (3) $k$ is uncountable and $R$ is noncommutative modulo its Jacobson radical. In particular, (1) and (2) verify Klein's conjecture for numerous well known classes of domains, over countable fields, not covered in the prior literature.
Intuitive neuromyoelectric control of a dexterous bionic arm using a modified Kalman filter

Abstract Background Multi-articulate prostheses are capable of performing dexterous hand movements. However, clinically available control strategies fail to provide users with intuitive, independent and proportional control over multiple degrees of freedom (DOFs) in real-time. New Method We detail the use of a modified Kalman filter (MKF) to provide intuitive, independent and proportional control over six-DOF prostheses such as the DEKA “LUKE” arm. Input features include neural firing rates recorded from Utah Slanted Electrode Arrays and mean absolute value of intramuscular electromyographic (EMG) recordings. Ad-hoc modifications include thresholds and non-unity gains on the output of a Kalman filter. Results We demonstrate that both neural and EMG data can be combined effectively. We also highlight that modifications can be optimized to significantly improve performance relative to an unmodified Kalman filter. Thresholds significantly reduced unintended movement and promoted more independent control of the different DOFs. Gains were significantly greater than one and served to ease movement initiation. Optimal modifications can be determined quickly offline and translate to functional improvements online. Using a portable take-home system, participants performed various activities of daily living. Comparison with Existing Methods In contrast to pattern recognition, the MKF allows users to continuously modulate their force output, which is critical for fine dexterity. The MKF is also computationally efficient and can be trained in less than five minutes. Conclusions The MKF can be used to explore the functional and psychological benefits associated with long-term, at-home control of dexterous prosthetic hands.
Promoter-specific dynamics of TATA-binding protein association with the human genome

Transcription factor binding to target sites in vivo is a dynamic process that involves cycles of association and dissociation, with individual proteins differing in their binding dynamics. The dynamics at individual sites on a genomic scale have been investigated in yeast cells, but comparable experiments have not been done in multicellular eukaryotes. Here, we describe a tamoxifen-inducible, time-course ChIP-seq approach to measure transcription factor binding dynamics at target sites throughout the human genome. As observed in yeast cells, the TATA-binding protein (TBP) typically displays rapid turnover at RNA polymerase (Pol) II-transcribed promoters, slow turnover at Pol III promoters, and very slow turnover at the Pol I promoter. Turnover rates vary widely among Pol II promoters in a manner that does not correlate with the level of TBP occupancy. Human Pol II promoters with slow TBP dissociation preferentially contain a TATA consensus motif, support high transcriptional activity of downstream genes, and are linked with specific activators and chromatin remodelers. These properties of human promoters with slow TBP turnover differ from those of yeast promoters with slow turnover. These observations suggest that TBP binding dynamics differentially affect promoter function and gene expression, possibly at the level of transcriptional reinitiation/bursting.
FD-SOI Enabled mmWave Telecommunication Applications and System Architectures

Due to ever increasing data traffic and the continued demand for high data rate on both fixed and mobile communication, future telecommunication radio access network will adopt mmWave frequencies. The use of phased array antenna system for directed and steerable beamforming will enable mmWave radio interface to have lower transmit power per antenna element than that in sub 6GHz cellular user equipment (UE) and access points. In addition to beamforming, mmWave system also needs to be close to the antenna array with minimum chip-to-chip interconnect to reduce loss and increased power efficiency driving SOC integration for mmWave frontend and transceiver. Both lower transmit power and drive for SOC integration makes mmWave capable Silicon technologies (Partially and Fully depleted SOI, SiGe & bulk CMOS) In this talk, different beamforming architecture options of mmWave telecommunication system will be highlighted along with key FOM's and chip partitioning covering both UE and base stations. Two key challenges of mmWave phased array system are the power efficiency of Transmitters and thermal power budget of the overall system. Hence, differentiation among silicon technologies will be based on Max. Power Output (Psat), power efficiency (PAE) at operating point of power amplifiers and losses between antenna and Transmitter/Receiver. The beamforming architectures and partitioning of chips will be determined by the EIRP, frequency band and amount of DC power dissipated in the system. There's a trade-off between area scaling of SOC and thermal power density to be dissipated. The challenge will be more severe as we will move to higher mmWave frequencies (> 60GH) as the array dimension will shrink, the beamforming SOC areas have to be scaled to be accommodated close to antenna elements. High thermal density needs to be addressed by improved power efficiency of the Silicon technology, improved thermal impedance of the package. As an example of Silicon implementation for mmWave beamforming radio, we'll provide examples of Fully Depleted SOI (FDSOI) based mmWave beamforming systems to show how FDSOI can address different system level challenges and enable SOC integration at high overall power efficiency. The intrinsic technology FOM's that impact the mmWave performance of different components and overall system will be discussed with examples based on measured silicon data. Roadmap of FDSOI technology generations to address the power and performance challenges of mmWave systems with ever increasing frequencies will be highlighted. The talk will conclude with comparisons among different silicon technologies for mmWave telecommunication systems and highlighting merits and demerits of different system architectures that can be addressed by combination of different Silicon technologies in comparison with FDSOI based implementations.
A Faster Algorithm for Minimum-cost Bipartite Perfect Matching in Planar Graphs

