cssi/SciSciGPT-SciSciNet huggingface dataset:
Drilling Holes Through Balls and Cubes

Suppose that we are allowed to drill a cylindrical hole of any radius through the center of a unit ball or a unit cube in a given direction. Which radius will maximize the total exposed surface area (ESA) of the finished object? If we are allowed to drill two or three mutually orthogonal cylindrical holes of the same radius, which common radius will maximize the total ESA?
Nucleon Gluon Distribution Function from 2+1+1-Flavor Lattice QCD

The parton distribution functions (PDFs) provide process-independent information about the quarks and gluons inside hadrons. Although the gluon PDF can be obtained from a global fit to experimental data, it is not constrained well in the large-$x$ region. Theoretical gluon-PDF studies are much fewer than those of the quark PDFs. In this work, we present the first lattice-QCD results that access the $x$-dependence of the gluon unpolarized PDF of the nucleon. The lattice calculation is carried out with nucleon momenta up to 2.16 GeV, lattice spacing $a\approx0.12$ fm, with valence pion masses of 310 and 690 MeV. We use reduced Ioffe-time distributions to cancel the renormalization and implement a one-loop perturbative pseudo-PDF matching to the lightcone distribution. Our matrix element results in coordinate space are consistent with those obtained from the global PDF fits of CT18 NNLO and NNPDF3.1 NNLO. Our fitted gluon PDF extrapolated to the physical pion mass gives consistent results in the $x > 0.3$ region.
Oral Hsp90 Inhibitor, SNX-5422, Attenuates SARS-CoV-2 Replication and Dampens Inflammation in Airway Cells

Currently available SARS-CoV-2 therapeutics are targeted towards moderately to severely ill patients and require intravenous infusions, with limited options for exposed or infected patients with no or mild symptoms. While vaccines have demonstrated protective efficacy, vaccine hesitancy and logistical distribution challenges will delay their ability to end the pandemic. Hence, there is a need for rapidly translatable, easy-to-administer-therapeutics, that can prevent SARS-CoV-2 disease progression, when administered in the early stages of infection. We demonstrate that an orally bioavailable Hsp90 inhibitor, SNX-5422, currently in clinical trials as an anti-cancer therapeutic, inhibits SARS-CoV-2 replication in vitro at a high selectivity index. SNX-5422 treatment of human primary airway epithelial cells dampened expression of inflammatory pathways associated with poor SARS-CoV-2 disease outcomes. Additionally, SNX-5422 interrupted expression of host factors that are crucial for SARS-CoV-2 replication machinery. Development of SNX-5422 as SARS-CoV-2-early-therapy will dampen disease severity, resulting in better clinical outcomes and reduced hospitalizations. Funding: This work was supported by a grant from Open Philanthropy (R.G and S.R.P) and COVID-19 research startup funds (S.R.P). Conflict of Interest: The authors declare no competing 338 interests. A patent has been filed with Duke University for use of SNX-5422 as an anti-SARS-CoV-2 therapeutic.
Lossless Complex-Valued Optical-Field Control with Compound Metaoptics

Forming a desired optical-field distribution from a given source requires precise spatial control of a field's amplitude and phase. Low-loss metasurfaces that allow extreme phase and polarization control of optical fields have been demonstrated over the past few years. However, metasurfaces that provide amplitude control have remained lossy, utilizing mechanisms such as reflection, absorption, or polarization loss to control amplitude. Here, we describe the amplitude and phase manipulation of optical fields without loss, by using two lossless phase-only metasurfaces separated by a distance. We first demonstrate a combined beam former and splitter optical component using this approach. Next, we show a high-quality computer-generated three-dimensional hologram. The proposed metaoptic platform combines the advantage of lossless complex-valued field control with a physically small thickness. This approach could lead to low-profile, three-dimensional holographic displays, compact optical components, and high precision optical tweezers for micro-particle manipulation.
SSLIDE: Sound Source Localization for Indoors Based on Deep Learning

This paper presents SSLIDE, Sound Source Localization for Indoors using DEep learning, which applies deep neural networks (DNNs) with encoder-decoder structure to localize sound sources with random positions in a continuous space. The spatial features of sound signals received by each microphone are extracted and represented as likelihood surfaces for the sound source locations in each point. Our DNN consists of an encoder network followed by two decoders. The encoder obtains a compressed representation of the input likelihoods. One decoder resolves the multipath caused by reverberation, and the other decoder estimates the source location. Experiments based on both the simulated and experimental data show that our method can not only outperform multiple signal classification (MUSIC), steered response power with phase transform (SRP-PHAT), sparse Bayesian learning (SBL), and a competing convolutional neural network (CNN) approach in the reverberant environment but also achieve a good generalization performance.
Consistent Recurrent Neural Networks For 3d Neuron Segmentation.

