cssi/SciSciGPT-SciSciNet huggingface dataset:
Effectively Unbiased FID and Inception Score and Where to Find Them

This paper shows that two commonly used evaluation metrics for generative models, the Frechet Inception Distance (FID) and the Inception Score (IS), are biased -- the expected value of the score computed for a finite sample set is not the true value of the score. Worse, the paper shows that the bias term depends on the particular model being evaluated, so model A may get a better score than model B simply because model A's bias term is smaller. This effect cannot be fixed by evaluating at a fixed number of samples. This means all comparisons using FID or IS as currently computed are unreliable. We then show how to extrapolate the score to obtain an effectively bias-free estimate of scores computed with an infinite number of samples, which we term FID Infinity and IS Infinity. In turn, this effectively bias-free estimate requires good estimates of scores with a finite number of samples. We show that using Quasi-Monte Carlo integration notably improves estimates of FID and IS for finite sample sets. Our extrapolated scores are simple, drop-in replacements for the finite sample scores. Additionally, we show that using low discrepancy sequence in GAN training offers small improvements in the resulting generator.
Skyrmion-skyrmion interaction in a magnetic film

Interaction of two skyrmions stabilized by the ferromagnetic exchange, Dzyaloshinskii-Moriya interaction (DMI), and external magnetic field has been studied numerically on a 2D lattice of size large compared to the separation, d, between the skyrmions. We show that two skyrmions of the same chirality (determined by the symmetry of the crystal) repel. In accordance with earlier analytical results, their long-range pair interaction falls out with the separation as exp(-d/δ H ), where δ H is the magnetic screening length, independent of the DMI. The prefactor in this expression depends on the DMI that drives the repulsion. The latter results in the spiral motion of the two skyrmions around each other, with the separation between them growing logarithmically with time. When two skyrmions of the total topological charge Q = 2 are pushed close to each other, the discreteness of the atomic lattice makes them collapse into one skyrmion of charge Q = 1 below a critical separation. Experiment is proposed that would allow one to measure the interaction between two skyrmions by holding them in positions with two magnetic tips. Our findings should be of value for designing topologically protected magnetic memory based upon skyrmions.
Good-Enough Compositional Data Augmentation

We propose a simple data augmentation protocol aimed at providing a compositional inductive bias in conditional and unconditional sequence models. Under this protocol, synthetic training examples are constructed by taking real training examples and replacing (possibly discontinuous) fragments with other fragments that appear in at least one similar environment. The protocol is model-agnostic and useful for a variety of tasks. Applied to neural sequence-to-sequence models, it reduces error rate by as much as 87% on diagnostic tasks from the SCAN dataset and 16% on a semantic parsing task. Applied to n-gram language models, it reduces perplexity by roughly 1% on small corpora in several languages.
Robust Encodings: A Framework for Combating Adversarial Typos.

Despite excellent performance on many tasks, NLP systems are easily fooled by small adversarial perturbations of inputs. Existing procedures to defend against such perturbations are either (i) heuristic in nature and susceptible to stronger attacks or (ii) provide guaranteed robustness to worst-case attacks, but are incompatible with state-of-the-art models like BERT. In this work, we introduce robust encodings (RobEn): a simple framework that confers guaranteed robustness, without making compromises on model architecture. The core component of RobEn is an encoding function, which maps sentences to a smaller, discrete space of encodings. Systems using these encodings as a bottleneck confer guaranteed robustness with standard training, and the same encodings can be used across multiple tasks. We identify two desiderata to construct robust encoding functions: perturbations of a sentence should map to a small set of encodings (stability), and models using encodings should still perform well (fidelity). We instantiate RobEn to defend against a large family of adversarial typos. Across six tasks from GLUE, our instantiation of RobEn paired with BERT achieves an average robust accuracy of 71.3% against all adversarial typos in the family considered, while previous work using a typo-corrector achieves only 35.3% accuracy against a simple greedy attack.
TYPICAL-MEDIUM THEORY OF MOTT–ANDERSON LOCALIZATION

The Mott and the Anderson routes to localization have long been recognized as the two basic processes that can drive the metal–insulator transition (MIT). Theories separately describing each of these mechanisms were discussed long ago, but an accepted approach that can include both has remained elusive. The lack of any obvious static symmetry distinguishing the metal from the insulator poses another fundamental problem, since an appropriate static order parameter cannot be easily found. More recent work, however, has revisited the original arguments of Anderson and Mott, which stressed that the key diference between the metal end the insulator lies in the dynamics of the electron. This physical picture has suggested that the "typical" (geometrically averaged) escape rate from a given lattice site should be regarded as the proper dynamical order parameter for the MIT, one that can naturally describe both the Anderson and the Mott mechanism for localization. This article provides an overview of the recent results obtained from the corresponding Typical-Medium Theory, which provided new insight into the the two-fluid character of the Mott–Anderson transition.
How To Backdoor Federated Learning.