Given a weighted planar bipartite graph G(A ∪ B, E) where each edge has an integer edge cost, we give an O(n4/3log nC) time algorithm to compute minimum-cost perfect matching; here C is the maximum edge cost in the graph. The previous best-known planarity exploiting algorithm has a running time of O(n3/2log n) and is achieved by using planar separators (Lipton and Tarjan ’80).Our algorithm is based on the bit-scaling paradigm (Gabow and Tarjan ’89). For each scale, our algorithm first executes O(n1/3) iterations of Gabow and Tarjan’s algorithm in O(n4/3) time leaving only O(n2/3) vertices unmatched. Next, it constructs a compressed residual graph H with O(n2/3) vertices and O(n) edges. This is achieved by using an r-division of the planar graph G with r=n2/3. For each partition of the r-division, there is an edge between two vertices of H if and only if they are connected by a directed path inside the partition. Using existing efficient shortest-path data structures, the remaining O(n2/3) vertices are matched by iteratively computing a minimum-cost augmenting path, each taking O(n2/3) time. Augmentation changes the residual graph, so the algorithm updates the compressed representation for each partition affected by the change in O(n2/3) time. We bound the total number of affected partitions over all the augmenting paths by O(n2/3 log n). Therefore, the total time taken by the algorithm is O(n4/3).
Should I Stay or Should I Grow? Using Voluntary Disclosure to Elicit Market Feedback

We explore the use of voluntary disclosure by managers to solicit market-feedback. Using managerial capital expenditure forecasts, we find that managers adjust annual capital expenditures upward (downward) in response to positive (negative) stock market reactions to capital expenditure forecasts, but only for those forecast announcements that stimulate rather than discourage informed trading. These capex adjustments motivated by market feedback correlate with higher future performance and are stronger (weaker) when outsiders (managers) are more informed. Finally, we show that managers are more likely to issue and learn from capex forecasts when pre-disclosure stock prices are affected by transitory non-fundamental shocks.
Detection of Quiescent Phases in Echocardiography Data using Non-Linear Filtering and Boundary Detection

In order to detect the quasi-stationary states of the heart within a cardiac cycle from echocardiography data, we present an algorithm that uses non-linear filtering and boundary detection. The non-linear filtering algorithm involves anisotropic diffusion to remove the speckle noise from the data and to smoothen the homogeneous regions while preserving the edges. Following this, we perform binary thresholding and boundary detection, and observe the positional changes in the region of interest. From a series of echocardiography images, we derived the regions of cardiac quiescence, which we then plotted on the electrocardiograph (ECG) R-R interval. It is observed that the quiescence occurs in the diastolic region of the ECG signal, but the position and length of quiescence varies across multiple cardiac cycles for the same individual.
Predicate Encryption from Bilinear Maps and One-Sided Probabilistic Rank

In predicate encryption for a function f, an authority can create ciphertexts and secret keys which are associated with ‘attributes’. A user with decryption key \(K_y\) corresponding to attribute y can decrypt a ciphertext \(CT_x\) corresponding to a message m and attribute x if and only if \(f(x,y)=0\). Furthermore, the attribute x remains hidden to the user if \(f(x,y) \ne 0\).
Recruiting RyRs to Open in a Ca2+ Release Unit: Single-RyR Gating Properties Make RyR Group Dynamics

