cssi/SciSciGPT-SciSciNet huggingface dataset:
Stein's method for steady-state diffusion approximations of M/Ph/n + M systems

We consider $M/\mathit{Ph}/n+M$ queueing systems in steady state. We prove that the Wasserstein distance between the stationary distribution of the normalized system size process and that of a piecewise Ornstein–Uhlenbeck (OU) process is bounded by $C/\sqrt{\lambda}$, where the constant $C$ is independent of the arrival rate $\lambda$ and the number of servers $n$ as long as they are in the Halfin-Whitt parameter regime. For each integer $m>0$, we also establish a similar bound for the difference of the $m$th steady-state moments. For the proofs, we develop a modular framework that is based on Stein’s method. The framework has three components: Poisson equation, generator coupling, and state space collapse. The framework, with further refinement, is likely applicable to steady-state diffusion approximations for other stochastic systems.
MnasNet: Platform-Aware Neural Architecture Search for Mobile

Designing convolutional neural networks (CNN) for mobile devices is challenging because mobile models need to be small and fast, yet still accurate. Although significant efforts have been dedicated to design and improve mobile CNNs on all dimensions, it is very difficult to manually balance these trade-offs when there are so many architectural possibilities to consider. In this paper, we propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. Unlike previous work, where latency is considered via another, often inaccurate proxy (e.g., FLOPS), our approach directly measures real-world inference latency by executing the model on mobile phones. To further strike the right balance between flexibility and search space size, we propose a novel factorized hierarchical search space that encourages layer diversity throughout the network. Experimental results show that our approach consistently outperforms state-of-the-art mobile CNN models across multiple vision tasks. On the ImageNet classification task, our MnasNet achieves 75.2% top-1 accuracy with 78ms latency on a Pixel phone, which is 1.8× faster than MobileNetV2 with 0.5% higher accuracy and 2.3× faster than NASNet with 1.2% higher accuracy. Our MnasNet also achieves better mAP quality than MobileNets for COCO object detection. Code is at https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet.
Jet formation in MHD simulations

In this talk I review the current status of jet formation in direct numerical simulations of black-hole accretion disks and magnetospheres. I address the following critical questions: What constitutes the jet? What is the launching mechanism? Where is the launching point of the jet? What is the Lorentz factor? What is the opening angle? How is the jet collimated? Just as importantly, I also discuss how dependent the answers to the above questions are on factors such as the initial conditions of the simulation. I end by discussing possible future directions for this research.
Spectral Gaps for the Two-Species Product Vacua and Boundary States Models on the d -Dimensional Lattice

We study the two-species Product Vacua and Boundary States (PVBS) models on the integer lattice $${\mathbb {Z}}^d$$ and prove the existence and non-existence of a spectral gap for all choices of parameters. The PVBS models are spin-1 quantum spin systems which are translation-invariant, frustration-free, and composed of nearest-neighbor non-commuting interactions with both an exclusion property and an interchange interaction between particle species. These models serve as possible representatives of families of automorphically equivalent gapped quantum spin-1 systems on $${\mathbb {Z}}^d$$ . The main result is that the two-species PVBS Hamiltonians have a positive spectral gap when gapped on both of the single-species subspaces and are gapless if gapless on either single-species subspace. The addition of a new particle species does not create any new gapless phases.
Defensive Quantization: When Efficiency Meets Robustness

Neural network quantization is becoming an industry standard to efficiently deploy deep learning models on hardware platforms, such as CPU, GPU, TPU, and FPGAs. However, we observe that the conventional quantization approaches are vulnerable to adversarial attacks. This paper aims to raise people's awareness about the security of the quantized models, and we designed a novel quantization methodology to jointly optimize the efficiency and robustness of deep learning models. We first conduct an empirical study to show that vanilla quantization suffers more from adversarial attacks. We observe that the inferior robustness comes from the error amplification effect, where the quantization operation further enlarges the distance caused by amplified noise. Then we propose a novel Defensive Quantization (DQ) method by controlling the Lipschitz constant of the network during quantization, such that the magnitude of the adversarial noise remains non-expansive during inference. Extensive experiments on CIFAR-10 and SVHN datasets demonstrate that our new quantization method can defend neural networks against adversarial examples, and even achieves superior robustness than their full-precision counterparts while maintaining the same hardware efficiency as vanilla quantization approaches. As a by-product, DQ can also improve the accuracy of quantized models without adversarial attack.
Convex pentagons that admit i-block transitive tilings

