cssi/SciSciGPT-SciSciNet huggingface dataset:
Alternative Promoters Drive Human Cytomegalovirus Reactivation from Latency

Reactivation from latency requires reinitiation of viral gene expression and culminates in the production of infectious progeny. The major immediate early promoter (MIEP) of human cytomegalovirus (HCMV) drives the expression of crucial lytic cycle transactivators but is silenced during latency in hematopoietic progenitor cells (HPCs). Because the MIEP has poor activity in HPCs, it is unclear how viral transactivators are expressed during reactivation. It has been presumed that viral gene expression is reinitiated via de-repression of the MIEP. We demonstrate that immediate early transcripts arising from reactivation originate predominantly from alternative promoters within the canonical major immediate early locus. Disruption of these intronic promoters results in striking defects in re-expression of viral genes and viral genome replication in the THP-1 latency model. Furthermore, we show that these promoters are necessary for efficient reactivation in primary CD34+ HPCs. Our findings shift the paradigm for HCMV reactivation by demonstrating that promoter switching governs reactivation from viral latency in a context-specific manner.
DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction

Reconstructing 3D shapes from single-view images has been a long-standing research problem. In this paper, we present DISN, a Deep Implicit Surface Net- work which can generate a high-quality detail-rich 3D mesh from a 2D image by predicting the underlying signed distance fields. In addition to utilizing global image features, DISN predicts the projected location for each 3D point on the 2D image and extracts local features from the image feature maps. Combin- ing global and local features significantly improves the accuracy of the signed distance field prediction, especially for the detail-rich areas. To the best of our knowledge, DISN is the first method that constantly captures details such as holes and thin structures present in 3D shapes from single-view images. DISN achieves the state-of-the-art single-view reconstruction performance on a variety of shape categories reconstructed from both synthetic and real images. Code is available at https://github.com/laughtervv/DISN. The supplemen- tary can be found at https://xharlie.github.io/images/neurips_ 2019_supp.pdf
Envy-Free Classification

In classic fair division problems such as cake cutting and rent division, envy-freeness requires that each individual (weakly) prefer his allocation to anyone else's. On a conceptual level, we argue that envy-freeness also provides a compelling notion of fairness for classification tasks, especially when individuals have heterogeneous preferences. Our technical focus is the generalizability of envy-free classification, i.e., understanding whether a classifier that is envy free on a sample would be almost envy free with respect to the underlying distribution with high probability. Our main result establishes that a small sample is sufficient to achieve such guarantees, when the classifier in question is a mixture of deterministic classifiers that belong to a family of low Natarajan dimension.
Contextual Bandits with Cross-Learning

In the classical contextual bandits problem, in each round $t$, a learner observes some context $c$, chooses some action $a$ to perform, and receives some reward $r_{a,t}(c)$. We consider the variant of this problem where in addition to receiving the reward $r_{a,t}(c)$, the learner also learns the values of $r_{a,t}(c')$ for all other contexts $c'$; i.e., the rewards that would have been achieved by performing that action under different contexts. This variant arises in several strategic settings, such as learning how to bid in non-truthful repeated auctions (in this setting the context is the decision maker's private valuation for each auction). We call this problem the contextual bandits problem with cross-learning. The best algorithms for the classical contextual bandits problem achieve $\tilde{O}(\sqrt{CKT})$ regret against all stationary policies, where $C$ is the number of contexts, $K$ the number of actions, and $T$ the number of rounds. We demonstrate algorithms for the contextual bandits problem with cross-learning that remove the dependence on $C$ and achieve regret $\tilde{O}(\sqrt{KT})$ (when contexts are stochastic with known distribution), $\tilde{O}(K^{1/3}T^{2/3})$ (when contexts are stochastic with unknown distribution), and $\tilde{O}(\sqrt{KT})$ (when contexts are adversarial but rewards are stochastic). We simulate our algorithms on real auction data from an ad exchange running first-price auctions (showing that they outperform traditional contextual bandit algorithms).
Property, predation and socialist reality

Economics as a social science is about exchange and the institutions within which exchange relationships are formed and transactions are executed. Yoram Barzel's contribution to economics and political economy reflect this focus on exchange and institutions. In this paper, I will sketch a theory of real-existing socialist economies from a property rights/public choice perspective. Then I present the puzzles in political economy that such a system confronted in attempting to create a prosperous, regenerating and competitive economic system. I then conclude with a short discussion of the future for a progressive research program in political economy that takes institutional change seriously.
Improving Performance of End-to-End ASR on Numeric Sequences.