We present a recurrent network for 3D reconstruction of neurons that sequentially generates binary masks for every object in an image with spatio-temporal consistency. Our network models consistency in two parts: (i) local, which allows exploring non-occluding and temporally-adjacent object relationships with bi-directional recurrence. (ii) non-local, which allows exploring long-range object relationships in the temporal domain with skip connections. Our proposed network is end-to-end trainable from an input image to a sequence of object masks, and, compared to methods relying on object boundaries, its output does not require post-processing. We evaluate our method on three benchmarks for neuron segmentation and achieved state-of-the-art performance on the SNEMI3D [1] challenge.
Identification of phosphosites that alter protein thermal stability.

ABSTRACT Proteomics has enabled the cataloguing of 100,000s of protein phosphorylation sites 1, however we lack methods to systematically annotate their function. Phosphorylation has numerous biological functions, yet biochemically all involve changes in protein structure and interactions. These biochemical changes can be recapitulated by measuring the difference in stability between the protein and the phosphoprotein. Building on recent work, we present a method to infer phosphosite functionality by reliably measuring such differences at the proteomic scale.
Thruster-Specific Force Estimation and Trending of Cassini Hydrazine Thrusters at Saturn

The Cassini spacecraft has been in orbit around Saturn since 2004 and has since been approved for both a first and second extended mission. As hardware reaches and exceeds its documented life expectancy, it becomes vital to closely monitor hardware performance. The performance of the 1-N hydrazine attitude control thrusters is especially important to study, because the spacecraft is currently operating on the back-up thruster branch. Early identification of hardware degradation allows more time to develop mitigation strategies. There is no direct measure of an individual thruster's thrust magnitude, but these values can be estimated by post-processing spacecraft telemetry. This paper develops an algorithm to calculate the individual thrust magnitudes using Euler's equation. The algorithm correctly shows the known degradation in the first thruster branch, validating the approach. Results for the current thruster branch show nominal performance as of August, 2015.
On seven conjectures of Kedlaya and Medvedovsky

Abstract In a paper of Kedlaya and Medvedovsky [KM19] , the number of distinct dihedral mod 2 modular representations of prime level N was calculated, and a conjecture on the dimension of the space of level N weight 2 modular forms giving rise to each representation was stated. In this paper we prove this conjecture.
Lorentz-covariant dissipative Lagrangian systems

The concept of dissipative Hamiltonian system is converted to Lorentz-covariant form, with evolution generated jointly by two scalar functionals, the Lagrangian action and the global entropy. A bracket formulation yields the local covariant laws of energy-momentum conservation and of entropy production. The formalism is illustrated by a derivation of the covariant Landau kinetic equation.
A Functional Perspective on Learning Symmetric Functions with Neural Networks

Symmetric functions, which take as input an unordered, fixed-size set, are known to be universally representable by neural networks that enforce permutation invariance. These architectures only give guarantees for fixed input sizes, yet in many practical applications, including point clouds and particle physics, a relevant notion of generalization should include varying the input size. In this work we treat symmetric functions (of any size) as functions over probability measures, and study the learning and representation of neural networks defined on measures. By focusing on shallow architectures, we establish approximation and generalization bounds under different choices of regularization (such as RKHS and variation norms), that capture a hierarchy of functional spaces with increasing degree of non-linear learning. The resulting models can be learned efficiently and enjoy generalization guarantees that extend across input sizes, as we verify empirically.
Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection

Despite significant progress in neural abstractive summarization, recent studies have shown that the current models are prone to generating summaries that are unfaithful to the original context. To address the issue, we study contrast candidate generation and selection as a model-agnostic post-processing technique to correct the extrinsic hallucinations (i.e. information not present in the source text) in unfaithful summaries. We learn a discriminative correction model by generating alternative candidate summaries where named entities and quantities in the generated summary are replaced with ones with compatible semantic types from the source document. This model is then used to select the best candidate as the final output summary. Our experiments and analysis across a number of neural summarization systems show that our proposed method is effective in identifying and correcting extrinsic hallucinations. We analyze the typical hallucination phenomenon by different types of neural summarization systems, in hope to provide insights for future work on the direction.
Reverse mathematics and Weihrauch analysis motivated by finite complexity theory

We extend a study by Lempp and Hirst of infinite versions of some problems from finite complexity theory, using an intuitionistic version of reverse mathematics and techniques of Weihrauch analysis.
Using accounting earnings and aggregate economic indicators to estimate firm-level systematic risk