The problem of classifying the convex pentagons that admit tilings of the plane is a long-standing unsolved problem. Previous to this article, there were 14 known distinct kinds of convex pentagons that admit tilings of the plane. Five of these types admit tile-transitive tilings (i.e. there is a single transitivity class with respect to the symmetry group of the tiling). The remaining 9 types do not admit tile-transitive tilings, but do admit either 2-block transitive tilings or 3-block transitive tilings; these are tilings comprised of clusters of 2 or 3 pentagons such that these clusters form tile-2-transitive or tile-3-transitive tilings. In this article, we present some combinatorial results concerning pentagons that admit i-block transitive tilings for $$i \in \mathbb {N}$$ . These results form the basis for an automated approach to finding all pentagons that admit i-block transitive tilings for each $$i \in \mathbb {N}$$ . We will present the methods of this algorithm and the results of the computer searches so far, which includes a complete classification of all pentagons admitting i-block transitive tilings for $$i \le 4$$ , among which is a new 15th type of convex pentagon that admits a tile-3-transitive tiling.
Endomorphism rings of reductions of Drinfeld modules

Abstract Let A = F q [ T ] be the polynomial ring over F q , and F be the field of fractions of A. Let ϕ be a Drinfeld A-module of rank r ≥ 2 over F. For all but finitely many primes p ◁ A , one can reduce ϕ modulo p to obtain a Drinfeld A-module ϕ ⊗ F p of rank r over F p = A / p . The endomorphism ring E p = End F p ( ϕ ⊗ F p ) is an order in an imaginary field extension K of F of degree r. Let O p be the integral closure of A in K, and let π p ∈ E p be the Frobenius endomorphism of ϕ ⊗ F p . Then we have the inclusion of orders A [ π p ] ⊂ E p ⊂ O p in K. We prove that if End F alg ( ϕ ) = A , then for arbitrary non-zero ideals n , m of A there are infinitely many p such that n divides the index χ ( E p / A [ π p ] ) and m divides the index χ ( O p / E p ) . We show that the index χ ( E p / A [ π p ] ) is related to a reciprocity law for the extensions of F arising from the division points of ϕ. In the rank r = 2 case we describe an algorithm for computing the orders A [ π p ] ⊂ E p ⊂ O p , and give some computational data.
Heavy-Tailed Independent Component Analysis

Independent component analysis (ICA) is the problem of efficiently recovering a matrix A a#x2208; Rn×n from i.i.d. Observations of X=AS where S a#x2208; Rn is a random vector with mutually independent coordinates. This problem has been intensively studied, but all existing efficient algorithms with provable guarantees require that the coordinates Si have finite fourth moments. We consider the heavy-tailed ICA problem where we do not make this assumption, about the second moment. This problem also has received considerable attention in the applied literature. In the present work, we first give a provably efficient algorithm that works under the assumption that for constant a#x3B3; > 0, each Si has finite (1+a#x03B3;)-moment, thus substantially weakening the moment requirement condition for the ICA problem to be solvable. We then give an algorithm that works under the assumption that matrix A has orthogonal columns but requires no moment assumptions. Our techniques draw ideas from convex geometry and exploit standard properties of the multivariate spherical Gaussian distribution in a novel way.
The power of duples (in self-assembly): It's not so hip to be square

