cssi/SciSciGPT-SciSciNet huggingface dataset:
Fast algorithms for segmented regression

We study the fixed design segmented regression problem: Given noisy samples from a piecewise linear function f, we want to recover f up to a desired accuracy in mean-squared error. Previous rigorous approaches for this problem rely on dynamic programming (DP) and, while sample efficient, have running time quadratic in the sample size. As our main contribution, we provide new sample near-linear time algorithms for the problem that - while not being minimax optimal - achieve a significantly better sample-time tradeoff on large datasets compared to the DP approach. Our experimental evaluation shows that, compared with the DP approach, our algorithms provide a convergence rate that is only off by a factor of 2 to 4, while achieving speedups of three orders of magnitude.
Moduli of twisted orbifold sheaves

Abstract We study stacks of slope-semistable twisted sheaves on orbisurfaces with projective coarse spaces and prove that in certain cases they have many of the asymptotic properties enjoyed by the moduli of slope-semistable sheaves on smooth projective surfaces.
Verisig: verifying safety properties of hybrid systems with neural network controllers

This paper presents Verisig, a hybrid system approach to verifying safety properties of closed-loop systems using neural networks as controllers. We focus on sigmoid-based networks and exploit the fact that the sigmoid is the solution to a quadratic differential equation, which allows us to transform the neural network into an equivalent hybrid system. By composing the network's hybrid system with the plant's, we transform the problem into a hybrid system verification problem which can be solved using state-of-the-art reachability tools. We show that reachability is decidable for networks with one hidden layer and decidable for general networks if Schanuel's conjecture is true. We evaluate the applicability and scalability of Verisig in two case studies, one from reinforcement learning and one in which the neural network is used to approximate a model predictive controller.
Online Learning of Combinatorial Objects via Extended Formulation

The standard techniques for online learning of combinatorial objects perform multiplicative updates followed by projections into the convex hull of all the objects. However, this methodology can be expensive if the convex hull contains many facets. For example, the convex hull of $n$-symbol Huffman trees is known to have exponentially many facets (Maurras et al., 2010). We get around this difficulty by exploiting extended formulations (Kaibel, 2011), which encode the polytope of combinatorial objects in a higher dimensional "extended" space with only polynomially many facets. We develop a general framework for converting extended formulations into efficient online algorithms with good relative loss bounds. We present applications of our framework to online learning of Huffman trees and permutations. The regret bounds of the resulting algorithms are within a factor of $O(\sqrt{\log(n)})$ of the state-of-the-art specialized algorithms for permutations, and depending on the loss regimes, improve on or match the state-of-the-art for Huffman trees. Our method is general and can be applied to other combinatorial objects.
Self-supervised learning of motion capture

Current state-of-the-art solutions for motion capture from a single camera are optimization driven: they optimize the parameters of a 3D human model so that its re-projection matches measurements in the video (e.g. person segmentation, optical flow, keypoint detections etc.). Optimization models are susceptible to local minima. This has been the bottleneck that forced using clean green-screen like backgrounds at capture time, manual initialization, or switching to multiple cameras as input resource. In this work, we propose a learning based motion capture model for single camera input. Instead of optimizing mesh and skeleton parameters directly, our model optimizes neural network weights that predict 3D shape and skeleton configurations given a monocular RGB video. Our model is trained using a combination of strong supervision from synthetic data, and self-supervision from differentiable rendering of (a) skeletal keypoints, (b) dense 3D mesh motion, and (c) human-background segmentation, in an end-to-end framework. Empirically we show our model combines the best of both worlds of supervised learning and test-time optimization: supervised learning initializes the model parameters in the right regime, ensuring good pose and surface initialization at test time, without manual effort. Self-supervision by back-propagating through differentiable rendering allows (unsupervised) adaptation of the model to the test data, and offers much tighter fit than a pretrained fixed model. We show that the proposed model improves with experience and converges to low-error solutions where previous optimization methods fail.
PARAPH: Presentation Attack Rejection by Analyzing Polarization Hypotheses

For applications such as airport border control, biometric technologies that can process many capture subjects quickly, efficiently, with weak supervision, and with minimal discomfort are desirable. Facial recognition is particularly appealing because it is minimally invasive yet offers relatively good recognition performance. Unfortunately, the combination of weak supervision and minimal invasiveness makes even highly accurate facial recognition systems susceptible to spoofing via presentation attacks. Thus, there is great demand for an effective and low cost system capable of rejecting such attacks. To this end we introduce PARAPH – a novel hardware extension that exploits different measurements of light polarization to yield an image space in which presentation media are readily discernible from Bona Fide facial characteristics. The PARAPH system is inexpensive with an added cost of less than 10 US dollars. The system makes two polarization measurements in rapid succession, allowing them to be approximately pixel-aligned, with a frame rate limited by the camera, not the system. There are no moving parts above the molecular level, due to the efficient use of twisted nematic liquid crystals. We present evaluation images using three presentation attack media next to an actual face – high quality photos on glossy and matte paper and a video of the face on an LCD. In each case, the actual face in the image generated by PARAPH is structurally discernible from the presentations, which appear either as noise (print attacks) or saturated images (replay attacks).
On Bergeron's Positivity Problem for $q$-Binomial Coefficients