Abstract In cardiac myocytes, clusters of type-2 ryanodine receptors (RyR2s) release Ca2+ from the sarcoplasmic reticulum (SR) via a positive feedback mechanism in which fluxed Ca2+ activates nearby RyRs. Although the general principles of this are understood, less is known about how single-RyR gating properties define the RyR group dynamics in an array of many channels. Here, we examine this using simulations with three models of RyR gating that have identical open probabilities: the commonly used two-state Markov gating model, one that utilizes multiple exponentials to fit single-channel open time (OT) and closed time (CT) distributions, and an extension of this multiexponential model that also includes experimentally measured correlations between single-channel OTs and CTs. The simulations of RyR clusters that utilize the multiexponential gating model produce infrequent Ca2+ release events with relatively few open RyRs. Ca2+ release events become even smaller when OT/CT correlations are included. This occurs because the correlations produce a small but consistent bias against recruiting more RyRs to open during the middle of a Ca2+ release event, between the initiation and termination phases (which are unaltered compared to the uncorrelated simulations). In comparison, the two-state model produces frequent, large, and long Ca2+ release events because it had a recruitment bias in favor of opening more RyRs. This difference stems from the two-state model’s single-RyR OT and CT distributions being qualitatively different from the experimental ones. Thus, the details of single-RyR gating can profoundly affect SR Ca2+ release even if open probability and mean OTs and CTs are identical. We also show that Ca2+ release events can terminate spontaneously without any reduction in SR [Ca2+], luminal regulation, Ca2+-dependent inactivation, or physical coupling between RyRs when Ca2+ flux is below a threshold value. This supports and extends the pernicious attrition/induction decay hypothesis that SR Ca2+ release events terminate below a threshold Ca2+ flux.
Juror gender and confession evidence: an exploratory study of effects on empathy and trial outcomes for juvenile defendants

We explored how relations among juror gender, confession evidence, and empathy impacted verdicts for a juvenile defendant accused of a serious crime. Jury-eligible women and men (N = 132) participated in a mock trial involving a girl defendant who had either maintained her innocence, confessed voluntarily, or confessed under coercion. Participants reviewed case materials, received juror instructions, and reported their verdict and empathy for the girl defendant. A manipulation check ensured participants attended to details surrounding the confession and participants were grouped by whether they perceived the confession as voluntary or coerced. A logistic regression analysis examined main and interactive effects of juror gender and perceived-confession condition on jurors’ verdicts, and the PROCESS regression macro-examined whether effects revealed therein could be explained by empathy toward the juvenile. Juror gender did not have a significant indirect effect on verdicts in either the no-confession, B = .05, 95% CI [− .005, .85], or perceived-coerced-confession conditions, B = − .12, 95% CI [− .36, .02]. However, women’s lower levels of empathy translated into a higher conviction rate among women versus men for the juvenile who was perceived as confessing voluntarily, B = − .28, 95% CI [− .75, − .02]. Women and men differ in ways that may sometimes produce outcomes that are less favorable to juvenile defendants. Implications for jury practice and process are considered.
Neonatal platelet count trends during inhaled nitric oxide therapy

Recent debate has focused on the significance of platelets generated in lung tissue. Here, we retrospectively analyzed platelet count changes in neonates requiring inhaled nitric oxide (iNO) pulmonary vasodilation therapy for pulmonary hypertension. There were no clinically significant changes in platelet count upon iNO initiation or during iNO therapy. Unexpectedly, platelet counts increased after iNO cessation. These findings argue against a clinically meaningful untapped pulmonary repository of megakaryocytes and platelets in this context, although acute platelet count increases might be expected after discontinuing iNO in some patients. Further work is needed to clarify the underlying etiology for these observations, and to better delineate the mechanisms for platelet count increases after recovery from lung injury.
Grain boundary properties of elemental metals

Abstract The structure and energy of grain boundaries (GBs) are essential for predicting the properties of polycrystalline materials. In this work, we use high-throughput density functional theory calculations workflow to construct the Grain Boundary Database (GBDB), the largest database of DFT-computed grain boundary properties to date. The database currently encompasses 327 GBs of 58 elemental metals, including 10 common twist or symmetric tilt GBs for body-centered cubic (bcc) and face-centered cubic (fcc) systems and the Σ7 [0001] twist GB for hexagonal close-packed (hcp) systems. In particular, we demonstrate a novel scaled-structural template approach for HT GB calculations, which reduces the computational cost of converging GB structures by a factor of ∼ 3 − 6 . The grain boundary energies and work of separation are rigorously validated against previous experimental and computational data. Using this large GB dataset, we develop an improved predictive model for the GB energy of different elements based on the cohesive energy and shear modulus. The open GBDB represents a significant step forward in the availability of first principles GB properties, which we believe would help guide the future design of polycrystalline materials.
Risk attitudes over small and large stakes recalibrated