In this paper we define the Dupled abstract Tile Assembly Model (DaTAM), which is a slight extension to the abstract Tile Assembly Model (aTAM) that allows for not only the standard square tiles, but also “duple” tiles which are rectangles pre-formed by the joining of two square tiles. We show that the addition of duples allows for powerful behaviors of self-assembling systems at temperature 1, meaning systems which exclude the requirement of cooperative binding by tiles (i.e., the requirement that a tile must be able to bind to at least 2 tiles in an existing assembly if it is to attach). Cooperative binding is conjectured to be required in the standard aTAM for Turing universal computation and the efficient self-assembly of shapes, but we show that in the DaTAM these behaviors can in fact be exhibited at temperature 1. We then show that the DaTAM doesn’t provide asymptotic improvements over the aTAM in its ability to efficiently build thin rectangles. Finally, we present a series of results which prove that the temperature-2 aTAM and temperature-1 DaTAM have mutually exclusive powers. That is, each is able to self-assemble shapes that the other can’t, and each has systems which cannot be simulated by the other. Beyond being of purely theoretical interest, these results have practical motivation as duples have already proven to be useful in laboratory implementations of DNA-based tiles.
On the Relationship between Data Efficiency and Error for Uncertainty Sampling

While active learning offers potential cost savings, the actual data efficiency---the reduction in amount of labeled data needed to obtain the same error rate---observed in practice is mixed. This paper poses a basic question: when is active learning actually helpful? We provide an answer for logistic regression with the popular active learning algorithm, uncertainty sampling. Empirically, on 21 datasets from OpenML, we find a strong inverse correlation between data efficiency and the error rate of the final classifier. Theoretically, we show that for a variant of uncertainty sampling, the asymptotic data efficiency is within a constant factor of the inverse error rate of the limiting classifier.
Mixed degree number field computations

We present a method for computing complete lists of number fields in cases where the Galois group, as an abstract group, appears as a Galois group in smaller degree. We apply this method to find the 25 octic fields with Galois group $${{\mathrm{PSL}}}_2(7)$$ and smallest absolute discriminant. We carry out a number of related computations, including determining the octic field with Galois group $$2^3{:}{{\mathrm{GL}}}_3(2)$$ of smallest absolute discriminant.
DNS study for the origin of the flow randomization in late boundary layer transition

This paper is devoted to the investigation of the origin and mechanism of randomization in late boundary layer transition over a flat plate without pressure gradient. The flow randomization is a crucial phase before flow transition to the turbulent state. According to existing literatures, the randomization was caused by the big background noises and non-periodic spanwise boundary conditions. It was assumed that the large ring structure is affected by background noises first, and then the change of large ring structure affects the small length scales quickly, which directly leads to randomization and formation of turbulence. However, by careful analysis of our high order DNS results, we believe that the internal instability of multiple ring cycles structure is the main reason. What we observed is that randomization begins when the third cycle overlaps the first and second cycles. A significant asymmetric phenomenon is originated from the second cycle in the middle of both streamwise and spanwise directions. More technically, a visible asymmetric phenomenon in the middle vortex ring cycle starts at time step t=16.25T and x=838.9{\delta}in where the top and bottom level rings are still completely symmetric. The non-symmetric structure of middle level ring affects the small length scale in boundary layer bottom quickly. The randomization phenomenon spreads to top level through ejections. Finally, the whole flow domain becomes randomized. A hypothesis of C- and K-types shift is given as a possible mechanism of flow randomization.
Unusual Negative Formation Enthalpies and Atomic Ordering in Isovalent Alloys of Transition Metal Dichalcogenide Monolayers

