cssi/SciSciGPT-SciSciNet huggingface dataset:
Why do corporations become criminals? Ownership, hidden actions, and crime as an agency cost

Abstract We examine the relationship between ownership structure and corporate crime. Our approach draws upon two lines of research: (1) the theory of the firm which poses ownership as a critical incentive mechanism and (2) the economic theory of corporate crime, which emphasizes the role played by top management in affecting crime in the corporation. We find that crime occurs less frequently among firms in which management has a larger ownership stake. Our results imply that penalizing `corporations' (shareholders) deters crime, and that corporate crime tends not to benefit shareholders, ex ante. Rather than being something shareholders have encouraged, corporate crime appears to reflect an agency cost limited but not optimally eliminated through the costly efforts of top management. The evidence is consistent with the notion that ownership structure plays an important role in aligning the hidden actions of top management with the shareholder interest.
GEOS-C orbit determination with Satellite to Satellite Tracking

This report studies the feasibility of employing satellite to satellite tracking in lieu of ground based tracking to satisfy the orbit determination requirements of the GEOS-C mission. It is shown that with proper estimation procedures it is possible to obtain from Satellite to Satellite Tracking data a GEOS-C orbit whose altitude error averages about 1 meter. The usefulness of this data type for geopotential recovery is also indicated.
Large sulfur isotope fractionation in lunar volcanic glasses reveals the magmatic differentiation and degassing of the Moon.

Sulfur isotope variations in mantle-derived lavas provide important constraints on the evolution of planetary bodies. Here, we report the first in situ measurements of sulfur isotope ratios dissolved in primitive volcanic glasses and olivine-hosted melt inclusions recovered from the Moon by the Apollo 15 and 17 missions. The new data reveal large variations in 34S/32S ratios, which positively correlates with sulfur and titanium contents within and between the distinct compositional groups of volcanic glasses analyzed. Our results uncover several magmatic events that fractionated the primordial sulfur isotope composition of the Moon: the segregation of the lunar core and the crystallization of the lunar magma ocean, which led to the formation of the heterogeneous sources of the lunar magmatism, followed by magma degassing during generation, transport, and eruption of the lunar lavas. Whether the Earth’s and Moon’s interiors share a common 34S/32S ratio remains a matter of debate.
First Principles, Explicit Interface Studies of Oxygen Vacancy and Chloride in Alumina Films for Corrosion Applications

Pitting corrosion is a much-studied and technologically relevant subject. However, the fundamental mechanisms responsible for the breakdown of the passivating oxide layer are still subjects of debate. Chloride anions are known to accelerate corrosion; relevant hypotheses include Cl insertion into positively charged oxygen vacancies in the oxide film, and Cl adsorption on passivating oxide surfaces, substituting for surface hydroxyl groups. In this work, we conduct large-scale first principles modeling of explicit metal/Al(2)O(3) interfaces to investigate the energetics and electronic structures associated with these hypotheses. The explicit interface models allow electron transfer that mimics electrochemical events, and the establishment of the relation between atomic structures at different interfaces and the electronic band alignment. For multiple model interfaces, we find that doubly charged oxygen vacancies, which are key ingredients of the point defect model (PDM) often used to analyze corrosion data, can only occur in the presence of a potential gradient that raises the voltage. Cl-insertion into oxide films can be energetically favorable in some oxygen vacancy sites, depending on the voltage. We also discuss the challenges associated with explicit DFT modeling of these complex interfaces.
Deep neural networks-based denoising models for CT imaging and their efficacy

Most of the Deep Neural Networks (DNNs) based CT image denoising literature shows that DNNs outperform traditional iterative methods in terms of metrics such as the RMSE, the PSNR and the SSIM. In many instances, using the same metrics, the DNN results from low-dose inputs are also shown to be comparable to their high-dose counterparts. However, these metrics do not reveal if the DNN results preserve the visibility of subtle lesions or if they alter the CT image properties such as the noise texture. Accordingly, in this work, we seek to examine the image quality of the DNN results from a holistic viewpoint for low-dose CT image denoising. First, we build a library of advanced DNN denoising architectures. This library is comprised of denoising architectures such as the DnCNN, U-Net, Red-Net, GAN, etc. Next, each network is modeled, as well as trained, such that it yields its best performance in terms of the PSNR and SSIM. As such, data inputs (e.g. training patch-size, reconstruction kernel) and numeric-optimizer inputs (e.g. minibatch size, learning rate, loss function) are accordingly tuned. Finally, outputs from thus trained networks are further subjected to a series of CT bench testing metrics such as the contrast-dependent MTF, the NPS and the HU accuracy. These metrics are employed to perform a more nuanced study of the resolution of the DNN outputs' low-contrast features, their noise textures, and their CT number accuracy to better understand the impact each DNN algorithm has on these underlying attributes of image quality.
Baryonic Higgs and dark matter

We discuss the correlation between dark matter and Higgs decays in gauge theories where the dark matter is predicted from anomaly cancellation. In these theories, the Higgs responsible for the breaking of the gauge symmetry generates the mass for the dark matter candidate. We investigate the Higgs decays in the minimal gauge theory for Baryon number. After imposing the dark matter density and direct detection constraints, we find that the new Higgs can have a large branching ratio into two photons or into dark matter. Furthermore, we discuss the production channels and the unique signatures at the Large Hadron Collider.
Evaluating how lethal management affects poaching of Mexican wolves.

