cssi/SciSciGPT-SciSciNet huggingface dataset:
A PMU Based Islanding Detection Scheme Immune to Additive Instrumentation Channel Errors

Traditional synchrophasor measurement-based islanding detection techniques have primarily relied on voltage angle measurements and/or their derivatives for successfully detecting islands. However, relatively high instrumentation channel errors associated with phasor measurement unit (PMU) data, can significantly degrade islanding detection accuracies. In this paper, a new islanding detection scheme employing cumulative sum of change in voltage phase angle difference (CUSPAD) is proposed, which is immune to additive instrumentation channel errors in the PMU measurements. The robustness of the proposed islanding detection algorithm is established through application to an 18-bus test system and the IEEE 118-bus system having different wind energy penetration levels. Comparative analysis of the accuracies of the proposed approach (CUSPAD) and the conventional angle difference (AD) approach prove the former's superior performance when additive instrumentation channel errors are present.
Improved rheometry of yield stress fluids using bespoke fractal 3D printed vanes

To enable robust rheological measurements of the properties of yield stress fluids, we introduce a class of modified vane fixtures with fractal-like cross-sectional structures. A greater number of outer contact edges leads to increased kinematic homogeneity at the point of yielding and beyond. The vanes are 3D printed (3DP) using a desktop stereolithography machine, making them inexpensive (disposable), chemically compatible with a wide range of solvents, and readily adaptable as a base for further design innovations. To complete the tooling set, we introduce a textured 3DP cup, which attaches to a standard rheometer base. We discuss general design criteria for 3DP rheometer vanes, including consideration of sample volume displaced by the vanes, stress homogeneity, and secondary flows that constrain the parameter space of potential designs. We also develop a conversion from machine torque to material shear stress for vanes with an arbitrary number of arms. We compare a family of vane designs by measuring the viscosity of Newtonian calibration oils with error <5% relative to reference measurements made with a cone-and-plate geometry. We measure the flow curve of a simple Carbopol yield stress fluid and show that a 24-armed 3DP fractal vane agrees within 1% of reference measurements made with a roughened cone-and-plate geometry. Last, we demonstrate use of the 24-armed fractal vane to probe the thixo-elastoviscoplastic response of a Carbopol-based hair gel, a jammed emulsion (mayonnaise), and a strongly alkaline carbon black-based battery slurry.
Minimizers of convex functionals with small degeneracy set

We study the question whether Lipschitz minimizers of $$\int F(\nabla u)\,dx$$ in $$\mathbb {R}^n$$ are $$C^1$$ when F is strictly convex. Building on work of De Silva–Savin, we confirm the $$C^1$$ regularity when $$D^2F$$ is positive and bounded away from finitely many points that lie in a 2-plane. We then construct a counterexample in $$\mathbb {R}^4$$, where F is strictly convex but $$D^2F$$ degenerates on the Clifford torus. Finally we highlight a connection between the case $$n = 3$$ and a result of Alexandrov in classical differential geometry, and we make a conjecture about this case.
Switched Linear Systems Meet Markov Decision Processes: Stability Guaranteed Policy Synthesis

Switched linear systems are time-varying nonlinear systems whose dynamics switch between different modes, where each mode corresponds to different linear dynamics. They arise naturally to model unexpected failures, environment uncertainties, or system noises during system operation. In this paper, we consider a special class of switched linear systems where the mode switches are governed by Markov decision processes (MDPs). We study the problem of synthesizing a policy in an MDP that stabilizes the switched system. Given a policy, the switched linear system becomes a Markov jump linear system whose stability conditions have been extensively studied. We make use of these stability conditions and propose three different computation approaches to find the stabilizing policy. We derive our first approach by extending the stability conditions for a Markov jump linear system to handle switches governed by an MDP. This approach requires finding a feasible solution of a set of bilinear matrix inequalities, which makes policy synthesis typically challenging. To improve scalability, we provide two approaches based on convex optimization. We give three examples to compare our proposed solutions.
Comparison of block-based and hybrid-based environments in transferring programming skills to text-based environments.

Teachers face several challenges when presenting the fundamental concepts of programming in the classroom. Several tools are introduced to give a visual dimension to support the learning process. These tools rely on code blocks, easily manipulated in a plug and play fashion, to build a program. These block-based tools intend to familiarize students with programming logic, before diving into text-based programming languages such as Java, Python, etc. However; when transitioning from block-based to text-based programming, students often encounter a gap in their learning. The student may not be able to apply block-based foundations in a text-based environment. To bridge the gap between both environments, we developed a hybrid-based learning approach. We found that on average a hybrid-based approach increases the students understanding of programming foundations, memorization, and ease of transition by more than 30% when compared to a block-based to text-based learning approach. Finally, we provide the community with an open source, hybrid-based learning tool that can be used by students when learning programming concepts or for future studies.
Fraud and Market Participation: Social Relations as a Moderator of Organizational Misconduct:

This paper extends organizational research on distrust to explain the effects of organizational misconduct on continued market participation after a fraud. I argue that social relations between fra...
Cohort Study, Historical †