Isovalent alloys of monolayer transition metal dichalcogenides (TMDs) MX2 (M = Mo, W; X = S, Se) have attracted much attention recently with great promise of expanding TMDs for more applications. In contrast to common substitutional isovalent semiconductor alloys which usually form disordered metastable phases with positive formation enthalpies (ΔH), TMD alloys unusually always have negative ΔH, indicating atomic ordering according to Hume–Rothery rules. As atomic ordering often plays important roles in determining alloy properties, understanding the negative ΔH and ordering effects in TMD alloys is thus of great importance and quite necessary. Using first-principles calculations, we reveal that the negative ΔH of cation-mixed TMD alloys results from energy gain due to charge transfer from weak Mo–X to nearest strong W–X bonds. For anion-mixed cases with stronger M–S bonds, the negative ΔH comes from energy gain due to charge transfer from Se to nearest S atoms. Such charge-transfer-induced energy gain al...
Differential modules on p-adic polyannuli

We consider variational properties of some numerical invariants, measuring convergence of local horizontal sections, associated to differential modules on polyannuli over a nonarchimedean field of characteristic 0. This extends prior work in the one-dimensional case of Christol, Dwork, Robba, Young, et al . Our results do not require positive residue characteristic; thus besides their relevance to the study of Swan conductors for isocrystals, they are germane to the formal classification of flat meromorphic connections on complex manifolds.
On unipotent and nilpotent pieces for classical groups

Abstract We show that the definition of unipotent (resp. nilpotent) pieces for classical groups given by Lusztig (resp. Lusztig and the author) coincides with the combinatorial definition using closure relations on unipotent classes (resp. nilpotent orbits). Moreover we give a closed formula for a map from the set of unipotent classes (resp. nilpotent orbits) in characteristic 2 to the set of unipotent classes in characteristic 0 such that the fibers are the unipotent (resp. nilpotent) pieces.
Formulations and algorithms for the multiple depot, fuel-constrained, multiple vehicle routing problem

We consider a multiple depot, multiple vehicle routing problem with fuel constraints. We are given a set of targets, a set of depots and a set of homogeneous vehicles, one for each depot. The depots are also allowed to act as refueling stations. The vehicles are allowed to refuel at any depot, and our objective is to determine a route for each vehicle with a minimum total cost such that each target is visited at least once by some vehicle, and the vehicles never run out fuel as it traverses its route. We refer to this problem as the Multiple Depot, Fuel-Constrained, Multiple Vehicle Routing Problem (FCMVRP). This paper presents four new mixed integer linear programming formulations to compute an optimal solution for the problem. Extensive computational results for a large set of instances are also presented.
Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation

Image segmentation is an important task in many medical applications. Methods based on convolutional neural networks attain state-of-the-art accuracy; however, they typically rely on supervised training with large labeled datasets. Labeling medical images requires significant expertise and time, and typical hand-tuned approaches for data augmentation fail to capture the complex variations in such images. We present an automated data augmentation method for synthesizing labeled medical images. We demonstrate our method on the task of segmenting magnetic resonance imaging (MRI) brain scans. Our method requires only a single segmented scan, and leverages other unlabeled scans in a semi-supervised approach. We learn a model of transformations from the images, and use the model along with the labeled example to synthesize additional labeled examples. Each transformation is comprised of a spatial deformation field and an intensity change, enabling the synthesis of complex effects such as variations in anatomy and image acquisition procedures. We show that training a supervised segmenter with these new examples provides significant improvements over state-of-the-art methods for one-shot biomedical image segmentation.
Network independent rates in distributed learning

We propose a novel belief update algorithm for Distributed Non-Bayesian learning over time-varying directed graphs, where a group of agents tries to collectively select a distribution that best describes the observed data. We show that the proposed update rule, inspired by the Push-Sum algorithm, is consistent; moreover we provide an explicit characterization of its convergence rate. Our main result states that, after a transient time, all agents will concentrate their beliefs at a network independent rate. Network independent rates were not available for other consensus based distributed learning algorithms on time-varying directed graphs.
Private selection from private candidates