We revisit the literature on using accounting earnings to estimate firm-level systematic risk, using macroeconomic indicators rather than listed-firm indexes to measure aggregate risk. Conventional listed-firm indexes reflect an unrepresentative subset of aggregate assets and thus are expected to substantially mismeasure aggregate and systematic risk (J Financ Econ 4, 129–176, Roll 1977). That choice dictates using earnings rather than returns to measure firm-level outcomes. Earnings and macroeconomic indicators both are primarily realized annual outcomes and thus are better aligned in time than forward-looking returns for capturing the contemporaneous co-movements that underlie systematic risk. Our macroeconomic indicators are chosen to reflect shocks to aggregate supply and demand, providing a parsimonious model that incorporates the two fundamental determinants of aggregate risk. We find that firms’ earnings-based sensitivities (betas) to aggregate supply and demand shocks are negatively correlated and explain the cross-section of returns better than conventional “index” betas. The earnings-based sensitivities are correlated with firm characteristics employed in empirical asset pricing models and explain one quarter of the explanatory power of those characteristics.
Pandemics and People

The views expressed in this article do not necessarily reflect the views of the journal or of ASM. As someone who has been involved in discussions about biopreparedness for the better part of the 21st century, albeit for human-made threats, the level of unpreparedness we found ourselves in with
Erosion of Polyurethane Insulation

Detailed description of the test program in which erosion of the spray foam insulation used in the S-II stage of the Saturn-V Apollo launch vehicle was investigated. The behavior of the spray foam was investigated at the elevated temperature and static pressure appropriate to the S-II stage environment, but in the absence of the aerodynamic shear stress.
Federated Multi-Armed Bandits.

Federated multi-armed bandits (FMAB) is a new bandit paradigm that parallels the federated learning (FL) framework in supervised learning. It is inspired by practical applications in cognitive radio and recommender systems, and enjoys features that are analogous to FL. This paper proposes a general framework of FMAB and then studies two specific federated bandit models. We first study the approximate model where the heterogeneous local models are random realizations of the global model from an unknown distribution. This model introduces a new uncertainty of client sampling, as the global model may not be reliably learned even if the finite local models are perfectly known. Furthermore, this uncertainty cannot be quantified a priori without knowledge of the suboptimality gap. We solve the approximate model by proposing Federated Double UCB (Fed2-UCB), which constructs a novel “double UCB” principle accounting for uncertainties from both arm and client sampling. We show that gradually admitting new clients is critical in achieving an O(log(T)) regret while explicitly considering the communication loss. The exact model, where the global bandit model is the exact average of heterogeneous local models, is then studied as a special case. We show that, somewhat surprisingly, the order-optimal regret can be achieved independent of the number of clients with a careful choice of the update periodicity. Experiments using both synthetic and real-world datasets corroborate the theoretical analysis and demonstrate the effectiveness and efficiency of the proposed algorithms.
Towards Indirect Top-Down Road Transport Emissions Estimation

Road transportation is one of the largest sectors of greenhouse gas (GHG) emissions affecting climate change. Tackling climate change as a global community will require new capabilities to measure and inventory road transport emissions. However, the large scale and distributed nature of vehicle emissions make this sector especially challenging for existing inventory methods. In this work, we develop machine learning models that use satellite imagery to perform indirect top-down estimation of road transport emissions. Our initial experiments focus on the United States, where a bottom-up inventory was available for training our models. We achieved a mean absolute error (MAE) of 39.5 kg CO 2 of annual road transport emissions, calculated on a pixel-by-pixel (100 m2) basis in Sentinel-2 imagery. We also discuss key model assumptions and challenges that need to be addressed to develop models capable of generalizing to global geography. We believe this work is the first published approach for automated indirect top-down estimation of road transport sector emissions using visual imagery and represents a critical step towards scalable, global, near-real-time road transportation emissions inventories that are measured both independently and objectively.
Generative hypergraph clustering: from blockmodels to modularity

Hypergraphs are a natural modeling paradigm for networked systems with multiway interactions. A standard task in network analysis is the identification of closely related or densely interconnected nodes. We propose a probabilistic generative model of clustered hypergraphs with heterogeneous node degrees and edge sizes. Approximate maximum likelihood inference in this model leads to a clustering objective that generalizes the popular modularity objective for graphs. From this, we derive an inference algorithm that generalizes the Louvain graph community detection method, and a faster, specialized variant in which edges are expected to lie fully within clusters. Using synthetic and empirical data, we demonstrate that the specialized method is highly scalable and can detect clusters where graph-based methods fail. We also use our model to find interpretable higher-order structure in school contact networks, U.S. congressional bill cosponsorship and committees, product categories in copurchasing behavior, and hotel locations from web browsing sessions.
Cassini Maneuver Experience: Ending the Equinox Mission