Federated learning enables thousands of participants to construct a deep learning model without sharing their private training data with each other. For example, multiple smartphones can jointly train a next-word predictor for keyboards without revealing what individual users type. We demonstrate that any participant in federated learning can introduce hidden backdoor functionality into the joint global model, e.g., to ensure that an image classifier assigns an attacker-chosen label to images with certain features, or that a word predictor completes certain sentences with an attacker-chosen word. We design and evaluate a new model-poisoning methodology based on model replacement. An attacker selected in a single round of federated learning can cause the global model to immediately reach 100% accuracy on the backdoor task. We evaluate the attack under different assumptions for the standard federated-learning tasks and show that it greatly outperforms data poisoning. Our generic constrain-and-scale technique also evades anomaly detection-based defenses by incorporating the evasion into the attacker's loss function during training.
Graph Coloring via Degeneracy in Streaming and Other Space-Conscious Models

We study the problem of coloring a given graph using a small number of colors in several well-established models of computation for big data. These include the data streaming model, the general graph query model, the massively parallel communication (MPC) model, and the CONGESTED-CLIQUE and the LOCAL models of distributed computation. On the one hand, we give algorithms with sublinear complexity, for the appropriate notion of complexity in each of these models. Our algorithms color a graph G using κ(G)⋅(1+o(1)) colors, where κ(G) is the degeneracy of G: this parameter is closely related to the arboricity α(G). As a function of κ(G) alone, our results are close to best possible, since the optimal number of colors is κ(G)+1. For several classes of graphs, including real-world "big graphs," our results improve upon the number of colors used by the various (Δ(G)+1)-coloring algorithms known for these models, where Δ(G) is the maximum degree in G, since Δ(G) ⩾ κ(G) and can in fact be arbitrarily larger than κ(G). On the other hand, we establish certain lower bounds indicating that sublinear algorithms probably cannot go much further. In particular, we prove that any randomized coloring algorithm that uses at most κ(G)+O(1) colors would require Ω(n²) storage in the one pass streaming model, and Ω(n²) many queries in the general graph query model, where n is the number of vertices in the graph. These lower bounds hold even when the value of κ(G) is known in advance; at the same time, our upper bounds do not require κ(G) to be given in advance.
Forward backward doubly stochastic differential equations and the optimal filtering of diffusion processes

The connection between forward backward doubly stochastic differential equations and the optimal filtering problem is established without using the Zakai's equation. The solutions of forward backward doubly stochastic differential equations are expressed in terms of conditional law of a partially observed Markov diffusion process. It then follows that the adjoint time-inverse forward backward doubly stochastic differential equations governs the evolution of the unnormalized filtering density in the optimal filtering problem.
Late ESCRT Machinery Mediates The Recycling And Rescue of Invariant Surface Glycoprotein 65 in Trypanosoma brucei

The Endosomal Sorting Complex Required for Transport machinery consists of four protein complexes (ESCRT 0-IV) and the post ESCRT ATPase Vps4. ESCRT mediates cargo delivery for lysosomal degradation via formation of multivesicular bodies. Trypanosoma brucei contains orthologues of ESCRT I-III and Vps4. Trypanosomes also have an ubiquitinylated invariant surface glycoprotein (ISG65) that is delivered to the lysosome by ESCRT, however, we previously implicated TbVps4 in rescue and recycling of ISG65. Here we use conditional silencing to investigate the role of TbVps24, a phosphoinositide-binding ESCRT III component, on protein trafficking. TbVps24 localises to the TbRab7+ late endosome, and binds PI(3,5)P2 , the product of the TbFab1 kinase, both of which also localise to late endosomes. TbVps24 silencing is lethal, and negatively affects biosynthetic trafficking of the lysosomal markers p67 and TbCathepsin L. However, the major phenotype of silencing is accelerated degradation and depletion of the surface pool of ISG65. Thus, TbVps24 silencing phenocopies that of TbVps4 in regard to ISG65 trafficking. This presents a paradox since we have previously found that depletion of TbFab1 completely blocks ISG65 turnover. We propose a model in which late ESCRT components operate at two sites, one PI(3,5)P2 -dependent (degradation) and one PI(3,5)P2 -independent (recycling), to regulate ISG65 homeostasis.
Dynamic Power Allocation and Virtual Cell Formation for Throughput-Optimal Vehicular Edge Networks in Highway Transportation

