cssi/SciSciGPT-SciSciNet huggingface dataset:
Gradient flow formulation and second order numerical method for motion by mean curvature and contact line dynamics on rough surface

We study the dynamics of a droplet moving on an inclined rough surface in the absence of inertial and viscous stress effects. In this case, the dynamics of the droplet is a purely geometric motion in terms of the wetting domain and the capillary surface. Using a single graph representation, we interpret this geometric motion as a gradient flow on a Hilbert manifold. We propose unconditionally stable first/second order numerical schemes to simulate this geometric motion of the droplet, which is described using motion by mean curvature coupled with moving contact lines. The schemes are based on (i) explicit moving boundaries, which decouple the dynamic updates of the contact lines and the capillary surface, (ii) a semi-Lagrangian method on moving grids and (iii) a predictor-corrector method with a nonlinear elliptic solver upto second order accuracy. For the case of quasi-static dynamics with continuous spatial variable in the numerical schemes, we prove the stability and convergence of the first/second order numerical schemes. To demonstrate the accuracy and long-time validation of the proposed schemes, several challenging computational examples - including breathing droplets, droplets on inhomogeneous rough surfaces and quasi-static Kelvin pendant droplets - are constructed and compared with exact solutions to quasi-static dynamics obtained by desingularized differential-algebraic system of equations (DAEs).
Weil representation and Arithmetic Fundamental Lemma

By a global approach, we prove the arithmetic fundamental lemma conjecture for unitary groups in $n$ variables over $\mathbb{Q}_p$ when $p\geq n$.
Modeling the impact of racial and ethnic disparities on COVID-19 epidemic dynamics.

Background: The impact of variable infection risk by race and ethnicity on the dynamics of SARS CoV-2 spread is largely unknown.Methods: Here, we fit structured compartmental models to seroprevalence data from New York State and analyze how herd immunity thresholds (HITs), final sizes, and epidemic risk changes across groups.Results: A simple model where interactions occur proportionally to contact rates reduced the HIT, but more realistic models of preferential mixing within groups increased the threshold toward the value observed in homogeneous populations. Across all models, the burden of infection fell disproportionately on minority populations: in a model fit to Long Island serosurvey and census data, 81% of Hispanics or Latinos were infected when the HIT was reached compared to 34% of non-Hispanic whites.Conclusions: Our findings, which are meant to be illustrative and not best estimates, demonstrate how racial and ethnic disparities can impact epidemic trajectories and result in unequal distributions of SARS-CoV-2 infection.Funding: K.C.M. was supported by National Science Foundation GRFP grant DGE1745303. Y.H.G. and M.L. were funded by the Morris-Singer Foundation. M.L. was supported by SeroNet cooperative agreement U01 CA261277.
Optimal Spectrum Partitioning and Licensing in Tiered Access Under Stochastic Market Models

We consider the problem of partitioning a spectrum band into $M$ channels of equal bandwidth, and then further assigning these $M$ channels into $P$ licensed channels and $M-P$ unlicensed channels. Licensed channels can be accessed both for licensed and opportunistic use following a tiered structure that has a higher priority for licensed use. Unlicensed channels can be accessed only for opportunistic use. We address the following question in this paper. Given a market setup, what values of $M$ and $P$ maximize the net spectrum utilization of the spectrum band? While this problem is fundamental, it is highly relevant practically, e.g., in the context of partitioning the recently proposed Citizens Broadband Radio Service band. If $M$ is too high or too low, it may decrease spectrum utilization due to limited channel capacity or due to wastage of channel capacity, respectively. If $P$ is too high (low), it will not incentivize the wireless operators who are primarily interested in unlicensed channels (licensed channels) to join the market. These tradeoffs are captured in our optimization problem which manifests itself as a two-stage Stackelberg game. We design an algorithm to solve the Stackelberg game and hence find the optimal $M$ and $P$ . The algorithm design also involves an efficient Monte Carlo integrator to evaluate the expected value of the involved random variables like spectrum utilization and operators’ revenue. We also benchmark our algorithms using numerical simulations.
Don’t Look Back: An Online Beat Tracking Method Using RNN and Enhanced Particle Filtering

Online beat tracking (OBT) has always been a challenging task. Due to the inaccessibility of future data and the need to make inference in real-time. We propose Don’t Look back! (DLB), a novel approach optimized for efficiency when performing OBT. DLB feeds the activations of a unidirectional RNN into an enhanced Monte-Carlo localization model to infer beat positions. Most preexisting OBT methods either apply some offline approaches to a moving window containing past data to make predictions about future beat positions or must be primed with past data at startup to initialize. Meanwhile, our proposed method only uses activation of the current time frame to infer beat positions. As such, without waiting at the beginning to receive a chunk, it provides an immediate beat tracking response, which is critical for many OBT applications. DLB significantly improves beat tracking accuracy over state-of-the-art OBT methods, yielding a similar performance to offline methods.
Developing Motion Code Embedding for Action Recognition in Videos

