cssi/SciSciGPT-SciSciNet huggingface dataset:
Towards affordable on-track testing for autonomous vehicle — A Kriging-based statistical approach

This paper discusses the use of Kriging model in Automated Vehicle evaluation. We explore how a Kriging model can help reduce the number of experiments or simulations in the Accelerated Evaluation procedure. We also propose an adaptive sampling scheme for selecting samples to construct the Kriging model. Application examples in the lane change scenario are presented to illustrate the proposed methods.
Intermediate Data Caching Optimization for Multi-Stage and Parallel Big Data Frameworks

In the era of big data and cloud computing, large amounts of data are generated from user applications and need to be processed in the datacenter. Data-parallel computing frameworks, such as Apache Spark, are widely used to perform such data processing at scale. Specifically, Spark leverages distributed memory to cache the intermediate results, represented as Resilient Distributed Datasets (RDDs). This gives Spark an advantage over other parallel frameworks for implementations of iterative machine learning and data mining algorithms, by avoiding repeated computation or hard disk accesses to retrieve RDDs. By default, caching decisions are left at the programmer's discretion, and the LRU policy is used for evicting RDDs when the cache is full. However, when the objective is to minimize total work, LRU is woefully inadequate, leading to arbitrarily suboptimal caching decisions. In this paper, we design an algorithm for multi-stage big data processing platforms to adaptively determine and cache the most valuable intermediate datasets that can be reused in the future. Our solution automates the decision of which RDDs to cache: this amounts to identifying nodes in a direct acyclic graph (DAG) representing computations whose outputs should persist in the memory. Our experiment results show that our proposed cache optimization solution can improve the performance of machine learning applications on Spark decreasing the total work to recompute RDDs by 12%.
Learning Latent Local Conversation Modes for Predicting Community Endorsement in Online Discussions

Many social media platforms offer a mechanism for readers to react to comments, both positively and negatively, which in aggregate can be thought of as community endorsement. This paper addresses the problem of predicting community endorsement in online discussions, leveraging both the participant response structure and the text of the comment. The different types of features are integrated in a neural network that uses a novel architecture to learn latent modes of discussion structure that perform as well as deep neural networks but are more interpretable. In addition, the latent modes can be used to weight text features thereby improving prediction accuracy.
Fashionista: A Fashion-aware Graphical System for Exploring Visually Similar Items

To build a fashion recommendation system, we need to help users retrieve fashionable items that are visually similar to a particular query, for reasons ranging from searching alternatives (i.e., substitutes), to generating stylish outfits that are visually consistent, among other applications. In domains like clothing and accessories, such considerations are particularly paramount as the visual appearance of items is a critical feature that guides users' decisions. However, existing systems like Amazon and eBay still rely mainly on keyword search and recommending loosely consistent items (e.g. based on co-purchasing or browsing data), without an interface that makes use of visual information to serve the above needs. In this paper, we attempt to fill this gap by designing and implementing an image-based query system, called Fashionista, which provides a graphical interface to help users efficiently explore those items that are not only visually similar to a given query, but which are also fashionable, as determined by visually-aware recommendation approaches. Methodologically, Fashionista learns a low-dimensional visual space as well as the evolution of fashion trends from large corpora of binary feedback data such as purchase histories of Women's Clothing & Accessories from Amazon, which we use for this demonstration.
A Bernoulli-Gaussian physical watermark for detecting integrity attacks in control systems

We examine the merit of Bernoulli packet drops in actively detecting integrity attacks on control systems. The aim is to detect an adversary who delivers fake sensor measurements to a system operator in order to conceal their effect on the plant. Physical watermarks, or noisy additive Gaussian inputs, have been previously used to detect several classes of integrity attacks in control systems. In this paper, we consider the analysis and design of Gaussian physical watermarks in the presence of packet drops at the control input. On one hand, this enables analysis in a more general network setting. On the other hand, we observe that in certain cases, Bernoulli packet drops can improve detection performance relative to a purely Gaussian watermark. This motivates the joint design of a Bernoulli-Gaussian watermark which incorporates both an additive Gaussian input and a Bernoulli drop process. We characterize the effect of such a watermark on system performance as well as attack detectability in two separate design scenarios. Here, we consider a correlation detector for attack recognition. We then propose efficiently solvable optimization problems to intelligently select parameters of the Gaussian input and the Bernoulli drop process while addressing security and performance trade-offs. Finally, we provide numerical results which illustrate that a watermark with packet drops can indeed outperform a Gaussian watermark.
Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning.