Recognizing written domain numeric utterances (e.g. I need $1.25.) can be challenging for ASR systems, particularly when numeric sequences are not seen during training. This out-of-vocabulary (OOV) issue is addressed in conventional ASR systems by training part of the model on spoken domain utterances (e.g. I need one dollar and twenty five cents.), for which numeric sequences are composed of in-vocabulary numbers, and then using an FST verbalizer to denormalize the result. Unfortunately, conventional ASR models are not suitable for the low memory setting of on-device speech recognition. E2E models such as RNN-T are attractive for on-device ASR, as they fold the AM, PM and LM of a conventional model into one neural network. However, in the on-device setting the large memory footprint of an FST denormer makes spoken domain training more difficult. In this paper, we investigate techniques to improve E2E model performance on numeric data. We find that using a text-to-speech system to generate additional numeric training data, as well as using a small-footprint neural network to perform spoken-to-written domain denorming, yields improvement in several numeric classes. In the case of the longest numeric sequences, we see reduction of WER by up to a factor of 8.
Calculating Compensation Sums for Private Law Wrongs: Underlying Imprecisions, Necessary Questions, and Toward a Plausible Account of Damages for Lost Years of Life

The ubiquitous corrective-justice goals of “making a party whole” or “returning a party to the position she was in” are typically understood in monetary terms, and in this context it is fairly clear what these terms mean. If, as this Article argues, these corrective-justice goals should instead be understood in terms of something that has intrinsic value, such as happiness, various imprecisions come to the fore. This Article identifies and explores these imprecisions and, in so doing, articulates a novel framework that can be used for understanding and systematizing our approach to private law remedies. This is the Article’s first task. Next, the Article focuses on the imprecision that the law must grapple with whose implications are most salient: how to aggregate happiness across years of a life. This imprecision becomes significant in the context of torts that shorten a person’s life. The Article explores the appropriate measure of damages (under a corrective-justice theory) in cases in which a victim has her expected future shortened by a tort (e.g., medical malpractice or exposure to carcinogens), but in which she has not yet died. The fact that the victim is still alive makes it possible to compensate the victim herself directly for the value of life-years. Should she be compensated? The question, already critical in a number of cases, will substantially increase in prevalence with developments in science and technology in the coming years. This Article argues, contra current law in most states, that the law should take these types of cases seriously and that victims should be compensated if their loss of life-years constitutes a loss of happiness. The contrary position is in great tension with the commonsense intuition that losing life-years is one of the most (if not the most) serious harms that one can incur. But is our commonsense intuition correct? The Article proposes a three-step framework that can be used for addressing these questions of loss and getting to the appropriate measure of monetary compensation: (1) Determine which “happiness aggregation function” to espouse, (2) determine how much happiness (if any), according to one’s happiness aggregation function of choice, a plaintiff lost as a result of the harm; and (3) determine how much monetary compensation will bring about a transfer of happiness to the plaintiff that will equal the amount that she lost (according to one’s happiness aggregation function of choice).
An Algebraic Characterization of the Point-Pushing Subgroup

Abstract The point-pushing subgroup P ( Σ g ) of the mapping class group Mod ( Σ g , 1 ) of a surface with marked point is an embedding of π 1 ( Σ g ) given by pushing the marked point around loops. We prove that for g ≥ 3 , the subgroup P ( Σ g ) is the unique normal, genus g surface subgroup of Mod ( Σ g , 1 ) . As a corollary to this uniqueness result, we give a new proof that Out ( Mod ± ( Σ g , 1 ) ) = 1 , where Out denotes the outer automorphism group; a proof which does not use automorphisms of complexes of curves. Ingredients in our proof of this characterization theorem include combinatorial group theory, representation theory, the Johnson theory of the Torelli group, surface topology, and the theory of Lie algebras.
Analytical Performance Models for NoCs with Multiple Priority Traffic Classes

Networks-on-chip (NoCs) have become the standard for interconnect solutions in industrial designs ranging from client CPUs to many-core chip-multiprocessors. Since NoCs play a vital role in system performance and power consumption, pre-silicon evaluation environments include cycle-accurate NoC simulators. Long simulations increase the execution time of evaluation frameworks, which are already notoriously slow, and prohibit design-space exploration. Existing analytical NoC models, which assume fair arbitration, cannot replace these simulations since industrial NoCs typically employ priority schedulers and multiple priority classes. To address this limitation, we propose a systematic approach to construct priority-aware analytical performance models using micro-architecture specifications and input traffic. Our approach decomposes the given NoC into individual queues with modified service time to enable accurate and scalable latency computations. Specifically, we introduce novel transformations along with an algorithm that iteratively applies these transformations to decompose the queuing system. Experimental evaluations using real architectures and applications show high accuracy of 97% and up to 2.5× speedup in full-system simulation.
Distributed Change Detection via Average Consensus over Networks

Distributed change-point detection has been a fundamental problem when performing real-time monitoring using sensor networks. We propose a distributed detection algorithm, where each sensor only exchanges CUSUM statistic with their neighbors based on the average consensus scheme, and an alarm is raised when local consensus statistic exceeds a prespecified global threshold. We provide theoretical performance bounds showing that the performance of the fully distributed scheme can match the centralized algorithms under some mild conditions. Numerical experiments demonstrate the good performance of the algorithm, especially, in detecting asynchronous changes.
Restless Video Bandits: Optimal SVC Streaming in a Multi-User Wireless Network