In this work, we propose a motion embedding strategy known as motion codes, which is a vectorized representation of motions based on a manipulation's salient mechanical attributes. These motion codes provide a robust motion representation, and they are obtained using a hierarchy of features called the motion taxonomy. We developed and trained a deep neural network model that combines visual and semantic features to identify the features found in our motion taxonomy to embed or annotate videos with motion codes. To demonstrate the potential of motion codes as features for machine learning tasks, we integrated the extracted features from the motion embedding model into the current state-of-the-art action recognition model. The obtained model achieved higher accuracy than the baseline model for the verb classification task on egocentric videos from the EPIC-KITCHENS dataset.
A Neural Acoustic Echo Canceller Optimized Using An Automatic Speech Recognizer and Large Scale Synthetic Data

We consider the problem of recognizing speech utterances spoken to a device which is generating a known sound waveform; for example, recognizing queries issued to a digital assistant which is generating responses to previous user inputs. Previous work has proposed building acoustic echo cancellation (AEC) models for this task that optimize speech enhancement metrics using both neural network as well as signal processing approaches.Since our goal is to recognize the input speech, we consider enhancements which improve word error rates (WERs) when the predicted speech signal is passed to an automatic speech recognition (ASR) model. First, we augment the loss function with a term that produces outputs useful to a pre-trained ASR model and show that this augmented loss function improves WER metrics. Second, we demonstrate that augmenting our training dataset of real world examples with a large synthetic dataset improves performance. Crucially, applying SpecAugment style masks to the reference channel during training aids the model in adapting from synthetic to real domains. In experimental evaluations, we find the proposed approaches improve performance, on average, by 57% over a signal processing baseline and 45% over the neural AEC model without the proposed changes.
Size and momentum of an infalling particle in the black hole interior

The future interior of black holes in AdS/CFT can be described in terms of a quantum circuit. We investigate boundary quantities detecting properties of this quantum circuit. We discuss relations between operator size, quantum complexity, and the momentum of an infalling particle in the black hole interior. We argue that the trajectory of the infalling particle in the interior close to the horizon is related to the growth of operator size. The notion of size here differs slightly from the size which has previously been related to momentum of exterior particles and provides an interesting generalization. The fact that both exterior and interior momentum are related to operator size growth is a manifestation of complementarity.
Identifying treatment effects in the presence of confounded types

Abstract In this paper, I consider identification of treatment effects when the treatment is endogenous. The use of instrumental variables is a popular solution to deal with endogeneity, but this may give misleading answers when the instrument is invalid. I show that when an (unobserved) instrument is invalid due to correlation with the first stage unobserved heterogeneity, a proxy for the instrument helps partially identify not only the local average treatment effect, but also the entire potential outcomes distributions for compliers. I exploit the fact that the distribution of the observed outcome in each group defined by the treatment and the instrument is a mixture of the distributions of interest. I write the identified set in the form of conditional moment inequalities, and provide an easily implementable inference procedure. Under some tail restrictions, the potential outcomes distributions are point-identified for compliers. Finally, I illustrate my methodology on data from the National Longitudinal Survey of Young Men to estimate returns to college using college proximity as a proxy for the instrument low college cost. I find that a college degree increases the average hourly wage of the compliers by 15%–30%.
When band convergence is not beneficial for thermoelectrics.

Band convergence is considered a clear benefit to thermoelectric performance because it increases the charge carrier concentration for a given Fermi level, which typically enhances charge conductivity while preserving the Seebeck coefficient. However, this advantage hinges on the assumption that interband scattering of carriers is weak or insignificant. With first-principles treatment of electron-phonon scattering in the CaMg2Sb2-CaZn2Sb2 Zintl system and full Heusler Sr2SbAu, we demonstrate that the benefit of band convergence can be intrinsically negated by interband scattering depending on the manner in which bands converge. In the Zintl alloy, band convergence does not improve weighted mobility or the density-of-states effective mass. We trace the underlying reason to the fact that the bands converge at a one k-point, which induces strong interband scattering of both the deformation-potential and the polar-optical kinds. The case contrasts with band convergence at distant k-points (as in the full Heusler), which better preserves the single-band scattering behavior thereby successfully leading to improved performance. Therefore, we suggest that band convergence as thermoelectric design principle is best suited to cases in which it occurs at distant k-points.
Prioritizing heart transplantation during the COVID-19 pandemic.