The vision for precision medicine is to use individual patient characteristics to inform a personalized treatment plan that leads to the best possible healthcare for each patient. Mobile technologi...
Direct Visualization of Barrier Crossing Dynamics in a Driven Optical Matter System

A major impediment to a more complete understanding of barrier crossing and other single-molecule processes is the inability to directly visualize the trajectories and dynamics of atoms and molecules in reactions. Rather, the kinetics are inferred from ensemble measurements or the position of a transducer (e.g., an AFM cantilever) as a surrogate variable. Direct visualization is highly desirable. Here, we achieve the direct measurement of barrier crossing trajectories by using optical microscopy to observe position and orientation changes of pairs of Ag nanoparticles, i.e. passing events, in an optical ring trap. A two-step mechanism similar to a bimolecular exchange reaction or the Michaelis–Menten scheme is revealed by analysis that combines detailed knowledge of each trajectory, a statistically significant number of repetitions of the passing events, and the driving force dependence of the process. We find that while the total event rate increases with driving force, this increase is due to an increase...
Kottwitz’s nearby cycles conjecture for a class of unitary Shimura varieties

This paper proves that the nearby cycles complexes on a certain family of PEL local models are central with respect to the convolution product of sheaves on the corresponding affine flag varieties. As a corollary, the semisimple trace functions defined using the action of Frobenius on those nearby cycles complexes are, via the sheaf-function dictionary, in the centers of the corresponding Iwahori–Hecke algebras. This is commonly referred to as Kottwitz’s conjecture. The reductive groups associated with the PEL local models under consideration are unramified unitary similitude groups with even dimension. The proof follows the method of Haines and Ngo (Compos Math 133:117–150, 2002). Upon completion of the first version of this paper, Pappas and Zhu released a preprint (now published as Pappas and Zhu in Invent Math 194(1):147–254, 2013) which contained within its scope the main theorem of this paper. However, the methods of Pappas and Zhu (2013) are very different, and some of the proofs from this paper have been useful in forthcoming work of Haines–Stroh.
Synthesizing Normalized Faces from Facial Identity Features

We present a method for synthesizing a frontal, neutral-expression image of a persons face, given an input face photograph. This is achieved by learning to generate facial landmarks and textures from features extracted from a facial-recognition network. Unlike previous generative approaches, our encoding feature vector is largely invariant to lighting, pose, and facial expression. Exploiting this invariance, we train our decoder network using only frontal, neutral-expression photographs. Since these photographs are well aligned, we can decompose them into a sparse set of landmark points and aligned texture maps. The decoder then predicts landmarks and textures independently and combines them using a differentiable image warping operation. The resulting images can be used for a number of applications, such as analyzing facial attributes, exposure and white balance adjustment, or creating a 3-D avatar.
The time-dependent reconstructed evolutionary process with a key-role for mass-extinction events

The homogeneous reconstructed evolutionary process is a birth-death process without observed extinct lineages. Each species evolves independently with the same diversification rate-speciation rate, λ(t), and extinction rate, μ(t)-that may change over time. The process is commonly applied to model species diversification where the data are reconstructed phylogenies, e.g. trees estimated from present-day molecular data, and used to infer diversification rates. In the present paper I develop the general probability density of a reconstructed tree under any homogeneous, time-dependent birth-death process. I demonstrate how to adapt this probability density when conditioning on the survival of one or two initial lineages, or on the process realizing n species, and also how to transform between the probability density of a reconstructed tree and the probability density of the speciation times. I demonstrate the use of the general time-dependent probability density functions by deriving the probability density of a reconstructed tree under a birth-death-shift model with explicit mass-extinction events. I extend these functions to several special cases, including the pure-birth process, the pure-death process, the birth-death process, and the critical-branching process. Thus, I specify equations for the most commonly used birth-death models in a unified framework (e.g. same condition and same data) using a common notation.
The Kontsevich constants for the volume of the moduli of curves and topological recursion

Author(s): Chapman, Kevin M.; Mulase, Motohico; Safnuk, Brad | Abstract: We give an Eynard-Orantin type topological recursion formula for the canonical Euclidean volume of the combinatorial moduli space of pointed smooth algebraic curves. The recursion comes from the edge removal operation on the space of ribbon graphs. As an application we obtain a new proof of the Kontsevich constants for the ratio of the Euclidean and the symplectic volumes of the moduli space of curves.
Estimating the Fundamental Limits is Easier Than Achieving the Fundamental Limits

We show through case studies that it is easier to estimate the fundamental limits of data processing than to construct the explicit algorithms to achieve those limits. Focusing on binary classification, data compression, and prediction under logarithmic loss, we show that in the finite space setting, when it is possible to construct an estimator of the limits with vanishing error with $n$ samples, it may require at least $n\ln n$ samples to construct an explicit algorithm to achieve the limits.
Active Model Learning and Diverse Action Sampling for Task and Motion Planning