Despite illegal killing (poaching) being the major cause of death among large carnivores globally, little is known about the effect of implementing lethal management policies on poaching. Two oppos...
K-meson experiments

Abstract This paper presents a brief review of the status of K-meson interactions with nuclei. Emphasis is placed on 1s shell nuclei with representative examples drawn from experiments on heavier nuclear systems. Directions for future developments are discussed.
HHAR-net: Hierarchical Human Activity Recognition using Neural Networks.

Activity recognition using built-in sensors in smart and wearable devices provides great opportunities to understand and detect human behavior in the wild and gives a more holistic view of individuals’ health and well being. Numerous computational methods have been applied to sensor streams to recognize different daily activities. However, most methods are unable to capture different layers of activities concealed in human behavior. Also, the performance of the models starts to decrease with increasing the number of activities. This research aims at building a hierarchical classification with Neural Networks to recognize human activities based on different levels of abstraction. We evaluate our model on the Extrasensory dataset; a dataset collected in the wild and containing data from smartphones and smartwatches. We use a two-level hierarchy with a total of six mutually exclusive labels namely, “lying down”, “sitting”, “standing in place”, “walking”, “running”, and “bicycling” divided into “stationary” and “non-stationary”. The results show that our model can recognize low-level activities (stationary/non-stationary) with 95.8% accuracy and overall accuracy of 92.8% over six labels. This is 3% above our best performing baseline (HHAR-net is shared as an open source tool at https://github.com/mehrdadfazli/HHAR-Net).
Investigating microwave loss of SiGe using superconducting transmon qubits

Silicon-germanium (SiGe) is a material that possesses a multitude of applications ranging from transistors to electro-optical modulators and quantum dots. The diverse properties of SiGe also make it attractive to implementations involving superconducting quantum computing. Here, we demonstrate the fabrication of transmon quantum bits on SiGe layers and investigate the microwave loss properties of SiGe at cryogenic temperatures and single photon microwave powers. We find relaxation times of up to 100 μs, corresponding to a quality factor Q above 4 M for large pad transmons. The high Q values obtained indicate that the SiGe/Si heterostructure is compatible with state-of-the-art performance of superconducting quantum circuits.
Forced Waves of Parabolic–Elliptic Keller–Segel Models in Shifting Environments

The current paper is concerned with the forced waves of Keller–Segel chemoattraction systems in shifting environments of the form, 0.1 $$\begin{aligned} {\left\{ \begin{array}{ll} u_t=u_{xx}-\chi (uv_x)_x +u(r(x-ct)-bu),\quad x\in \mathbb {R}\\ 0=v_{xx}- \nu v+\mu u,\quad x\in \mathbb {R}, \end{array}\right. } \end{aligned}$$ where $$\chi $$ , b, $$\nu $$ , and $$\mu $$ are positive constants, $$c\in \mathbb {R}$$ , the resource function r(x) is globally Holder continuous, bounded, $$r^*=\sup _{x\in {\mathbb R}}r(x)>0$$ , $$r(\pm \infty ):=\lim _{x\rightarrow \pm \infty }r(x)$$ exist, and either $$r(-\infty )<0<r(\infty )$$ , or $$r(\pm \infty )<0$$ . Assume that $$b>2\chi \mu $$ . In the case that $$r(-\infty )<0<r(\infty )$$ , it is shown that (0.1) has a forced wave solution connecting $$(\frac{r^*}{b},\frac{\mu }{\nu }\frac{r^*}{b})$$ and (0, 0) with speed c provided that $$c>\frac{\chi \mu r^*}{2\sqrt{\nu }(b-\chi \mu )}- 2\sqrt{\frac{r^*(b-2\chi \mu )}{b-\chi \mu }}$$ . In the case that $$r(\pm \infty )<0$$ , it is shown that (0.1) has a forced wave solution connecting (0, 0) and (0, 0) with speed c provided that $$\chi $$ is sufficiently small and $$\lambda _\infty >0$$ , where $$\lambda _\infty $$ is the generalized principal eigenvalue of the operator $$u(\cdot )\mapsto u_{xx}(\cdot )+cu_{x}(\cdot )+r(\cdot )u(\cdot )$$ on $${\mathbb R}$$ in certain sense. Some numerical simulations are also carried out. The simulations indicate the existence of forced wave solutions in some parameter regions which are not covered in the theoretical results, induce several problems to be further studied, and also provide some illustration of the theoretical results.
Inhibition of the phosphoinositide 3-kinase-AKT-cyclic GMP-c-Jun N-terminal kinase signaling pathway attenuates the development of morphine tolerance in a mouse model of neuropathic pain.