A historical cohort study, also called a retrospective cohort study or a nonconcurrent cohort study, refers to a cohort whose members were at risk for disease outcomes before the study was initiated. Historical cohort studies are widely used in occupational epidemiology because they allow one to take advantage of employment records to identify cohort membership and exposures, and because one does not need to wait for disease outcomes before analyzing the data. As in other cohort studies, historical cohort studies allow one to estimate disease incidence rates as well as relative risks and other measures of exposure effect. Because administrative databases are often used, however, there is a potential for inadequate data to define cohort membership (selection bias), exposure status, and covariates needed to control for confounding, such as smoking status. In addition, there may be special problems with loss to follow-up and misclassification of health outcomes. Historical cohort studies may be supplemented with special nested case–control or case–cohort designs to obtain additional detailed information on selected cohort members. Keywords: retrospective cohort study; historical cohort study; occupational epidemiology; incidence rate; relative risk; selection bias; information bias; confounding
Single machine graph analytics on massive datasets using Intel optane DC persistent memory

Intel Optane DC Persistent Memory (Optane PMM) is a new kind of byte-addressable memory with higher density and lower cost than DRAM. This enables the design of affordable systems that support up to 6TB of randomly accessible memory. In this paper, we present key runtime and algorithmic principles to consider when performing graph analytics on extreme-scale graphs on Optane PMM and highlight principles that can apply to graph analytics on all large-memory platforms.To demonstrate the importance of these principles, we evaluate four existing shared-memory graph frameworks and one out-of-core graph framework on large real-world graphs using a machine with 6TB of Optane PMM. Our results show that frameworks using the runtime and algorithmic principles advocated in this paper (i) perform significantly better than the others and (ii) are competitive with graph analytics frameworks running on production clusters.
Variations in Mesospheric Neutral Densities from Rayleigh Lidar Observations at Utah State University

A Rayleigh lidar was operated from 1993 to 2004, at the Atmospheric Lidar Observatory (ALO; 41.7°N, 111.8°W) at the Center for Atmospheric and Space Sciences (CASS) on the campus of Utah State University (USU). Observations were carried out on over 900 nights, 729 of which had good data starting at 45 km and going upward toward 90 km. They were reduced for absolute temperatures and relative neutral number densities. The latter at 45 km can be put on an absolute basis by using atmospheric models that go up to at least 45 km. The models’ absolute number densities at 45 km are used to normalize the lidar observations, thereby providing absolute densities from 45 to 90 km. We examine these absolute density profiles for differences from the overall mean density profile to show altitudinal structure and seasonal variations.
Induced-charge electrokinetic phenomena

Abstract The field of nonlinear “induced-charge” electrokinetics is rapidly advancing, motivated by potential applications in microfluidics as well as by the unique opportunities it provides for probing fundamental scientific issues in electrokinetics. Over the past few years, several surprising theoretical predictions have been observed in experiments: (i) induced-charge electrophoresis of half-metallic Janus particles, perpendicular to a uniform AC field; (ii) microfluidic mixing around metallic structures by induced-charge electro-osmosis, and (iii) fast, high-pressure AC electro-osmotic pumping by non-planar electrode arrays, and ICEK effects upon the collective behavior of polarizable particle suspensions has been studied theoretically and computationally. A new experimental system enables a clean and direct comparison between theoretical predictions and measured ICEK flows, providing a route to fundamental studies of particular surfaces and high-throughput searches for optimal ICEK systems. Systematic discrepancies between theory and experiment have engendered the search for mechanisms, including new theories that account for electrochemical surface reactions, surface contamination, roughness, and the crowding of ions at high voltage. Promising directions for further research, both fundamental and applied, are discussed.
Physics basis for the advanced tokamak fusion power plant, ARIES-AT

Abstract The advanced tokamak is considered as the basis for a fusion power plant. The ARIES-AT design has an aspect ratio of A ≡ R / a = 4.0 , an elongation and triangularity of κ = 2.20 , δ = 0.90 (evaluated at the separatrix surface), a toroidal beta of β = 9.1 % (normalized to the vacuum toroidal field at the plasma center), which corresponds to a normalized beta of β N ≡ 100 × β / ( I P ( M A ) / a ( m ) B ( T ) ) = 5.4 . These beta values are chosen to be 10% below the ideal MHD stability limit. The bootstrap-current fraction is f BS ≡ I BS / I P = 0.91 . This leads to a design with total plasma current I P = 12.8 MA, and toroidal field of 11.1 T (at the coil edge) and 5.8 T (at the plasma center). The major and minor radii are 5.2 and 1.3 m. The effects of H-mode edge gradients and the stability of this configuration to non-ideal modes is analyzed. The current drive system consists of ICRF/FW for on-axis current drive and a Lower Hybrid system for off-axis. Transport projections are presented using the drift-wave based GLF23 model. The approach to power and particle exhaust using both plasma core and scrape-off-layer radiation is presented.
Avoiding 5/4-Powers on the Alphabet of Nonnegative Integers (Extended Abstract)

We identify the structure of the lexicographically least word avoiding 5/4-powers on the alphabet of nonnegative integers.
In silico simulations of erythrocyte aquaporins with quantitative in vitro validation.