F. Bergeron recently asked the intriguing question whether $\binom{b+c}{b}_q -\binom{a+d}{d}_q$ has nonnegative coefficients as a polynomial in $q$, whenever $a,b,c,d$ are positive integers, $a$ is the smallest, and $ad=bc$. We conjecture that, in fact, this polynomial is also always unimodal, and combinatorially show our conjecture for $a\le 3$ and any $b,c\ge 4$. The main ingredient will be a novel (and rather technical) application of Zeilberger's KOH theorem.
An improved physics model for multi-material identification in photon counting CT

Photon-counting computed tomography (PCCT) with energy discrimination capabilities hold great potentials to improve the limitations of the conventional CT, including better signal-to-noise ratio (SNR), improved contrast-to-noise ratio (CNR), lower radiation dose, and most importantly, simultaneous multiple material identification. One potential way of material identification is via calculation of effective atomic number (Zeff) and effective electron density (peeff) from PCCT image data. However, the current methods for calculating effective atomic number and effective electron density from PCCT image data are mostly based on semi-empirical models and accordingly are not sufficiently accurate. Here, we present a physics-based model to calculate the effective atomic number and effective electron density of various matters, including single element substances, molecular compounds, and multi-material mixtures as well. The model was validated over several materials under various combinations of energy bins. A PCCT system was simulated to generate the PCCT image data, and the proposed model was applied to the PCCT image data. Our model yielded a relative standard deviations for effective atomic numbers and effective electron densities at less than 1%. Our results further showed that five different materials can be simultaneously identified and well separated in a Zeff − peeff map. The model could serve as a basis for simultaneous material identification from PCCT.
Transport modeling of sedimenting particles in a turbulent pipe flow using Euler–Lagrange large eddy simulation

Abstract A volume-filtered Euler–Lagrange large eddy simulation methodology is used to predict the physics of turbulent liquid–solid slurry flow through a horizontal periodic pipe. A dynamic Smagorinsky model based on Lagrangian averaging is employed to account for the sub-filter scale effects in the liquid phase. A fully conservative immersed boundary method is used to account for the pipe geometry on a uniform cartesian grid. The liquid and solid phases are coupled through volume fraction and momentum exchange terms. Particle–particle and particle–wall collisions are modeled using a soft-sphere approach. Three simulations are performed by varying the superficial liquid velocity to be consistent with the experimental data by Dahl et al. (2003). Depending on the liquid flow rate, a particle bed can form and develop different patterns, which are discussed in light of regime diagrams proposed in the literature. The fluctuation in the height of the liquid-bed interface is characterized to understand the space and time evolution of these patterns. Statistics of engineering interest such as mean velocity, mean concentration, and mean streamwise pressure gradient driving the flow are extracted from the numerical simulations and presented. Sand hold-up calculated from the simulation results suggest that this computational strategy is capable of predicting critical deposition velocity.
A minimax approach to supervised learning

Given a task of predicting Y from X, a loss function L, and a set of probability distributions r on (X, Y), what is the optimal decision rule minimizing the worst-case expected loss over Γ? In this paper, we address this question by introducing a generalization of the maximum entropy principle. Applying this principle to sets of distributions with marginal on X constrained to be the empirical marginal, we provide a minimax interpretation of the maximum likelihood problem over generalized linear models as well as some popular regularization schemes. For quadratic and logarithmic loss functions we revisit well-known linear and logistic regression models. Moreover, for the 0-1 loss we derive a classifier which we call the minimax SVM. The minimax SVM minimizes the worst-case expected 0-1 loss over the proposed Γ by solving a tractable optimization problem. We perform several numerical experiments to show the power of the minimax SVM in outperforming the SVM.
On Nodes of Small Degrees and Degree Profile in Preferential Dynamic Attachment Circuits

We investigate the joint distribution of nodes of small degrees and the degree profile in preferential dynamic attachment circuits. In particular, we study the joint asymptotic distribution of the number of the nodes of outdegree 0 (terminal nodes) and outdegree 1 in a very large circuit. The expectation and variance of the number of those two types of nodes are both asymptotically linear with respect to the age of the circuit. We show that the numbers of nodes of outdegree 0 and 1 asymptotically follow a two-dimensional Gaussian law via multivariate martingale methods. The rate of convergence is derived analytically. We also study the exact distribution of the degree of a node, as the circuit ages, via a series of Polya-Eggenberger urn models with “hiccups” in between. The exact expectation and variance of the degree of nodes are determined by recurrence methods. Phase transitions of these degrees are discussed briefly. This is an extension of the abstract (Zhang 2016).
Nonlinear Langevin Equations for Wandering Patterns in Stochastic Neural Fields