Research presented here sought to determine if opioid induced tolerance is linked to activity changes within the PI3Kγ-AKT-cGMP-JNK intracellular signaling pathway in spinal cord or peripheral nerv...
Mispricing, short-sale constraints, and the cross-section of option returns

Abstract Motivated by the theory of demand-based option pricing in imperfect markets, we examine the relation between short-sale constraints and equity option returns, conditional on the level of mispricing in the underlying stock. We report a monotonic relation between various measures of short-sale constraints and delta-hedged returns of put options on overpriced stocks. This relation is robust to controls for firm attributes and limits to arbitrage proxies. Our findings suggest that while investors drive up the demand for these put options, dealers command a high premium as compensation for the increased market making risk. We do not find a robust relation for either put options on underpriced stocks or call options.
Blend Analysis of HATNet Transit Candidates

Candidate transiting planet systems discovered by wide-field ground- based surveys must go through an intensive follow-up procedure to distinguish the true transiting planets from the much more common false positives. Especially pernicious are configurations of three or more stars which produce radial velocity and light curves that are similar to those of single stars transited by a planet. In this contribution we describe the methods used by the HATNet team to reject these blends, giving a few illustrative examples.
An overview of neV probes of PeV scale physics – and of what's in between

Low-energy experiments which would identify departures from the Standard Model (SM) rely either on the unexpected observation of symmetry breaking, such as of CP or B, or on an observed significant deviation from a precise SM prediction. We discuss examples of each search strategy, and show that low-energy experiments can open windows on physics far beyond accessible collider energies. We consider how the use of a frequentist analysis framework can redress the impact of theoretical uncertainties in such searches – and how lattice QCD can help control them.
A parallel-in-time approach for wave-type PDEs

Numerical solutions to wave-type PDEs utilizing method-of-lines require the ODE solver’s stability domain to include a large stretch of the imaginary axis surrounding the origin. We show here that extrapolation based solvers of Gragg–Bulirsch–Stoer type can meet this requirement. Extrapolation methods utilize several independent time stepping sequences, making them highly suited for parallel execution. Traditional extrapolation schemes use all time stepping sequences to maximize the method’s order of accuracy. The present method instead maintains a desired order of accuracy while employing additional time stepping sequences to shape the resulting stability domain. We optimize the extrapolation coefficients to maximize the stability domain’s imaginary axis coverage. This yields a family of explicit schemes that approaches maximal time step size for wave propagation problems. On a computer with several cores we achieve both high order and fast time to solution compared with traditional ODE integrators.
A Pharmacological Interactome Platform for Discovery of Pain Mechanisms and Targets

Cells communicate with each other through ligand and receptor interactions. In the case of the peripheral nervous system, these ligand-receptor interactions shape sensory experience. In disease states, such as chronic pain, these ligand-receptor interactions can change the excitability of target neurons augmenting nociceptive input to the CNS. While the importance of these cell to neuron interactions are widely acknowledged, they have not been thoroughly characterized. We sought to address this by cataloging how peripheral cell types interact with sensory neurons in the dorsal root ganglion (DRG) using RNA sequencing datasets. Using single cell sequencing datasets from mouse we created a comprehensive interactome map for how mammalian sensory neurons interact with 42 peripheral cell types. We used this knowledge base to understand how specific cell types and sensory neurons interact in disease states. In mouse datasets, we created an interactome of colonic enteric glial cells in the naive and inflamed state with sensory neurons that specifically innervate this tissue. In human datasets, we created interactomes of knee joint macrophages from rheumatoid arthritis patients and pancreatic cancer samples with human DRG. Collectively, these interactomes highlight ligand-receptor interactions in mouse models and human disease states that reflect the complexity of cell to neuron signaling in chronic pain states. These interactomes also highlight therapeutic targets, such as the epidermal growth factor receptor ( EGFR ), which was a common interaction point emerging from our studies.
Effective randomness for continuous measures

We investigate which infinite binary sequences (reals) are effectively random with respect to some continuous (i.e., non-atomic) probability measure. We prove that for every n, all but countably many reals are n-random for such a measure, where n indicates the arithmetical complexity of the Martin-Lof tests allowed. The proof is based on a Borel determinacy argument and presupposes the existence of infinitely many iterates of the power set of the natural numbers. In the second part of the paper we present a metamathematical analysis showing that this assumption is indeed necessary. More precisely, there exists a computable function G such that, for any n, the statement `All but countably many reals are G(n)-random with respect to a continuous probability measure' cannot be proved in $ZFC^-_n$. Here $ZFC^-_n$ stands for Zermelo-Fraenkel set theory with the Axiom of Choice, where the Power Set Axiom is replaced by the existence of n-many iterates of the power set of the natural numbers. The proof of the latter fact rests on a very general obstruction to randomness, namely the presence of an internal definability structure.
Mesh refinement method for solving optimal control problems with nonsmooth solutions using jump function approximations