The objective of this work is to augment the basic abilities of a robot by learning to use new sensorimotor primitives to enable the solution of complex long-horizon problems. Solving long-horizon problems in complex domains requires flexible generative planning that can combine primitive abilities in novel combinations to solve problems as they arise in the world. In order to plan to combine primitive actions, we must have models of the preconditions and effects of those actions: under what circumstances will executing this primitive achieve some particular effect in the world? We use, and develop novel improvements on, state-of-the-art methods for active learning and sampling. We use Gaussian process methods for learning the conditions of operator effectiveness from small numbers of expensive training examples collected by experimentation on a robot. We develop adaptive sampling methods for generating diverse elements of continuous sets (such as robot configurations and object poses) during planning for solving a new task, so that planning is as efficient as possible. We demonstrate these methods in an integrated system, combining newly learned models with an efficient continuous-space robot task and motion planner to learn to solve long horizon problems more efficiently than was previously possible.
Interpolative Separable Density Fitting through Centroidal Voronoi Tessellation with Applications to Hybrid Functional Electronic Structure Calculations

The recently developed interpolative separable density fitting (ISDF) decomposition is a powerful way for compressing the redundant information in the set of orbital pairs and has been used to accelerate quantum chemistry calculations in a number of contexts. The key ingredient of the ISDF decomposition is to select a set of nonuniform grid points, so that the values of the orbital pairs evaluated at such grid points can be used to accurately interpolate those evaluated at all grid points. The set of nonuniform grid points, called the interpolation points, can be automatically selected by a QR factorization with column pivoting (QRCP) procedure. This is the computationally most expensive step in the construction of the ISDF decomposition. In this work, we propose a new approach to find the interpolation points based on the centroidal Voronoi tessellation (CVT) method, which offers a much less expensive alternative to the QRCP procedure when ISDF is used in the context of hybrid functional electronic struc...
Time-optimal velocity tracking control for differential drive robots

Abstract Nonholonomic wheeled mobile robots are often required to implement control algorithms designed for holonomic kinematic systems. This creates a velocity tracking problem for an actual wheeled mobile robot. In this paper, we investigate the issue of tracking a desired velocity in the least amount of time, for a differential drive nonholonomic wheeled mobile robot with torque inputs. The Pontryagin Maximum Principle provides time-optimal controls that must be implemented as open-loop commands to the motors. We propose two discontinuous state-based feedback control laws, such that the associated closed-loop systems track a desired velocity in minimum time. The feedback control laws are rigorously shown to produce only time-optimal trajectories, by constructing a regular synthesis for each control law. The availability of these time-optimal feedback control laws makes re-computation of open-loop time-optimal controls (due to changes in the desired velocity or input disturbances) unnecessary.
A Generative Product-of-Filters Model of Audio

Abstract: We propose the product-of-filters (PoF) model, a generative model that decomposes audio spectra as sparse linear combinations of "filters" in the log-spectral domain. PoF makes similar assumptions to those used in the classic homomorphic filtering approach to signal processing, but replaces hand-designed decompositions built of basic signal processing operations with a learned decomposition based on statistical inference. This paper formulates the PoF model and derives a mean-field method for posterior inference and a variational EM algorithm to estimate the model's free parameters. We demonstrate PoF's potential for audio processing on a bandwidth expansion task, and show that PoF can serve as an effective unsupervised feature extractor for a speaker identification task.
Studying cascading overload failures under high penetration of wind generation

While power systems are reliable infrastructures, their complex interconnectivities allow for propagation of disturbances through cascading failures which causes blackouts. Meanwhile the ever increasing penetration level of renewable generation into power grids introduces a massive amount of uncertainty to the grid that might have a severe impact on grid vulnerability to overload cascading failures. There are numerous studies in the literature that focus on modeling cascading failures with different approaches. However, there is a need for studies that simulate cascading failure considering the uncertainty coming from high penetration of renewable generation. In this study, the impacts of wind generation in terms of its penetration and uncertainty levels on grid vulnerability to cascading overload failures are studied. The simulation results on IEEE 300 bus system show that uncertainty coming from wind energy have severe impact on grid vulnerability to cascading overload failures. Results also suggest that higher penetration levels of wind energy if not managed appropriately will add to this severity due to injection of higher uncertainties into the grid.
From trees to seeds: On the inference of the seed from large trees in the uniform attachment model

We study the influence of the seed in random trees grown according to the uniform attachment model, also known as uniform random recursive trees. We show that different seeds lead to different distributions of limiting trees from a total variation point of view. To do this, we construct statistics that measure, in a certain well-defined sense, global “balancedness” properties of such trees. Our paper follows recent results on the same question for the preferential attachment model.
Individual planning in agent populations: exploiting anonymity and frame-action hypergraphs