We analyze the effects of additive, spatially extended noise on spatiotemporal patterns in continuum neural fields. Our main focus is how fluctuations impact patterns when they are weakly coupled to an external stimulus or another equivalent pattern. Showing the generality of our approach, we study both propagating fronts and stationary bumps. Using a separation of time scales, we represent the effects of noise in terms of a phase-shift of a pattern from its uniformly translating position at long time scales, and fluctuations in the pattern profile around its instantaneous position at short time scales. In the case of a stimulus-locked front, we show that the phase-shift satisfies a nonlinear Langevin equation (SDE) whose deterministic part has a unique stable fixed point. Using a linear-noise approximation, we thus establish that wandering of the front about the stimulus-locked state is given by an Ornstein--Uhlenbeck (OU) process. Analogous results hold for the relative phase-shift between a pair of mut...
On axioms of frobenius like structure in the theory of arrangements

A Frobenius manifold is a manifold with a flat metric and a Frobenius algebra structure on tangent spaces at points of the manifold such that the structure constants of multiplication are given by third derivatives of a potential function on the manifold with respect to flat coordinates. In this paper we present a modification of that notion coming from the theory of arrangements of hyperplanes. Namely, given natural numbers $n>k$, we have a flat $n$-dimensional manifold and a vector space $V$ with a non-degenerate symmetric bilinear form and an algebra structure on $V$, depending on points of the manifold, such that the structure constants of multiplication are given by $2k+1$-st derivatives of a potential function on the manifold with respect to flat coordinates. We call such a structure a Frobenius like structure . Such a structure arises when one has a family of arrangements of $n$ affine hyperplanes in ${\mathbb C}^k$ depending on parameters so that the hyperplanes move parallely to themselves when the parameters change. In that case a Frobenius like structure arises on the base ${\mathbb C}^n$ of the family.
Evaluating small sphere limit of the Wang-Yau quasi-local energy

In this article, we study the small sphere limit of the Wang–Yau quasi-local energy defined in Wang and Yau (Phys Rev Lett 102(2):021101, 2009, Commun Math Phys 288(3):919–942, 2009). Given a point p in a spacetime N, we consider a canonical family of surfaces approaching p along its future null cone and evaluate the limit of the Wang–Yau quasi-local energy. The evaluation relies on solving an “optimal embedding equation” whose solutions represent critical points of the quasi-local energy. For a spacetime with matter fields, the scenario is similar to that of the large sphere limit found in Chen et al. (Commun Math Phys 308(3):845–863, 2011). Namely, there is a natural solution which is a local minimum, and the limit of its quasi-local energy recovers the stress-energy tensor at p. For a vacuum spacetime, the quasi-local energy vanishes to higher order and the solution of the optimal embedding equation is more complicated. Nevertheless, we are able to show that there exists a solution that is a local minimum and that the limit of its quasi-local energy is related to the Bel–Robinson tensor. Together with earlier work (Chen et al. 2011), this completes the consistency verification of the Wang–Yau quasi-local energy with all classical limits.
GaKCo: a Fast GApped k-mer string Kernel using COunting

String Kernel (SK) techniques, especially those using gapped k-mers as features (gk), have obtained great success in classifying sequences like DNA, protein, and text. However, the state-of-the-art gk-SK runs extremely slow when we increase the dictionary size (\(\varSigma \)) or allow more mismatches (M). This is because current gk-SK uses a trie-based algorithm to calculate co-occurrence of mismatched substrings resulting in a time cost proportional to \(O(\varSigma ^{M})\). We propose a fast algorithm for calculating Gapped k-mer Kernel using Counting (GaKCo). GaKCo uses associative arrays to calculate the co-occurrence of substrings using cumulative counting. This algorithm is fast, scalable to larger \(\varSigma \) and M, and naturally parallelizable. We provide a rigorous asymptotic analysis that compares GaKCo with the state-of-the-art gk-SK. Theoretically, the time cost of GaKCo is independent of the \(\varSigma ^{M}\) term that slows down the trie-based approach. Experimentally, we observe that GaKCo achieves the same accuracy as the state-of-the-art and outperforms its speed by factors of 2, 100, and 4, on classifying sequences of DNA (5 datasets), protein (12 datasets), and character-based English text (2 datasets). (GaKCo is shared as an open source tool at https://github.com/QData/GaKCo-SVM). Code and data related to this chapter are available at: https://doi.org/10.6084/m9.figshare.5434825.
Nudging at Scale: Experimental Evidence from FAFSA Completion Campaigns

Abstract Do successful local nudge interventions maintain efficacy when scaled state or nationwide? We investigate, through two randomized controlled trials, the impact of a national and state-level campaign encouraging students to apply for financial aid for college. The campaigns collectively reached over 800,000 students, with multiple treatment arms patterned after prior local interventions in order to explore potential mechanisms. We find no impacts on aid receipt or college enrollment overall or for any subgroups. We find no evidence that different approaches to message framing, delivery, or timing, or access to one-on-one advising affected campaign efficacy. We discuss why nudge strategies that work locally may be hard to scale effectively.
Towards a theory of multi-parameter geometrical variational problems: Fibre bundles, differential forms, and Riemannian quasiconvexity