In this paper, we consider the problem of optimal scalable video delivery to mobile users in wireless networks given arbitrary Quality Adaptation (QA) mechanisms. In current practical systems, QA and wireless channel scheduling are performed independently by the content provider and network operator, respectively. While most research has been focused on jointly optimizing these two tasks, the high complexity that comes with a joint approach makes the implementation impractical. Therefore, we present a scheduling mechanism that takes the QA logic of each user as input and optimizes the scheduling accordingly. Hence, there is no need for centralized QA and cross-layer interactions are minimized. We model the QA-adaptive scheduling and the jointly optimal problem as a Restless Bandit and a Multi-user Semi Markov Decision Process, respectively, in order to compare the loss incurred by not employing a jointly optimal scheme. We then present heuristic algorithms in order to achieve the optimal outcome of the Restless Bandit solution, assuming the base station has knowledge of the underlying quality adaptation of each user (QA-Aware). We also present a simplified heuristic without the need for any knowledge of the QA logic at the base station (QA-Blind). We show that our QA-Aware strategy can achieve up to two times improvement in network utilization compared to popular baseline algorithms such as Proportional Fairness. We also show that the QA-Blind strategy performs very close to the QA-Aware scheme while providing significant reduction in complexity. Furthermore, we provide a testbed implementation of the QA-Blind scheme in order to compare it with baseline algorithms in a real network setting.
Fall Risk Prediction in Multiple Sclerosis Using Postural Sway Measures: A Machine Learning Approach.

Numerous postural sway metrics have been shown to be sensitive to balance impairment and fall risk in individuals with MS. Yet, there are no guidelines concerning the most appropriate postural sway metrics to monitor impairment. This investigation implemented a machine learning approach to assess the accuracy and feature importance of various postural sway metrics to differentiate individuals with MS from healthy controls as a function of physiological fall risk. 153 participants (50 controls and 103 individuals with MS) underwent a static posturography assessment and a physiological fall risk assessment. Participants were further classified into four subgroups based on fall risk: controls, low-risk MS (n = 34), moderate-risk MS (n = 27), high-risk MS (n = 42). Twenty common sway metrics were derived following standard procedures and subsequently used to train a machine learning algorithm (random forest – RF, with 10-fold cross validation) to predict individuals’ fall risk grouping. The sway-metric based RF classifier had high accuracy in discriminating controls from MS individuals (>86%). Sway sample entropy was identified as the strongest feature for classification of low-risk MS individuals from healthy controls. Whereas for all other comparisons, mediolateral sway amplitude was identified as the strongest predictor for fall risk groupings.
A novel interplay between GEFs orchestrates Cdc42 activity during cell polarity and cytokinesis in fission yeast.

Cdc42, a conserved regulator of cell polarity, is activated by two GEFs, Gef1 and Scd1, in fission yeast. Why the cell needs two GEFs is unclear, given that they are partially redundant and activate the same GTPase. Using the GEF localization pattern during cytokinesis as a paradigm, we report a novel interplay between Gef1 and Scd1 that spatially modulates Cdc42. We find that Gef1 promotes Scd1 localization to the division site during cytokinesis through the recruitment of the scaffold Scd2 via a Cdc42 feedforward pathway. Similarly, in interphase Gef1 promotes Scd1 recruitment at the new end to enable the transition from monopolar to bipolar growth. Reciprocally, Scd1 restricts Gef1 localization to prevent ectopic Cdc42 activation during cytokinesis to promote cell separation, and to maintain cell shape during interphase. Our findings reveal an elegant regulatory pattern in which Gef1 primes Cdc42 activation at new sites to initiate Scd1-dependent polarized growth, while Scd1 restricts Gef1 to sites of polarization. We propose that crosstalk between GEFs is a conserved mechanism that orchestrates Cdc42 activation during complex cellular processes.
Timing of Quaternary geomagnetic reversals and excursions in volcanic and sedimentary archives

Abstract We review the ages of Quaternary magnetic polarity reversals and magnetic excursions. Improvements in radioisotopic 40Ar/39Ar dating of reversals and excursions reflect the use of multi-collector mass spectrometers, along with ultra-low backgrounds, laser-based incremental heating methods, and adoption of 28.201 Ma for the Fish Canyon sanidine (FCs) standard age (Kuiper et al., 2008). These improvements have brought 40Ar/39Ar ages of reversals and excursions into close correspondence with astrochronological ages based on the correlation of δ18O data to LR04 (Lisiecki and Raymo, 2005) and, in a few cases, correlation to ice-core chronologies. Ages of two polarity reversals, the Matuyama-Brunhes boundary and base Olduvai, are at odds with ages given in popular geomagnetic polarity timescales. Sedimentary records of the Laschamp and Iceland Basin excursion are numerous and widespread, and attest to global manifestation and global synchronicity of these excursions. Records of other Quaternary excursions are less numerous. The Blake excursion (∼115 ka) has been recorded in North Atlantic sediments, Chinese loess, Indian Ocean volcanics, and Spanish speleothems. The Santa Rosa excursion (∼932 ka) has been recorded in North Atlantic and Phillipine Basin sediments, as well as in volcanic rocks from New Mexico and Galapagos. The Punaruu excursion (∼1115 ka) has been observed in sediments from the North Atlantic and California Margin as well as in volcanic rocks on Tahiti. Uniformity of individual excursion ages, using astrochronological, 40Ar/39Ar and U–Th methods, argues for synchronous global manifestation of excursions, strengthening the usefulness of magnetic excursions (and reversals) in Quaternary stratigraphy.
Flexible MEC service consumption through edge host zoning in 5G networks