Interactive partially observable Markov decision processes (I-POMDP) provide a formal framework for planning for a self-interested agent in multiagent settings. An agent operating in a multiagent environment must deliberate about the actions that other agents may take and the effect these actions have on the environment and the rewards it receives. Traditional I-POMDPs model this dependence on the actions of other agents using joint action and model spaces. Therefore, the solution complexity grows exponentially with the number of agents thereby complicating scalability. In this paper, we model and extend anonymity and context-specific independence - problem structures often present in agent populations - for computational gain. We empirically demonstrate the efficiency from exploiting these problem structures by solving a new multiagent problem involving more than 1,000 agents.
Online Dynamic Programming

We consider the problem of repeatedly solving a variant of the same dynamic programming problem in successive trials. An instance of the type of problems we consider is to find a good binary search tree in a changing environment. At the beginning of each trial, the learner probabilistically chooses a tree with the n keys at the internal nodes and the n + 1 gaps between keys at the leaves. The learner is then told the frequencies of the keys and gaps and is charged by the average search cost for the chosen tree. The problem is online because the frequencies can change between trials. The goal is to develop algorithms with the property that their total average search cost (loss) in all trials is close to the total loss of the best tree chosen in hindsight for all trials. The challenge, of course, is that the algorithm has to deal with exponential number of trees. We develop a general methodology for tackling such problems for a wide class of dynamic programming algorithms. Our framework allows us to extend online learning algorithms like Hedge and Component Hedge to a significantly wider class of combinatorial objects than was possible before.
C -EQUIVALENCES OF GRAPHS

Several relations on graphs, including primitive equivalence, ex- plosion equivalence and strong shift equivalence, are examined and shown to preserve either the graph groupoid, a construction of Kumjian, Pask, Rae- burn, and Renault, or the groupoid of a pointed version of the graph. Thus these relations preserve either the isomorphism class or the Morita equiva- lence class of the graph C -algebra, as defined by Kumjian, Pask, and Rae- burn.
Bayesian nonparametric kernel-learning

Kernel methods are ubiquitous tools in machine learning. However, there is often little reason for the common practice of selecting a kernel a priori. Even if a universal approximating kernel is selected, the quality of the finite sample estimator may be greatly affected by the choice of kernel. Furthermore, when directly applying kernel methods, one typically needs to compute a $N \times N$ Gram matrix of pairwise kernel evaluations to work with a dataset of $N$ instances. The computation of this Gram matrix precludes the direct application of kernel methods on large datasets, and makes kernel learning especially difficult. In this paper we introduce Bayesian nonparmetric kernel-learning (BaNK), a generic, data-driven framework for scalable learning of kernels. BaNK places a nonparametric prior on the spectral distribution of random frequencies allowing it to both learn kernels and scale to large datasets. We show that this framework can be used for large scale regression and classification tasks. Furthermore, we show that BaNK outperforms several other scalable approaches for kernel learning on a variety of real world datasets.
Qualitatively characterizing neural network optimization problems

Abstract: Training neural networks involves solving large-scale non-convex optimization problems. This task has long been believed to be extremely difficult, with fear of local minima and other obstacles motivating a variety of schemes to improve optimization, such as unsupervised pretraining. However, modern neural networks are able to achieve negligible training error on complex tasks, using only direct training with stochastic gradient descent. We introduce a simple analysis technique to look for evidence that such networks are overcoming local optima. We find that, in fact, on a straight path from initialization to solution, a variety of state of the art neural networks never encounter any significant obstacles.
Irrational behavior of algebraic discrete valuations

Abstract We study algebraic discrete valuations dominating normal local domains of dimension two. We construct a family of examples to show that the Hilbert–Samuel function of the associated graded ring of the valuation can fail to be asymptotically of the form: quasi-polynomial plus a bounded function. We also show that the associated multiplicity can be irrational, or even transcendental.
Ranks of Jacobians in towers of function fields

.2. Endomorphismalgebrasof superelliptic Jacobians2.1. Notation. In this section, we collect some results on the endomorphism al-gebras of certain superelliptic Jacobians. Throughout the paper, k will denote analgebraic closure of k.If X and Y are abelian varieties over k, then we write End(X) and Hom(X,Y)for the corresponding ring and group of homomorphisms over k.Let f be a non-constant polynomial with coeﬃcients in k and without multipleroots. We write m for the degree of f, R
Stanley sequences with odd character

Given a set of integers containing no 3-term arithmetic progressions, one constructs a Stanley sequence by choosing integers greedily without forming such a progression. Independent Stanley sequences are a "well-structured" class of Stanley sequences with two main parameters: the character $\lambda(A)$ and the repeat factor $\rho(A)$. Rolnick conjectured that for every $\lambda \in \mathbb{N}_0\backslash\{1, 3, 5, 9, 11, 15\}$, there exists an independent Stanley sequence $S(A)$ such that $\lambda(A) =\lambda$. This paper demonstrates that $\lambda(A) \not\in \{1, 3, 5, 9, 11, 15\}$ for any independent Stanley sequence $S(A)$.
The Bruhat order on clans