We are concerned with the relaxation and existence theories of a general class of geometrical minimisation problems, with action integrals defined via differential forms over fibre bundles. We find natural algebraic and analytic conditions which give rise to a relaxation theory. Moreover, we propose the notion of ``Riemannian quasiconvexity'' for cost functions whose variables are differential forms on Riemannian manifolds, which extends the classical quasiconvexity condition in the Euclidean settings. The existence of minimisers under the Riemannian quasiconvexity condition has been established. This work may serve as a tentative generalisation of the framework developed in the recent paper: B. Dacorogna and W. Gangbo, Quasiconvexity and relaxation in optimal transportation of closed differential forms, \textit{Arch. Ration. Mech. Anal.} (2019), to appear. DOI: \texttt{this https URL}.
On the Correctness and Sample Complexity of Inverse Reinforcement Learning

Inverse reinforcement learning (IRL) is the problem of finding a reward function that generates a given optimal policy for a given Markov Decision Process. This paper looks at an algorithmic-independent geometric analysis of the IRL problem with finite states and actions. A L1-regularized Support Vector Machine formulation of the IRL problem motivated by the geometric analysis is then proposed with the basic objective of the inverse reinforcement problem in mind: to find a reward function that generates a specified optimal policy. The paper further analyzes the proposed formulation of inverse reinforcement learning with $n$ states and $k$ actions, and shows a sample complexity of $O(d^2 \log (nk))$ for transition probability matrices with at most $d$ non-zeros per row, for recovering a reward function that generates a policy that satisfies Bellman's optimality condition with respect to the true transition probabilities.
3D cell culture stimulates the secretion of in vivo like extracellular vesicles.

For studying cellular communications ex-vivo, a two-dimensional (2D) cell culture model is currently used as the “gold standard”. 2D culture models are also widely used in the study of RNA expression profiles from tumor cells secreted extracellular vesicles (EVs) for tumor biomarker discovery. Although the 2D culture system is simple and easily accessible, the culture environment is unable to represent in vivo extracellular matrix (ECM) microenvironment. Our study observed that 2D- culture derived EVs showed significantly different profiles in terms of secretion dynamics and essential signaling molecular contents (RNAs and DNAs), when compared to the three-dimensional (3D) culture derived EVs. By performing small RNA next-generation sequencing (NGS) analysis of cervical cancer cells and their EVs compared with cervical cancer patient plasma EV-derived small RNAs, we observed that 3D- culture derived EV small RNAs differ from their parent cell small RNA profile which may indicate a specific sorting process. Most importantly, the 3D- culture derived EV small RNA profile exhibited a much higher similarity (~96%) to in vivo circulating EVs derived from cervical cancer patient plasma. However, 2D- culture derived EV small RNA profile correlated better with only their parent cells cultured in 2D. On the other hand, DNA sequencing analysis suggests that culture and growth conditions do not affect the genomic information carried by EV secretion. This work also suggests that tackling EV molecular alterations secreted into interstitial fluids can provide an alternative, non-invasive approach for investigating 3D tissue behaviors at the molecular precision. This work could serve as a foundation for building precise models employed in mimicking in vivo tissue system with EVs as the molecular indicators or transporters. Such models could be used for investigating tumor biomarkers, drug screening, and understanding tumor progression and metastasis.
Systems of Points with Coulomb Interactions

Large ensembles of points with Coulomb interactions arise in various settings of condensed matter physics, classical and quantum mechanics, statistical mechanics, random matrices and even approximation theory, and give rise to a variety of questions pertaining to calculus of variations, Partial Differential Equations and probability. We will review these as well as "the mean-field limit" results that allow to derive effective models and equations describing the system at the macroscopic scale. We then explain how to analyze the next order beyond the mean-field limit, giving information on the system at the microscopic level. In the setting of statistical mechanics, this allows for instance to observe the effect of the temperature and to connect with crystallization questions.
A Case for Stale Synchronous Distributed Model for Declarative Recursive Computation

A large class of traditional graph and data mining algorithms can be concisely expressed in Datalog, and other Logic-based languages, once aggregates are allowed in recursion. In fact, for most BigData algorithms, the difficult semantic issues raised by the use of non-monotonic aggregates in recursion are solved by Pre-Mappability ( reM), a property that assures that for a program with aggregates in recursion there is an equivalent aggregate-stratified program. In this paper we show that, by bringing together the formal abstract semantics of stratified programs with the efficient operational one of unstratified programs, reM can also facilitate and improve their parallel execution. We prove that reM-optimized lock-free and decomposable parallel semi-naive evaluations produce the same results as the single executor programs. Therefore, reM can be assimilated into the data-parallel computation plans of different distributed systems, irrespective of whether these follow bulk synchronous parallel (BSP) or asynchronous computing models. In addition, we show that non-linear recursive queries can be evaluated using a hybrid stale synchronous parallel (SSP) model on distributed environments. After providing a formal correctness proof for the recursive query evaluation with reM under this relaxed synchronization model, we present experimental evidence of its benefits.
'Distributed' signal processing: new opportunities and challenges