Abstract In this paper I provide bounds on the marginal rate of substitution between losing $x and winning $y, starting from wealth level $w, for a risk averse individual that rejects a small stake gamble for a range of initial wealth levels. I then prove a theorem that can be used to identify the kinds of large stakes that would be rejected by any such individual. The theorems allow us to understand how much risk aversion is embedded in anyone’s rejections of certain small stakes gambles and provide tighter connections between the results in Rabin (2000) and the received theory of decision making under risk.
Fairness for Robust Log Loss Classification

Developing classification methods with high accuracy that also avoid unfair treatment of different groups has become increasingly important for data-driven decision making in social applications. Many existing methods enforce fairness constraints on a selected classifier (e.g., logistic regression) by directly forming constrained optimizations. We instead re-derive a new classifier from the first principles of distributional robustness that incorporates fairness criteria into a worst-case logarithmic loss minimization. This construction takes the form of a minimax game and produces a parametric exponential family conditional distribution that resembles truncated logistic regression. We present the theoretical benefits of our approach in terms of its convexity and asymptotic convergence. We then demonstrate the practical advantages of our approach on three benchmark fairness datasets.
On extensions of the Loomis-Whitney inequality and Ball's inequality for concave, homogeneous measures

Abstract The Loomis-Whitney inequality states that the volume of a convex body is bounded by the product of volumes of its projections onto orthogonal hyperplanes. We provide an extension of both this fact and a generalization of this fact due to Ball to the context of q−concave, 1 q − homogeneous measures.
Topology of tropical moduli of weighted stable curves

The moduli space $\Delta_{g,w}$ of tropical $w$-weighted stable curves of volume $1$ is naturally identified with the dual complex of the divisor of singular curves in Hassett's spaces of $w$-weighted stable curves. If at least two of the weights are $1$, we prove that $\Delta_{0,w}$ is homotopic to a wedge sum of spheres, possibly of varying dimensions. Under additional natural hypotheses on the weight vector, we establish explicit formulas for the Betti numbers of the spaces. We exhibit infinite families of weights for which the space $\Delta_{0,w}$ is disconnected and for which the fundamental group of $\Delta_{0,w}$ has torsion. In the latter case, the universal cover is shown to have a natural modular interpretation. This places the weighted variant of the space in stark contrast to the heavy/light cases studied previously by Vogtmann and Cavalieri-Hampe-Markwig-Ranganathan. Finally, we prove a structural result relating the spaces of weighted stable curves in genus $0$ and $1$, and leverage this to extend several of our genus $0$ results to the spaces $\Delta_{1,w}$.
Lost in translation: “Risks,” “needs,” and “evidence” in implementing the First Step Act

In this article, we focus on two highly problematic issues in the manner in which the First Step Act of 2018 is being implemented by the Bureau of Prisons: an uncritical separation of "dynamic risks" and "criminogenic needs"; and a spurious reliance on "evidence-based" interventions to reduce recidivism risk. We argue that if the Act is to live up to its promise of being a game-changing development in efforts to reduce crime while simultaneously shrinking mass incarceration, "needs assessment" must be subject to vastly increased empirical attention, variable and causal risk factors must be identified and validly assessed, and interventions to reduce risk must be rigorously evaluated both for their fidelity of implementation and impact on recidivism. Rather than further proliferating programs that ostensibly reduce risk, we believe that serious consideration should be given to the Bureau of Prisons offering one signature, well-established cognitive-behavioral program that can simultaneously address multiple risk factors for moderate and high-risk prisoners.
Context-Specific Nested Effects Models.