Background Coronavirus disease 2019 (COVID-19) has significantly impacted the healthcare landscape in the United States in a variety of ways including a nation-wide reduction in operative volume. The impact of COVID-19 on the availability of donor organs and the impact on solid organ transplant remains unclear. We examine the impact of COVID-19 on a single, large-volume heart transplant program. Methods A retrospective chart review was performed examining all adult heart transplants performed at a single institution between March 2020 and June 2020. This was compared to the same time frame in 2019. We examined incidence of primary graft dysfunction, continuous renal replacement therapy (CRRT) and 30-day survival. Results From March to June 2020, 43 orthotopic heart transplants were performed compared to 31 performed during 2019. Donor and recipient demographics demonstrated no differences. There was no difference in 30-day survival. There was a statistically significant difference in incidence of postoperative CRRT (9/31 vs. 3/43; p = .01). There was a statistically significant difference in race (23 W/8B/1AA vs. 30 W/13B; p = .029). Conclusion We demonstrate that a single, large-volume transplant program was able to grow volume with little difference in donor variables and clinical outcomes following transplant. While multiple reasons are possible, most likely the reduction of volume at other programs allowed us to utilize organs to which we would not have previously had access. More significantly, our growth in volume was coupled with no instances of COVID-19 infection or transmission amongst patients or staff due to an aggressive testing and surveillance program.
MiSebastes: An eDNA metabarcoding primer set for rockfishes (genus Sebastes)

Environmental DNA (eDNA) is a promising biomonitoring tool for marine ecosystems, but its effectiveness for North Pacific coastal fishes is limited by the inability of existing barcoding primers to differentiate among rockfishes in the genus Sebastes. Comprised of 110 commercially and ecologically important species, this recent radiation is exceptionally speciose, and exhibits high sequence similarity among species at standard barcoding loci. Here, we report new Sebastes-specific metabarcoding primers that target mitochondrial cytochrome B. Amongst the 110 Sebastes species, 85 unique barcodes (of which 62 are species-specific) were identified in our amplicon region based on available reference sequences. The majority of the remaining barcodes are shared by only two species. Importantly, MiSebastes yield unique barcodes for 28 of 44 commercially harvested species in California, a dramatic improvement compared to the widely employed MiFish-U 12 S primers which only recover one of 44. Tests of these primers in an aquarium mesocosm containing 16 rockfish species confirms the utility of these new primers for eDNA metabarcoding, providing an important biomonitoring tool for these key coastal marine fishes.
Nonlinear stochastic parabolic partial differential equations with a monotone operator of the Ladyzenskaya-Smagorinsky type, driven by a Lévy noise

Abstract The aim of this article is to show the global existence of both martingale and pathwise solutions of stochastic equations with a monotone operator, of the Ladyzenskaya-Smagorinsky type, driven by a general Levy noise. The classical approach based on using directly the Galerkin approximation is not valid. Instead, our approach is based on using appropriate approximations for the monotone operator, Galerkin approximations and on the theory of martingale solutions.
Exploring Task Placement for Edge-to-Cloud Applications using Emulation

A vast and growing number of IoT applications connect physical devices, such as scientific instruments, technical equipment, machines, and cameras, across heterogenous infrastructure from the edge to the cloud to provide responsive, intelligent services while complying with privacy and security requirements. However, the integration of heterogeneous IoT, edge, and cloud technologies and the design of end-to-end applications that seamlessly work across multiple layers and types of infrastructures is challenging. A significant issue is resource management and the need to ensure that the right type and scale of resources is allocated on every layer to fulfill the application’s processing needs. As edge and cloud layers are increasingly tightly integrated, imbalanced resource allocations and sub-optimally placed tasks can quickly deteriorate the overall system performance. This paper proposes an emulation approach for the investigation of task placements across the edge-to-cloud continuum. We demonstrate that emulation can address the complexity and many degrees-of-freedom of the problem, allowing us to investigate essential deployment patterns and trade-offs. We evaluate our approach using a machine learning-based workload, demonstrating the validity by comparing emulation and real-world experiments. Further, we show that the right task placement strategy has a significant impact on performance – in our experiments, between 5% and 65% depending on the scenario.
Improving Uncertainty Calibration of Deep Neural Networks via Truth Discovery and Geometric Optimization

Deep Neural Networks (DNNs), despite their tremendous success in recent years, could still cast doubts on their predictions due to the intrinsic uncertainty associated with their learning process. Ensemble techniques and post-hoc calibrations are two types of approaches that have individually shown promise in improving the uncertainty calibration of DNNs. However, the synergistic effect of the two types of methods has not been well explored. In this paper, we propose a truth discovery framework to integrate ensemble-based and post-hoc calibration methods. Using the geometric variance of the ensemble candidates as a good indicator for sample uncertainty, we design an accuracy-preserving truth estimator with provably no accuracy drop. Furthermore, we show that post-hoc calibration can also be enhanced by truth discovery-regularized optimization. On large-scale datasets including CIFAR and ImageNet, our method shows consistent improvement against state-of-the-art calibration approaches on both histogram-based and kernel density-based evaluation metrics. Our codes are available at https://github.com/horsepurve/truly-uncertain.
Neural networks in structural analysis and design: An overview