Summary form only given. We are at a novel crossroads in technology where we are witnessing the confluence of computing, communicating and networking. A number of exciting applications are both driving and being driven by this confluence, including low-power sensor networks, large-scale ad hoc wireless networks, and wireless multimedia transmission. Many of these applications demand a move away from classical centralized architectures and algorithms towards more decentralized and distributed ones. Signal processing plays a key role in this revolution- not in isolation but rather as a pivotal interdisciplinary systems component, intimately integrated with communications, information theory, coding theory, and networking protocols. Sensor networks represent a particularly rich applications base. We would provide a snapshot of the sensor network related activities in a number of research groups at Berkeley. Motivated by the communications and computational constraints imposed by large-scale low-power sensor networks, we would describe some of our signal processing centric research including: (i) distributed sampling; (ii) distributed source coding; (iii) distributed estimation; and (iv) robust transmission. We would highlight the key foundational role played by multi-user information theory, particularly the so-called area of side-information coding for both source coding (compression) and channel coding (transmission). A deeper look reveals a beautiful functional duality between source and channel coding with side-information. This unexpectedly unifies a host of seemingly unrelated problem areas like distributed compression, digital watermarking, multimedia transmission over packet-error networks, and seamless digital upgrade of analog TV. Finally, as a microcosm of the expressive power of interdisciplinary thinking, we would describe a novel video compression paradigm dubbed PRISM (power-efficient, robust, hlh-compression, syndrome-based multimedia coding). PRISM's architecture, in stark contrast to that driving current video codecs like MPEG, allows for a novel shifting of the computational complexity from the encoder to the decoder, making it ideally suited for "uplink" transmission scenarios in wireless multimedia and surveillance applications.
A neural ensemble correlation code for sound category identification

Humans and other animals effortlessly identify natural sounds and categorize them into behaviorally relevant categories. Yet, the acoustic features and neural transformations that enable sound recognition and the formation of perceptual categories are largely unknown. Here, using multichannel neural recordings in the auditory midbrain of unanesthetized female rabbits, we first demonstrate that neural ensemble activity in the auditory midbrain displays highly structured correlations that vary with distinct natural sound stimuli. These stimulus-driven correlations can be used to accurately identify individual sounds using single-response trials, even when the sounds do not differ in their spectral content. Combining neural recordings and an auditory model, we then show how correlations between frequency-organized auditory channels can contribute to discrimination of not just individual sounds but sound categories. For both the model and neural data, spectral and temporal correlations achieved similar categorization performance and appear to contribute equally. Moreover, both the neural and model classifiers achieve their best task performance when they accumulate evidence over a time frame of approximately 1-2 seconds, mirroring human perceptual trends. These results together suggest that time-frequency correlations in sounds may be reflected in the correlations between auditory midbrain ensembles and that these correlations may play an important role in the identification and categorization of natural sounds.
Weakly Supervised Segmentation by a Deep Geodesic Prior

The performance of the state-of-the-art image segmentation methods heavily relies on the high-quality annotations, which are not easily affordable, particularly for medical data. To alleviate this limitation, in this study, we propose a weakly supervised image segmentation method based on a deep geodesic prior. We hypothesize that integration of this prior information can reduce the adverse effects of weak labels in segmentation accuracy. Our proposed algorithm is based on a prior information, extracted from an auto-encoder, trained to map objects’ geodesic maps to their corresponding binary maps. The obtained information is then used as an extra term in the loss function of the segmentor. In order to show efficacy of the proposed strategy, we have experimented segmentation of cardiac substructures with clean and two levels of noisy labels (L1, L2). Our experiments showed that the proposed algorithm boosted the performance of baseline deep learning-based segmentation for both clean and noisy labels by \(4.4\%\), \(4.6\%\)(L1), and \(6.3\%\)(L2) in dice score, respectively. We also showed that the proposed method was more robust in the presence of high-level noise due to the existence of shape priors.
Electrified cone formation in perfectly conducting viscous liquids: Self-similar growth irrespective of Reynolds number