Differentially Private algorithms often need to select the best amongst many candidate options. Classical works on this selection problem require that the candidates’ goodness, measured as a real-valued score function, does not change by much when one person’s data changes. In many applications such as hyperparameter optimization, this stability assumption is much too strong. In this work, we consider the selection problem under a much weaker stability assumption on the candidates, namely that the score functions are differentially private. Under this assumption, we present algorithms that are near-optimal along the three relevant dimensions: privacy, utility and computational efficiency.Our result can be seen as a generalization of the exponential mechanism and its existing generalizations. We also develop an online version of our algorithm, that can be seen as a generalization of the sparse vector technique to this weaker stability assumption. We show how our results imply better algorithms for hyperparameter selection in differentially private machine learning, as well as for adaptive data analysis.
Simultaneous Clustering and Estimation of Heterogeneous Graphical Models

We consider joint estimation of multiple graphical models arising from heterogeneous and high-dimensional observations. Unlike most previous approaches which assume that the cluster structure is given in advance, an appealing feature of our method is to learn cluster structure while estimating heterogeneous graphical models. This is achieved via a high dimensional version of Expectation Conditional Maximization (ECM) algorithm (Meng and Rubin, 1993). A joint graphical lasso penalty is imposed on the conditional maximization step to extract both homogeneity and heterogeneity components across all clusters. Our algorithm is computationally efficient due to fast sparse learning routines and can be implemented without unsupervised learning knowledge. The superior performance of our method is demonstrated by extensive experiments and its application to a Glioblastoma cancer dataset reveals some new insights in understanding the Glioblastoma cancer. In theory, a non-asymptotic error bound is established for the output directly from our high dimensional ECM algorithm, and it consists of two quantities: statistical error (statistical accuracy) and optimization error (computational complexity). Such a result gives a theoretical guideline in terminating our ECM iterations.
Urban land use and transportation planning for climate change mitigation: A theoretical framework

Abstract Cities account for 75% of global greenhouse gas (GHG) emissions from energy use, and their share is increasing due to rapid urbanization. While compact urban forms with public transit are viewed as important strategies for reducing emissions, environmental benefits must be weighed against the costs of public transit infrastructure, road improvements to alleviate congestion in dense urban space, and more expensive housing resulting from land use restrictions. The literature largely lacks a theoretical framework for assessing these tradeoffs. This paper derives analytical insights into urban land use and transportation planning for climate change mitigation by formulating a social planner’s utility maximization problem. The planner chooses the residential densities of urban zones as well as investments in road and public transit infrastructures that link these zones to the city center. Road travel is subject to congestion. Any feasible solution must accommodate a fixed total population and ensure that residents of all zones have the same maximum utility. GHG emissions associated with housing, road travel, and public transit generate damages. Analytical results show that incorporating GHG damages into urban planning always leads to an optimal solution with a more compact urban form, and reduces automobile travel in each zone if a specific condition involving the marginal congestion cost and the marginal effectiveness of road investment is satisfied. Numerical examples demonstrate that near-optimal emissions reductions and utility improvements can be achieved via public transit investment and mode shifting even if the planner inherits and cannot modify a suboptimal land use and road configuration.
Gradient Descent for One-Hidden-Layer Neural Networks: Polynomial Convergence and SQ Lower Bounds.

We study the complexity of training neural network models with one hidden nonlinear activation layer and an output weighted sum layer. We analyze Gradient Descent applied to learning a bounded target function on $n$ real-valued inputs. We give an agnostic learning guarantee for GD: starting from a randomly initialized network, it converges in mean squared loss to the minimum error (in $2$-norm) of the best approximation of the target function using a polynomial of degree at most $k$. Moreover, for any $k$, the size of the network and number of iterations needed are both bounded by $n^{O(k)}\log(1/\epsilon)$. In particular, this applies to training networks of unbiased sigmoids and ReLUs. We also rigorously explain the empirical finding that gradient descent discovers lower frequency Fourier components before higher frequency components. We complement this result with nearly matching lower bounds in the Statistical Query model. GD fits well in the SQ framework since each training step is determined by an expectation over the input distribution. We show that any SQ algorithm that achieves significant improvement over a constant function with queries of tolerance some inverse polynomial in the input dimensionality $n$ must use $n^{\Omega(k)}$ queries even when the target functions are restricted to a set of $n^{O(k)}$ degree-$k$ polynomials, and the input distribution is uniform over the unit sphere; for this class the information-theoretic lower bound is only $\Theta(k \log n)$. Our approach for both parts is based on spherical harmonics. We view gradient descent as an operator on the space of functions, and study its dynamics. An essential tool is the Funk-Hecke theorem, which explains the eigenfunctions of this operator in the case of the mean squared loss.
Comparison of movement rate with different initial moment-to-force ratios.