A mesh refinement method is described for solving optimal control problems using Legendre-Gauss-Radau collocation. The method detects discontinuities in the control solution by employing an edge detection scheme based on jump function approximations. When discontinuities are identified, the mesh is refined with a targeted $h$-refinement approach whereby the discontinuity locations are bracketed with mesh points. The remaining smooth portions of the mesh are refined using previously developed techniques. The method is demonstrated on two examples, and results indicate that the method solves optimal control problems with discontinuous control solutions using fewer mesh refinement iterations and less computation time when compared with previously developed methods.
Gambler’s ruin estimates on finite inner uniform domains

Gambler’s ruin estimates can be viewed as harmonic measure estimates for finite Markov chains which are absorbed (or killed) at boundary points. We relate such estimates to properties of the underlying chain and its Doob transform. Precisely, we show that gambler’s ruin estimates reduce to a good understanding of the Perron–Frobenius eigenfunction and eigenvalue whenever the underlying chain and its Doob transform are Harnack Markov chains. Finite inner-uniform domains (say, in the square grid Zn) provide a large class of examples where these ideas apply and lead to detailed estimates. In general, understanding the behavior of the Perron–Frobenius eigenfunction remains a challenge.
COVID-19: Short term prediction model using daily incidence data.

Background Prediction of the dynamics of new SARS-CoV-2 infections during the current COVID-19 pandemic is critical for public health planning of efficient health care allocation and monitoring the effects of policy interventions. We describe a new approach that forecasts the number of incident cases in the near future given past occurrences using only a small number of assumptions. Methods Our approach to forecasting future COVID-19 cases involves 1) modeling the observed incidence cases using a Poisson distribution for the daily incidence number, and a gamma distribution for the series interval; 2) estimating the effective reproduction number assuming its value stays constant during a short time interval; and 3) drawing future incidence cases from their posterior distributions, assuming that the current transmission rate will stay the same, or change by a certain degree. Results We apply our method to predicting the number of new COVID-19 cases in a single state in the U.S. and for a subset of counties within the state to demonstrate the utility of this method at varying scales of prediction. Our method produces reasonably accurate results when the effective reproduction number is distributed similarly in the future as in the past. Large deviations from the predicted results can imply that a change in policy or some other factors have occurred that have dramatically altered the disease transmission over time. Conclusion We presented a modelling approach that we believe can be easily adopted by others, and immediately useful for local or state planning.
On Julia Limiting Directions in Higher Dimensions

For a quasiregular mapping $$f:{\mathbb {R}}^n \rightarrow {\mathbb {R}}^n$$ , with $$n\ge 2$$ , a Julia limiting direction $$\theta \in S^{n-1}$$ arises from a sequence $$(x_n)_{n=1}^{\infty }$$ contained in the Julia set of f, with $$|x_n| \rightarrow \infty $$ and $$x_n/|x_n| \rightarrow \theta $$ . Julia limiting directions have been extensively studied for entire and meromorphic functions in the plane. In this paper, we focus on Julia limiting directions in higher dimensions. First, we give conditions under which every direction is a Julia limiting direction. Our methods show that if a quasi-Fatou component contains a sectorial domain, then there is a polynomial bound on the growth in the sector. Second, we give a sufficient, but not necessary, condition in $${\mathbb {R}}^3$$ for a set $$E\subset S^2$$ to be the set of Julia limiting directions for a quasiregular mapping. The methods here will require showing that certain sectorial domains in $${\mathbb {R}}^3$$ are ambient quasiballs. This is a contribution to the notoriously hard problem of determining which domains are the image of the unit ball $${\mathbb {B}}^3$$ under an ambient quasiconformal mapping of $${\mathbb {R}}^3$$ onto itself.
Do all 5d SCFTs descend from 6d SCFTs

We present examples of 5d SCFTs that serve as counter-examples to a recently actively studied conjecture according to which it should be possible to obtain all 5d SCFTs by integrating out BPS particles from 6d SCFTs compactified on a circle. We further observe that it is possible to obtain these 5d SCFTs from 6d SCFTs if one allows integrating out BPS strings as well. Based on this observation, we propose a revised version of the conjecture according to which it should be possible to obtain all 5d SCFTs by integrating out both BPS particles and BPS strings from 6d SCFTs compactified on a circle. We describe a general procedure to integrate out BPS strings from a 5d theory once a geometric description of the 5d theory is given. We also discuss the consequences of the revised conjecture for the classification program of 5d SCFTs.
Offsetting Policy Feedback Effects: Evidence from the Affordable Care Act

The US welfare state provides key benefits indirectly. The Affordable Care Act (ACA), for example, uses a package including exchanges, subsidies, and penalties to increase health insurance enrollme...
Data Evaluation and Enhancement for Quality Improvement of Machine Learning