Above a critical field strength, the free surface of an electrified, perfectly conducting viscous liquid, such as a liquid metal, is known to develop an accelerating protrusion resembling a cusp with a conic tip. Field self-enhancement from tip sharpening is reported to generate divergent power law growth in finite time of the forces acting in that region. Previous studies have established that tip sharpening proceeds via a self-similar process in two distinguished limits—the Stokes regime and the inviscid regime. Using finite element simulations to track the shape and forces acting at the tip of an electrified protrusion in a perfectly conducting Newtonian liquid, we demonstrate that the conic tip always undergoes self-similar growth irrespective of the Reynolds number. The blowup exponents at the conic apex for all terms in the Navier-Stokes equation and the normal stress boundary condition at the moving interface reveal the dominant forces at play as the Reynolds number increases. Rescaling of the tip shape by the power law representing the divergence in capillary stress at the apex yields an excellent collapse onto a universal cone shape with an interior half-angle dependent on the Maxwell stress. The rapid acceleration of the liquid interface also generates a thin interfacial boundary layer characterized by a significant rate of strain. Additional details of the modeled flow, applicable to cone growth in systems such as liquid metal ion sources, help dispel prevailing misconceptions that dynamic cones resemble conventional Taylor cones or that viscous stresses at a finite Reynolds number can be neglected.
#Cyberbullying in the digital age: Exploring people's opinions with text mining

This study used text mining to investigate people's insights about cyberbullying. English-language tweets were collected and analyzed by R software. Our analysis demonstrated three major themes: (a) the major actions that needed to be taken into consideration (e.g. guiding parents and teachers to cyberbullying prevention, funding schools to fight cyberbullying), (b) certain events that were important to people (e.g. the Michigan cyberbullying law), and (c) people's major concerns in this regard (e.g. mental health issues among students). Parents and teachers have an important role in educating, informing, warning, preventing, and protecting against cyberbullying behaviors. The frequency of negative sentiments was almost 2.45 times more than positive sentiments.
ON STOCHASTIC COMPARISONS OF ORDER STATISTICS FROM HETEROGENEOUS EXPONENTIAL SAMPLES

We show that the kth order statistic from a heterogeneous sample of n ≥ k exponential random variables is larger than that from a homogeneous exponential sample in the sense of star ordering, as conjectured by Xu and Balakrishnan [14]. As a consequence, we establish hazard rate ordering for order statistics between heterogeneous and homogeneous exponential samples, resolving an open problem of Pǎltǎnea [11]. Extensions to general spacings are also presented.
Synthesis and enzymatic cyclization of (3S)-14-fluoro-2,3-oxidosqualene

Abstract A convergent asymmetric synthesis led to ( 3S )-14-fluoro-2,3-oxidosqualene (14-FOS, 16 ), which was cyclized by bacterial squalene:hopane cyclase to a monocarbocyclic product with a bridged ether and a 2:3 mixture of bicyclic alcohols. 14-FOS was neither a substrate nor an inhibitor for vertebrate oxidosqualene:lanosterol cyclase.
Structural complementarity facilitates E7820-mediated degradation of RBM39 by DCAF15.

The investigational drugs E7820, indisulam and tasisulam (aryl-sulfonamides) promote the degradation of the splicing factor RBM39 in a proteasome-dependent mechanism. While the activity critically depends on the cullin RING ligase substrate receptor DCAF15, the molecular details remain elusive. Here we present the cryo-EM structure of the DDB1–DCAF15–DDA1 core ligase complex bound to RBM39 and E7820 at a resolution of 4.4 A, together with crystal structures of engineered subcomplexes. We show that DCAF15 adopts a new fold stabilized by DDA1, and that extensive protein–protein contacts between the ligase and substrate mitigate low affinity interactions between aryl-sulfonamides and DCAF15. Our data demonstrate how aryl-sulfonamides neo-functionalize a shallow, non-conserved pocket on DCAF15 to selectively bind and degrade RBM39 and the closely related splicing factor RBM23 without the requirement for a high-affinity ligand, which has broad implications for the de novo discovery of molecular glue degraders. Cryo-EM and crystal structural analysis of DDB1–DCAF15–DDA1 in complex with E7820 and RBM39 reveal that aryl-sulfonamides reshape the surface of the cullin RING ligase substrate receptor DCAF15 to bind and degrade the splicing factor RBM39.
Unexpected Hole Doping of Graphene by Osmium Adatoms

The electronic transport of monolayer graphene devices is studied before and after \emph{in situ} deposition of a sub-monolayer coating of osmium adatoms. Unexpectedly, and unlike all other metallic adatoms studied to date, osmium adatoms shift the charge neutrality point to more positive gate voltages. This indicates that osmium adatoms act as electron acceptors and thus leave the graphene hole-doped. Analysis of transport data suggest that Os adatoms behave as charged impurity scatterers, albeit with a surprisingly low charge-doping efficiency. The charge neutrality point of graphene is found to vary non-monotonically with gate voltage as the sample is warmed to room temperature, suggesting that osmium diffuses on the surface but is not completely removed.
A Forensic Qualitative Analysis of Contributions to Wikipedia from Anonymity Seeking Users