Multi-access Edge Computing (MEC) is commonly recognized as a key supporting technology for the emerging 5G systems. When deployed in fully virtualized networks, i.e., following the Network Function Virtualization (NFV) paradigm, it will enable a multitude of new applications and use cases. However, the growing number of devices, combined with the vastly increasing traffic demand, call for low End-to-End (E2E) latency packet transfer and processing in an NFV environment, both in user and control plane. In this paper, focusing on control plane packet traffic, we investigate the general case of a MEC application consuming a MEC service running on a different MEC host. To enable flexible MEC platform service consumption at different localities, based on a state-of-the-art statistical model of the total processing time, we define latency-aware proximity zones around MEC servers hosting MEC application instances. Exemplary scenarios exhibit the E2E performance benefit of introducing the awareness of proximity zones around MEC hosts and service producing MEC application instances. This performance-aware service consumption will be beneficial in view of the future evolution towards distributed computing systems.
AFFECT VARIABILITY AND SLEEP: EMOTIONAL UPS AND DOWNS ARE RELATED TO A POORER NIGHT’S REST

Abstract Objectives Many studies have documented the strong associations between well-being and mean levels of both positive and negative affect. A growing number of studies are examining how fluctuations in daily reported emotional experience, known as affect variability, is related to health outcomes. Sleep is a critical correlate of healthy in functioning in late life. This study examines associations between positive and negative affect variability and facets of self-reported sleep behavior among older adults. Methods Participants ( N = 277) completed a 5 to 6 day ecological momentary assessment. The first survey upon waking asked participants about their sleep the previous night, and participants rated their positive and negative affect every three subsequent hours during waking hours. Results Regression models indicate that greater variability in daily positive affect is associated with fewer hours of sleep ( b = −0.648, p = .04) and greater morning tiredness (b = 0.67, p = .006) even after adjusting for mean levels of affect. Although greater negative affect variability is associated with worse sleep quality ( b = −0.77, p = .02) and greater morning tiredness ( b = 0.91, p = .004), these associations are no longer significant once mean negative affect is added into the model. Conclusion Findings support theory describing the downside in the variability of positive affect, and suggest that fluctuations in positive affect are related to poor sleep outcomes that have implications for overall health and well-being.
Prostate Cancer Inference via Weakly-Supervised Learning using a Large Collection of Negative MRI

Recent advances in medical imaging techniques have led to significant improvements in the management of prostate cancer (PCa). In particular, multi-parametric MRI (mp-MRI) continues to gain clinical acceptance as the preferred imaging technique for non-invasive detection and grading of PCa. However, the machine learning-based diagnosis systems for PCa are often constrained by the limited access to accurate lesion ground truth annotations for training. The performance of the machine learning system is highly dependable on both quality and quantity of lesion annotations associated with histopathologic findings, resulting in limited scalability and clinical validation. Here, we propose the baseline MRI model to alternatively learn the appearance of mp-MRI using radiology-confirmed negative MRI cases via weakly supervised learning. Since PCa lesions are case-specific and highly heterogeneous, it is assumed to be challenging to synthesize PCa lesions using the baseline MRI model, while it would be relatively easier to synthesize the normal appearance in mp-MRI. We then utilize the baseline MRI model to infer the pixel-wise suspiciousness of PCa by comparing the original and synthesized MRI with two distance functions. We trained and validated the baseline MRI model using 1,145 negative prostate mp-MRI scans. For evaluation, we used separated 232 mp-MRI scans, consisting of both positive and negative MRI cases. The 116 positive MRI scans were annotated by radiologists, confirmed with post-surgical whole-gland specimens. The suspiciousness map was evaluated by receiver operating characteristic (ROC) analysis for PCa lesions versus non-PCa regions classification and free-response receiver operating characteristic (FROC) analysis for PCa localization. Our proposed method achieved 0.84 area under the ROC curve and 77.0% sensitivity at one false positive per patient in FROC analysis.
From Lurkers to Workers: Predicting Voluntary Contribution and Community Welfare

In an online community, users can interact with fellow community members by voluntarily contributing to existing discussion threads or by starting new threads. In practice, however, the vast majori...
Differentially Private Smart Metering: Implementation, Analytics, and Billing