The Cassini-Huygens spacecraft was launched in 1997 on a mission to observe Saturn and its many moons. After a seven-year interplanetary cruise, it entered a Saturnian orbit for a four-year Prime Mission in 2004 and began a two-year Equinox Mission in 2008. It has been approved for another seven-year mission, the Solstice Mission, starting in October 2010. This paper highlights significant maneuver activities performed from July 2009 to June 2010. We present results for the 45 maneuvers during this time. The successful navigation of the Cassini orbiter can be attributed in part to the accurate maneuver performance, which has greatly exceeded pre-launch expectations.
Analytical Design of a High Performance Stability and Control Augmentation System for a Hingeless Rotor Helicopter

A stability and control augmentation system (SCAS) was designed based on a set of comprehensive performance criteria. Linear optimal control theory was applied to determine appropriate feedback gains for the stability augmentation system (SAS). The helicopter was represented by six-degree-of-freedom rigid body equations of motion and constant factors were used as weightings for state and control variables. The ratio of these factors was employed as a parameter for SAS analysis and values of the feedback gains were selected on this basis to satisfy three of the performance criteria for full and partial state feedback systems. A least squares design method was then applied to determine control augmentation system (CAS) cross feed gains to satisfy the remaining seven performance criteria. The SCAS gains were then evaluated by nine degree-of-freedom equations which include flapping motion and conclusions drawn concerning the necessity of including the pitch/regressing and roll/regressing modes in SCAS analyses.
Money Growing on Trees: A Classroom Game about Payments for Ecosystem Services and Tropical Deforestation.

Payments for ecosystem services programs use a market-based approach to pursue environmental goals. While they are common policy tools, key concepts that can determine their efficacy are nuanced and hard to grasp. We present a new interactive game that explores the functioning and implications of payments for ecosystem services programs. Participants play the role of rural households in a developing country. They decide individually or as groups whether to enter into contracts to receive payment from the United Nations REDD+ program to refrain from harvesting from a local forest. The game explores topics including: payments for ecosystem services programs; climate change; tropical deforestation; cost-effectiveness; additionality; contract fraud and enforcement; and community resource management. We provide customizable materials, a detailed reading list, and prompts for discussion.
Realization, Internal Stability, and Controller Synthesis

We have witnessed the emergence of several controller parameterizations and the corresponding synthesis methods, including Youla, system level, input-output, and many other new proposals. Meanwhile, under the same synthesis method, there are multiple realizations to adopt. Different synthesis methods/realizations target different plants/scenarios. Except for some case-by-case studies, we don't currently have a unified framework to understand their relationships. To address the issue, we show that existing controller synthesis methods and realization proposals are all special cases of a simple lemma, the realization-stability lemma. The lemma leads to easier equivalence proofs among existing methods and enables the formulation of a general controller synthesis problem, which provides a unified foundation for controller synthesis and realization derivation.
Becoming Good at AI for Good

AI for good (AI4G) projects involve developing and applying artificial intelligence (AI) based solutions to further goals in areas such as sustainability, health, humanitarian aid, and social justice. Developing and deploying such solutions must be done in collaboration with partners who are experts in the domain in question and who already have experience in making progress towards such goals. Based on our experiences, we detail the different aspects of this type of collaboration broken down into four high-level categories: communication, data, modeling, and impact, and distill eleven takeaways to guide such projects in the future. We briefly describe two case studies to illustrate how some of these takeaways were applied in practice during our past collaborations.
Single-photon vibrometry

We propose and demonstrate a single-photon sensitive technique for optical vibrometry. It uses high speed photon counting to sample the modulated backscattering from a vibrating target. Designed for remote vibration sensing with ultralow photon flux, we show that this technique can detect small displacements down to 110 nm and resolve vibration frequencies from DC up to several kilohertz, with ≤0.01 detected photons per pulse. This single-photon sensitive optical vibrometry may find important applications in acousto-optic sensing and imaging, especially in photon-starved environments.
Compactness of Composition Operators on the Bergman Spaces of Convex Domains and Analytic Discs

We study the compactness of composition operators on the Bergman spaces of certain bounded convex domains in ℂn with non-trivial analytic discs contained in the boundary. As a consequence we characterize that compactness of the composition operator with a continuous symbol (up to the closure) on the Bergman space of the polydisc.
Intelligent Network Layer for Cyber-Physical Systems Security

Cyber-Physical System (CPS) has made a tremendous progress in recent years and also disrupted many technical fields such as smart industries, smart health, smart transportation etc. to flourish the nations economy. However, CPS Security is still one of the concerns for wide adoption owing to high number of devices connecting to the internet and the traditional security solutions may not be suitable to protect the advanced, application specific attacks. This paper presents a programmable device network layer architecture to combat attacks and efficient network monitoring in heterogeneous environment CPS applications. We leverage Industrial control systems (ICS) to discuss the existing issues, highlighting the importance of advanced network layer for CPS. The programmable data plane language (P4) is introduced to detect well known HELLO Flood attack with minimal efforts in the network level and also used to featuring the potential solutions for security.
Understanding the Representation and Representativeness of Age in AI Data Sets