This paper investigates highly mobile vehicular networks from users' perspectives in highway transportation. Particularly, a centralized software-defined architecture is introduced in which centralized resources can be assigned, programmed, and controlled using the anchor nodes (ANs) of the edge servers. Unlike the legacy networks, where a typical user is served from only one access point (AP), in the proposed system model, a vehicle user is served from multiple APs simultaneously. While this increases the reliability and the spectral efficiency of the assisted users, it also necessitates an accurate power allocation in all transmission time slots. As such, a joint user association and power allocation problem is formulated to achieve enhanced reliability and weighted user sum rate. However, the formulated problem is a complex combinatorial problem, remarkably hard to solve. Therefore, fine-grained machine learning algorithms are used to efficiently optimize joint user associations and power allocations of the APs in a highly mobile vehicular network. Furthermore, a distributed single-agent reinforcement learning algorithm, namely SARL-MARL, is proposed which obtains nearly identical genie-aided optimal solutions within a nominal number of training episodes than the baseline solution. Simulation results validate that our solution outperforms existing schemes and can attain genie-aided optimal performances.
Continuous Schauder frames for Banach spaces

We introduce the notion of a continuous Schauder frame for a Banach space. This is both a generalization of continuous frames for Hilbert spaces and a generalization of unconditional Schauder frames for Banach spaces. Furthermore, we generalize the properties shrinking and boundedly complete to the continuous Schauder frame setting, and prove that many of the fundamental James theorems still hold in this general context.
Cross-App Interference Threats in Smart Homes: Categorization, Detection and Handling

Internet of Thing platforms prosper home automation applications (apps). Prior research concerns intra-app security. Our work reveals that automation apps, even secured individually, still cause a family of threats when they interplay, termed as Cross-App Interference (CAI) threats. We systematically categorize such threats and encode them using satisfiability modulo theories (SMT). We present HomeGuard, a system for detecting and handling CAI threats in real deployments. A symbolic executor is built to extract rule semantics, and instrumentation is utilized to capture configuration during app installation. Rules and configuration are checked against SMT models, the solutions of which indicate the existence of corresponding CAI threats. We further combine app functionalities, device attributes and CAI types to label the risk level of CAI instances. In our evaluation, HomeGuard discovers 663 CAI instances from 146 SmartThings market apps, imposing minor latency upon app installation and no runtime overhead.
A Successive-Elimination Approach to Adaptive Robotic Source Seeking

In this article, we study an adaptive source seeking problem, in which a mobile robot must identify the strongest emitter(s) of a signal in an environment with background emissions. Background signals may be highly heterogeneous and can mislead algorithms that are based on receding horizon control. We propose $\mathtt {AdaSearch}$ , a general algorithm for adaptive source seeking in the face of heterogeneous background noise. $\mathtt {AdaSearch}$ combines global trajectory planning with principled confidence intervals in order to concentrate measurements in promising regions while guaranteeing sufficient coverage of the entire area. Theoretical analysis shows that $\mathtt {AdaSearch}$ confers gains over a uniform sampling strategy when the distribution of background signals is highly variable. Simulation experiments demonstrate that when applied to the problem of radioactive source-seeking, $\mathtt {AdaSearch}$ outperforms both uniform sampling and a receding time horizon information-maximization approach based on the current literature. We also demonstrate $\mathtt {AdaSearch}$ in hardware, providing further evidence of its potential for real-time implementation.
Fluorescence-detected Fourier transform electronic spectroscopy by phase-tagged photon counting.

Fluorescence-detected Fourier transform (FT) spectroscopy is a technique in which the relative paths of an optical interferometer are controlled to excite a material sample, and the ensuing fluorescence is detected as a function of the interferometer path delay and relative phase. A common approach to enhance the signal-to-noise ratio in these experiments is to apply a continuous phase sweep to the relative optical path, and to detect the resulting modulated fluorescence using a phase-sensitive lock-in amplifier. In many important situations, the fluorescence signal is too weak to be measured using a lock-in amplifier, so that photon counting techniques are preferred. Here we introduce an approach to low-signal fluorescence-detected FT spectroscopy, in which individual photon counts are assigned to a modulated interferometer phase (‘phase-tagged photon counting,’ or PTPC), and the resulting data are processed to construct optical spectra. We studied the fluorescence signals of a molecular sample excited resonantly by a pulsed coherent laser over a range of photon flux and visibility levels. We compare the performance of PTPC to standard lock-in detection methods and establish the range of signal parameters over which meaningful measurements can be carried out. We find that PTPC generally outperforms the lock-in detection method, with the dominant source of measurement uncertainty being associated with the statistics of the finite number of samples of the photon detection rate.
Size-dependent patterns of cell proliferation and migration in freely-expanding epithelia