Advances in systems biology have made clear the importance of network models for capturing knowledge about complex relationships in gene regulation, metabolism, and cellular signaling. A common app...
Simulation of Transitional and Bursty Wireless Visual Sensor Networks

Communication links in low-power wireless visual sensor networks (WVSNs) are subject to short-term and long-term noise variations. These variations can cause a WVSN to exhibit prolonged or periodic transitional or bursty transmission performance. In this paper, we present our work on how to generate noise traces that simulate real-world transitional and bursty network behavior in TOSSIM. We develop a toolset called BurstyGen for TOSSIM which can facilitate WVSN protocol designers and application developers to better understand WVSN performance under these conditions. BurstyGen allows users to model both short time-scale and long time-scale variations in WVSN noise environments for the simulation and testing of WVSN system algorithms and protocols.
Bayesian Topological Learning for Brain State Classification

Investigation of human brain states through electroencephalograph (EEG) signals is a crucial step in human-machine communications. However, classifying and analyzing EEG signals are challenging due to their noisy, nonlinear and nonstationary nature. Current methodologies for analyzing these signals often fall short because they have several regularity assumptions baked in. This work provides an effective, flexible and noise-resilient scheme to analyze EEG by extracting pertinent information while abiding by the 3N (noisy, nonlinear and nonstationary) nature of data. We implement a topological tool, namely persistent homology, that tracks the evolution of topological features over time intervals and incorporates individual's expectations as prior knowledge by means of a Bayesian framework to compute posterior distributions. Relying on these posterior distributions, we apply Bayes factor classification to noisy EEG measurements. The performance of this Bayesian classification scheme is then compared with other existing methods for EEG signals.
Auptimizer - an Extensible, Open-Source Framework for Hyperparameter Tuning

Tuning machine learning models at scale, especially finding the right hyperparameter values, can be difficult and time-consuming. In addition to the computational effort required, this process also requires some ancillary efforts including engineering tasks (e.g., job scheduling) as well as more mundane tasks (e.g., keeping track of the various parameters and associated results). We present Auptimizer, a general Hyperparameter Optimization (HPO) framework to help data scientists speed up model tuning and bookkeeping. With Auptimizer, users can use all available computing resources in distributed settings for model training. The user-friendly system design simplifies creating, controlling, and tracking of a typical machine learning project. The design also allows researchers to integrate new HPO algorithms. To demonstrate its flexibility, we show how Auptimizer integrates a few major HPO techniques (from random search to neural architecture search). The code is available at https://github.com/LGE-ARC-AdvancedAI/auptimizer.
CO Gas and Dust Outbursts from Centaur 29P/Schwassmann–Wachmann

29P/Schwassmann Wachmann is an unusual solar system object. Originally classified as a short-period comet, it is now known as a Centaur that recently transferred to its current orbit, and may become a Jupiter Family comet. It has exhibited a dust coma for over 90 years, and regularly undergoes significant dust outbursts. Carbon monoxide is routinely detected in high amounts and is typically assumed to play a large role in generating the quiescent dust coma and outbursts. To test this hypothesis, we completed two 3-month long observing campaigns of the CO J=2-1 rotational line using the Arizona Radio Observatory 10m Sub-millimeter Telescope during 2016 and 2018-2019, and compared the results to visible magnitudes obtained at the same time. As the Centaur approached its 2019 perihelion, the quiescent dust coma grew $\sim$45\% in brightness, while it is unclear whether the quiescent CO production rate also increased. A doubling of the CO production rate on 2016 Feb 28.6 UT did not trigger an outburst nor a rise in dust production for at least 10 days. Similarly, two dust outbursts occurred in 2018 while CO production continued at quiescent rates. Two other dust outbursts may show gas involvement. The data indicate that CO- and dust-outbursts are not always well-correlated. This may be explained if CO is not always substantially incorporated with the dust component in the nucleus, or if CO is primarily released through a porous material. Additionally, other minor volatiles or physical processes may help generate dust outbursts.
Vascular progenitors generated from tankyrase inhibitor-regulated naïve diabetic human iPSC potentiate efficient revascularization of ischemic retina

Here, we report that the functionality of vascular progenitors (VP) generated from normal and disease-primed conventional human induced pluripotent stem cells (hiPSC) can be significantly improved by reversion to a tankyrase inhibitor-regulated human naive epiblast-like pluripotent state. Naive diabetic vascular progenitors (N-DVP) differentiated from patient-specific naive diabetic hiPSC (N-DhiPSC) possessed higher vascular functionality, maintained greater genomic stability, harbored decreased lineage-primed gene expression, and were more efficient in migrating to and re-vascularizing the deep neural layers of the ischemic retina than isogenic diabetic vascular progenitors (DVP). These findings suggest that reprogramming to a stable naive human pluripotent stem cell state may effectively erase dysfunctional epigenetic donor cell memory or disease-associated aberrations in patient-specific hiPSC. More broadly, tankyrase inhibitor-regulated naive hiPSC (N-hiPSC) represent a class of human stem cells with high epigenetic plasticity, improved multi-lineage functionality, and potentially high impact for regenerative medicine. hPSCs in culture acquire a more naive pluripotent state upon tankyrase inhibition. Here, the authors show that tankyrase inhibitor-regulated naive hiPSCs from diabetic donors generate more vascular progenitors and more efficient engraftment into mouse retina than conventional PSCs.
4×112 Gbps/fiber CWDM VCSEL Arrays for Co-Packaged Interconnects