Modelling water and membrane lipids is an essential element in the computational research of biophysical/biochemical processes such as water transport across the cell membrane. In this study, we examined the accuracies of two popular water models, TIP3P and TIP4P, in the molecular dynamics simulations of erythrocyte aquaporins (AQP1 and AQP3). We modelled the erythrocyte membrane as an asymmetric lipid bilayer with appropriate lipid compositions of its inner and outer leaflet, in comparison with a symmetric lipid bilayer of a single lipid type. We computed the AQP1/3 permeabilities with the transition state theory with full correction for recrossing events. We also conducted cell swelling assays for water transport across the erythrocyte membrane. The experimental results agree with the TIP3P water–erythrocyte membrane model, in confirmation of the expected accuracy of the erythrocyte membrane model, the TIP3P water model, and the CHARMM parameters for water–protein interactions.
Computer Science Education: Online Content Modules and Professional Development for Secondary Teachers in West Tennessee – A Case Study

With ongoing efforts in the United States to further develop the availability of computer science education in the public schools, federal, state, and local educational agencies are increasing efforts to encourage and promote the inclusion of computer science and programming skills in the middle school curriculum (Grover and Pea in Comput. Sci. Educ. 25:199–237, 2015) [1]. The goal for the Online Content Modules: Computer Science in the Middle Grades project was the development of five online content modules with a focus on computer science instruction in three public school districts in West Tennessee, and disseminated through a week-long professional development summer institute.
Exploit Clues From Views: Self-Supervised and Regularized Learning for Multiview Object Recognition

Multiview recognition has been well studied in the literature and achieves decent performance in object recognition and retrieval task. However, most previous works rely on supervised learning and some impractical underlying assumptions, such as the availability of all views in training and inference time. In this work, the problem of multiview self-supervised learning (MV-SSL) is investigated, where only image to object association is given. Given this setup, a novel surrogate task for self-supervised learning is proposed by pursuing ``object invariant" representation. This is solved by randomly selecting an image feature of an object as object prototype, accompanied with multiview consistency regularization, which results in view invariant stochastic prototype embedding (VISPE). Experiments shows that the recognition and retrieval results using VISPE outperform that of other self-supervised learning methods on seen and unseen data. VISPE can also be applied to semi-supervised scenario and demonstrates robust performance with limited data available. Code is available at https://github.com/chihhuiho/VISPE
Board Risk Oversight and Corporate Tax-Planning Practices

Risk oversight by the board of directors is a key component of a firm's enterprise risk management framework, and recently, boards have paid more attention to their firm's tax-planning activities. In this study, we use a hand-collected sample of proxy statement disclosures about the board's role in risk oversight and provide evidence that risk oversight is negatively associated with both tax uncertainty and overall tax burdens. We find that risk oversight is most strongly associated with positions that yield permanent tax benefits and also with less risky tax-planning activities. Overall, the evidence suggests that board risk oversight is associated with more effective tax-planning practices.
Hierarchical Generation of Molecular Graphs using Structural Motifs

Graph generation techniques are increasingly being adopted for drug discovery. Previous graph generation approaches have utilized relatively small molecular building blocks such as atoms or simple cycles, limiting their effectiveness to smaller molecules. Indeed, as we demonstrate, their performance degrades significantly for larger molecules. In this paper, we propose a new hierarchical graph encoder-decoder that employs significantly larger and more flexible graph motifs as basic building blocks. Our encoder produces a multi-resolution representation for each molecule in a fine-to-coarse fashion, from atoms to connected motifs. Each level integrates the encoding of constituents below with the graph at that level. Our autoregressive coarse-to-fine decoder adds one motif at a time, interleaving the decision of selecting a new motif with the process of resolving its attachments to the emerging molecule. We evaluate our model on multiple molecule generation tasks, including polymers, and show that our model significantly outperforms previous state-of-the-art baselines.
Inhibition and Crystal Structure of the Human DHTKD1-Thiamin Diphosphate Complex

DHTKD1 is the E1 component of the 2-oxoadipate dehydrogenase complex, an enzyme involved in the catabolism of (hydroxy-)lysine and tryptophan. Mutations in DHTKD1 have been associated with 2-aminoa...
A Large Effective Phonon Magnetic Moment in a Dirac Semimetal

We investigated the magnetoterahertz response of the Dirac semimetal Cd3As2 and observed a particularly low frequency optical phonon as well as a very prominent and field-sensitive cyclotron resonance. As the cyclotron frequency is tuned with the field to pass through the phonon, the phonon becomes circularly polarized, as shown by a notable splitting in its response to right- and left-hand polarized light. This splitting can be expressed as an effective phonon magnetic moment that is approximately 2.7 times the Bohr magneton, which is almost 4 orders of magnitude larger than ab initio calculations predict for phonon magnetic moments in nonmagnetic insulators. This exceedingly large value is due to the coupling of the phonons to the cyclotron motion and is controlled directly by the electron-phonon coupling constant. This field-tunable circular-polarization-selective coupling provides new functionality for nonlinear optics to create light-induced topological phases in Dirac semimetals.
Distributed and retinotopically asymmetric processing of coherent motion in mouse visual cortex