Cells do not exist in isolation. Instead, they form tissues, where individual cells make contact with their neighbors and form microscopic ‘architectures’. Epithelia are a type of tissue where cells are arranged in flat sheets, and are found in organs such as the lining of the kidney or the skin. Tissues need to grow, especially early in life. If tissues are damaged – for example, if the skin is cut or grazed – cells also need to divide (to create new healthy cells) and move as a group (to close the wound). Such coordinated motions result in cells exhibiting distinct group behaviors, similar to those observed within crowds of people or schools of fish. If coordination breaks down, problems can happen such as uncoordinated tissue growth seen in cancer. However, how cell movements are coordinated is still not fully understand. For example, researchers know that cells’ positions within a group can determine how they behave, meaning that even the same type of cell could behave differently at the edge or center of a tissue. This suggests that the initial size and shape of a tissue should influence its subsequent growth and behavior; however, the nature of this influence is still largely unknown. Heinrich et al. therefore wanted to determine the differences in the way larger and smaller tissues grow. Microscope imaging was used to track the growth of circular, artificial tissues made from single-layered sheets of dog kidney cells grown in the laboratory. Comparing how quickly the tissues expanded revealed that the area of tissue circles that started out smaller increased at a much faster rate than that of tissue circles that were larger to begin with. This turned out to be because the edges of the tissues grew at a constant speed, independent of their initial size or shape, but circles with a smaller area have a larger proportion of cells on their edges. The motions of the cells at the center of the tissues had no effect on how the edges of the tissue grew. A final observation was that the way tissues of a given size behaved depended on whether they had grown to be that size, or they started off that big. These results shed light on how groups of cells interact in growing tissues. In the future, this information could be used to predict how different tissues grow over time, potentially helping scientists engineer better artificial tissues or organs for transplantation.
GPT-GNN: Generative Pre-Training of Graph Neural Networks

Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data. However, training GNNs requires abundant task-specific labeled data, which is often arduously expensive to obtain. One effective way to reduce the labeling effort is to pre-train an expressive GNN model on unlabelled data with self-supervision and then transfer the learned model to downstream tasks with only a few labels. In this paper, we present the GPT-GNN framework to initialize GNNs by generative pre-training. GPT-GNN introduces a self-supervised attributed graph generation task to pre-train a GNN so that it can capture the structural and semantic properties of the graph. We factorize the likelihood of graph generation into two components: 1) attribute generation and 2) edge generation. By modeling both components, GPT-GNN captures the inherent dependency between node attributes and graph structure during the generative process. Comprehensive experiments on the billion-scale open academic graph and Amazon recommendation data demonstrate that GPT-GNN significantly outperforms state-of-the-art GNN models without pre-training by up to 9.1% across various downstream tasks?
Multi-Product Dynamic Pricing in High-Dimensions with Heterogeneous Price Sensitivity

We consider the problem of multi-product dynamic pricing, in a contextual setting, for a seller of differentiated products. In this environment, the customers arrive over time and products are described by high-dimensional feature vectors. Each customer chooses a product according to the widely used Multinomial Logit (MNL) choice model and her utility depends on the product features as well as the prices offered. The seller a-priori does not know the parameters of the choice model but can learn them through interactions with customers. The seller's goal is to design a pricing policy that maximizes her cumulative revenue. This model is motivated by online marketplaces such as Airbnb platform and online advertising. We measure the performance of a pricing policy in terms of regret, which is the expected revenue loss with respect to a clairvoyant policy that knows the parameters of the choice model in advance and always sets the revenue-maximizing prices. We propose a pricing policy, named M3P, that achieves a T-period regret of O(log(Td)($\sqrt T $ +d log(T))) under heterogeneous price sensitivity for products with features of dimension d. We also use tools from information theory to prove that no policy can achieve worst-case T-regret better than Ω($\sqrt T $).
Intensity Discriminability of Electrocutaneous and Intraneural Stimulation Pulse Frequency in Intact Individuals and Amputees

Electrical stimulation of residual nerves can be used to provide amputees with intuitive sensory feedback. An important aspect of this artificial sensory feedback is the ability to convey the magnitude of tactile stimuli. Using classical psychophysical methods, we quantified the just-noticeable differences for electrocutaneous stimulation pulse frequency in both intact participants and one transradial amputee. For the transradial amputee, we also quantified the just-noticeable difference of intraneural microstimulation pulse frequency via chronically implanted Utah Slanted Electrode Arrays. We demonstrate that intensity discrimination is similar across conditions: intraneural microstimulation of the residual nerves, electrocutaneous stimulation of the reinnervated skin on the residual limb, and electrocutaneous stimulation of intact hands. We also show that intensity discrimination performance is significantly better at lower pulse frequencies than at higher pulse frequencies – a finding that’s unique to electrocutaneous and intraneural stimulation and suggests that supplemental sensory cues may be present at lower pulse frequencies. These results can help guide the implementation of artificial sensory feedback for sensorized bionic arms.Clinical Relevance— Intraneural and electrocutaneous artificial sensory feedback are comparable in their ability to convey the magnitude of tactile stimuli via pulse frequency.
String Diagrams for Assembly Planning