A diverse representation of different demographic groups in AI training data sets is important in ensuring that the models will work for a large range of users. To this end, recent efforts in AI fairness and inclusion have advocated for creating AI data sets that are well-balanced across race, gender, socioeconomic status, and disability status. In this paper, we contribute to this line of work by focusing on the representation of age by asking whether older adults are represented proportionally to the population at large in AI data sets. We examine publicly-available information about 92 face data sets to understand how they codify age as a case study to investigate how the subjects' ages are recorded and whether older generations are represented. We find that older adults are very under-represented; five data sets in the study that explicitly documented the closed age intervals of their subjects included older adults (defined as older than 65 years), while only one included oldest-old adults (defined as older than 85 years). Additionally, we find that only 24 of the data sets include any age-related information in their documentation or metadata, and that there is no consistent method followed across these data sets to collect and record the subjects' ages. We recognize the unique difficulties in creating representative data sets in terms of age, but raise it as an important dimension that researchers and engineers interested in inclusive AI should consider.
Prevalence of mesothelin expression in peritoneal disease from colorectal and appendiceal cancers.

BACKGROUND Mesothelin is a cell surface glycoprotein overexpressed in 28%-58% of colorectal cancer (CRC). We hypothesized that CRC mesothelin expression contributes to peritoneal spread and that it is selectively overexpressed in those with peritoneal metastasis versus distant metastasis. METHODS This case-controlled study involved mesothelin immunohistochemistry staining of tumor specimens from patients with metastatic CRC/appendiceal cancers between 2017 and 2019. Staining reactivity was graded from trace to 4+ (low ≤1+; high >1+). Staining patterns were characterized on global (focal/patchy/diffuse) and cellular (apical/cytoplasmic) levels. Immunostaining of normal mesothelial cells served as internal control. RESULTS Thirty-one patients were identified: 11 peritoneal (study) and 20 distant metastasis (control). The control group did not include appendiceal cancers. The study group had greater proportion of high staining reactivity (55% vs. 5%; odds ratio [OR] = 20.4, 95% confidence interval [CI] 1.96-211.8). The study group had more diffuse (36% vs. 0%; OR = 22.2, 95% CI 1.1-465.3) and cytoplasmic staining patterns (73% vs. 28%; OR = 6.9, 95% CI 1.3-37.2). CONCLUSION Mesothelin expression is higher in CRC/appendiceal cancers with peritoneal metastasis than those with distant metastasis. Immunohistochemistry staining patterns suggestive of propensity towards peritoneal metastasis include diffuse and cytoplasmic staining. Mesothelin may be a potential target for novel treatments of CRC/appendiceal carcinoma with peritoneal involvement.
Lyman-alpha filter prototype to enable astronomical photometry in the Lyman ultraviolet

Observations of astronomical objects in the far ultraviolet (FUV wavelengths span 900-1800 A) from earth's orbit has been impeded due to bright Lyman- geocoronal emission. The Johns Hopkins Rocket Group is developing a hydrogen absorption cell that would act as a narrow band Lyman- rejection filter to enable space-based photometric observation in bandpasses that span over the Lyman ultraviolet region shortward of the geocoronal line. While this technology has been applied to various planetary missions with single element photomultiplier detectors it has yet to be used on near earth orbiting satellites with a multi-element detector. We are working to develop a cell that could be easily incorporated into future Lyman ultraviolet missions. The prototype cell is a low-pressure (~few torr) chamber sealed between a pair of MgF2 windows allowing transmission down to 1150 A. It is filled with molecular hydrogen that is converted to its neutral atomic form in the presence of a hot tungsten lament, which allows for the absorption of the Lyman- photons. Molecular hydrogen is stored in a fully saturated non-evaporable getter module (St707TM), which allows the cell pressure to be increased under a modest application of heat (a 20 degree rise from room temperature has produced a rise in pressure from 0.6 to 10 torr). Testing is now underway using a vacuum ultraviolet monochromator to characterize the cell optical depth to Lyman- photons as functions of pressure and tungsten filament current. We will present these results, along with a discussion of enabled science in broadband photometric applications.
Radon Cumulative Distribution Transform Subspace Modeling for Image Classification