We give an explicit description of the closure containment order (or "Bruhat order") on the set of orbits of $$GL_p \times GL_q$$GLp×GLq on the flag variety $$GL_{p+q}/B$$GLp+q/B, relative to the parametrization of the orbits by combinatorial objects called "clans". This leads to a corresponding description of the closures of such orbits as sets of flags satisfying certain incidence conditions encoded by the parametrizing clans.
Normal bundles of rational curves in projective space

Let $$b_{\bullet }$$ be a sequence of integers $$1 < b_1 \le b_2 \le \cdots \le b_{n-1}$$ . Let $${\text {M}}_e(b_{\bullet })$$ be the space parameterizing nondegenerate, immersed, rational curves of degree e in $$\mathbb {P}^n$$ such that the normal bundle has the splitting type $$\bigoplus _{i=1}^{n-1}\mathcal {O}(e+b_i)$$ . When $$n=3$$ , celebrated results of Eisenbud, Van de Ven, Ghione and Sacchiero show that $${\text {M}}_e(b_{\bullet })$$ is irreducible of the expected dimension. We show that when $$n \ge 5$$ , these loci are generally reducible with components of higher than the expected dimension. We give examples where the number of components grows linearly with n. These generalize an example of Alzati and Re.
SSH (Sketch, Shingle, & Hash) for Indexing Massive-Scale Time Series.

Similarity search on time series is a frequent operation in large-scale data-driven applications. Sophisticated similarity measures are standard for time series matching, as they are usually misaligned. Dynamic Time Warping or DTW is the most widely used similarity measure for time series because it combines alignment and matching at the same time. However, the alignment makes DTW slow. To speed up the expensive similarity search with DTW, branch and bound based pruning strategies are adopted. However, branch and bound based pruning are only useful for very short queries (low dimensional time series), and the bounds are quite weak for longer queries. Due to the loose bounds branch and bound pruning strategy boils down to a brute-force search. To circumvent this issue, we design SSH (Sketch, Shingle, & Hashing), an efficient and approximate hashing scheme which is much faster than the state-of-the-art branch and bound searching technique: the UCR suite. SSH uses a novel combination of sketching, shingling and hashing techniques to produce (probabilistic) indexes which align (near perfectly) with DTW similarity measure. The generated indexes are then used to create hash buckets for sub-linear search. Our results show that SSH is very effective for longer time sequence and prunes around 95% candidates, leading to the massive speedup in search with DTW. Empirical results on two large-scale benchmark time series data show that our proposed method can be around 20 times faster than the state-of-the-art package (UCR suite) without any significant loss in accuracy.
An Improved Bound for Minimizing the Total Weighted Completion Time of Coflows in Datacenters

In data-parallel computing frameworks, intermediate parallel data is often produced at various stages which needs to be transferred among servers in the datacenter network (e.g., the shuffle phase in MapReduce). A stage often cannot start or be completed unless all the required data pieces from the preceding stage are received. Coflow is a recently proposed networking abstraction to capture such communication patterns. We consider the problem of efficiently scheduling coflows with release dates in a shared datacenter network so as to minimize the total weighted completion time of coflows. Several heuristics have been proposed recently to address this problem, as well as a few polynomial-time approximation algorithms with provable performance guarantees. Our main result in this paper is a polynomial-time deterministic algorithm that improves the prior known results. Specifically, we propose a deterministic algorithm with approximation ratio of 5, which improves the prior best known ratio of 12. For the special case when all coflows are released at time zero, our deterministic algorithm obtains approximation ratio of 4 which improves the prior best known ratio of 8. The key ingredient of our approach is an improved linear program formulation for sorting the coflows followed by a simple list scheduling policy. Extensive simulation results, using both synthetic and real traffic traces, are presented that verify the performance of our algorithm and show improvement over the prior approaches.
Occupancy distributions of membrane proteins in heterogeneous liposome populations.