We demonstrate a 4×112 Gbps/fiber VCSEL link using a co-packaged coarse wavelength division multiplexing (CWDM) optical module. A complete co-packaged CWDM module can achieve a 2.668 Tb/s aggregated bandwidth by assembling four 1×6 VCSEL arrays.
Disciplined quasiconvex programming

We present a composition rule involving quasiconvex functions that generalizes the classical composition rule for convex functions. This rule complements well-known rules for the curvature of quasiconvex functions under increasing functions and pointwise maximums. We refer to the class of optimization problems generated by these rules, along with a base set of quasiconvex and quasiconcave functions, as disciplined quasiconvex programs. Disciplined quasiconvex programming generalizes disciplined convex programming, the class of optimization problems targeted by most modern domain-specific languages for convex optimization. We describe an implementation of disciplined quasiconvex programming that makes it possible to specify and solve quasiconvex programs in CVXPY 1.0.
The Local Geometry of Testing in Ellipses: Tight Control via Localized Kolmogorov Widths

Mean testing over ellipses is a decision problem that arises in several applications, including non-parametric goodness-of-fit testing, signal detection in cognitive radio, and regression function testing in reproducing kernel Hilbert spaces. We study the local geometry of such testing problems with compound alternatives. Given samples of a Gaussian random vector, the goal is to distinguish whether the mean is equal to a known vector within an ellipse, or equal to some other unknown vector in the ellipse. While past work on such problems has focused on the difficulty in a global sense, we study difficulty in a way that is localized to each vector within the ellipse. Our main result is to give sharp upper and lower bounds on the localized minimax testing radius in terms of an explicit formula involving the Kolmogorov width of the ellipse intersected with a Euclidean ball. When applied to particular examples, our general theorems yield interesting rates that were not known before: as a particular case, for testing in Sobolev ellipses of smoothness $\alpha $ , we demonstrate rates that vary from $(\sigma ^{2})^{\frac {4 \alpha }{4 \alpha + 1}}$ , corresponding to the classical global rate, to the faster rate $(\sigma ^{2})^{\frac {8 \alpha }{8 \alpha + 1}}$ , achievable for vectors at favorable locations within the ellipse. We also show that the optimal test for this problem is achieved by a linear projection test that is based on an explicit lower-dimensional projection of the observation vector.
GraphOne: A Data Store for Real-time Analytics on Evolving Graphs

There is a growing need to perform a diverse set of real-time analytics (batch and stream analytics) on evolving graphs to deliver the values of big data to users. The key requirement from such applications is to have a data store to support their diverse data access efficiently, while concurrently ingesting fine-grained updates at a high velocity. Unfortunately, current graph systems, either graph databases or analytics engines, are not designed to achieve high performance for both operations; rather, they excel in one area that keeps a private data store in a specialized way to favor their operations only. To address this challenge, we have designed and developed GraphOne, a graph data store that abstracts the graph data store away from the specialized systems to solve the fundamental research problems associated with the data store design. It combines two complementary graph storage formats (edge list and adjacency list) and uses dual versioning to decouple graph computations from updates. Importantly, it presents a new data abstraction, GraphView, to enable data access at two different granularities of data ingestions (called data visibility) for concurrent execution of diverse classes of real-time graph analytics with only a small data duplication. Experimental results show that GraphOne is able to deliver 11.40× and 5.36× average speedup in ingestion rate against LLAMA and Stinger, the two state-of-the-art dynamic graph systems, respectively. Further, they achieve an average speedup of 8.75× and 4.14× against LLAMA and 12.80× and 3.18× against Stinger for BFS and PageRank analytics (batch version), respectively. GraphOne also gains over 2,000× speedup against Kickstarter, a state-of-the-art stream analytics engine in ingesting the streaming edges and performing streaming BFS when treating first half as a base snapshot and rest as streaming edge in a synthetic graph. GraphOne also achieves an ingestion rate of two to three orders of magnitude higher than graph databases. Finally, we demonstrate that it is possible to run concurrent stream analytics from the same data store.
The Characteristics, Valuation Methods, and Information Use of Valuation Specialists