Perception of visual motion is important for a range of ethological behaviors in mammals. In primates, specific visual cortical regions are specialized for processing of coherent visual motion. However, whether mouse visual cortex has a similar organization remains unclear, despite powerful genetic tools available for measuring population neural activity. Here, we use widefield and 2-photon calcium imaging of transgenic mice to measure mesoscale and cellular responses to coherent motion. Imaging of primary visual cortex (V1) and higher visual areas (HVAs) during presentation of natural movies and random dot kinematograms (RDKs) reveals varied responsiveness to coherent motion, with stronger responses in dorsal stream areas compared to ventral stream areas. Moreover, there is considerable anisotropy within visual areas, such that neurons representing the lower visual field are more responsive to coherent motion. These results indicate that processing of visual motion in mouse cortex is distributed heterogeneously both across and within visual areas.
The Polynomial Carleson operator

We prove affirmatively the one dimensional case of a conjecture of Stein regarding the $L^p$-boundedness of the Polynomial Carleson operator, for $1<p<\infty$. The proof is based on two new ideas: i) developing a framework for \emph{higher-order wave-packet analysis} that is consistent with the time-frequency analysis of the (generalized) Carleson operator, and ii) a new tile discretization of the time-frequency plane that has the major consequence of \emph{eliminating the exceptional sets} from the analysis of the Carleson operator. As a further consequence, we are able to provide the full $L^p$ boundedness range and prove directly -- without interpolation techniques -- the strong $L^2$ bound for the (generalized) Carleson operator, answering a question raised by C. Fefferman.
Brexit, the Misrepresentation of Democracy, and the Rock of Gibraltar

This short essay makes three points regarding Brexit that have not been widely considered in public or academic debate. First, Brexit advocates (Leavers) successfully misrepresented the referendum of June 2016 as a definitive expression of democratic will. (“The people have spoken.”) The slim majority result was less than such an expression, particularly because it ignored intercommunal and intergenerational democratic values—most profoundly, overriding clear majorities in Scotland and Northern Ireland which had voted to remain in the EU. Second, even though within a year of that referendum, a majority of Britons (Remainers) had come to oppose Brexit, political leadership among the Remainers was woefully ineffective. Some, like Prime Minister Theresa May, simply changed sides, wrongheadedly accepting the people-have-spoken rhetoric. Other Remainers continued to make their case, but did not effectively argue that people had not spoken. Perhaps, most crucially, the British “first past the post” electoral system provided no viable remain option in the December 2019 Parliamentary elections. Put simply, in the December 2019 Parliamentary elections, Remainers had no one to vote for. Third, hopes that the UK could easily rejoin the European Union (encouraged in part by remarks of EU leaders) are false. Article 49 of the Treaty on European Union (TEU) requires that a state which has left the EU should go through a rigorous admission process. Since Article 49 requires unanimity among EU member states, any member with an objection to Britain rejoining could block readmission. Most troubling for Britain could be Spain’s long-standing claim for the return of Gibraltar to which the UK would be loathe to accede and from which Spain would be loathe to retreat.
An Inverse Dynamics Approach to Control Lyapunov Functions

With the goal of moving towards implementation of increasingly dynamic behaviors on underactuated systems, this paper presents an optimization-based approach for solving full-body dynamics based controllers on underactuated bipedal robots. The primary focus of this paper is on the development of an alternative approach to the implementation of controllers utilizing control Lyapunov function based quadratic programs. This approach utilizes many of the desirable aspects from successful inverse dynamics based controllers in the literature, while also incorporating a variant of control Lyapunov functions that renders better convergence in the context of tracking outputs. The principal benefits of this formulation include a greater ability to add costs which regulate the resulting behavior of the robot. In addition, the model error-prone inertia matrix is used only once, in a non-inverted form. The result is a successful demonstration of the controller for walking in simulation, and applied on hardware in real-time for dynamic crouching.
Broad Area Search and Detection of Surface-to-Air Missile Sites Using Spatial Fusion of Component Object Detections From Deep Neural Networks

Here, we demonstrate how deep neural network (DNN) detections of multiple constitutive or component objects that are part of a larger, more complex, and encompassing feature can be spatially fused to improve the search, detection, and retrieval (ranking) of the larger complex feature. First, scores computed from a spatial clustering algorithm are normalized to a reference space so that they are independent of image resolution and DNN input chip size. Then, multiscale DNN detections from various component objects are fused to improve the detection and retrieval of DNN detections of a larger complex feature. We demonstrate the utility of this approach for broad area search and detection of surface-to-air missile (SAM) sites that have a very low occurrence rate (only 16 sites) over a $\sim$ 90 000 km2 study area in SE China. The results demonstrate that spatial fusion of multiscale component-object DNN detections can reduce the detection error rate of SAM Sites by $>$ 85% while still maintaining a 100% recall. The novel spatial fusion approach demonstrated here can be easily extended to a wide variety of other challenging object search and detection problems in large-scale remote sensing image datasets.
Deciphering serous ovarian carcinoma histopathology and platinum response by convolutional neural networks.