By choice or by necessity, some contributors to commons-based peer production sites use privacy-protecting services to remain anonymous. As anonymity seekers, users of the Tor network have been cast both as ill-intentioned vandals and as vulnerable populations concerned with their privacy. In this study, we use a dataset drawn from a corpus of Tor edits to Wikipedia to uncover the character of Tor users' contributions. We build in-depth narrative descriptions of Tor users' actions and conduct a thematic analysis that places their editing activity into seven broad groups. We find that although their use of a privacy-protecting service marks them as unusual within Wikipedia, the character of many Tor users' contributions is in line with the expectations and norms of Wikipedia. However, our themes point to several important places where lack of trust promotes disorder, and to contributions where risks to contributors, service providers, and communities are unaligned.
Are we Asking the Right Questions in MovieQA

Joint vision and language tasks like visual question answering are fascinating because they explore high-level understanding, but at the same time, can be more prone to language biases. In this paper, we explore the biases in the MovieQA dataset and propose a strikingly simple model which can exploit them. We find that using the right word embedding is of utmost importance. By using an appropriately-trained word embedding, about half the Question-Answers (QAs) can be answered by looking at the questions and answers alone, completely ignoring narrative context from video clips, subtitles, and movie scripts. Compared to the best published papers on the leaderboard, our simple question+answer only model improves accuracy by 5% for video + subtitle category, 5% for subtitle, 15% for DVS and 6% higher for scripts.
Metallo‐supramolecular Crosslinked Polyurethanes

The effects of incorporating metal‐binding ligands as chain extenders in polyurethane elastomers were investigated. Segmented polyurethanes based on 2 kDa poly(tetramethylene oxide) (PTMO) and 4,4‐methylenebis(cyclohexyl isocyanate) were polymerized using a two‐step process in which 2,6‐bis(1‐ethyl‐5‐(methoxymethyl)‐1H‐benzo[d]imidazol‐2‐yl)pyridine was added as a chain extender. The resulting polyurethanes were then metallated using stoichiometric amounts of Zn(II) metal salts with different counterions. The resulting metallopolymers have substantially improved Young's moduli, increased failure stress, and improved thermomechanical behavior. The materials were microphase‐separated into anisotropic hard domains within a PTMO matrix. Simultaneous small‐angle X‐ray scattering and tensile testing revealed the minority hard segment domains remain relatively intact during elongation, likely due to the strength of the metal–ligand complex. © 2019 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2019, 57, 1744–1757
Measurement errors in the binary instrumental variable model

Author(s): Jiang, Z; Ding, P | Abstract: Instrumental variable methods can identify causal effects even when the treatment and outcome are confounded. We study the problem of imperfect measurements of the binary instrumental variable, treatment and outcome.We first consider nondifferential measurement errors, that is, the mismeasured variable does not depend on other variables given its true value.We show that the measurement error of the instrumental variable does not bias the estimate, that the measurement error of the treatment biases the estimate away from zero, and that the measurement error of the outcome biases the estimate toward zero. Moreover, we derive sharp bounds on the causal effects without additional assumptions. These bounds are informative because they exclude zero. We then consider differential measurement errors, and focus on sensitivity analyses in those settings.
A new primal-dual weak galerkin finite element method for ill-posed elliptic cauchy problems

Abstract A new numerical method is devised and analyzed for a type of ill-posed elliptic Cauchy problems by using the primal–dual weak Galerkin finite element method. This new primal–dual weak Galerkin algorithm is robust and efficient in the sense that the system arising from the scheme is symmetric, well-posed, and is satisfied by the exact solution (if it exists). An error estimate of optimal order is established for the corresponding numerical solutions in a scaled residual norm. In addition, a mathematical convergence is established in a weak L 2 topology for the new numerical method. Numerical results are reported to demonstrate the efficiency of the primal–dual weak Galerkin method as well as the accuracy of the numerical approximations.
Tone and vowel quality

Abstract The study investigates the influence of tone on vowel quality. Speakers of a tone language, Taiwanese, were instructed to produce vowels [i], [e], [a], [ɔ] and [u] with two contrasting tones, the high tone and the low tone. These vowels associated with either the high or the low tone are meaningful monosyllabic words in this Chinese dialect. Results of analysis show that vowel quality is in fact affected by tonal difference, although not in a systematic fashion for different vowels and for different speakers.
Complexity, development, and evolution in morphogenetic collective systems

Many living and nonliving complex systems can be modeled and understood as collective systems made of heterogeneous components that self-organize and generate nontrivial morphological structures and behaviors. This chapter presents a brief overview of our recent effort that investigated various aspects of such morphogenetic collective systems. We first propose a theoretical classification scheme that distinguishes four complexity levels of morphogenetic collective systems based on the nature of their components and interactions. We conducted a series of computational experiments using a self-propelled particle swarm model to investigate the effects of (1) heterogeneity of components, (2) differentiation/re-differentiation of components, and (3) local information sharing among components, on the self-organization of a collective system. Results showed that (a) heterogeneity of components had a strong impact on the system’s structure and behavior; (b) dynamic differentiation/re-differentiation of components and local information sharing helped the system maintain spatially adjacent, coherent organization; (c) dynamic differentiation/re-differentiation contributed to the development of more diverse structures and behaviors; and (d) stochastic re-differentiation of components naturally realized a self-repair capability of self-organizing morphologies. We also explored evolutionary methods to design novel self-organizing patterns, using interactive evolutionary computation and spontaneous evolution within an artificial ecosystem. These self-organizing patterns were found to be remarkably robust against dimensional changes from 2D to 3D, although evolution worked efficiently only in 2D settings.
Decisions of firm risk and the role of organizational identity