Abstract There has been considerable recent interest in the application of neural networks in problems of structural analysis and design. The present paper provides an overview of the state-of-the-art in this emerging field, including a survey of published applications in structural engineering. Such applications have included, among others, the use of neural networks in modeling non-linear analysis of structures, as a rapid reanalysis capability in optimal design, and in developing problem parameter sensitivity of optimal solutions for use in multilevel decomposition based design. While most of the applications reported in the literature have been restricted to the use of the multilayer perceptron architecture and minor variations thereof, other network architectures have also been successfully explored, including the adaptive resonance theory network, the counterpropagation network, and the Hopfield-Tank model. Applications of these architectures have included solution of inverse analysis problems, pattern matching in structural analysis and design, and as an optimization tool for generically difficult optimization problems, in particular, those characterized as being NP-complete.
SecureNN: Efficient and Private Neural Network Training

Neural Networks (NN) provide a powerful method for machine learning training and inference. To effectively train, it is desirable for multiple parties to combine their data — however, doing so conflicts with data privacy. In this work, we provide novel three-party secure computation protocols for various NN building blocks such as matrix multiplication, convolutions, Rectified Linear Units, Maxpool, normalization and so on. This enables us to construct three-party secure protocols for training and inference of several NN architectures such that no single party learns any information about the data. Experimentally, we implement our system over Amazon EC2 servers in different settings. Our work advances the state-of-the-art of secure computation for neural networks in three ways: Scalability: We are the first work to provide neural network training on Convolutional Neural Networks (CNNs) that have an accuracy of >99% on the MNIST dataset; Performance: For secure inference, our system outperforms prior 2 and 3-server works (SecureML, MiniONN, Chameleon, Gazelle) by 6x-113x (with larger gains obtained in more complex networks). Our total execution times are 2-4x faster than even just the online times of these works. For secure training, compared to the only prior work (SecureML) that considered a much smaller fully connected network, our protocols are 79x and 7x faster than their 2 and 3-server protocols. In the WAN setting, these improvements are more dramatic and we obtain an improvement of 553x! Security: Our protocols provide two kinds of security: full security (privacy and correctness) against one semi-honest corruption and the notion of privacy against one malicious corruption [Araki et al. CCS’16]. All prior works only provide semi-honest security and ours is the first system to provide any security against malicious adversaries for the secure computation of complex algorithms such as neural network inference and training. Our gains come from a significant improvement in communication through the elimination of expensive garbled circuits and oblivious transfer protocols.
Learning Domain-Agnostic Visual Representation for Computational Pathology Using Medically-Irrelevant Style Transfer Augmentation.

Suboptimal generalization of machine learning models on unseen data is a key challenge which hampers the clinical applicability of such models to medical imaging. Although various methods such as domain adaptation and domain generalization have evolved to combat this challenge, learning robust and generalizable representations is core to medical image understanding, and continues to be a problem. Here, we propose STRAP ( S tyle TR ansfer A ugmentation for histo P athology), a form of data augmentation based on random style transfer from non-medical style sources such as artistic paintings, for learning domain-agnostic visual representations in computational pathology. Style transfer replaces the low-level texture content of an image with the uninformative style of randomly selected style source image, while preserving the original high-level semantic content. This improves robustness to domain shift and can be used as a simple yet powerful tool for learning domain-agnostic representations. We demonstrate that STRAP leads to state-of-the-art performance, particularly in the presence of domain shifts, on two particular classification tasks in computational pathology. Our code is available at https://github.com/rikiyay/style-transfer-for-digital-pathology .
Pollution abatement with disruptive R&D investment

Abstract This paper examines a model of investment in abatement where polluting firms produce output while investing in RD (2) produce output alone; or (3) engage in both activities. We evaluate how the emergence of each result is affected by the market structure in which firms compete and by the severity of spillover effects. We then measure welfare levels in each equilibrium profile. Overall, we show that firms endogenously choose to focus on R&D only when the market is concentrated and spillover effects are small. In other type of industries, our findings indicate that firms may focus on output production or engage in both activities under relatively large conditions.
Strange Metals as Ersatz Fermi Liquids.

A long-standing mystery of fundamental importance in correlated electron physics is to understand strange non-Fermi liquid metals that are seen in diverse quantum materials. A striking experimental feature of these metals is a resistivity that is linear in temperature ($T$). In this Letter we ask what it takes to obtain such non-Fermi liquid physics down to zero temperature in a translation invariant metal. If in addition the full frequency ($\ensuremath{\omega}$) dependent conductivity satisfies $\ensuremath{\omega}/T$ scaling, we argue that the $T$-linear resistivity must come from the intrinsic physics of the low energy fixed point. Combining with earlier arguments that compressible translation invariant metals are ``ersatz Fermi liquids'' with an infinite number of emergent conserved quantities, we obtain powerful and practical conclusions. We show that there is necessarily a diverging susceptibility for an operator that is odd under inversion and time reversal symmetries, and has zero crystal momentum. We discuss a few other experimental consequences of our arguments, as well as potential loopholes, which necessarily imply other exotic phenomena.
Home Sweet Home? The Efficacy of Rental Restrictions to Promote Neighborhood Stability