Ovarian cancer causes 151,900 deaths per year worldwide. Treatment and prognosis are primarily determined by the histopathologic interpretation in combination with molecular diagnosis. However, the relationship between histopathology patterns and molecular alterations is not fully understood, and it is difficult to predict patients’ chemotherapy response using the known clinical and histological variables. We analyzed the whole-slide histopathology images, RNA-Seq, and proteomics data from 587 primary serous ovarian adenocarcinoma patients and developed a systematic algorithm to integrate histopathology and functional omics findings and to predict patients’ response to platinum-based chemotherapy. Our convolutional neural networks identified the cancerous regions with areas under the receiver operating characteristic curve (AUCs) > 0.95 and classified tumor grade with AUCs > 0.80. Functional omics analysis revealed that expression levels of proteins participated in innate immune responses and catabolic pathways are associated with tumor grade. Quantitative histopathology analysis successfully stratified patients with different response to platinum-based chemotherapy (P = 0.003). These results indicated the potential clinical utility of quantitative histopathology evaluation in tumor cell detection and chemotherapy response prediction. The developed algorithm is easily extensible to other tumor types and treatment modalities.
RIT-Eyes: Rendering of near-eye images for eye-tracking applications

Deep neural networks for video-based eye tracking have demonstrated resilience to noisy environments, stray reflections, and low resolution. However, to train these networks, a large number of manually annotated images are required. To alleviate the cumbersome process of manual labeling, computer graphics rendering is employed to automatically generate a large corpus of annotated eye images under various conditions. In this work, we introduce a synthetic eye image generation platform that improves upon previous work by adding features such as an active deformable iris, an aspherical cornea, retinal retro-reflection, gaze-coordinated eye-lid deformations, and blinks. To demonstrate the utility of our platform, we render images reflecting the represented gaze distributions inherent in two publicly available datasets, NVGaze and OpenEDS. We also report on the performance of two semantic segmentation architectures (SegNet and RITnet) trained on rendered images and tested on the original datasets.
Graphs that contain multiply transitive matchings

Abstract Let Γ be a finite, undirected, connected, simple graph. We say that a matching M is a permutable m -matching if M contains m edges and the subgroup of Aut ( Γ ) that fixes the matching M setwise allows the edges of M to be permuted in any fashion. A matching M is 2-transitive if the setwise stabilizer of M in Aut ( Γ ) can map any ordered pair of distinct edges of M to any other ordered pair of distinct edges of M . We provide constructions of graphs with a permutable matching; we show that, if Γ is an arc-transitive graph that contains a permutable m -matching for m ⩾ 4 , then the degree of Γ is at least m ; and, when m is sufficiently large, we characterize the locally primitive, arc-transitive graphs of degree m that contain a permutable m -matching. Finally, we classify the graphs that have a 2-transitive perfect matching and also classify graphs that have a permutable perfect matching.
Thermal Properties of Glassy and Molten Planetary Candidate Lavas

Abstract Heat transport plays a crucial role in igneous processes, and the thermal evolution of terrestrial bodies. Thermal conductivity (k) is a product of density (ρ), thermal diffusivity (D) and specific heat (CP). We measured D and CP as a function of temperature for a suite of planetary analog lavas relevant to the Moon, Mars, Mercury, Io and Vesta. Heat capacity was measured using differential scanning calorimetry on glasses and liquids covering temperatures from 400 to 1750 ​K; ρ was measured using the Archimedean method; and D was measured using laser-flash analysis on glasses from room temperature up to their melting point, which is slightly above the glass transition (Tg). Although the values of D and CP depend on both temperature and composition, we found no systematic variation of D with chemical or structural parameters. Glass data are described by the equation D g l a s s = 2.305 ± 0.22 T − 0.2567 ± 0.015 + 7.796 ± 6.2 × 10 − 5 T where D is in mm2s−1 and T is in K, with 2σ uncertainty of 0.06 ​mm2 ​s−1. Thermal diffusivity of the tholeiitic liquids above Tg is Dliquid ​= ​0.36 ​± ​0.07 mm2s-1, but the temperature-dependence cannot be constrained due to viscous flow and changing sample geometry at higher temperatures. The model for D presented here, in combination with already available models to calculate CP and ρ, allows prediction of thermal conductivity. For tholeiitic glasses, k decreases from ~1.5 ​± ​0.3 ​W ​m−1K−1 ​at ~295 ​K to ~1.3 ​± ​0.3 ​W ​m−1K−1 ​at Tg at ~1000 ​K. For tholeiitic liquids, k decreases from ~1.6 ​± ​0.3 ​W ​m−1K−1 ​at Tg to ~1.3 ​± ​0.4 ​W ​m−1K−1 ​at 1500 ​K. We recommend a generic value of 1.3 ​W ​m−1 K−1 for k of tholeiitic and basaltic lava instead of the commonly assumed 1.0 ​W ​m−1 K−1. Future work should aim to constrain the temperature dependence of D for liquids, for which a novel experimental approach is needed.
Evaluation of deacetylase inhibition in metaplastic breast carcinoma using multiple derivations of preclinical models of a new patient-derived tumor