Smart power grids offer to revolutionize power distribution by sharing granular power usage data, though this same data sharing can reveal a great deal about users, and there are serious privacy concerns for customers. In this paper, we address these concerns using differential privacy. Differential privacy is a statistical notion of privacy that adds noise to provide privacy guarantees. One privacy threat is the aggregation of time series data, and we therefore apply a trajectory-level form of differential privacy to guard against such privacy threats. In particular, we consider input-perturbation privacy, which adds noise directly to sensitive data streams before sharing them. We apply it in this work to provide privacy guarantees on an individual basis. We then address the impact of privacy upon two key grid stakeholders: the utility and the accuracy of its analytics of interest, as well as customers and the financial impact upon their utility bills. Both impacts are shown to be modest, even with strong privacy guarantees. Simulation results are provided using actual power usage data, demonstrating the viability of this approach in practice.
Chemical unfolding of protein domains induces shape change in programmed protein hydrogels

Programmable behavior combined with tailored stiffness and tunable biomechanical response are key requirements for developing successful materials. However, these properties are still an elusive goal for protein-based biomaterials. Here, we use protein-polymer interactions to manipulate the stiffness of protein-based hydrogels made from bovine serum albumin (BSA) by using polyelectrolytes such as polyethyleneimine (PEI) and poly-L-lysine (PLL) at various concentrations. This approach confers protein-hydrogels with tunable wide-range stiffness, from ~10–64 kPa, without affecting the protein mechanics and nanostructure. We use the 6-fold increase in stiffness induced by PEI to program BSA hydrogels in various shapes. By utilizing the characteristic protein unfolding we can induce reversible shape-memory behavior of these composite materials using chemical denaturing solutions. The approach demonstrated here, based on protein engineering and polymer reinforcing, may enable the development and investigation of smart biomaterials and extend protein hydrogel capabilities beyond their conventional applications. Tailoring and programing the behavior of protein biomaterials is complex. Here, the authors report on the use of polyelectrolytes for controlling the stiffness to allow programing of protein hydrogels and generate reversible shape changes via folding and unfolding reactions.
Fast BFS-Based Triangle Counting on GPUs

In this paper, we propose a novel method to compute triangle counting on GPUs. Unlike previous formulations of graph matching, our approach is BFS-based by traversing the graph in an all-source-BFS manner and thus can be mapped onto GPUs in a massively parallel fashion. Our implementation uses the Gunrock programming model and we evaluate our implementation in runtime and memory consumption compared with previous state-of-the-art work. We sustain a peak traversed-edges-per-second (TEPS) rate of nearly 10 GTEPS. Our algorithm is the most scalable and parallel among all existing GPU implementations and also outperforms all existing CPU distributed implementations. This work specifically focuses on leveraging our implementation on the triangle counting problem for the Subgraph Isomorphism Graph Challenge 2019, demonstrating a geometric mean speedup over the 2018 champion of $3.84 \times $.
Transnational Lawyering and Legal Resistance in National Courts: Palestinian Cases Before the Israeli Supreme Court

This Note explores the strategies of transnational cause lawyers working within national courts. It begins by documenting the emerging use of international norms and arguments for the purpose of mobilizing local communities and affecting domestic laws. The use of transnational law today broadens the legal imagination of lawyers beyond their national borders, constraints, and traditional audiences. Redefining the boundaries of a legal victory, transnational law provides lawyers with tools to continue bringing legal challenges while avoiding the dilemmas of legitimating oppressive legal structures.
Robust Optimization of Unconstrained Binary Quadratic Problems

In this paper we focus on the unconstrained binary quadratic optimisation model, maximise xtQx, x binary, and consider the problem of identifying optimal solutions that are robust with respect to perturbations in the Q matrix. We are motivated to find robust, or stable, solutions because of the uncertainty inherent in the big data origins of Q and limitations in computer numerical precision, particularly in a new class of quantum annealing computers. Experimental design techniques are used to generate a diverse subset of possible scenarios, from which robust solutions are identified. An illustrative example with practical application to business decision making is examined. The approach presented also generates a surface response equation which is used to estimate upper bounds in constant time for Q instantiations within the scenario extremes. In addition, a theoretical framework for the robustness of individual xi variables is considered by examining the range of Q values over which the xi are predetermined.
Orthogonal polynomial expansions to evaluate stop-loss premiums

A numerical method is proposed to evaluate the survival function of a compound distribution and the stop-loss premiums associated with a non-proportional global reinsurance treaty. The method relies on a representation of the probability density function in terms of Laguerre polynomials and the gamma density. We compare the method against a well established Laplace transform inversion technique at the end of the paper.
Complex Manifolds With Negative Curvature Operator

We prove that compact complex manifolds with admitting metrics with negative Chern curvature operator either admit a $dd^c$-exact positive (1,1) current, or are Kahler with ample canonical bundle. In the case of complex surfaces we obtain a complete classification. The proofs rely on a global existence and convergence result for the pluriclosed flow.
CAG: A Real-Time Low-Cost Enhanced-Robustness High-Transferability Content-Aware Adversarial Attack Generator