We present a new supervised image classification method applicable to a broad class of image deformation models. The method makes use of the previously described Radon Cumulative Distribution Transform (R-CDT) for image data, whose mathematical properties are exploited to express the image data in a form that is more suitable for machine learning. While certain operations such as translation, scaling, and higher-order transformations are challenging to model in native image space, we show the R-CDT can capture some of these variations and thus render the associated image classification problems easier to solve. The method—utilizing a nearest-subspace algorithm in the R-CDT space—is simple to implement, non-iterative, has no hyper-parameters to tune, is computationally efficient, label efficient, and provides competitive accuracies to state-of-the-art neural networks for many types of classification problems. In addition to the test accuracy performances, we show improvements (with respect to neural network-based methods) in terms of computational efficiency (it can be implemented without the use of GPUs), number of training samples needed for training, as well as out-of-distribution generalization. The Python code for reproducing our results is available at Shifat-E-Rabbi et al. (Python code implementing the Radon cumulative distribution transform subspace model for image classification. https://github.com/rohdelab/rcdt_ns_classifier ).
Understanding Medical Conversations: Rich Transcription, Confidence Scores & Information Extraction

In this paper, we describe novel components for extracting clinically relevant information from medical conversations which will be available as Google APIs. We describe a transformer-based Recurrent Neural Network Transducer (RNN-T) model tailored for long-form audio, which can produce rich transcriptions including speaker segmentation, speaker role labeling, punctuation and capitalization. On a representative test set, we compare performance of RNN-T models with different encoders, units and streaming constraints. Our transformer-based streaming model performs at about 20% WER on the ASR task, 6% WDER on the diarization task, 43% SER on periods, 52% SER on commas, 43% SER on question marks and 30% SER on capitalization. Our recognizer is paired with a confidence model that utilizes both acoustic and lexical features from the recognizer. The model performs at about 0.37 NCE. Finally, we describe a RNN-T based tagging model. The performance of the model depends on the ontologies, with F-scores of 0.90 for medications, 0.76 for symptoms, 0.75 for conditions, 0.76 for diagnosis, and 0.61 for treatments. While there is still room for improvement, our results suggest that these models are sufficiently accurate for practical applications.
Complexity of randomized algorithms for underdamped Langevin dynamics

We establish an information complexity lower bound of randomized algorithms for simulating underdamped Langevin dynamics. More specifically, we prove that the worst $L^2$ strong error is of order $\Omega(\sqrt{d}\, N^{-3/2})$, for solving a family of $d$-dimensional underdamped Langevin dynamics, by any randomized algorithm with only $N$ queries to $\nabla U$, the driving Brownian motion and its weighted integration, respectively. The lower bound we establish matches the upper bound for the randomized midpoint method recently proposed by Shen and Lee [NIPS 2019], in terms of both parameters $N$ and $d$.
An Analysis Of Entire Space Multi-Task Models For Post-Click Conversion Prediction

Industrial recommender systems are frequently tasked with approximating probabilities for multiple, often closely related, user actions. For example, predicting if a user will click on an advertisement and if they will then purchase the advertised product. The conceptual similarity between these tasks has promoted the use of multi-task learning: a class of algorithms that aim to bring positive inductive transfer from related tasks. Here, we empirically evaluate multi-task learning approaches with neural networks for an online advertising task. Specifically, we consider approximating the probability of post-click conversion events (installs) (CVR) for mobile app advertising on a large-scale advertising platform, using the related click events (CTR) as an auxiliary task. We use an ablation approach to systematically study recent approaches that incorporate both multitask learning and “entire space modeling” which train the CVR on all logged examples rather than learning a conditional likelihood of conversion given clicked. Based on these results we show that several different approaches result in similar levels of positive transfer from the data-abundant CTR task to the CVR task and offer some insight into how the multi-task design choices address the two primary problems affecting the CVR task: data sparsity and data bias. Our findings add to the growing body of evidence suggesting that standard multi-task learning is a sensible approach to modelling related events in real-world large-scale applications and suggest the specific multitask approach can be guided by ease of implementation in an existing system.
Abstract sequential task control is facilitated by practice and embedded motor sequences.

Everyday task sequences, such as cooking, contain overarching goals (completing the meal), subgoals (prepare vegetables), and motor actions (chopping). Such tasks generally are considered hierarchical because superordinate levels (e.g., goals) affect performance at subordinate levels (e.g., subgoals and motor actions). However, there is debate as to whether this hierarchy is "strict" with unidirectional, top-down influences, and it is unknown if and how practice affects performance at the superordinate levels. To investigate these questions, we manipulated practice with sequences at the goal and motor action levels using an abstract, or nonmotor, task sequence paradigm (Desrochers et al., 2015; Schneider & Logan, 2006). In three experiments, participants performed memorized abstract task sequences composed of simple tasks (e.g., color/shape judgements), where some contained embedded motor response sequences. We found that practice facilitated performance and reduced control costs for abstract task sequences and subordinate tasks. The interrelation was different between the hierarchical levels, demonstrating a strict relationship between abstract task sequence goals and subgoals and a nonstrict relationship between subgoal and motor response levels. Under some conditions, the motor response level influenced the abstract task sequence level in a nonstrict manner. Further, manipulating the presence or absence of a motor sequence after learning indicated that these effects were not the result of an integrated representation produced by practice. These experiments provide evidence for a mixed hierarchical model of task sequences and insight into the distinct roles of practice and motor processing in efficiently executing task sequences in daily life. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Sim2Sim Evaluation of a Novel Data-Efficient Differentiable Physics Engine for Tensegrity Robots