Metaplastic breast carcinoma (MBC) is a clinically aggressive and rare subtype of breast cancer, with similar features to basal-like breast cancers. Due to rapid growth rates and characteristic heterogeneity, MBC is often unresponsive to standard chemotherapies; and novel targeted therapeutic discovery is urgently needed. Histone deacetylase inhibitors (DACi) suppress tumor growth and metastasis through regulation of the epithelial-to-mesenchymal transition axis in various cancers, including basal-like breast cancers. We utilized a new MBC patient-derived xenograft (PDX) to examine the effect of DACi therapy on MBC. Cell morphology, cell cycle-associated gene expressions, transwell migration, and metastasis were evaluated in patient-derived cells and tumors after treatment with romidepsin and panobinostat. Derivations of our PDX model, including cells, spheres, organoids, explants, and in vivo implanted tumors were treated. Finally, we tested the effects of combining DACi with approved chemotherapeutics on relative cell biomass. DACi significantly suppressed the total number of lung metastasis in vivo using our PDX model, suggesting a role for DACi in preventing circulating tumor cells from seeding distal tissue sites. These data were supported by our findings that DACi reduced cell migration, populations, and expression of mesenchymal-associated genes. While DACi treatment did affect cell cycle-regulating genes in vitro, tumor growth was not affected compared to controls. Importantly, gene expression results varied depending on the cellular or tumor system used, emphasizing the importance of using multiple derivations of cancer models in preclinical therapeutic discovery research. Furthermore, DACi sensitized and produced a synergistic effect with approved oncology therapeutics on inherently resistant MBC. This study introduced a role for DACi in suppressing the migratory and mesenchymal phenotype of MBC cells through regulation of the epithelial-mesenchymal transition axis and suppression of the CTC population. Preliminary evidence that DACi treatment in combination with MEK1/2 inhibitors exerts a synergistic effect on MBC cells was also demonstrated.
MicroED Elucidation of Diverse Solid‐State Packing in a Family of Electron‐Deficient Expanded Helicenes

Solid-state packing plays a defining role in the properties of a molecular organic material, but it is difficult to elucidate in the absence of single crystals that are suitable for X-ray diffraction. Here, we demonstrate the coupling of divergent synthesis with microcrystal electron diffraction (MicroED) for rapid assessment of solid-state packing motifs, using a class of chiral nanocarbons – expanded helicenes – as a proof of concept. Two highly selective oxidative dearomatizations of a readily-accessible helicene provided a divergent route to four electron-deficient analogues containing quinone or quinoxaline units. Crystallization efforts consistently yielded microcrystals that were unsuitable for single crystal X-ray diffraction, but ideal for MicroED. This technique facilitated the elucidation of solid-state structures of all five compounds with <1.1 A resolution. The otherwise-inaccessible data revealed a range of notable packing behavior, including four different space groups, homochirality in a crystal for a helicene with an extremely low enantiomerization barrier, and nanometer scale cavities. The results of this study suggest that MicroED will soon become an indispensable tool for high-throughput investigations in pursuit of next-generation organic materials.
Deep transfer learning for reducing health care disparities arising from biomedical data inequality

As artificial intelligence (AI) is increasingly applied to biomedical research and clinical decisions, developing unbiased AI models that work equally well for all ethnic groups is of crucial importance to health disparity prevention and reduction. However, the biomedical data inequality between different ethnic groups is set to generate new health care disparities through data-driven, algorithm-based biomedical research and clinical decisions. Using an extensive set of machine learning experiments on cancer omics data, we find that current prevalent schemes of multiethnic machine learning are prone to generating significant model performance disparities between ethnic groups. We show that these performance disparities are caused by data inequality and data distribution discrepancies between ethnic groups. We also find that transfer learning can improve machine learning model performance for data-disadvantaged ethnic groups, and thus provides an effective approach to reduce health care disparities arising from data inequality among ethnic groups.
Signomial and Polynomial Optimization via Relative Entropy and Partial Dualization

We describe a generalization of the Sums-of-AM/GM-Exponential (SAGE) methodology for relative entropy relaxations of constrained signomial and polynomial optimization problems. Our approach leverages the fact that SAGE certificates conveniently and transparently blend with convex duality, in a way which enables partial dualization of certain structured constraints. This more general approach retains key properties of ordinary SAGE relaxations (e.g. sparsity preservation), and inspires a projective method of solution recovery which respects partial dualization. We illustrate the utility of our methodology with a range of examples from the global optimization literature, along with a publicly available software package.
Toward Multimodal Modeling of Emotional Expressiveness