Assembly planning is a difficult problem for companies. Many disciplines such as design, planning, scheduling, and manufacturing execution need to be carefully engineered and coordinated to create successful product assembly plans. Recent research in the field of design for assembly has proposed new methodologies to design product structures in such a way that their assembly is easier. However, present assembly planning approaches lack the engineering tool support to capture all the constraints associated to assembly planning in a unified manner. This paper proposes CompositionalPlanning, a string diagram based framework for assembly planning. In the proposed framework, string diagrams and their compositional properties serve as the foundation for an engineering tool where CAD designs interact with planning and scheduling algorithms to automatically create high-quality assembly plans. These assembly plans are then executed in simulation to measure their performance and to visualize their key build characteristics. We demonstrate the versatility of this approach in the LEGO assembly domain. We developed two reference LEGO CAD models that are processed by CompositionalPlanning’s algorithmic pipeline. We compare sequential and parallel assembly plans in a Minecraft simulation and show that the time-to-build performance can be optimized by our algorithms.
The Capacity of Quantum Neural Networks

Quantum neural networks (QNN) are a promising application of near-term quantum computers. We present an information theory of QNN's expressive power, which we apply to an example optical QNN based on a Gaussian Boson Sampler.
More than STEM: spillovers from higher education institution infrastructure investments in the arts

Higher education institutions (HEIs) represent an enormous density of investment and resources, concentrating infrastructure spending and creating high human capital citizens and knowledge spillovers increasingly seen as critical to advancing regional quality of life. While HEIs’ prominent role in promoting regional economic growth, innovation, and attractiveness receives considerable research attention, most of that attention is paid to aspects of HEIs that are directly related to STEM activity. There are various theories that suggest spillovers from non-STEM activity at HEIs as well, specifically in the arts. This study examines whether spillovers occur for HEIs’ large capital investments in the arts. Specifically, we focus on HEI investments in arts physical infrastructure and whether these investments have any effects on regional-level business activity, including jobs and firms. To analyze HEI spillovers of physical arts infrastructure on regional jobs and firms, we use construction starts data on building projects from Dodge Analytics, Inc. and data from the Integrated Postsecondary Education Data System, which include administrative data for every college, university, and technical/vocational institution that participates in the federal student financial aid programs. We couple these data with public data on regional-level socioeconomic indicators from the U.S. Census Bureau’s Zip-code Business Patterns data. We employ a quasi-experimental propensity-score matching design in order to control for a host of HEI and regional-level characteristics in examining the impact of infrastructure investments. The results suggest strong and consistent spillover effects (i.e. overall and specifically in the arts industry) for regions with HEIs that make these investments.
Elasticity in Apéry Sets

A numerical semigroup S is an additive subsemigroup of the nonnegative integers, containing zero, with finite complement. Its multiplicity m is its smallest nonzero element. The Apery set of S is t...
SafeRoute: Learning to Navigate Streets Safely in an Urban Environment

Recent studies show that 85% of women have changed their traveled routes to avoid harassment and assault. Despite this, current mapping tools do not empower users with information to take charge of their personal safety. We propose SafeRoute, a novel solution to the problem of navigating cities and avoiding street harassment and crime. Unlike other street navigation applications, SafeRoute introduces a new type of path generation via deep reinforcement learning. This enables us to successfully optimize for multi-criteria path-finding and incorporate representation learning within our framework. Our agent learns to pick favorable streets to create a safe and short path with a reward function that incorporates safety and efficiency. Given access to recent crime reports in many urban cities, we train our model for experiments in Boston, New York, and San Francisco. We test our model on areas of these cities, specifically the populated downtown regions with high foot traffic. We evaluate SafeRoute and successfully improve over state-of-the-art methods by up to 17% in local average distance from crimes while decreasing path length by up to 7%.
Safety-Critical Control Synthesis for Network Systems With Control Barrier Functions and Assume-Guarantee Contracts

This article aims at the safety-critical control synthesis of network systems such that the satisfaction of the safety constraints can be guaranteed. To handle the large state dimension of such systems, an assume-guarantee contract is used to break the large synthesis problem into smaller subproblems. Parameterized signal temporal logic (pSTL) is used to formally describe the behaviors of the subsystems which we use as the template for the contract. We show that robust control invariant sets (RCIs) for the subsystems can be composed to form a robust control invariant set (RCI) for the whole network system under a valid assume-guarantee contract. An epigraph algorithm is proposed to solve for a contract that is valid, an approach that has linear complexity for sparse networks, which leads to a RCI for the whole network system. Implemented with control barrier function (CBF), the state of each subsystem is guaranteed to stay within the safe set. Furthermore, we propose a contingency tube model predictive control approach based on the RCI, which is capable of handling severe contingencies, including topology changes of the network. A power grid example is used to demonstrate the proposed method. The simulation result includes both set point control and contingency recovery, and the safety constraint is always satisfied.
A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism

We propose an integrated deep-generative framework, that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tractography to extract predictive biomarkers of a disease. The generative part of our framework is a structurally-regularized Dynamic Dictionary Learning (sr-DDL) model that decomposes the dynamic rs-fMRI correlation matrices into a collection of shared basis networks and time varying patient-specific loadings. This matrix factorization is guided by the DTI tractography matrices to learn anatomically informed connectivity profiles. The deep part of our framework is an LSTM-ANN block, which models the temporal evolution of the patient sr-DDL loadings to predict multidimensional clinical severity. Our coupled optimization procedure collectively estimates the basis networks, the patient-specific dynamic loadings, and the neural network weights. We validate our framework on a multi-score prediction task in 57 patients diagnosed with Autism Spectrum Disorder (ASD). Our hybrid model outperforms state-of-the-art baselines in a five-fold cross validated setting and extracts interpretable multimodal neural signatures of brain dysfunction in ASD.
Shedding light on moiré excitons: A first-principles perspective.

Moire superlattices in van der Waals (vdW) heterostructures could trap long-lived interlayer excitons. These moire excitons could form ordered quantum dot arrays, paving the way for unprecedented optoelectronic and quantum information applications. Here, we perform first-principles simulations to shed light on moire excitons in twisted MoS2/WS2 heterostructures. We provide direct evidence of localized interlayer moire excitons in vdW heterostructures. The interlayer and intralayer moire potentials are mapped out based on spatial modulations of energy gaps. Nearly flat valence bands are observed in the heterostructures. The dependence of spatial localization and binding energy of the moire excitons on the twist angle of the heterostructures is examined. We explore how vertical electric field can be tuned to control the position, polarity, emission energy, and hybridization strength of the moire excitons. We predict that alternating electric fields could modulate the dipole moments of hybridized moire excitons and suppress their diffusion in moire lattices.
RobNorm: model-based robust normalization method for labeled quantitative mass spectrometry proteomics data.

Motivation Data normalization is an important step in processing proteomics data generated in mass spectrometry experiments, which aims to reduce sample-level variation and facilitate comparisons of samples. Previously published methods for normalization primarily depend on the assumption that the distribution of protein expression is similar across all samples. However, this assumption fails when the protein expression data is generated from heterogenous samples, such as from various tissue types. This led us to develop a novel data-driven method for improved normalization to correct the systematic bias meanwhile maintaining underlying biological heterogeneity. Results To robustly correct the systematic bias, we used the density-power-weight method to down-weigh outliers and extended the one-dimensional robust fitting method described in the previous work to our structured data. We then constructed a robustness criterion and developed a new normalization algorithm, called RobNorm.In simulation studies and analysis of real data from the genotype-tissue expression project, we compared and evaluated the performance of RobNorm against other normalization methods. We found that the RobNorm approach exhibits the greatest reduction in systematic bias while maintaining across-tissue variation, especially for datasets from highly heterogeneous samples. Availabilityand implementation https://github.com/mwgrassgreen/RobNorm. Supplementary information Supplementary data are available at Bioinformatics online.
Searching for Efficient Architecture for Instrument Segmentation in Robotic Surgery

Segmentation of surgical instruments is an important problem in robot-assisted surgery: it is a crucial step towards full instrument pose estimation and is directly used for masking of augmented reality overlays during surgical procedures. Most applications rely on accurate real-time segmentation of high-resolution surgical images. While previous research focused primarily on methods that deliver high accuracy segmentation masks, majority of them can not be used for real-time applications due to their computational cost. In this work, we design a light-weight and highly-efficient deep residual architecture which is tuned to perform real-time inference of high-resolution images. To account for reduced accuracy of the discovered light-weight deep residual network and avoid adding any additional computational burden, we perform a differentiable search over dilation rates for residual units of our network. We test our discovered architecture on the EndoVis 2017 Robotic Instruments dataset and verify that our model is the state-of-the-art in terms of speed and accuracy tradeoff with a speed of up to 125 FPS on high resolution images.
Residually finite rationally p groups

In this paper, we develop the theory of residually finite rationally p (RFRp) groups, where p is a prime. We first prove a series of results about the structure of finitely generated RFRp groups (e...
Prospects for neutron star equation of state constraints using “recycled” millisecond pulsars

“Recycled” millisecond pulsars are a variety of rapidly spinning neutron stars that typically show thermal X-ray radiation due to the heated surface of their magnetic polar caps. Detailed numerical modeling of the rotation-induced thermal X-ray pulsations observed from recycled millisecond pulsars, including all relevant relativistic and stellar atmospheric effects, has been identified as a promising approach towards an astrophysical determination of the true neutron star mass-radius relation, and by extension the state of cold matter at densities exceeding those of atomic nuclei. Herein, I review the basic model and methodology commonly used to extract information regarding neutron star structure from the pulsed X-ray radiation observed from millisecond pulsars. I also summarize the results of past X-ray observations of these objects and the prospects for precision neutron star mass-radius measurements with the upcoming Neutron Star Interior Composition Explorer (NICER) X-ray timing mission.
Design and analysis of communication protocols for quantum repeater networks