Deep neural networks (DNNs) are vulnerable to adversarial attack despite their tremendous success in many artificial intelligence fields. Adversarial attack is a method that causes the intended misclassfication by adding imperceptible perturbations to legitimate inputs. To date, researchers have developed numerous types of adversarial attack methods. However, from the perspective of practical deployment, these methods suffer from several drawbacks such as long attack generating time, high memory cost, insufficient robustness and low transferability. To address the drawbacks, we propose a Content-aware Adversarial Attack Generator (CAG) to achieve real-time, low-cost, enhanced-robustness and high-transferability adversarial attack. First, as a type of generative model-based attack, CAG shows significant speedup (at least 500 times) in generating adversarial examples compared to the state-of-the-art attacks such as PGD and C&W. Furthermore, CAG only needs a single generative model to perform targeted attack to any targeted class. Because CAG encodes the label information into a trainable embedding layer, it differs from prior generative model-based adversarial attacks that use n different copies of generative models for n different targeted classes. As a result, CAG significantly reduces the required memory cost for generating adversarial examples. Moreover, CAG can generate adversarial perturbations that focus on the critical areas of input by integrating the class activation maps information in the training process, and hence improve the robustness of CAG attack against the state-of-art adversarial defenses. In addition, CAG exhibits high transferability across different DNN classifier models in black-box attack scenario by introducing random dropout in the process of generating perturbations. Extensive experiments on different datasets and DNN models have verified the real-time, low-cost, enhanced-robustness, and high-transferability benefits of CAG.
A high order hybridizable discontinuous Galerkin method for incompressible miscible displacement in heterogeneous media

Abstract An hybridizable discontinuous Galerkin method of arbitrary high order is formulated to solve the miscible displacement problem in porous media. The spatial discretization is combined with a sequential algorithm that decouples the flow and the transport equations. Hybridization produces a linear system for the globally coupled degrees of freedom, that is smaller in size compared to the system resulting from the interior penalty discontinuous Galerkin methods. We study the impact of increasing the polynomial order on the accuracy of the solution. Numerical experiments show that the method converges optimally and that it is robust for highly heterogeneous porous media in two and three dimensions.
Asymptotically symmetric spaces with hereditarily non-unique spreading models

We examine a variant of a Banach space $\mathfrak{X}_{0,1}$ defined by Argyros, Beanland, and the second named author that has the property that it admits precisely two spreading models in every infinite dimensional subspace. We prove that this space is asymptotically symmetric and thus it provides a negative answer to a problem of Junge, the first. named author, and Odell.
Monopulse radar tracking using an adaptive interacting multiple model method with extended Kalman filters

In this paper we investigate an adaptive interacting multiple model (AIMM) tracker using the extended Kalman filter. This adaptive algorithm is based on the interacting multiple model (IMM) tracking technique with the addition of an adaptive acceleration model to track behavior that falls in between the fixed model dynamics. In previous research, we found that the adaptive model matches more closely the true system dynamics when the target kinematics lie in between the fixed models, thus improving the overall performance of the tracking system. We also showed that the AIMM outperforms other existing adaptive approaches while reducing computational complexity. In this paper, we further investigate these superior qualities of the AIMM by considering a more realistic radar-tracking scenario where monopulse radar range, azimuth, and elevation measurements are processed using extended Kalman filters in the AIMM. Here a more complex 3D simulation is implemented instead of the simplified 2D problem considered in our previous research. Again, the result how that the AIMM outperforms the classical IMM when the target is maneuvering.
A formal proof of the independence of the continuum hypothesis

We describe a formal proof of the independence of the continuum hypothesis (CH) in the Lean theorem prover. We use Boolean-valued models to give forcing arguments for both directions, using Cohen forcing for the consistency of ¬ CH and a σ-closed forcing for the consistency of CH.
Inflation, proton decay, and Higgs-portal dark matter in $SO(10) \times U(1)_\psi $

We propose a simple non-supersymmetric grand unified theory (GUT) based on the gauge group $$SO(10) \times U(1)_\psi $$. The model includes 3 generations of fermions in $$\mathbf{16}$$ ($$+1$$), $$\mathbf{10}$$ ($$-2$$) and $$\mathbf{1}$$ ($$+4$$) representations. The $$\mathbf{16}$$-plets contain Standard Model (SM) fermions plus right-handed neutrinos, and the $$\mathbf{10}$$-plet and the singlet fermions are introduced to make the model anomaly-free. Gauge coupling unification at $$M_{GUT} \simeq 5 \times 10^{15}{-}10^{16}$$ GeV is achieved by including an intermediate Pati–Salam breaking at $$M_{I} \simeq 10^{12}{-}10^{11}$$ GeV, which is a natural scale for the seesaw mechanism. For $$M_{I} \simeq 10^{12}{-}10^{11}$$, proton decay will be tested by the Hyper-Kamiokande experiment. The extra fermions acquire their masses from $$U(1)_\psi $$ symmetry breaking, and a $$U(1)_\psi $$ Higgs field drives a successful inflection-point inflation with a low Hubble parameter during inflation, $$H_{inf} \ll M_{I}$$. Hence, cosmologically dangerous monopoles produced from SO(10) and PS breakings are diluted away. This is the first SO(10) model we are aware of in which relatively light intermediate mass ($$\sim 10^{10}{-}10^{12}$$ GeV) primordial monopoles can be adequately suppressed. The reheating temperature after inflation can be high enough for successful leptogenesis. With the Higgs field contents of our model, a $$\mathbf{Z}_2$$ symmetry remains unbroken after GUT symmetry breaking, and the lightest mass eigenstate among linear combinations of the $$\mathbf{10}$$-plet and the singlet fermions serves as a Higgs-portal dark matter (DM). We identify the parameter regions to reproduce the observed DM relic density while satisfying the current constraint from the direct DM detection experiments. The present allowed region will be fully covered by the future direct detection experiments such as LUX-ZEPLIN DM experiment. In the presence of the extra fermions, the SM Higgs potential is stabilized up to $$M_{I}$$.
Extending the Race between Education and Technology