We provide insights into the inputs and valuation models used by valuation specialists. We survey 172 valuation specialists and conduct several follow-up interviews covering various topics, includi...
Optimal Power Flow Using Graph Neural Networks

Optimal power flow (OPF) is one of the most important optimization problems in the energy industry. In its simplest form, OPF attempts to find the optimal power that the generators within the grid have to produce to satisfy a given demand. Optimality is measured with respect to the cost that each generator incurs in producing this power. The OPF problem is non-convex due to the sinusoidal nature of electrical generation and thus is difficult to solve. Using small angle approximations leads to a convex problem known as DC OPF, but this approximation is no longer valid when power grids are heavily loaded. Many approximate solutions have been since put forward, but these do not scale to large power networks. In this paper, we propose using graph neural networks (which are localized, scalable parametrizations of network data) trained under the imitation learning framework to approximate a given optimal solution. While the optimal solution is costly, it is only required to be computed for network states in the training set. During test time, the GNN adequately learns how to compute the OPF solution. Numerical experiments are run on the IEEE-30 and IEEE-118 test cases.
Sharp bounds for multilinear curved Kakeya, restriction and oscillatory integral estimates away from the endpoint

We revisit the multilinear Kakeya, curved Kakeya, restriction, and oscillatory integral estimates that were obtained in paper of Bennett, Carbery, and the author using a heat flow monotonicity method applied to a fractional Cartesian product, together with induction on scales arguments. Many of these estimates contained losses of the form $R^\varepsilon$ (or $\log^{O(1)} R$) for some scale factor $R$. By further developing the heat flow method, and applying it directly for the first time to the multilinear curved Kakeya and restriction settings, we are able to eliminate these losses, as long as the exponent $p$ stays away from the endpoint. In particular, we establish global multilinear restriction estimates away from the endpoint, without any curvature hypotheses on the hypersurfaces.
Glioma-Induced Alterations in Neuronal Activity and Neurovascular Coupling during Disease Progression

Diffusely infiltrating gliomas are known to cause alterations in cortical function, vascular disruption, and seizures. These neurological complications present major clinical challenges, yet their underlying mechanisms and causal relationships to disease progression are poorly characterized. Here, we follow glioma progression in awake Thy1-GCaMP6f mice using in vivo wide-field optical mapping to monitor alterations in both neuronal activity and functional hemodynamics. The bilateral synchrony of spontaneous neuronal activity gradually decreases in glioma-infiltrated cortical regions, while neurovascular coupling becomes progressively disrupted compared to uninvolved cortex. Over time, mice develop diverse patterns of high amplitude discharges and eventually generalized seizures that appear to originate at the tumors' infiltrative margins. Interictal and seizure events exhibit positive neurovascular coupling in uninfiltrated cortex; however, glioma-infiltrated regions exhibit disrupted hemodynamic responses driving seizure-evoked hypoxia. These results reveal a landscape of complex physiological interactions occurring during glioma progression and present new opportunities for exploring novel biomarkers and therapeutic targets.
Robust Q-learning

Abstract–Q-learning is a regression-based approach that is widely used to formalize the development of an optimal dynamic treatment strategy. Finite dimensional working models are typically used to...
Using Randomness to Improve Robustness of Tree-based Models Against Evasion Attacks

Machine learning models have been widely used in security applications. However, it is well-known that adversaries can adapt their attacks to evade detection. There has been some work on making machine learning models more robust to such attacks. However, one simple but promising approach called randomization is under-explored. In addition, most existing works focus on models with differentiable error functions while tree-based models do not have such error functions but are quite popular because they are easy to interpret. This paper proposes a novel randomization-based approach to improve robustness of tree-based models against evasion attacks. The proposed approach incorporates randomization into both model training time and model application time (meaning when the model is used to detect attacks). We also apply this approach to random forest, an existing ML method which already has incorporated randomness at training time but still often fails to generate robust models. We proposed a novel weighted-random-forest method to generate more robust models and a clustering method to add randomness at model application time. We also proposed a theoretical framework to provide a lower bound for adversaries' effort. Experiments on intrusion detection and spam filtering data show that our approach further improves robustness of random-forest method.
Wrongful Discharge Laws and Asymmetric Cost Behavior