Emotional expressiveness captures the extent to which a person tends to outwardly display their emotions through behavior. Due to the close relationship between emotional expressiveness and behavioral health, as well as the crucial role that it plays in social interaction, the ability to automatically predict emotional expressiveness stands to spur advances in science, medicine, and industry. In this paper, we explore three related research questions. First, how well can emotional expressiveness be predicted from visual, linguistic, and multimodal behavioral signals? Second, how important is each behavioral modality to the prediction of emotional expressiveness? Third, which behavioral signals are reliably related to emotional expressiveness? To answer these questions, we add highly reliable transcripts and human ratings of perceived emotional expressiveness to an existing video database and use this data to train, validate, and test predictive models. Our best model shows promising predictive performance on this dataset (RMSE=0.65, R^2=0.45, r=0.74). Multimodal models tend to perform best overall, and models trained on the linguistic modality tend to outperform models trained on the visual modality. Finally, examination of our interpretable models' coefficients reveals a number of visual and linguistic behavioral signals---such as facial action unit intensity, overall word count, and use of words related to social processes---that reliably predict emotional expressiveness.
Communication and Bargaining Breakdown: An Empirical Analysis

Bargaining breakdown plagues bargaining in environments with incomplete information. Does communication help or harm? On May 23, 2016, eBay Germany's Best Offer platform introduced unstructured communication allowing desktop users, but not the mobile users, to accompany offers with a message. Using this natural experiment, our difference-in-differences approach documents a 14% decrease in the the rate of breakdown among compliers. Though adoption is immediate, the effect is not. We show, using text analysis, that the dynamics are consistent with repeat players learning how to use communication in bargaining, and that the messaging strategies of experienced sellers are correlated with successful bargaining. Full paper available at: https://www.nber.org/papers/w27984
Unitary signings and induced subgraphs of Cayley graphs of \(\mathbb{Z}_2^{n}\)

Boolean functions play an important role in many different areas of computer science. The _local sensitivity_ of a Boolean function $f:\{0,1\}^n\to \{0,1\}$ on an input $x\in\{0,1\}^n$ is the number of coordinates whose flip changes the value of $f(x)$, i.e., the number of i's such that $f(x)\not=f(x+e_i)$, where $e_i$ is the $i$-th unit vector. The _sensitivity_ of a Boolean function is its maximum local sensitivity. In other words, the sensitivity measures the robustness of a Boolean function with respect to a perturbation of its input. Another notion that measures the robustness is block sensitivity. The _local block sensitivity_ of a Boolean function $f:\{0,1\}^n\to \{0,1\}$ on an input $x\in\{0,1\}^n$ is the number of disjoint subsets $I$ of $\{1,..,n\}$ such that flipping the coordinates indexed by $I$ changes the value of $f(x)$, and the _block sensitivity_ of $f$ is its maximum local block sensitivity. Since the local block sensitivity is at least the local sensitivity for any input $x$, the block sensitivity of $f$ is at least the sensitivity of $f$. The next example demonstrates that the block sensitivity of a Boolean function is not linearly bounded by its sensitivity. Fix an integer $k\ge 2$ and define a Boolean function $f:\{0,1\}^{2k^2}\to\{0,1\}$ as follows: the coordinates of $x\in\{0,1\}^{2k^2}$ are split into $k$ blocks of size $2k$ each and $f(x)=1$ if and only if at least one of the blocks contains exactly two entries equal to one and these entries are consecutive. While the sensitivity of the function $f$ is $2k$, its block sensitivity is $k^2$. The Sensitivity Conjecture, made by Nisan and Szegedy in 1992, asserts that the block sensitivity of a Boolean function is polynomially bounded by its sensivity. The example above shows that the degree of such a polynomial must be at least two. The Sensitivity Conjecture has been recently proven by Huang in [Annals of Mathematics 190 (2019), 949-955](https://doi.org/10.4007/annals.2019.190.3.6). He proved the following combinatorial statement that implies the conjecture (with the degree of the polynomial equal to four): any subset of more than half of the vertices of the $n$-dimensional cube $\{0,1\}^n$ induces a subgraph that contains a vertex with degree at least $\sqrt{n}$. The present article extends this result as follows: every Cayley graph with the vertex set $\{0,1\}^n$ and any generating set of size $d$ (the vertex set is viewed as a vector space over the binary field) satisfies that any subset of more than half of its vertices induces a subgraph that contains a vertex of degree at least $\sqrt{d}$. In particular, when the generating set consists of the $n$ unit vectors, the Cayley graph is the $n$-dimensional hypercube.
Conformal Contractions and Lower Bounds on the Density of Harmonic Measure

We give a concrete sufficient condition for a simply-connected domain to be the image of the unit disk under a nonexpansive conformal map. This class of domains is also characterized by having sufficiently dense harmonic measure. The relation with the harmonic measure provides a natural higher-dimensional analogue of this problem, which is also addressed.
Effects on the structure of the universe of an accelerating expansion