Abstract Studies of membrane protein structure and function often rely on reconstituting the protein into lipid bilayers through the formation of liposomes. Many measurements conducted in proteoliposomes, e.g. transport rates, single-molecule dynamics, monomer-oligomer equilibrium, require some understanding of the occupancy statistics of the liposome population for correct interpretation of the results. In homogenous liposomes, this is easy to calculate as the act of protein incorporation can be described by the Poisson distribution. However, in reality, liposomes are heterogeneous, which alters the statistics of occupancy in several ways. Here, we determine the liposome occupancy distribution for membrane protein reconstitution while considering liposome size heterogeneity. We calculate the protein occupancy for a homogenous population of liposomes with radius r = 200 nm, representing an idealization of vesicles extruded through 400 nm pores and compare it to the right-skewed distribution of 400 nm 2:1 POPE:POPG vesicles. As is the case for E. coli polar lipids, this synthetic composition yields a sub-population of small liposomes, 25–30 nm in radius with a long tail of larger vesicles. Previously published microscopy data of the co-localization of the CLC-ec1 Cl−/H+ transporter with liposomes, and vesicle occupancy measurements using functional transport assays, shows agreement with the heterogeneous 2:1 POPE:POPG population. Next, distributions of 100 nm and 30 nm extruded 2:1 POPE:POPG liposomes are measured by cryo-electron microscopy, demonstrating that extrusion through smaller pores does not shift the peak, but reduces polydispersity arising from large liposomes. Single-molecule photobleaching analysis of CLC-ec1-Cy5 shows the 30 nm extruded population increases the ‘Poisson-dilution’ range, reducing the probability of vesicles with more than one protein at higher protein/lipid densities. These results demonstrate that the occupancy distributions of membrane proteins into vesicles can be accurately predicted in heterogeneous populations with experimental knowledge of the liposome size distribution. This article is part of a Special Issue entitled: Molecular biophysics of membranes and membrane proteins.
Imperfect gaps in Gap-ETH and PCPs

We study the role of perfect completeness in probabilistically checkable proof systems (PCPs) and give a way to transform a PCP with imperfect completeness to one with perfect completeness, when the initial gap is a constant. We show that PCPc,s[r, q] ⊆ PCP1,s'[r + O(1), q + O(r)] for c --- s = Ω(1) which in turn implies that one can convert imperfect completeness to perfect in linear-sized PCPs for NP with a O(log n) additive loss in the query complexity q. We show our result by constructing a "robust circuit" using threshold gates. These results are a gap amplification procedure for PCPs, (when completeness is not 1) analogous to questions studied in parallel repetition [22] and pseudorandomness [15] and might be of independent interest.We also investigate the time-complexity of approximating perfectly satisfiable instances of 3SAT versus those with imperfect completeness. We show that the Gap-ETH conjecture without perfect completeness is equivalent to Gap-ETH with perfect completeness, i.e. MAX 3SAT(1 --- ∈, 1 --- δ), δ > ∈ has 2o(n) algorithms if and only if MAX 3SAT(1, 1 --- δ) has 2o(n) algorithms. We also relate the time complexities of these two problems in a more fine-grained way to show that T2(n) ≤ T1(n(log log n)O(1)), where T1(n),T2(n) denote the randomized time-complexity of approximating MAX 3SAT with perfect and imperfect completeness respectively.
Social Media-based User Embedding: A Literature Review

Automated representation learning is behind many recent success stories in machine learning. It is often used to transfer knowledge learned from a large dataset (e.g., raw text) to tasks for which only a small number of training examples are available. In this paper, we review recent advance in learning to represent social media users in low-dimensional embeddings. The technology is critical for creating high performance social media-based human traits and behavior models since the ground truth for assessing latent human traits and behavior is often expensive to acquire at a large scale. In this survey, we review typical methods for learning a unified user embeddings from heterogeneous user data (e.g., combines social media texts with images to learn a unified user representation). Finally we point out some current issues and future directions.
Integrating discussion and digital media to increase classroom interaction

Abstract A push has been made over the past decade to utilize “short and sweet” media clips in the classroom to engage students with the material while being cognizant of shortening attention spans. While useful, short clips may lack depth and not be able to connect topics across lessons. Longer video clips often have depth, but students may not be as engaged with the material. This paper presents a method of “breaking” longer clips into smaller segments to account for potential attention deficits and integrates classroom discussion in between segments to engage students with the material.
Technical, Political, and Social Issues in Archaeological Collections Data Management

Managing collections means ensuring that the data about them are useful, available, and accurate. In addition to the technical aspects of data management, there are layers of political and social structure that direct the construction and use of collections data. The Minnesota Historical Society (MNHS) employs a set of data standards that allows us to gather electronic cataloging data from a wide community of archaeology researchers who are depositing collections at our institution. Though met with initial resistance, these standards have facilitated publication in Open Context as linked open data. Furthermore, institutional discussions concerning Creative Commons licensing and the cultural sensitivity of collections data were precipitated by publication, highlighting the role of social agreement in data management. We found that successful employment of data standards must take into account the needs of the various stakeholders and further their interests. Standards will be most useful and successful when they are lightweight, are supported by training and documentation, and exist as part of a system that allows for more than one way to characterize the collections.
Modeling regular, process—structured networks *