Introduction The objective of this clinical prospective study was to evaluate the effect of the 2 treatment strategies, translation or controlled tipping, followed by root correction on canine retraction efficiency, specifically canine movement rate. Methods Twenty-one patients who needed bilateral maxillary canine retraction to close extraction space as part of their treatment plan were selected for this study. Segmental T-loops designed for controlled tipping or for translation were applied randomly to each side. Two digital maxillary dental casts (taken before and after treatment) were used to measure the tooth displacements of each patient. The coordinate system located at the center of canine crown on the pretreatment model with the 3 axes defined in the mesial-distal (M-D), buccal-lingual, and occlusal-gingival directions was used to express the 6 tooth displacement components. The movement rates on the occlusal plane and in the M-D direction were computed. Movement rates were calculated by dividing the M-D displacements or the resultant displacement on the occlusal plane with the corresponding treatment time. Results T-Loops for controlled tipping moved canines faster (33.3% on occlusal plane and 38.5% in the M-D direction) than T-loops for translation. The differences were statistically significant (P = 0.041 on the occlusal plane and 0.020 in the M-D direction). Conclusions Moment-to-force ratio (M/F) affects the canine movement rate in a maxillary canine retraction treatment with the use of a segmented T-loop mechanism. Within the neighborhood of the ratio for translation, lower M/F moves the canine faster than higher M/F both on the occlusal plane and in the M-D direction.
Genetic Selection for Small Molecule Production in Competitive Microfluidic Droplets.

Biosensors can be used to screen or select for small molecule production in engineered microbes. However, mutations to the biosensor that interfere with accurate signal transduction are common, pro...
An Advanced Simulation Framework of an Integrated Vehicle-Powertrain Eco-Operation System for Electric Buses

Activities of transit buses traveling along arterial roads and city streets consist of frequent stops and idling events at many predictable occasions, e.g., loading/unloading passengers at bus stops, approaching traffic signals or stop signs, and going through recurrent traffic congestion, etc. Besides designing transit buses with electric powertrain systems that can save a noticeable amount of energy thanks to regenerative breaking, this urban traffic environment also unfolds a number of opportunities to further improve their energy efficiency via vehicle connectivity and autonomy. Therefore, this paper proposes a complete and novel simulation framework of integrated vehicle/powertrain eco-operation system for electric buses (Eco-bus) by co-optimizing the vehicle dynamics and powertrain (VD&PT) controls. A comprehensive evaluation of the proposed system on mobility benefits and energy savings has been conducted over various traffic conditions. Simulation results are presented to showcase the superiority of the proposed simulation framework of the Eco-bus compared to the conventional bus, particularly in terms of mobility and energy efficiency aspects.
Antithymocyte Globulin Antibody Titer Congruent With Kidney Transplantation: Analysis of Incidence, Outcomes, Cost, and Alternative Targets.