We analyze how the performance of a quantum-repeater network depends on the protocol employed to distribute entanglement, and we find that the choice of repeater-to-repeater link protocol has a profound impact on communication rate as a function of hardware parameters. We develop numerical simulations of quantum networks using different protocols, where the repeater hardware is modeled in terms of key performance parameters, such as photon generation rate and collection efficiency. These parameters are motivated by recent experimental demonstrations in quantum dots, trapped ions, and nitrogen-vacancy centers in diamond. We find that a quantum-dot repeater with the newest protocol ("MidpointSource") delivers the highest communication rate when there is low probability of establishing entanglement per transmission, and in some cases the rate is orders of magnitude higher than other schemes. Our simulation tools can be used to evaluate communication protocols as part of designing a large-scale quantum network.
Quantum Groverian geodesic paths with gravitational and thermal analogies

We present a unifying variational calculus derivation of Groverian geodesics for both quantum state vectors and quantum probability amplitudes. In the first case, we show that horizontal affinely parametrized geodesic paths on the Hilbert space of normalized vectors emerge from the minimization of the length specified by the Fubini–Study metric on the manifold of Hilbert space rays. In the second case, we demonstrate that geodesic paths for probability amplitudes arise by minimizing the length expressed in terms of the Fisher information. In both derivations, we find that geodesic equations are described by simple harmonic oscillators (SHOs). However, while in the first derivation the frequency of oscillations is proportional to the (constant) energy dispersion $$\Delta E$$ of the Hamiltonian system; in the second derivation the frequency of oscillations is proportional to the square root $$\sqrt{\mathcal {F}}$$ of the (constant) Fisher information. Interestingly, by setting these two frequencies equal to each other, we recover the well-known Anandan–Aharonov relation linking the squared speed of evolution of an Hamiltonian system with its energy dispersion. Finally, upon transitioning away from the quantum setting, we discuss the universality of the emergence of geodesic motion of SHO type in the presence of conserved quantities by analyzing two specific phenomena of gravitational and thermodynamical origin, respectively.
Hyperparameter Ensembles for Robustness and Uncertainty Quantification

Ensembles over neural network weights trained from different random initialization, known as deep ensembles, achieve state-of-the-art accuracy and calibration. The recently introduced batch ensembles provide a drop-in replacement that is more parameter efficient. In this paper, we design ensembles not only over weights, but over hyperparameters to improve the state of the art in both settings. For best performance independent of budget, we propose hyper-deep ensembles, a simple procedure that involves a random search over different hyperparameters, themselves stratified across multiple random initializations. Its strong performance highlights the benefit of combining models with both weight and hyperparameter diversity. We further propose a parameter efficient version, hyper-batch ensembles, which builds on the layer structure of batch ensembles and self-tuning networks. The computational and memory costs of our method are notably lower than typical ensembles. On image classification tasks, with MLP, LeNet, ResNet 20 and Wide ResNet 28-10 architectures, we improve upon both deep and batch ensembles.
Modeling the Impact of Baryons on Subhalo Populations with Machine Learning

We identify subhalos in dark matter–only (DMO) zoom-in simulations that are likely to be disrupted due to baryonic effects by using a random forest classifier trained on two hydrodynamic simulations of Milky Way (MW)–mass host halos from the Latte suite of the Feedback in Realistic Environments (FIRE) project. We train our classifier using five properties of each disrupted and surviving subhalo: pericentric distance and scale factor at first pericentric passage after accretion and scale factor, virial mass, and maximum circular velocity at accretion. Our five-property classifier identifies disrupted subhalos in the FIRE simulations with an 85% out-of-bag classification score. We predict surviving subhalo populations in DMO simulations of the FIRE host halos, finding excellent agreement with the hydrodynamic results; in particular, our classifier outperforms DMO zoom-in simulations that include the gravitational potential of the central galactic disk in each hydrodynamic simulation, indicating that it captures both the dynamical effects of a central disk and additional baryonic physics. We also predict surviving subhalo populations for a suite of DMO zoom-in simulations of MW-mass host halos, finding that baryons impact each system consistently and that the predicted amount of subhalo disruption is larger than the host-to-host scatter among the subhalo populations. Although the small size and specific baryonic physics prescription of our training set limits the generality of our results, our work suggests that machine-learning classification algorithms trained on hydrodynamic zoom-in simulations can efficiently predict realistic subhalo populations.
The Discovery and Origin of a Very High-velocity Cloud Toward M33