Homeownership is an enduring and fundamental American tradition whose economic and social benefits are well examined and have received renewed attention in recent articles and books. Tax laws encourage homeownership; debtor-creditor and property laws protect homeowners; and constitutional protections defend homeowners from governmental attempts to exercise eminent domain.The current economic and housing crises have forced commentators and policymakers to reexamine the connection between traditional conceptions of homeownership and economic stability, particularly for low-income residents. This article questions that traditional conception by exploring how local governments, in an effort to promote regulatory land use goals, frequently limit homeowners' power to freely alienate property. In particular, I consider state and local efforts to restrict residential real estate rentals and to curb residential real estate speculation.In so doing, I attempt to strike the proper balance of legal protections concerning homeownership. Do we over protect traditional conceptions of homeownership at the expense of other important values? If the answer is yes, then do limitations on owners' alienation rights achieve worthwhile goals? Or are we perhaps offering owners too little in terms of ownership options? Owners may need more flexibility in the compromise between shelter and commodity, or between on-site and off-site risk factors. These questions of "too much" or "too little" suggest that the issues of rental restrictions and limits on speculation are ones that jurisdictions will soon be compelled to reconsider.
Structure of the ancient TRPY1 channel from Saccharomyces cerevisiae reveals mechanisms of modulation by lipids and calcium.

Summary Transient receptor potential (TRP) channels emerged in fungi as mechanosensitive osmoregulators. The Saccharomyces cerevisiae vacuolar TRP yeast 1 (TRPY1) is the most studied TRP channel from fungi, but the structure and details of channel modulation remain elusive. Here, we describe the full-length cryoelectron microscopy structure of TRPY1 at 3.1 A resolution in a closed state. The structure, despite containing an evolutionarily conserved and archetypical transmembrane domain, reveals distinctive structural folds for the cytosolic N and C termini, compared with other eukaryotic TRP channels. We identify an inhibitory phosphatidylinositol 3-phosphate (PI(3)P) lipid-binding site, along with two Ca2+-binding sites: a cytosolic site, implicated in channel activation and a vacuolar lumen site, implicated in inhibition. These findings, together with data from microsecond-long molecular dynamics simulations and a model of a TRPY1 open state, provide insights into the basis of TRPY1 channel modulation by lipids and Ca2+, and the molecular evolution of TRP channels.
Electron ion interactions in crystal channels: collisions in ultra-dense electron media

Dielectronic excitation of H-like S, Ca and Ti is shown to occur in the dense electron gas of a crystal channel. Cross sections for collisional ionization of the short lived excited states can then be determined. Ionic excitation can also be achieved by resonant coherent excitation in which case specific m states can be excited for further study.
A New Lecture-Tutorial for Teaching Interferometry to Astro 101 Students

The ground-breaking image of a black hole’s event horizon, which captured the public’s attention and imagination in April 2019, was captured using the power of interferometry: many separate telescopes working together to observe the cosmos in incredible detail. Many recent astrophysical discoveries that have revolutionized the scientific community’s understanding of the cosmos were made by interferometers such as LIGO, ALMA, and the Event Horizon Telescope. Astro 101 instructors who want their students to learn the science behind these discoveries must teach about interferometry. Decades of research show that using active learning strategies can significantly increase students’ learning and reduce achievement gaps between different demographic groups over what is achieved from traditional lecture-based instruction. As part of an effort to create active learning materials on interferometry, we developed and tested a new Lecture-Tutorial to help Astro 101 students learn about key properties of astronomical interferometers. This paper describes this new Lecture-Tutorial and presents evidence for its effectiveness from a study conducted with 266 Astro 101 students at the University of North Carolina at Chapel Hill.
Fully automated noncoplanar radiation therapy treatment planning.

PURPOSE To perform fully automated noncoplanar (NC) treatment planning, we propose a method called NC-POPS to produce NC plans using the Pareto optimal projection search (POPS) algorithm. METHODS NC radiation therapy treatment planning has the potential to improve dosimetric quality as compared to traditional coplanar techniques. Likewise, automated treatment planning algorithms can reduce a planner's active treatment planning time and remove inter-planner variability. Our NC-POPS algorithm extends the original POPS algorithm to the NC setting with potential applications to both intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT). The proposed algorithm consists of two main parts: (1) NC beam angle optimization (BAO) and (2) fully automated inverse planning using the POPS algorithm. RESULTS We evaluate the performance of NC-POPS by comparing between various NC and coplanar configurations. To evaluate plan quality, we compute the homogeneity index (HI), conformity index (CI), and dose-volume histogram statistics for various organs-at-risk (OARs). As compared to the evaluated coplanar baseline methods, the proposed NC-POPS method achieves significantly better OAR sparing, comparable or better dose conformity, and similar dose homogeneity. CONCLUSIONS Our proposed NC-POPS algorithm provides a modular approach for fully automated treatment planning of NC IMRT cases with the potential to substantially improve treatment planning workflow and plan quality.
Discriminative adversarial domain generalization with meta-learning based cross-domain validation