Learning policies in simulation is promising for reducing human effort when training robot controllers. This is especially true for soft robots that are more adaptive and safe but also more difficult to accurately model and control. The sim2real gap is the main barrier to successfully transfer policies from simulation to a real robot. System identification can be applied to reduce this gap but traditional identification methods require a lot of manual tuning. Data-driven alternatives can tune dynamical models directly from data but are often data hungry, which also incorporates human effort in collecting data. This work proposes a data-driven, end-to-end differentiable simulator focused on the exciting but challenging domain of tensegrity robots. To the best of the authors' knowledge, this is the first differentiable physics engine for tensegrity robots that supports cable, contact, and actuation modeling. The aim is to develop a reasonably simplified, data-driven simulation, which can learn approximate dynamics with limited ground truth data. The dynamics must be accurate enough to generate policies that can be transferred back to the ground-truth system. As a first step in this direction, the current work demonstrates sim2sim transfer, where the unknown physical model of MuJoCo acts as a ground truth system. Two different tensegrity robots are used for evaluation and learning of locomotion policies, a 6-bar and a 3-bar tensegrity. The results indicate that only 0.25\% of ground truth data are needed to train a policy that works on the ground truth system when the differentiable engine is used for training against training the policy directly on the ground truth system.
Characteristic polynomials of random banded Hessenberg matrices and Hermite-Pad\'e approximation

We consider a class of random banded Hessenberg matrices with independent entries having identical distributions along diagonals. The distributions may be different for entries belonging to different diagonals. For a sequence of $$n\times n$$ matrices in the class considered, we investigate the asymptotic behavior of their empirical spectral distribution as n tends to infinity.
Non-fillable Augmentations of Twist Knots

We establish new examples of augmentations of Legendrian twist knots that cannot be induced by orientable Lagrangian fillings. To do so, we use a version of the Seidel-Ekholm-Dimitroglou Rizell isomorphism with local coefficients to show that any Lagrangian filling point in the augmentation variety of a Legendrian knot must lie in the injective image of an algebraic torus with dimension equal to the first Betti number of the filling. This is a Floer-theoretic version of a result from microlocal sheaf theory. For the augmentations in question, we show that no such algebraic torus can exist.
A Finite Element Method for Two-Phase Flow with Material Viscous Interface

This paper studies a model of two-phase flow with an immersed material viscous interface and a finite element method for numerical solution of the resulting system of PDEs. The interaction between the bulk and surface media is characterized by no-penetration and slip with friction interface conditions. The system is shown to be dissipative and a model stationary problem is proved to be well-posed. The finite element method applied in this paper belongs to a family of unfitted discretizations. The performance of the method when model and discretization parameters vary is assessed. Moreover, an iterative procedure based on the splitting of the system into bulk and surface problems is introduced and studied numerically.
Zero-Shot Dialogue Disentanglement by Self-Supervised Entangled Response Selection

Dialogue disentanglement aims to group utterances in a long and multi-participant dialogue into threads. This is useful for discourse analysis and downstream applications such as dialogue response selection, where it can be the first step to construct a clean context/response set. Unfortunately, labeling all reply-to links takes quadratic effort w.r.t the number of utterances: an annotator must check all preceding utterances to identify the one to which the current utterance is a reply. In this paper, we are the first to propose a zero-shot dialogue disentanglement solution. Firstly, we train a model on a multi-participant response selection dataset harvested from the web which is not annotated; we then apply the trained model to perform zero-shot dialogue disentanglement. Without any labeled data, our model can achieve a cluster F1 score of 25. We also fine-tune the model using various amounts of labeled data. Experiments show that with only 10% of the data, we achieve nearly the same performance of using the full dataset.
Luminous solar neutrinos. I. Dipole portals