Poor data quality has a direct impact on the performance of the machine learning system that is built on the data. As a demonstrated effective approach for data quality improvement, transfer learning has been widely used to improve machine learning quality. However, the “quality improvement” brought by transfer learning was rarely rigorously validated, and some of the quality improvement results were misleading. This article first exposed the hidden quality problem in the datasets used to build a machine learning system for normalizing medical concepts in social media text. The system was claimed to have achieved the best performance compared to existing work on a machine learning task. However, the results of our experiments showed that the “best performance” was due to the poor quality of the datasets and the defective validation process. To address the data quality issue and build a high-performance medical concept normalization system, we developed a transfer-learning-based strategy for data quality enhancement and system performance improvement. The results of the experiments showed a strong correlation between the quality of the datasets and the performance of the machine learning system. The results also demonstrated that a rigorous evaluation of data quality is necessary for guiding the quality improvement of machine learning. Therefore, we propose a data quality evaluation framework that includes the quality criteria and their corresponding evaluation approaches. The data validation process, the performance improvement strategy, and the data quality evaluation framework discussed in this article can be used for machine learning researchers and practitioners to build high-performance machine learning systems. The code and datasets used in this research are available in GitHub ( https://github.com/haihua0913/dataEvaluationML ).
A Structural Model of Homophily and Clustering in Social Networks

I develop and estimate a structural model of network formation with heterogeneous players and latent community structure, whose equilibrium homophily and clustering levels match those usually obser...
Mumford’s influence on the moduli theory of algebraic varieties

We give a short appreciation of Mumford's work on the moduli of varieties by putting it into historical context. By reviewing earlier works we highlight the innovations introduced by Mumford. Then we discuss recent developments whose origins can be traced back to Mumford's ideas.
LSTMs and Deep Residual Networks for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management.

To avoid serious diabetic complications, people with type 1 diabetes must keep their blood glucose levels (BGLs) as close to normal as possible. Insulin dosages and carbohydrate consumption are important considerations in managing BGLs. Since the 1960s, models have been developed to forecast blood glucose levels based on the history of BGLs, insulin dosages, carbohydrate intake, and other physiological and lifestyle factors. Such predictions can be used to alert people of impending unsafe BGLs or to control insulin flow in an artificial pancreas. In past work, we have introduced an LSTM-based approach to blood glucose level prediction aimed at “what-if” scenarios, in which people could enter foods they might eat or insulin amounts they might take and then see the effect on future BGLs. In this work, we invert the “what-if” scenario and introduce a similar architecture based on chaining two LSTMs that can be trained to make either insulin or carbohydrate recommendations aimed at reaching a desired BG level in the future. Leveraging a recent state-of-the-art model for time series forecasting, we then derive a novel architecture for the same recommendation task, in which the two LSTM chain is used as a repeating block inside a deep residual architecture. Experimental evaluations using real patient data from the OhioT1DM dataset show that the new integrated architecture compares favorably with the previous LSTM-based approach, substantially outperforming the baselines. The promising results suggest that this novel approach could potentially be of practical use to people with type 1 diabetes for self-management of BGLs.
Soft Tensegrity Systems for Planetary Landing and Exploration

During the last decade, tensegrity systems have been the focus of numerous investigations exploring the possibility of adopting them for planetary landing and exploration applications. Early approaches mainly focused on locomotion aspects related to tensegrity systems, where mobility was achieved by actuating the cable members of the system. Later efforts focused on understanding energy storage mechanisms of tensegrity systems undergoing landing events. More precisely, it was shown that under highly dynamic events, buckling of individual members of a tensegrity structure does not necessarily imply structural failure, suggesting that efficient structural design of planetary landers could be achieved by allowing its compression members to buckle. In this work, we combine both aspects of previous research on tensegrity structures, showing a possible lattice-like structural configuration able to withstand impact events, store pre-impact kinetic energy, and utilize a part of that energy for the locomotion process. Our work shows the feasibility of this proposed approach via both experimental and computational means.
Decentralized Optimization on Time-Varying Directed Graphs Under Communication Constraints

We consider the problem of decentralized optimization where a collection of agents, each having access to a local cost function, communicate over a time-varying directed network and aim to minimize the sum of those functions. In practice, the amount of information that can be exchanged between the agents is limited due to communication constraints. We propose a communication-efficient algorithm for decentralized convex optimization that rely on sparsification of local updates exchanged between neighboring agents in the network. In directed networks, message sparsification alters column-stochasticity – a property that plays an important role in establishing convergence of decentralized learning tasks. We propose a decentralized optimization scheme that relies on local modification of mixing matrices, and show that it achieves $\mathcal{O}\left( {\frac{{\ln T}}{{\sqrt T }}} \right)$ convergence rate in the considered settings. Experiments validate theoretical results and demonstrate efficacy of the proposed algorithm.
Generative Speech Coding with Predictive Variance Regularization

The recent emergence of machine-learning based generative models for speech suggests a significant reduction in bit rate for speech codecs is possible. However, the performance of generative models deteriorates significantly with the distortions present in real-world input signals. We argue that this deterioration is due to the sensitivity of the maximum likelihood criterion to outliers and the ineffectiveness of modeling a sum of independent signals with a single autoregressive model. We introduce predictive-variance regularization to reduce the sensitivity to outliers, resulting in a significant increase in performance. We show that noise reduction to remove unwanted signals can significantly increase performance. We provide extensive subjective performance evaluations that show that our system based on generative modeling provides state-of-the-art coding performance at 3 kb/s for real-world speech signals at reasonable computational complexity.
Derlin rhomboid pseudoproteases employ substrate engagement and lipid distortion function for retrotranslocation of ER multi-spanning membrane substrates