An interesting strategy to exploit micro-technology is to interconnect microcomputers (i.e., microprocessor with local memory) in a regular dataflow network of low degree. The network is regular so that each computer is a similar "building block" with regard to the number of connecting data buses. The number of buses is small not only because microcomputers are inherently limited in their bus capacity but also because many incident buses lead to switching and "memory port" complexity, which is difficult to handle by micro-circuits. Such complexity is one of the reasons why shared memory "mainframe" computers are relatively expensive. In essence, the low-degree, regular network approach replaces the hard-wired switching with programmed message-passing. Since the processor node will not be fully utilized by productive computing, some of the capacity can profitably be used for such message-handling.
The Precariat Class Structure and Income Inequality among US Workers: 1980–2018:

I apply the precariat class schema developed by Standing to the US workforce to illustrate an increased polarization between those who do and do not have quality jobs from 1980 to 2018. I use a dec...
Rigidity for von Neumann algebras

We survey some of the progress made recently in the classification of von Neumann algebras arising from countable groups and their measure preserving actions on probability spaces. We emphasize results which provide classes of (W$^*$-superrigid) actions that can be completely recovered from their von Neumann algebras and II$_1$ factors that have a unique Cartan subalgebra. We also present cocycle superrigidity theorems and some of their applications to orbit equivalence. Finally, we discuss several recent rigidity results for von Neumann algebras associated to groups.
D-modules in birational geometry

It is well known that numerical quantities arising from the theory of D-modules are related to invariants of singularities in birational geometry. This paper surveys a deeper relationship between the two areas, where the numerical connections are enhanced to sheaf theoretic constructions facilitated by the theory of mixed Hodge modules. The emphasis is placed on the recent theory of Hodge ideals.
Molecular profiling predicts meningioma recurrence and reveals loss of DREAM complex repression in aggressive tumors

Meningiomas account for one-third of all primary brain tumors. Although typically benign, about 20% of meningiomas are aggressive, and despite the rigor of the current histopathological classification system there remains considerable uncertainty in predicting tumor behavior. Here, we analyzed 160 tumors from all 3 World Health Organization (WHO) grades (I through III) using clinical, gene expression, and sequencing data. Unsupervised clustering analysis identified 3 molecular types (A, B, and C) that reliably predicted recurrence. These groups did not directly correlate with the WHO grading system, which classifies more than half of the tumors in the most aggressive molecular type as benign. Transcriptional and biochemical analyses revealed that aggressive meningiomas involve loss of the repressor function of the DREAM complex, which results in cell-cycle activation; only tumors in this category tend to recur after full resection. These findings should improve our ability to predict recurrence and develop targeted treatments for these clinically challenging tumors.
Computational Screening of Current Collectors for Enabling Anode-Free Lithium Metal Batteries

Lithium metal cells are key for achieving high specific energy for electrification of transportation and aviation. Anode-free cells are lithium metal cells involving no excess lithium with the high...
Hadrons and Nuclei

In 2018, the USQCD Collaboration’s Executive Committee organized several subcommittees to recognize future opportunities and formulate possible goals for lattice field theory calculations in several physics areas. The conclusions of these studies, along with community input, are presented in seven white papers. Here, we discuss opportunities for lattice QCD calculations related to the structure and spectroscopy of hadrons and nuclei. An overview of recent lattice calculations of the structure of the proton and other hadrons is presented along with prospects for future extensions. Progress and prospects of hadronic spectroscopy and the study of resonances in the light, strange and heavy quark sectors are summarized. Finally recent advances in the study of light nuclei from lattice QCD are addressed and the scope of future investigations that are currently envisioned is outlined.
Primordial fluctuations of scale factor in closed Universe in Einstein$-$Cartan gravity

We consider a homogeneous and isotropic Universe, described by the minisuperspace Lagrangian with the scale factor as a generalized coordinate. We show that the energy of a closed Universe is zero....
Microgrid Value of Ramping

The growing penetration of renewable generation in distribution networks, primarily deployed by end-use electricity customers, is changing the traditional load profile and inevitably makes supply-load balancing more challenging for grid operators. Leveraging the potential flexibility of existing microgrids, that is to help with supply-load balance locally, is a viable solution to cope with this challenge and mitigate existing net load variability and intermittency in distribution networks. This paper discusses this timely topic and determines the microgrid value of ramping based on its available reserve using a cost-benefit analysis. To this end, a microgrid ramping-oriented optimal scheduling model is developed and tested through numerical simulations to prove the effectiveness and the merits of the proposed approach in microgrid ramping valuation.
Adaptive introgression during environmental change can weaken reproductive isolation