Solar neutrinos upscattering inside the Earth can source unstable particles that subsequently decay inside large volume detectors (e.g., neutrino experiments). Contrary to naive expectations, when the decay length is much shorter than the radius of the Earth (rather than the length of the detector), the event rate is independent of the decay length. In this paper, we study a neutrino-dipole portal (transition dipole operator) and show that existing data from Borexino and Super-Kamiokande probes previously untouched parameter space in the 0.5--20 MeV regime, complementing recent cosmological and supernova bounds. We discuss similarities and differences with luminous dark matter and comment on future prospects for analogous signals stemming from atmospheric neutrinos. A companion paper explores an analogous mass-mixing portal.
Exploiting Chain Rule and Bayes' Theorem to Compare Probability Distributions

To measure the difference between two probability distributions, referred to as the source and target, respectively, we exploit both the chain rule and Bayes' theorem to construct conditional transport (CT), which is constituted by both a forward component and a backward one. The forward CT is the expected cost of moving a source data point to a target one, with their joint distribution defined by the product of the source probability density function (PDF) and a source-dependent conditional distribution, which is related to the target PDF via Bayes' theorem. The backward CT is defined by reversing the direction. The CT cost can be approximated by replacing the source and target PDFs with their discrete empirical distributions supported on mini-batches, making it amenable to implicit distributions and stochastic gradient descent-based optimization. When applied to train a generative model, CT is shown to strike a good balance between mode-covering and mode-seeking behaviors and strongly resist mode collapse. On a wide variety of benchmark datasets for generative modeling, substituting the default statistical distance of an existing generative adversarial network with CT is shown to consistently improve the performance. PyTorch-style code is provided.
Tuza's conjecture for random graphs

A celebrated conjecture of Zs. Tuza says that in any (finite) graph, the minimum size of a cover of triangles by edges is at most twice the maximum size of a set of edge-disjoint triangles. Resolving a recent question of Bennett, Dudek, and Zerbib, we show that this is true for random graphs; more precisely: \[ \mbox{for any $p=p(n)$, $\mathbb P(\mbox{$G_{n,p}$ satisfies Tuza's Conjecture})\rightarrow 1 $ (as $n\rightarrow\infty$).} \]
Critical Assemblies: Dragon Burst Assembly and Solution Assemblies

This work reviewed the historical literature associated with the Dragon experiment and Water Boiler reactors operated at Los Alamos during the Manhattan Project. Frisch's invited talk given at the Fast Burst Reactor Conference held the University of New Mexico in Albuquerque, NM in 1969 is quoted. From the literature review, basic models for the Dragon experiment and for a Water Boiler type assembly (aqueous homogeneous reactor) were created that can be used for conducting multi-physics simulations for criticality excursion studies. This methodology utilizes the coupled neutronic-hydrodynamic method to perform a time-dependent dynamic simulation of a criticality excursion. MCNP was utilized to calculate important nuclear kinetic parameters that were incorporated into the models. Simulation results compared reasonably well with historic data.
They Teach Us How to Teach Them: Teacher Preparation for the 21st Century

This article describes an English language arts teacher preparation perspective that considers middle school students as part of the teacher educator team. Based on a recognized gap in the literature about students as powerful partners, the authors undertook a 3-year study to explore the question, “What do preservice middle school teachers learn when middle school students assume the role of pedagogical experts?” Using the ever-popular young adult novel, The Outsiders, as a nexus of literature study and an integration of technology and music, the authors created The Outsiders Project. They collected extensive qualitative data, including detailed field notes, preservice teachers’ reflections, and digital videos, across the 3 years to analyze preservice teachers’ views about the power of middle school students as teacher educators.
Organic solids: Traditional semiconductors or fermi glasses?

A brief review is presented of the concepts required to describe the dynamics of charge injected into “semiconducting” Van der Waals organic solids. Analysis of experimental photoemission spectra reveals that photoinjected holes behave like radical cations localized on the individual molecular moities which comprise the solid. Examination of measured drift mobilities, especially from naphthalene, suggest that transport in extended energy band states may occur at low temperature (T≲100 K) but that phonon-assisted hopping between localized states occurs at higher temperatures.
Microdialysis crystallization chamber: Design of a solvent exchangeable microsialysis crystallization system for manual or automatic control of protein crystal growth at microgravity

Abstract Several microdialysis crystallizing chambers (MDCC) are being developed to be used in microgravity (μG) situations where crystallization experiments could be performed on unmanned space flights of long duration. Disturbance of the crystal during growth and during recovery is to kept to a minimum.
Polarization Effects in Electron-Atom Collisions

The three talks upon which this article is based are concerned with certain aspects of the electron-atom collision problem, particularly those which concern angular momentum orientation in both selection and analysis of collision partners in electron-atom collisions. I will not try to make this a comprehensive review of the subject. There are by now a lajge number of review articles and portions of books that do this; some of these are listed in the references at appropriate places. Rather, I thought that this might be a good opportunity to give some interesting background, early history, and some pedagogic material and, of course, some prejudicial viewpoints from the work of myself and colleagues at New York University.