Nearly one-third of proteins are initially targeted to the endoplasmic reticulum (ER) membrane where they are correctly folded, assembled, and then delivered to their final cellular destinations. In order to prevent the accumulation of misfolded membrane proteins, ER associated degradation (ERAD) moves these clients from the ER membrane to the cytosol; a process known as retrotranslocation. Our recent work in S. cerevisiae has revealed a derlin rhomboid pseudoprotease, Dfm1, is involved in the retrotranslocation of ubiquitinated ERAD membrane substrates. In this study, we sought to understand the mechanism associated with Dfm1s actions and found that Dfm1s conserved rhomboid residues are critical for membrane protein retrotranslocation. Specifically, we identified several retrotranslocation-deficient Loop 1 mutants that display impaired binding to membrane substrates. Furthermore, Dfm1 has retained the lipid thinning functions of its rhomboid protease predecessors to facilitate in the removal of ER membrane substrates. We find this substrate engagement and lipid thinning feature is conserved in its human homolog, Derlin-1. Utilizing interaction studies and molecular dynamics simulations, this work reveals that derlin rhomboid pseudoproteases employ novel mechanisms of substrate engagement and lipid thinning for catalyzing extraction of multi-spanning membrane substrates.
AI for radiographic COVID-19 detection selects shortcuts over signal

Artificial intelligence (AI) researchers and radiologists have recently reported AI systems that accurately detect COVID-19 in chest radiographs. However, the robustness of these systems remains unclear. Using state-of-the-art techniques in explainable AI, we demonstrate that recent deep learning systems to detect COVID-19 from chest radiographs rely on confounding factors rather than medical pathology, creating an alarming situation in which the systems appear accurate, but fail when tested in new hospitals. We observe that the approach to obtain training data for these AI systems introduces a nearly ideal scenario for AI to learn these spurious ‘shortcuts’. Because this approach to data collection has also been used to obtain training data for the detection of COVID-19 in computed tomography scans and for medical imaging tasks related to other diseases, our study reveals a far-reaching problem in medical-imaging AI. In addition, we show that evaluation of a model on external data is insufficient to ensure AI systems rely on medically relevant pathology, because the undesired ‘shortcuts’ learned by AI systems may not impair performance in new hospitals. These findings demonstrate that explainable AI should be seen as a prerequisite to clinical deployment of machine-learning healthcare models. The urgency of the developing COVID-19 epidemic has led to a large number of novel diagnostic approaches, many of which use machine learning. DeGrave and colleagues use explainable AI techniques to analyse a selection of these approaches and find that the methods frequently learn to identify features unrelated to the actual disease.
Long Penetration Mode Counterflowing Jets for Supersonic Slender Configurations — A Numerical Study

A novel approach of using counterflowing jets positioned strategically on the aircraft and exploiting its long penetration mode (LPM) of interaction towards sonic-boom mitigation forms the motivation for this study. Given that most previous studies on the counterflowing LPM jet have all been on blunt bodies and at high supersonic or hypersonic flow conditions, exploring the feasibility to obtain a LPM jet issuing from a slender body against low supersonic freestream conditions is the main focus of this study. Computational fluid dynamics computations of axisymmetric models (cone-cylinder and quartic geometry), of relevance to NASA's High Speed project, are carried out using the space-time conservation element solution element viscous flow solver with unstructured meshes. A systematic parametric study is conducted to determine the optimum combination of counterflowing jet size, mass flow rate, and nozzle geometry for obtaining LPM jets. Details from these computations will be used to assess the potential of the LPM counterflowing supersonic jet as a means of active flow control for enabling supersonic flight over land and to establish the knowledge base for possible future implementation of such technologies.
Automated Extraction of Grade, Stage, and Quality Information From Transurethral Resection of Bladder Tumor Pathology Reports Using Natural Language Processing

PurposeBladder cancer is initially diagnosed and staged with a transurethral resection of bladder tumor (TURBT). Patient survival is dependent on appropriate sampling of layers of the bladder, but pathology reports are dictated as free text, making large-scale data extraction for quality improvement challenging. We sought to automate extraction of stage, grade, and quality information from TURBT pathology reports using natural language processing (NLP).MethodsPatients undergoing TURBT were retrospectively identified using the Northwestern Enterprise Data Warehouse. An NLP algorithm was then created to extract information from free-text pathology reports and was iteratively improved using a training set of manually reviewed TURBTs. NLP accuracy was then validated using another set of manually reviewed TURBTs, and reliability was calculated using Cohen’s κ.ResultsOf 3,042 TURBTs identified from 2006 to 2016, 39% were classified as benign, 35% as Ta, 11% as T1, 4% as T2, and 10% as isolated carcinoma in situ...
Molecular vibrational frequencies from analytic Hessian of constrained nuclear–electronic orbital density functional theory