Abstract The generalization capability of machine learning models, which refers to generalizing the knowledge for an “unseen” domain via learning from one or multiple seen domain(s), is of great importance to develop and deploy machine learning applications in the real-world conditions. Domain Generalization (DG) techniques aim to enhance such generalization capability of machine learning models, where the learnt feature representation and the classifier are two crucial factors to improve generalization and make decisions. In this paper, we propose Discriminative Adversarial Domain Generalization (DADG) with meta-learning-based cross-domain validation. Our proposed framework tries to learn a domain-invariant feature representation from source domains and generalize it to the unseen domains. It contains two main components that work synergistically to build a domain-generalized Deep Neural Network (DNN) model: (i) discriminative adversarial learning, which proactively learns a generalized feature representation on multiple “seen” domains, and (ii) meta-learning based cross domain validation, which simulates train/test domain shift via applying meta-learning techniques in the training process. In the experimental evaluation, a comprehensive comparison has been made among our proposed approach and other existing approaches on three benchmark datasets. The results shown that DADG consistently outperforms a strong baseline DeepAll, and outperforms the other existing DG algorithms in most of the evaluation cases.
Robotic Grasping of Fully-Occluded Objects using RF Perception

We present the design, implementation, and evaluation of RF-Grasp, a robotic system that can grasp fully-occluded objects in unknown and unstructured environments. Unlike prior systems that are constrained by the line-of-sight perception of vision and infrared sensors, RF-Grasp employs RF (Radio Frequency) perception to identify and locate target objects through occlusions, and perform efficient exploration and complex manipulation tasks in non-line-of-sight settings.RF-Grasp relies on an eye-in-hand camera and batteryless RFID tags attached to objects of interest. It introduces two main innovations: (1) an RF-visual servoing controller that uses the RFID’s location to selectively explore the environment and plan an efficient trajectory toward an occluded target, and (2) an RF-visual deep reinforcement learning network that can learn and execute efficient, complex policies for decluttering and grasping.We implemented and evaluated an end-to-end physical prototype of RF-Grasp. We demonstrate it improves success rate and efficiency by up to 40-50% over a state-of-the-art baseline. We also demonstrate RF-Grasp in novel tasks such mechanical search of fully-occluded objects behind obstacles, opening up new possibilities for robotic manipulation. Qualitative results (videos) available at rfgrasp.media.mit.edu
End User Accounts of Dark Patterns as Felt Manipulation

Manipulation defines many of our experiences as a consumer, including subtle nudges and overt advertising campaigns that seek to gain our attention and money. With the advent of digital services that can continuously optimize online experiences to favor stakeholder requirements, increasingly designers and developers make use of "dark patterns"-forms of manipulation that prey on human psychology-to encourage certain behaviors and discourage others in ways that present unequal value to the end user. In this paper, we provide an account of end user perceptions of manipulation that builds on and extends notions of dark patterns. We report on the results of a survey of users conducted in English and Mandarin Chinese (n=169), including follow-up interviews from nine survey respondents. We used a card sorting method to support thematic analysis of responses from each cultural context, identifying both qualitatively-supported insights to describe end users' felt experiences of manipulative products and a continuum of manipulation. We further support this analysis through a descriptive analysis of survey results and the presentation of examples from the interviews. We conclude with implications for future research, considerations for public policy, and guidance on how to further empower and give users autonomy in their experiences with digital services.
Query-driven Segment Selection for Ranking Long Documents

Transformer-based rankers have shown state-of-the-art performance. However, their self-attention operation is mostly unable to process long sequences. One of the common approaches to train these rankers is to heuristically select some segments of each document, such as the first segment, as training data. However, these segments may not contain the query-related parts of documents. To address this problem, we propose query-driven segment selection from long documents to build training data. The segment selector provides relevant samples with more accurate labels and non-relevant samples which are harder to be predicted. The experimental results show that the basic BERT-based ranker trained with the proposed segment selector significantly outperforms that trained by the heuristically selected segments, and performs equally to the state-of-the-art model with localized self-attention that can process longer input sequences. Our findings open up new direction to design efficient transformer-based rankers.
Forecast Aggregation via Peer Prediction

Crowdsourcing enables the solicitation of forecasts on a variety of prediction tasks from distributed groups of people. How to aggregate the solicited forecasts, which may vary in quality, into an accurate final prediction remains a challenging yet critical question. Studies have found that weighing expert forecasts more in aggregation can improve the accuracy of the aggregated prediction. However, this approach usually requires access to the historical performance data of the forecasters, which are often not available. In this paper, we study the problem of aggregating forecasts without having historical performance data. We propose using peer prediction methods, a family of mechanisms initially designed to truthfully elicit private information in the absence of ground truth verification, to assess the expertise of forecasters, and then using this assessment to improve forecast aggregation. We evaluate our peer-prediction-aided aggregators on a diverse collection of 14 human forecast datasets. Compared with a variety of existing aggregators, our aggregators achieve a significant and consistent improvement on aggregation accuracy measured by the Brier score and the log score. Our results reveal the effectiveness of identifying experts to improve aggregation even without historical data.
Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness

We investigate the HSIC (Hilbert-Schmidt independence criterion) bottleneck as a regularizer for learning an adversarially robust deep neural network classifier. We show that the HSIC bottleneck enhances robustness to adversarial attacks both theoretically and experimentally. Our experiments on multiple benchmark datasets and architectures demonstrate that incorporating an HSIC bottleneck regularizer attains competitive natural accuracy and improves adversarial robustness, both with and without adversarial examples during training.
The mass budget necessary to explain ‘Oumuamua as a nitrogen iceberg

Abstract Recently, a nitrogen iceberg was proposed as a possible origin for the first interstellar object, 1I/2017 U1, also known as ‘Oumuamua. Here, we show that the mass budget in exo-Pluto planets necessary to explain the detection of ‘Oumuamua as a nitrogen iceberg chipped off from a planetary surface requires a mass of heavy elements exceeding the total quantity locked in stars with 95% confidence, making the scenario untenable because only a small fraction of the mass in stars ends in exo-Plutos.
Degree bounds for Gröbner bases of modules

Abstract Let F be a non-negatively graded free module over a polynomial ring K [ x 1 , … , x n ] generated by m basis elements. Let M be a submodule of F generated by elements with degrees bounded by D and dim F / M = r . We prove that if M is graded, the degree of the reduced Grobner basis of M for any term order is bounded by 2 [ 1 / 2 ( ( D m ) n − r m + D ) ] 2 r − 1 . If M is not graded, the bound is 2 [ 1 / 2 ( ( D m ) ( n − r ) 2 m + D ) ] 2 r . This is a generalization of Dube (1990) and Mayr-Ritscher (2013)'s bounds for ideals in a polynomial ring.
Why Europe Grew Rich and Asia Did Not: Global Economic Divergence, 1600–1850

Why Europe Grew Rich and Asia Did Not provides a striking new answer to the classic question of why Europe industrialised from the late eighteenth century and Asia did not. Drawing significantly from the case of India, Prasannan Parthasarathi shows that in the seventeenth and eighteenth centuries the advanced regions of Europe and Asia were more alike than different, both characterized by sophisticated and growing economies. Their subsequent divergence can be attributed to different competitive and ecological pressures that in turn produced varied state policies and economic outcomes. This account breaks with conventional views, which hold that divergence occurred because Europe possessed superior markets, rationality, science or institutions. It offers instead a groundbreaking rereading of global economic development that ranges from India, Japan and China to Britain, France and the Ottoman Empire and from the textile and coal industries to the roles of science, technology and the state.
Conservative management of gastric emphysema with hepatoportal venous gas

A 43-year-old man with a history of viral hepatitis and active intravenous drug use presented with a 4-day history of severe nausea and coffee-ground emesis, followed by acute onset of colicky abdominal pain and non-bloody diarrhoea. On examination, he was tachycardic, but otherwise haemodynamically stable and afebrile. There was tenderness in the right upper quadrant, with no rebound or guarding and bowel sounds were normoactive. Initial laboratory investigations revealed raised urea of 24 mM/L, normal lactate and no leucocytosis. A CT of the abdomen and pelvis with contrast revealed gastric pneumatosis (figure 1) with hepatoportal venous gas (HPVG; figure 2) and signs of parenchymal …
Iowa State University Swine Teaching Farm—A Farrow to Finish Farm

The Swine Teaching Farm provides swine and facilities for teaching support of over 15 courses each year. Students learn the entire breadth of the swine industry from breed identification in introductory classes to enterprise management and swine production systems. The farm hosts annually an extensive number of tours for local grade schools classes, 4-H and FFA clubs, and international groups. Hogs from the farm are shown and sold successfully in major national events.
Testimony of Brendan S. Maher ERISA Advisory Council United States Department of Labor August 29, 2013 Hearing: Pension De-Risking

This is my invited written statement on the phenomenon of "pension de-risking," before the ERISA Advisory Council, United States Department of Labor, on August 29, 2013.
Can developmental screening tests identify children who are developmentally at risk

Developmental screening tests are in widespread use, but few reliable and valid tests are available. The most frequently used screening instrument for detecting young children who are at risk for developmental delays is the Denver Developmental Screening Test (DDST). Although the DDST has excellent test specificity, overreferring few children, results from more than a dozen studies of the DDST's concurrent and predictive validity fail to replicate the original validation and demonstrate a uniformly poor sensitivity, ie, a high proportion of underreferrals. Whether samples are stratified by age, risk, duration of time between predictor and outcome, or type of outcome measure used, these studies demonstrate that the DDST underrefers children at nearly a 2:1 ratio. Several other screening tests with more optimal psychometric properties are presented. It is urged that caution be exercised in using tests for predicting the risk of developmental problems in young children unless the tests have acceptable levels of sensitivity and specificity.
Usulan Acceptance Sampling Plan Untuk Tape Yarn Produk Geotex 250 Studi Kasus: PT. Unggul Karya Semesta - Bogor