We report the detection of a largely ionized very-high velocity cloud (VHVC; $v_{\rm LSR}\sim-350$ km/s) toward M33 with the Hubble Space Telescope/Cosmic Origin Spectrograph. The VHVC is detected in OI, CII, SiII, and SiIII absorption along five sightlines separated by ~0.06-0.4 degree. On sub-degree scales, the velocities and ionic column densities of the VHVC remain relatively smooth with standard deviations of +/-14 km/s and +/-0.15 dex between the sightlines, respectively. The VHVC has a metallicity of [OI/HI]=-0.56+/-0.17 dex (Z=0.28+/-0.11 Z$_{\odot}$). Despite the position-velocity proximity of the VHVC to the ionized Magellanic Stream, the VHVC's higher metallicity makes it unlikely to be associated with the Stream, highlighting the complex velocity structure of this region of sky. We investigate the VHVC's possible origin by revisiting its surrounding HI environment. We find that the VHVC may be: (1) a MW CGM cloud, (2) related to a nearby HI VHVC -- Wright's Cloud, or (3) connected to M33's northern warp. Furthermore, the VHVC could be a bridge connecting Wright's Cloud and M33's northern warp, which would make it a Magellanic-like structure in the halo of M33.
Star-forming Filament Models

New models of star-forming filamentary clouds are presented, to quantify their properties and to predict their evolution. These 2D axisymmetric models describe filaments having no core, one low-mass core, and one cluster-forming core. They are based on Plummer-like cylinders and spheroids, bounded by a constant-density surface of finite extent. In contrast to 1D Plummer-like models, they have specific values of length and mass, they approximate observed column density maps, and their distributions of column density (N-pdfs) are pole-free. Each model can estimate the star-forming potential of a core-filament system, by identifying the zone of gas dense enough to form low-mass stars, and by counting the number of enclosed thermal Jeans masses. This analysis suggests that the Musca Center filament may be near the start of its star-forming life, with enough dense gas to make its first ~3 protostars, while the Coronet filament is near the midpoint of its star formation, with enough dense gas to add ~ 8 protostars to its ~20 known stars. In contrast L43 appears near the end of its star-forming life, since it lacks enough dense gas to add any new protostars to the 2 YSOs already known.
Nucleus capture by macroscopic dark matter

For a class of macroscopic dark matter with a large interaction strength with Standard Model particles, a nucleus could be captured by the dense, heavy dark matter as it traverses ordinary material. The radiated photon carries most of the binding energy and is a characteristic signature for dark matter detection. We develop analytic formulas and present numerical results for this radiative capture process in the low energy, non-dipole limit. Large-volume neutrino detectors like NOνA, JUNO, DUNE and Super(Hyper)-K may detect multi-hit or single-hit radiative capture events and can search for dark matter up to one gram in mass.
Photon masses in the landscape and the swampland

In effective quantum field theory, a spin-1 vector boson can have a technically natural small mass that does not originate from the Higgs mechanism. For such theories, which may be written in Stuckelberg form, there is no point in field space at which the mass is exactly zero. I argue that quantum gravity differs from, and constrains, effective field theory: arbitrarily small Stuckelberg masses are forbidden. In particular, the limit in which the mass goes to zero lies at infinite distance in field space, and this distance is correlated with a tower of modes becoming light according to the Swampland Distance Conjecture. Application of Tower or Sublattice variants of the Weak Gravity Conjecture makes this statement more precise: for a spin-1 vector boson with coupling constant e and Stuckelberg mass m, local quantum field theory breaks down at energies at or below ΛUV = min((mMPl/e)1/2, e1/3MPl). Combined with phenomenological constraints, this argument implies that the Standard Model photon must be exactly massless. It also implies that much of the parameter space for light dark photons, which are the target of many experimental searches, is compatible only with Higgs and not Stuckelberg mass terms. This significantly affects the experimental limits and cosmological histories of such theories. I explain various caveats and weak points of the arguments, including loopholes that could be targets for model-building.
DENSITY ESTIMATION FOR GROUPED DATA WITH APPLICATION TO LINE TRANSECT SAMPLING

Line transect sampling is a method used to estimate wildlife populations, with the resulting data often grouped in intervals. Estimating the density from grouped data can be challenging. In this paper we propose a kernel density estimator of wildlife population density for such grouped data. Our method uses a combined cross-validation and smoothed bootstrap approach to select the optimal bandwidth for grouped data. Our simulation study shows that with the smoothing parameter selected with this method, the estimated density from grouped data matches the true density more closely than with other approaches. Using smoothed bootstrap, we also construct bias-adjusted confidence intervals for the value of the density at the boundary. We apply the proposed method to two grouped data sets, one from a wooden stake study where the true density is known, and the other from a survey of kangaroos in Australia.