The race between education and technology provides a canonical framework that does an excellent job of explaining U.S. wage structure changes across the twentieth century. The framework involves secular increases in the demand for more-educated workers from skill-biased technological change, combined with variations in the supply of skills from changes in educational access. We expand the analysis backwards and forwards. The framework helps explain rising skill differentials in the nineteenth and twenty-first centuries, but needs to be augmented to illuminate the recent convexification of education returns and implied slowdown in the growth of the relative demand for college workers. Increased educational wage differentials explain 75 percent of the rise of U.S. wage inequality from 1980 to 2000 as compared to 38 percent for 2000 to 2017.
Parallel Approach to Sliding Window Sums.

Sliding window sums are widely used for string indexing, hashing, time series analysis and machine learning. New vector algorithms which utilize the advanced vector extension (AVX) instructions available on modern processors, or the parallel compute units on GPUs and FPGAs, would provide a significant performance boost.
Cost Recovery and Financial Viability of the Power Sector in Developing Countries : Insights from 15 Case Studies

This paper analyzes power utilities in 15 jurisdictions to understand the determinants of success for reforms aimed at improving financial viability and cost recovery in the power sector and the impacts of these reforms on metrics of sector performance. The analysis finds that electricity tariffs are rarely high enough to cover the full costs of service delivery, even where the cost of service is low, and that few countries adequately manage volatile costs and maintain cost recovery levels over time. Almost everywhere, power utilities often impose a substantial fiscal burden and contingent liabilities on government budgets. Over the past 30 years, cost recovery levels have increased on average, but progress has been uneven, with over half of the case study jurisdictions experiencing a decline compared with the pre-reform period. The record of reforms of price formation, especially tariff setting through regulatory agencies, is mixed. On average, countries that have made more progress on utility governance and decision making perform better on cost recovery. The paper concludes with proposed modifications to the conceptual framework underpinning the economic analysis of power sector reforms as well as immediate, practical implications for understanding cost recovery as part of the overall power sector reform agenda.
Design of a Fail-Safe Wearable Robot with Novel Extendable Arms for Ergonomic Accommodation during Floor Work

Aircraft manufacturing, construction, and agricultural production often involve workers maintaining uncomfortable postures, such as stooping and kneeling, for extended periods of time. We present a wearable robot, called MantisBot Alpha, that consists of two expandable robotic arms that brace a worker near the ground and allows them to perform bi-manual tasks. The key component of this new design is a novel linkage mechanism that provides adjustment of both the worker’s distance to the ground and their torso tilt. The mechanism link parameters are optimized such that a) its expansion rate is high enough to push off the human body from the ground and fully contract the scissor arm when not used, and b) it allows the worker to reach within a large space while c) it is light enough for wearability. The linkage mechanism also avoids the singularity problem in standard scissor mechanisms. The actuator design provides a fail-safe system. A prototype has been fabricated to demonstrate the feasibility of the system.
Streaming PCA: Matching matrix bernstein and near-optimal finite sample guarantees for oja's algorithm

This work provides improved guarantees for streaming principle component analysis (PCA). Given $A_1, \ldots, A_n\in \mathbb{R}^{d\times d}$ sampled independently from distributions satisfying $\mathbb{E}[A_i] = \Sigma$ for $\Sigma \succeq \mathbf{0}$, this work provides an $O(d)$-space linear-time single-pass streaming algorithm for estimating the top eigenvector of $\Sigma$. The algorithm nearly matches (and in certain cases improves upon) the accuracy obtained by the standard batch method that computes top eigenvector of the empirical covariance $\frac{1}{n} \sum_{i \in [n]} A_i$ as analyzed by the matrix Bernstein inequality. Moreover, to achieve constant accuracy, our algorithm improves upon the best previous known sample complexities of streaming algorithms by either a multiplicative factor of $O(d)$ or $1/\mathrm{gap}$ where $\mathrm{gap}$ is the relative distance between the top two eigenvalues of $\Sigma$. These results are achieved through a novel analysis of the classic Oja's algorithm, one of the oldest and most popular algorithms for streaming PCA. In particular, this work shows that simply picking a random initial point $w_0$ and applying the update rule $w_{i + 1} = w_i + \eta_i A_i w_i$ suffices to accurately estimate the top eigenvector, with a suitable choice of $\eta_i$. We believe our result sheds light on how to efficiently perform streaming PCA both in theory and in practice and we hope that our analysis may serve as the basis for analyzing many variants and extensions of streaming PCA.
Essential dimension of the moduli stack of polarized K3 surfaces