Perencanaan penerimaan sampel merupakan salah satu aplikasi system pengendalian kualitas yang merancang suatu teknik pengambilan sampel dengan jumlah sampel dan batas spesifikasi yang telah ditentukan. Untuk membuat rancangan ini terlebih dahulu ditentukan nilai untuk beberapa variabel, yaitu probabilitas menolak lot yang baik (producer’s risk), probabilitas menerima lot yang buruk (consumer’s risk), rata-rata lot berkualitas baik, rata-rata lot berkualitas buruk, dan standar deviasi proses. Variabel-variabel ini akan mempengaruhi perhitungan untuk menentukan jumlah sampel dan batas spesifikasi penerimaan sampel. Hasilnya berupa suatu rancangan di mana sampel yang akan diinspeksi adalah sejumlah 13 benang plastik (tape yarn), kemudian diukur kekuatan per deniernya (tenacity). Jika rata-rata tenacity sama dengan atau lebih dari 5,9 gram/D maka lot diterima, dan jika rata-rata tenacity kurang dari 5,9 gram/D maka lot ditolak. Kata kunci: pengendalian kualitas, perencanaan penerimaan sampel, perencanaan sampling untuk karakteristik variabel, batas spesifikasi tunggal, standar deviasi. Acceptance sampling plan is an application of quality control system that creates a sampling technique with certain sample size and specification limit. To plan this, it has to determine the value of variables, such as probability of rejecting good lot (producer’s risk), probability of accepting poor lot (consumer’s risk), good average quality, poor average quality, and process standard deviation. These variables will determine the sample size and acceptance specification limit. The result is a plan which the inspected sample size is 13 tape yarns, then its strength per denier (tenacity) is measured. If the sample average is equal to or more than 5,9 gram/D, accept lot; otherwise, reject lot. Keywords: quality control, acceptance sampling plans, variable sampling plan, single specification limit, standard deviation.
Early Maternal Behaviors Predictive of the Survival of Suckling Rats with Intergenerational Malnutrition

Mother-pup interactions during the first 12 hours post partum were studied in intergenerationally malnourished and control rats. Pup mortality during the suckling period was found to be associated with nutritional status with only 50% survival in the malnourished series, as compared to 100% survival in the control series. Litters with subsequent high pup mortality were distinguished by abnormal mothering patterns during the first 12 hours of life and reduced pup weight at the end of this period. High pup mortality was associated with increased active nursing, and reductions in passive nursing, pup licking, amount of contact with the pups and time spent in the nest by the lactating female. Thus, the stimulus to active nursing is most intense under conditions where the pups are least likely to survive, whereas the amount of stimulation provided to such pups is reduced. Maximal associations between early mother-pup interactions and subsequent pup weight and survival were present 7-9 hours after birth.
Cross-Layer Based Approach to Detect Idle Channels and Allocate Them Efficiently Using Markov Models

Cross-layer based approach is used in cognitive wireless networks for efficient utilization of unused spectrum by quick and correct detection of primary signals. In the current research, Su’s algorithm was modified and the RASH (Random Access by Sequential search and Hash organization) algorithm was proposed for quick detection of idle spectrum. Once the idle spectrum is detected, the Hidden Markov Model (HMM) is used to help the analysis of efficient utilization of the idle spectrum. The simulation results show that the proposed model will be helpful for better utilization of the idle spectrum.
Effects of Inorganic Electrolytes on the Membrane Transport and Metabolism of Serotonin and Norepinephrine by Synaptosomes

Publisher Summary This chapter summarizes the results of the experiments on the ion requirements for transport and storage of amines by synaptosomes. Many of the findings described in this chapter parallel those found earlier in the adrenergic nerve endings in heart slices. An important early finding in experiments with synaptosomes was that the autolysis of synaptosomes incubated at 37°, with the release of acetylcholine, was decreased if the synaptosomes were suspended in Krebs–Ringer. The essential results of preliminary studies indicate that synaptosomes incubated in Na-deficient media (as compared with those incubated in Krebs–bicarbonate medium) show a time-dependent swelling of their mitochondria and vesicles. Synaptosomes were prepared from homogenates of rat or rabbit brain stem, using discontinuous sucrose gradients essentially. During the preparation, other cell fractions are isolated, including the soluble and mitochondria1 fractions and membrane fragments. On the basis of histological and biochemical data, the chapter concludes that inhibition of uptake by Na + -free, K + -free, or ouabain-containing media up to at least 20 minutes is mainly the result of impairment of the membrane transport mechanism and not the disruption of cellular contents.