Recent experimental results from supernovae Ia observations have been interpreted to show that the rate of expansion of the universe is increasing. Other recent experimental results find strong indications that the universe is “flat.” In this paper, I investigate some solutions of Einstein's field equations which go smoothly between Schwarzschild's relativistic gravitational solution near a mass concentration to the Friedmann-Lemaitre expanding universe solution. In particular, the static, curved-space extension of the Lemaitre-Schwarzschild solution in vacuum is given. Uniqueness conditions are discussed. One of these metrics preserves the “cosmological equation.” We find that when the rate of expansion of the universe is increasing, space is broken up into domains of attraction. Outside a domain of attraction, the expansion of the universe is strong enough to accelerate a test particle away from the domain boundary. I give a domain-size–mass relationship. This relationship may very well be important to our understanding of the large scale structure of the universe.
Wilson loops in 5d long quiver gauge theories

Quiver gauge theories with a large number of nodes host a wealth of Wilson loop operators. Expectation values are obtained, using supersymmetric localization, for Wilson loops in the antisymmetric representations associated with each individual gauge node, for a sample of 5d long quiver gauge theories whose UV fixed points have holographic duals in Type IIB. The sample includes the TN theories and the results are uniformly given in terms of Bloch-Wigner functions. The holographic representation of the Wilson loops is identified. It comprises, for each supergravity solution, a two-parameter family of D3-branes which exactly reproduce the field theory results and identify points in the internal space with the faces of the associated 5-brane web. The expectation values of (anti)fundamental Wilson loops exhibit an enhanced scaling for many operators, which matches between field theory and supergravity.
Learning Manipulation Skills via Hierarchical Spatial Attention

Learning generalizable skills in robotic manipulation has long been challenging due to real-world sized observation and action spaces. One method for addressing this problem is attention focus—the robot learns where to attend its sensors and irrelevant details are ignored. However, these methods have largely not caught on due to the difficulty of learning a good attention policy and the added partial observability induced by a narrowed window of focus. This article addresses the first issue by constraining gazes to a spatial hierarchy. For the second issue, we identify a case where the partial observability induced by attention does not prevent Q-learning from finding an optimal policy. We conclude with real-robot experiments on challenging pick–place tasks demonstrating the applicability of the approach.
Relay: a new IR for machine learning frameworks

Machine learning powers diverse services in industry including search, translation, recommendation systems, and security. The scale and importance of these models require that they be efficient, expressive, and portable across an array of heterogeneous hardware devices. These constraints are often at odds; in order to better accommodate them we propose a new high-level intermediate representation (IR) called Relay. Relay is being designed as a purely-functional, statically-typed language with the goal of balancing efficient compilation, expressiveness, and portability. We discuss the goals of Relay and highlight its important design constraints. Our prototype is part of the open source NNVM compiler framework, which powers Amazon's deep learning framework MxNet.
CO LINE EMISSION FROM COMPACT NUCLEAR STARBURST DISKS AROUND ACTIVE GALACTIC NUCLEI

There is substantial evidence for a connection between star formation in the nuclear region of a galaxy and growth of the central supermassive black hole. Furthermore, starburst activity in the region around an active galactic nucleus (AGN) may provide the obscuration required by the unified model of AGNs. Molecular line emission is one of the best observational avenues to detect and characterize dense, star-forming gas in galactic nuclei over a range of redshift. This paper presents predictions for the carbon monoxide (CO) line features from models of nuclear starburst disks around AGNs. These small-scale ( 100 pc), dense and hot starbursts have CO luminosities similar to scaled-down ultra-luminous infrared galaxies and quasar host galaxies. Nuclear starburst disks that exhibit a pc-scale starburst and could potentially act as the obscuring torus show more efficient CO excitation and higher brightness temperature ratios than those without such a compact starburst. In addition, the compact starburst models predict strong absorption when J Upper 10, a unique observational signature of these objects. These findings allow for the possibility that CO spectral line energy distributions (SLEDs) could be used to determine if starburst disks are responsible for the obscuration in z 1 AGNs. Directly isolating the nuclear CO line emission of such compact regions around AGNs from galactic-scale emission will require high-resolution imaging or selecting AGN host galaxies with weak galactic-scale star formation. Stacking individual CO SLEDs will also be useful in detecting the predicted high-J features.
Electromigration dispersion in capillary electrophoresis.

In a previous paper (Ghosal and Chen in Bull. Math. Biol. 72:2047, 2010), it was shown that the evolution of the solute concentration in capillary electrophoresis is described by a nonlinear wave equation that reduced to Burger’s equation if the nonlinearity was weak. It was assumed that only strong electrolytes (fully dissociated) were present. In the present paper, it is shown that the same governing equation also describes the situation where the electrolytic buffer consists of a single weak acid (or base). A simple approximate formula is derived for the dimensionless peak variance which is shown to agree well with published experimental data.