Rabbit antithymocyte globulin (rATG) use for immunosuppression induction is widespread but is contraindicated by the presence of anti-rATG antibodies. This study reports the incidence of positive anti-rATG antibody titers in patients before and after renal transplant and evaluates associated outcomes and costs. In addition, it will correlate CD40L and interleukin (IL)-21 with anti-rATG antibody titers. Methods Clinical and billing records from the Indiana University Transplant Laboratory were reviewed for positive versus negative anti-rATG antibody titers, graft survival, and 7-day readmission costs between 2004 and 2018. Serum from patients with positive and negative rATG antibody titers were quantitated for CD40L and IL-21 by enzyme-linked immunosorbent assay. Results On average, between 2004 and May 2018, 163 kidney transplants per year were performed. Anti-rATG antibody titers were ordered for 17 patients/year, of which 18.2% were positive at 1:100 titer either pre- or post-transplant. Time to graft loss correlated with a positive rATG titer at time of readmission. Moreover, second kidney transplant increased the anti-rATG positive rate. A weak correlation was observed between anti-rATG titer and recipient age. Seven-day readmission treatment costs were significantly lower in patients with positive anti-rATG titer. IL-21 and CD40L were significantly greater in patients with positive anti-rATG titers after transplant when compared with negative anti rATG patients. Conclusions Positive anti-rATG antibody titer is associated with a significant negative impact on outcomes. Monitoring of anti-rATG antibody titer is recommended to optimize treatment options in patients, especially in the setting of second transplants. Elucidation of the mechanisms associated with positive anti-rATG antibody is required. IL-21 and CD40L are potential targets for future study.
A generalization of a 1998 unimodality conjecture of Reiner and Stanton

An interesting, and still wide open, conjecture of Reiner and Stanton predicts that certain "strange" symmetric differences of $q$-binomial coefficients are always nonnegative and unimodal. We extend their conjecture to a broader, and perhaps more natural, framework, by conjecturing that, for each $k\ge 5$, the polynomials $$f(k,m,b)(q)=\binom{m}{k}_q-q^{\frac{k(m-b)}{2}+b-2k+2}\cdot\binom{b}{k-2}_q$$ are nonnegative and unimodal for all $m\gg_k 0$ and $b\le \frac{km-4k+4}{k-2}$ such that $kb\equiv km$ (mod 2), with the only exception of $b=\frac{km-4k+2}{k-2}$ when this is an integer. Using the KOH theorem, we combinatorially show the case $k=5$. In fact, we completely characterize the nonnegativity and unimodality of $f(k,m,b)$ for $k\le 5$. (This also provides an isolated counterexample to Reiner-Stanton's conjecture when $k=3$.) Further, we prove that, for each $k$ and $m$, it suffices to show our conjecture for the largest $2k-6$ values of $b$.
Social Interaction With an Alcohol‐Intoxicated or Cocaine‐Injected Peer Selectively Alters Social Behaviors and Drinking in Adolescent Male and Female Rats

BACKGROUND Drinking alcohol is facilitated by social interactions with peers, especially during adolescence. The importance of peer social influences during adolescence on alcohol and substance use has recently received more attention. We have shown that social interaction with an alcohol-intoxicated peer influences adolescent alcohol drinking differently in male and female rats using the demonstrator-observer paradigm. The present set of experiments analyzed the social interaction session to determine changes in social behaviors and subsequent alcohol drinking in adolescent male and female rats. METHODS Specifically, in Experiment 1, we determined whether specific social behaviors were altered during interaction with an alcohol-intoxicated demonstrator administered 1.5 g/kg ethanol (EtOH) and assessed changes in EtOH intake in adolescent observers. Experiment 2 examined changes in voluntary saccharin consumption to determine whether social interaction with an alcohol-intoxicated demonstrator administered 1.5 g/kg EtOH altered consumption of a palatable solution. In Experiment 3, we administered saline, and a low (5 mg/kg) or high (20 mg/kg) dose of cocaine to the demonstrator and assessed changes in the adolescent observers to determine whether social interaction with a "drugged" peer altered social behaviors and voluntary EtOH intake. RESULTS We showed that social interaction with an alcohol-intoxicated demonstrator administered 1.5 g/kg EtOH (i) decreased social play and increased social investigation and social contact in adolescent male and female observers, (ii) did not alter nonsocial behaviors, (iii) did not alter saccharin consumption, and (iv) increased voluntary EtOH intake in adolescent female but not male observers. When the peer was injected with cocaine, (i) social play was dose-dependently decreased, (ii) there were no changes in other social or nonsocial behaviors, and (iii) voluntary EtOH intake in adolescent male and female observers was unaffected. CONCLUSIONS The present results are consistent and extend our previous work, showing that social interaction with an alcohol-intoxicated peer selectively alters social behaviors and alcohol drinking in adolescent rats. Females appear to be more sensitive to the elevating effects of social interaction on voluntary EtOH consumption.
Domestic Government Spending on Human Capital : A Cross-Country Analysis of Recent Trends