Exploiting the natural experiment created by the adoption of wrongful discharge laws (WDLs) across U.S. states, we examine the effect of legal protection against unjust employment termination on firm-level cost behavior. We find that the adoption of WDLs increases the asymmetric sensitivity of costs to activity (i.e., cost stickiness). The effect of WDLs on cost stickiness varies across firms and industries, as well as by adoption timing and multiplicity. Our evidence suggests that changes in the state-level legal environment have a significant effect on firm-level resource adjustment decisions and asymmetric cost behavior.
Energy-Efficient and Metastability-Immune Resilient Circuits for Dynamic Variation Tolerance

A 65 nm resilient circuit test-chip is implemented with timing-error detection and recovery circuits to eliminate the clock frequency guardband from dynamic supply voltage (V CC ) and temperature variations as well as to exploit path-activation probabilities for maximizing throughput. Two error-detection sequential (EDS) circuits are introduced to preserve the timing-error detection capability of previous EDS designs while lowering clock energy and removing datapath metastability. One EDS circuit is a dynamic transition detector with a time-borrowing datapath latch (TDTB). The other EDS circuit is a double-sampling static design with a time-borrowing datapath latch (DSTB). In comparison to previous EDS designs, TDTB and DSTB redirect the highly complex metastability problem from both the datapath and error path to only the error path, enabling a drastic simplification in managing metastability. From a survey of various EDS circuit options, TDTB represents the lowest clock energy EDS circuit known; DSTB represents the lowest clock energy static-EDS circuit with SER protection known. Error-recovery circuits are introduced to replay failing instructions at lower clock frequency to guarantee correct functionality. Relative to conventional circuits, test-chip measurements demonstrate that resilient circuits enable either 25%-32% throughput gain at equal V CC or at least 17% V CC reduction at equal throughput, corresponding to 31%-37% total power reduction.
The use of collateral in bilateral repurchase and securities lending agreements

Abstract We use unique data from U.S. BHC-affiliated securities dealers to study the use of collateral in bilateral repurchase and securities lending agreements. This is the first paper to provide stylized facts about this market, documenting, among other things, that most trades are backed by U.S. Treasuries and equities, and have either an overnight or open maturity. We also focus on how haircuts are determined in this market, highlighting the differences across asset classes. Finally, we document a negative correlation between haircut and interest rate, a prediction in line with a large set of theories; however this correlation is quite small in magnitude.
2 – SUPPLY AND DEMAND

Publisher Summary This chapter presents an overall view of the market along with the various conditions of supply and demand. A market is a place where goods or services are exchanged. Buyers and sellers battle each other over price, but their conflict is friendly because neither could survive without the other. Demand to an economist is a description of the way people feel about the things that they buy and how those feelings determine the amount of any particular good they want to buy. The chapter also explains the demand and the supply curve in detail. An increase in demand is the economist's term for anything that makes anyone want to buy more apples even if their price does not fall. Demand falls when goods become less desirable. This is decrease in demand. Supply is a total description of the way sellers choose the amounts and kinds of goods and services that they produce and sell. Higher prices give sellers an incentive to produce more. Lower prices make selling an unprofitable proposition, so fewer goods are offered for sale. Events that make production cheaper, more efficient, and generally increase profits cause an increase in supply. A decrease in supply occurs whenever events increase costs, slow down production, or otherwise reduce profits. The chapter also discusses the applications of supply and demand, review of market actions and economic controversies.
PALEOCLIMATE MODELING | Quaternary Environments

The modeling of Quaternary climate provides an opportunity to understand the balance of processes that maintain climate and its sensitivity to perturbation. However, models do not faithfully represent the balance of processes that are acting in nature. The primary objective of modeling Quaternary climate is to interpret these balances from climate proxy data. The century to multimillennial timescales and the range of processes involved in Quaternary climate change have forced modelers to design simpler, more parameterized, versions of comprehensive atmosphere–ocean general circulation models. A review of some common choices in experimental and model design and the strength and limitations of these choices for addressing science questions is presented. The use of a range of model complexities and multiple models can be helpful in illustrating where certain modeling assumptions or implicit limitations are important to the interpretation of modeling results.