Due to risk aversion, nonprofit firms are believed to have a lower propensity to innovate than for-profits. In turn, social enterprises have arisen as a popular alternative to nonprofits under the expectation that their for-profit practices will lead to increased social innovation. Yet, hybrid firms face uncertainty in their organizational identity, which may detract from their propensity to innovate. Further, there is little empirical work on the actual differences of risk-taking outside of for-profit firms. This paper assesses how the organizational identity of a firm impacts their decisions of risk. Using survey data from the US state of North Carolina, logistic estimations reveal that organizational identity has differential impacts across firm structures. Specifically, for-profit firms benefit from an entrepreneurial identity, while nonprofit firms are hindered by a hybrid identity. This paper provides important developments in our understanding of the role of organizational identity on risk-taking behavior beyond traditional for-profit firms.
GraphSAINT: Graph Sampling Based Inductive Learning Method

Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer sampling techniques to alleviate the "neighbor explosion" problem during minibatch training. We propose GraphSAINT, a graph sampling based inductive learning method that improves training efficiency and accuracy in a fundamentally different way. By changing perspective, GraphSAINT constructs minibatches by sampling the training graph, rather than the nodes or edges across GCN layers. Each iteration, a complete GCN is built from the properly sampled subgraph. Thus, we ensure fixed number of well-connected nodes in all layers. We further propose normalization technique to eliminate bias, and sampling algorithms for variance reduction. Importantly, we can decouple the sampling from the forward and backward propagation, and extend GraphSAINT with many architecture variants (e.g., graph attention, jumping connection). GraphSAINT demonstrates superior performance in both accuracy and training time on five large graphs, and achieves new state-of-the-art F1 scores for PPI (0.995) and Reddit (0.970).
CLEVRER: Collision Events for Video Representation and Reasoning

The ability to reason about temporal and causal events from videos lies at the core of human intelligence. Most video reasoning benchmarks, however, focus on pattern recognition from complex visual and language input, instead of on causal structure. We study the complementary problem, exploring the temporal and causal structures behind videos of objects with simple visual appearance. To this end, we introduce the CoLlision Events for Video REpresentation and Reasoning (CLEVRER) dataset, a diagnostic video dataset for systematic evaluation of computational models on a wide range of reasoning tasks. Motivated by the theory of human casual judgment, CLEVRER includes four types of question: descriptive (e.g., ‘what color’), explanatory (‘what’s responsible for’), predictive (‘what will happen next’), and counterfactual (‘what if’). We evaluate various state-of-the-art models for visual reasoning on our benchmark. While these models thrive on the perception-based task (descriptive), they perform poorly on the causal tasks (explanatory, predictive and counterfactual), suggesting that a principled approach for causal reasoning should incorporate the capability of both perceiving complex visual and language inputs, and understanding the underlying dynamics and causal relations. We also study an oracle model that explicitly combines these components via symbolic representations. CLEVRER will be made publicly available.
Optimal Perimeter Guarding With Heterogeneous Robot Teams: Complexity Analysis and Effective Algorithms

We perform structural and algorithmic studies of significantly generalized versions of the optimal perimeter guarding ( OPG ) problem. As compared with the original OPG where robots are uniform, in this paper, many mobile robots with heterogeneous sensing capabilities are to be deployed to optimally guard a set of one-dimensional segments. Two complimentary formulations are investigated where one limits the number of available robots ( OPG ${}_{LR}$ ) and the other seeks to minimize the total deployment cost ( ${\rm OPG}_{MC}$ ). In contrast to the original OPG which admits low-polynomial time solutions, both OPG ${}_{LR}$ and ${\rm OPG}_{MC}$ are computationally intractable with OPG ${}_{LR}$ being strongly NP-hard. Nevertheless, we develop fairly scalable pseudo-polynomial time algorithms for practical, fixed-parameter subcase of OPG ${}_{LR}$ ; we also develop pseudo-polynomial time algorithm for general ${\rm OPG}_{MC}$ and polynomial time algorithm for the fixed-parameter ${\rm OPG}_{MC}$ case. The applicability and effectiveness of selected algorithms are demonstrated through extensive numerical experiments.
Pathways to Nanocrystalline III-V (13-15) Compound Semiconductors

Discussed in this review are various solution-phase methodologies and specific chemical reactions involving molecular precursors and/or species derived there-from which have been utilized to synthesize various binary and ternary nanocrystalline III-V (13–15) compound semiconductors.