We will calculate the essential dimension of the moduli stack of polarized K3 surfaces, including the positive char case.
Hadwiger Numbers of Self-complementary Graphs

The Hadwiger number of a graph G, denoted by h(G), is the order of the largest complete minor of G. It is known that for any $$n\equiv 0,1 (\text {mod 4})$$ and any self-complementary graph G with n vertices, $$ \lfloor (n+1)/2 \rfloor \le h(G) \le \lfloor 3n/5\rfloor $$. In this article, we revisit the proof of the lower bound, and we prove that for all $$n\equiv 0,1 (\text {mod 4})$$ and $$ \lfloor (n+1)/2 \rfloor \le h \le \lfloor 3n/5\rfloor $$, there exists a self-complementary graph G with n vertices whose Hadwiger number is h. We visit topological properties of self-complementary graphs.
A Delsarte-Style Proof of the Bukh–Cox Bound

The line packing problem is concerned with the optimal packing of points in real or complex projective space so that the minimum distance between points is maximized. Until recently, all bounds on optimal line packings were known to be derivable from Delsarte’s linear program. Last year, Bukh and Cox introduced a new bound for the line packing problem using completely different techniques. In this paper, we use ideas from the Bukh–Cox proof to find a new proof of the Welch bound, and then we use ideas from Delsarte’s linear program to find a new proof of the Bukh–Cox bound. Hopefully, these unifying principles will lead to further refinements.
Spectra of Cayley graphs of the lamplighter group and random Schrödinger operators

We show that the lamplighter group L has a system of generators for which the spectrum of the discrete Laplacian on the Cayley graph is a union of an interval and a countable set of isolated points accumulating to a point outside this interval. This is the first example of a group with infinitely many gaps in the spectrum of its Cayley graph. The result is obtained by a careful study of spectral properties of a one-parametric family of convolution operators on L. Our results show that the spectrum is a pure point spectrum for each value of the parameter, the eigenvalues are solutions of algebraic equations involving Chebyshev polynomials of the second kind, and the topological structure of the spectrum makes a bifurcation when the parameter passes the points 1 and -1.
Bringing in the Outliers: A Sparse Subspace Clustering Approach to Learn a Dictionary of Mouse Ultrasonic Vocalizations

Mice vocalize in the ultrasonic range during social interactions. These vocalizations are used in neuroscience and clinical studies to tap into complex behaviors and states. The analysis of these ultrasonic vocalizations (USVs) has been traditionally a manual process, which is prone to errors and human bias, and is not scalable to large scale analysis. We propose a new method to automatically create a dictionary of USVs based on a two-step spectral clustering approach, where we split the set of USVs into inlier and outlier data sets. This approach is motivated by the known degrading performance of sparse subspace clustering with outliers. We apply spectral clustering to the inlier data set and later find the clusters for the outliers. We propose quantitative and qualitative performance measures to evaluate our method in this setting, where there is no ground truth. Our approach outperforms two baselines based on k-means and spectral clustering in all of the proposed performance measures, showing greater distances between clusters and more variability between clusters.
Correlated activity favors synergistic processing in local cortical networks in vitro at synaptically relevant timescales.

Neural information processing is widely understood to depend on correlations in neuronal activity. However, whether correlation is favorable or not is contentious. Here, we sought to determine how ...
Lifting the Veil: The Benefits of Cost Transparency

We investigate when and why the disclosure of cost information by firms can increase consumers’ purchase interest.
Thermodynamics of Mixing of n-Alkanes with Polyisobutylene

Abstract The volume changes on mixing polyisobutylene (PIB) with n-pentane, n-hexane, n-heptane, n-octane, n-decane, and n-hexadecane have been determined by direct measurements at 45°, and for n-heptane at 0 and 50° as well. They are negative in every case; the magnitude of the excess volume decreases with chain length, and increases with temperature. These results on volume changes, which are beyond the scope of conventional theories of polymer solutions, are rationally taken into account by the recent statistical mechanical theory of solutions which relates properties of the mixture to characteristics of the pure liquids manifested in their equation-of-state parameters. The negative enthalpies of mixing found by Delmas, Patterson, and Somcynsky for all of these systems with the exception of PIB-n-hexadecane are similarly shown to arise from negative equation-of-state contributions to the enthalpy which reflect differences between the liquid characteristics of n-alkane and PIB. The energy contributed by...