Nuclear quantum effects are important in a variety of chemical and biological processes. The constrained nuclear-electronic orbital density functional theory (cNEO-DFT) has been developed to include nuclear quantum effects in energy surfaces. Herein, we develop the analytic Hessian for cNEO-DFT energy with respect to the change in nuclear (expectation) positions, which can be used to characterize stationary points on energy surfaces and compute molecular vibrational frequencies. This is achieved by constructing and solving the multicomponent cNEO coupled-perturbed Kohn-Sham (cNEO-CPKS) equations, which describe the response of electronic and nuclear orbitals to the displacement of nuclear (expectation) positions. With the analytic Hessian, the vibrational frequencies of a series of small molecules are calculated and compared to those from conventional DFT Hessian calculations as well as those from the vibrational second-order perturbation theory (VPT2). It is found that even with a harmonic treatment, cNEO-DFT significantly outperforms DFT and is comparable to DFT-VPT2 in the description of vibrational frequencies in regular polyatomic molecules. Furthermore, cNEO-DFT can reasonably describe the proton transfer modes in systems with a shared proton, whereas DFT-VPT2 often faces great challenges. Our results suggest the importance of nuclear quantum effects in molecular vibrations, and cNEO-DFT is an accurate and inexpensive method to describe molecular vibrations.
Markets and Contractual Fairness

Contract is the quintessential legal institution of a market economy, but the contemporary philosophy of contract law is dominated by promissory and autonomy theories that tend to treat contract’s role in facilitating commerce as little more than a happy accident. It is thus striking that in his ambitious effort to generate such a purely transactional theory of contract law Peter Benson places markets at the center of his account of transactional fairness. His argues that the abstract equality of contracting parties requires equality of exchange, an equality mediated by the just price. Echoing the scholastic tradition, Benson identifies the just price with the competitive market price. We thus have a fascinating example of an anti-functionalist, anti-distributive theory of contract that nevertheless incorporates the market as an important theoretical element. In this essay, I evaluate Benson’s use of markets, placing it within both the broader discussion of markets in contract law theory and in the larger argument that he is making about contract law. Ultimately, Benson justifies the appeal to markets not because of any attractive distributional features of competitive prices but because such prices have a formal structure that satisfies the demands of Benson’s formal theory. It is an ingenious and subtle argument. However, I am not ultimately persuaded. While I think that the rule for unconscionability cases that Benson extracts from his concept of equality of exchange is defensible, I am not persuaded by Benson’s formal approach to the fairness of markets. Rather, I argue that competitive markets often do not have the formal features that Benson ascribes to them. Furthermore, a key aspect of his understanding of transactional fairness is best understood as resting on an attractive distributional feature of competitive markets rather than on their formal structure.
Almost minimizers for certain fractional variational problems

In this paper we introduce a notion of almost minimizers for certain variational problems governed by the fractional Laplacian, with the help of the Caffarelli-Silvestre extension. In particular, we study almost fractional harmonic functions and almost minimizers for the fractional obstacle problem with zero obstacle. We show that for a certain range of parameters, almost minimizers are almost Lipschitz or $C^{1,\beta}$-regular.
Transcriptome analysis of chloride intracellular channel knockdown in Drosophila identifies oxidation-reduction function as possible mechanism of altered sensitivity to ethanol sedation

Chloride intracellular channels (CLICs) are a unique family of evolutionarily conserved metamorphic proteins, switching between stable conformations based on redox conditions. CLICs have been implicated in a wide variety biological processes including ion channel activity, apoptosis, membrane trafficking, and enzymatic oxidoreductase activity. Understanding the molecular mechanisms by which CLICs engage in these activities is an area of active research. Here, the sole Drosophila melanogaster ortholog, Clic, was targeted for RNAi knockdown to identify genes and biological processes associated with Clic expression. Clic knockdown had a substantial impact on global transcription, altering expression of over 7% of transcribed Drosophila genes. Overrepresentation analysis of differentially expressed genes identified enrichment of Gene Ontology terms including Cytoplasmic Translation, Oxidation-Reduction Process, Heme Binding, Membrane, Cell Junction, and Nucleolus. The top term, Cytoplasmic Translation, was enriched almost exclusively with downregulated genes. Drosophila Clic and vertebrate ortholog Clic4 have previously been tied to ethanol sensitivity and ethanol-regulated expression. Clic knockdown-responsive genes from the present study were found to overlap significantly with gene sets from 4 independently published studies related to ethanol exposure and sensitivity in Drosophila. Bioinformatic analysis of genes shared between these studies revealed an enrichment of genes related to amino acid metabolism, protein processing, oxidation-reduction processes, and lipid particles among others. To determine whether the modulation of ethanol sensitivity by Clic may be related to co-regulated oxidation-reduction processes, we evaluated the effect of hyperoxia on ethanol sedation in Clic knockdown flies. Consistent with previous findings, Clic knockdown reduced acute ethanol sedation sensitivity in flies housed under normoxia. However, this effect was reversed by exposure to hyperoxia, suggesting a common set of molecular-genetic mechanism may modulate each of these processes. This study suggests that Drosophila Clic has a major influence on regulation of oxidative stress signaling and that this function overlaps with the molecular mechanisms of acute ethanol sensitivity in the fly.
Employing Connectome-Based Models to Predict Working Memory in Multiple Sclerosis.