Anthropogenic climate change is an urgent threat to species diversity1,2. One aspect of this threat is the merging of species through increased hybridization3. The primary mechanism for this collapse is thought to be the weakening of ecologically mediated reproductive barriers, as demonstrated in cases of ‘reverse speciation’4,5. Here, we expand on this idea and show that adaptive introgression between species adapting to a shared, moving climatic optimum can readily weaken any reproductive barrier, including those that are completely independent of climate. Using genetically explicit forward-time simulations, we show that genetic linkage between alleles conferring adaptation to a changing climate and alleles conferring reproductive isolation (intrinsic and/or non-climatic extrinsic) can lead to adaptive introgression facilitating the homogenization of reproductive isolation alleles. This effect causes the decay of species boundaries across a broad and biologically realistic parameter space. We explore how the magnitude of this effect depends on the rate of climate change, the genetic architecture of adaptation, the initial degree of reproductive isolation, the degree to which reproductive isolation is intrinsic versus extrinsic and the mutation rate. These results highlight a previously unexplored effect of rapid climate change on species diversity. Increased hybridization has the potential to threaten species diversity. Here population genetic computer simulations show that climate-induced adaptive introgression could readily lead to hybridization even when reproductive isolation is independent from climate.
Gaussian Conditionally Markov Sequences: Dynamic Models and Representations of Reciprocal and Other Classes

Conditionally Markov (CM) sequences are powerful mathematical tools for modeling problems. One class of CM sequences is the reciprocal sequence. In application, we need not only CM dynamic models, but also know how to design model parameters. Models of two important classes of nonsingular Gaussian (NG) CM sequences, called $CM_L$ and $CM_F$ models, and a model of the NG reciprocal sequence, called reciprocal $CM_L$ model, were presented in our previous works and their applications were discussed. In this paper, these models are studied in more detail, in particular their parameter design. It is shown that every reciprocal $CM_L$ model can be induced by a Markov model. Then, parameters of each reciprocal $CM_L$ model can be obtained from those of the Markov model. Also, it is shown that an NG $CM_L$ ( $CM_F$ ) sequence can be represented by a sum of an NG Markov sequence and an uncorrelated NG vector. This (necessary and sufficient) representation provides a basis for designing parameters of a $CM_L$ ( $CM_F$ ) model. From the CM viewpoint, a representation is also obtained for NG reciprocal sequences. This representation is simple and reveals an important property of reciprocal sequences. As a result, the significance of studying reciprocal sequences from the CM viewpoint is demonstrated. A full spectrum of dynamic models from a $CM_L$ model to a reciprocal $CM_L$ model is also presented. Some examples are presented for illustration.
Enhancing Adversarial Defense by k-Winners-Take-All

We propose a simple change to existing neural network structures for better defending against gradient-based adversarial attacks. Instead of using popular activation functions (such as ReLU), we advocate the use of k-Winners-Take-All (k-WTA) activation, a C0 discontinuous function that purposely invalidates the neural network model’s gradient at densely distributed input data points. The proposed k-WTA activation can be readily used in nearly all existing networks and training methods with no significant overhead. Our proposal is theoretically rationalized. We analyze why the discontinuities in k-WTA networks can largely prevent gradient-based search of adversarial examples and why they at the same time remain innocuous to the network training. This understanding is also empirically backed. We test k-WTA activation on various network structures optimized by a training method, be it adversarial training or not. In all cases, the robustness of k-WTA networks outperforms that of traditional networks under white-box attacks.
Matrix Scaling Limits in Finitely Many Iterations

The alternate row and column scaling algorithm applied to a positive \(n\times n\) matrix A converges to a doubly stochastic matrix S(A), sometimes called the Sinkhorn limit of A. For every positive integer n, a two parameter family of row but not column stochastic \(n\times n\) positive matrices is constructed that become doubly stochastic after exactly one column scaling.
Topological inference of manifolds with boundary

Abstract Given a set of data points sampled from some underlying space, there are two important challenges in geometric and topological data analysis when dealing with sampled data: reconstruction – how to assemble discrete samples into global structures, and inference – how to extract geometric and topological information from data that are high-dimensional, incomplete and noisy. Niyogi et al. (2008) have shown that by constructing an offset of the samples using a suitable offset parameter could provide reconstructions that preserve homotopy types therefore homology for densely sampled smooth submanifolds of Euclidean space without boundary. Chazal et al. (2009) and Attali et al. (2013) have introduced a parameterized set of sampling conditions that extend the results of Niyogi et al. to a large class of compact subsets of Euclidean space. Our work tackles data problems that fill a gap between the work of Niyogi et al. and Chazal et al. In particular, we give a probabilistic notion of sampling conditions for manifolds with boundary that could not be handled by existing theories. We also give stronger results that relate topological equivalence between the offset and the manifold as a deformation retract.