Background: Individuals with multiple sclerosis (MS) are vulnerable to deficits in working memory, but the search for neural correlates of working memory within circumscribed areas has been inconcl...
Analysis of the Impact of Mask-wearing in Viral Spread: Implications for COVID-19

Masks are used as part of a comprehensive strategy of measures to limit transmission and save lives during the COVID-19 pandemic. Research about the impact of mask-wearing in the COVID-19 pandemic has raised formidable interest across multiple disciplines. In this paper, we investigate the impact of mask-wearing in spreading processes over complex networks. This is done by studying a heterogeneous bond percolation process over a multi-type network model, where nodes can be one of two types (mask-wearing, and not-mask-wearing). We provide analytical results that accurately predict the expected epidemic size and probability of emergence as functions of the characteristics of the spreading process (e.g., transmission probabilities, inward and outward efficiency of the masks, etc.), the proportion of mask-wearers in the population, and the structure of the underlying contact network. In addition to the theoretical analysis, we also conduct extensive simulations on random networks. We also comment on the analogy between the mask-model studied here and the multiple-strain viral spreading model with mutations studied recently by Eletreby et al.
An experimental investigation of high-aspect-ratio cooling passages

An experimental investigation was conducted to evaluate the effectiveness of using high-aspect-ratio cooling passenges to improve the life and reduce the colant pressure drop in high-pressure rocket thrust chambers. A plug-nozzle rocket-engine test apparatus was used to test two cylindrical chambers with low-aspect-ratio cooling passages and one with high-aspect-ratio cooling passages. The chambers were cyclically tested and data were taken over a wide range of coolant mass flows. The results showed that for the same coolant pressure drop, the hot-gas-side wall temperature of the high-aspect-ratio chamber was 30 percent lower than the baseline low-aspect-ratio chamber, resulting in no fatigue damage to the wall. The coolant pressure drop for the high-aspect-ratio chamber was reduced in increments to one-half that of the baseline chamber, by reducing the coolant mass flow, and still resulted in a reduction in the hot-gas-side wall temperature when compared to the low-aspect-ratio chambers.
Joint Learning of 3D Shape Retrieval and Deformation

We propose a novel technique for producing high-quality 3D models that match a given target object image or scan. Our method is based on retrieving an existing shape from a database of 3D models and then deforming its parts to match the target shape. Unlike previous approaches that independently focus on either shape retrieval or deformation, we propose a joint learning procedure that simultaneously trains the neural deformation module along with the embedding space used by the retrieval module. This enables our network to learn a deformation-aware embedding space, so that retrieved models are more amenable to match the target after an appropriate deformation. In fact, we use the embedding space to guide the shape pairs used to train the deformation module, so that it invests its capacity in learning deformations between meaningful shape pairs. Furthermore, our novel part-aware deformation module can work with inconsistent and diverse part-structures on the source shapes. We demonstrate the benefits of our joint training not only on our novel framework, but also on other state- of-the-art neural deformation modules proposed in recent years. Lastly, we also show that our jointly-trained method outperforms various non-joint baselines.
AUV Buoyancy Control with Hard and Soft Actuators

Autonomous underwater vehicles (AUVs) find many applications in oceanography, environmental research, and inspection and maintenance of subsea energy assets. Subsea Resident AUVs remain in subsea for extended periods of time which could last for several months. Maintaining depth using traditional hard actuators (HAs) is very energy expensive. Mimicking aquatic creatures, it is shown in this paper that HAs and proposed soft actuators (SAs) can collaborate in a novel way. This collaboration can stabilize AUVs at any desired depth with minimum energy consumption at steady state. This is demonstrated using a laboratory AUV for which a nonlinear dynamic model was developed that uses experimentally validated system parameters. The AUV uses HAs to quickly reach any desired depth, while SAs generate volume change to adjust the system's buoyancy to maintain neutral buoyancy at the desired depth. In the neutral buoyancy state, the HAs shut off while the SAs stabilize and maintain the depth with virtually zero energy consumption. A control algorithm architecture is developed to manage the HA and SA collaboration. The HAs use a proportional controller with a dead-band, while the SAs use a proportional-derivative-acceleration (PDA) feedback controller. The ability of both types of actuators to mitigate disturbance forces are explored and analyzed. Simulation results show that SAs alone can reject small disturbances while using both SAs and HAs in collaboration can reject large disturbances. Simulation results demonstrate that combining traditional HAs with SAs leads to dynamic performance and very low energy consumption capabilities that cannot be achieved by either one alone.