Using a new data set comprised of publicly available information, this paper provides cross-country evidence on domestic government spending for human capital in recent years. Creating a measure of social spending that covers the three sectors of health, education, and social protection has proven to be a challenging task. Only for health spending is there high data coverage over time and across countries. Education and, especially, social protection display large gaps. Increases in social sector spending have generally been slow and unsteady. Although education spending in low-income countries has seen a stable and steady increase, spending on health has been remarkably flat. Human capital outcomes are only weakly correlated with spending in the three sectors. Finally, this paper discusses future research required to provide guidance on how much and what type of investment is needed to achieve high levels of human capital.
Successive Quadratic Upper-Bounding for Discrete Mean-Risk Minimization and Network Interdiction

The advances in conic optimization have led to its increased utilization for modeling data uncertainty. In particular, conic mean-risk optimization gained prominence in probabilistic and robust opt...
Information overload in group communication: from conversation to cacophony in the Twitch chat.

As social media replace traditional communication channels, we are often exposed to too much information to process. The presence of too many participants, for example, can turn online public space...
Kidney Disease Progression Does Not Decrease Intestinal Phosphorus Absorption in a Rat Model of Chronic Kidney Disease-Mineral Bone Disorder.

The Cy/+ rat has been characterized as a progressive model of chronic kidney disease-mineral bone disorder (CKD-MBD). We aimed to determine the effect of kidney disease progression on intestinal phosphorus absorption and whole-body phosphorus balance in this model. A total of 48 Cy/+ (CKD) and 48 normal littermates (NL) rats were studied at two ages: 20 weeks and 30 weeks, to model progressive kidney function decline at approximately 50% and 20% of normal kidney function. Sodium-dependent and sodium-independent intestinal phosphorus absorption efficiency were measured by the in situ jejunal ligated loop method using 33 P radioisotope. Our results show that CKD rats had slightly higher sodium-dependent phosphorus absorption compared to NL rats, and absorption decreased from 20 to 30 weeks. These results are in contrast to plasma 1,25OH2 D, which was lower in CKD rats. Gene expression of the major intestinal phosphorus transporter, NaPi-2b, was not different between CKD and NL rats in the jejunum but was lower in CKD rats versus NL rats in the duodenum. Jejunal ligated loop phosphorus absorption results are consistent with percent net phosphorus absorption results obtained from metabolic balance: higher net percent phosphorus absorption values in CKD rats compared with NL, and lower values in 30-week-olds compared with 20-week-olds. Phosphorus balance was negative (below zero) in CKD rats, significantly lower in 30-week-old rats compared with 20-week-old rats, and lower in CKD rats compared with NL rats at both ages. These results demonstrate no reduction in intestinal phosphorus absorption with progression of CKD despite lower 1,25OH2 D status when assessed by an in situ ligated loop test, which is in contrast to the majority of in vitro studies, and if confirmed in further studies, could challenge the physiological relevance of in vitro findings. © 2019 American Society for Bone and Mineral Research.
Bit-Flip Attack: Crushing Neural Network With Progressive Bit Search

Several important security issues of Deep Neural Network (DNN) have been raised recently associated with different applications and components. The most widely investigated security concern of DNN is from its malicious input, a.k.a adversarial example. Nevertheless, the security challenge of DNN’s parameters is not well explored yet. In this work, we are the first to propose a novel DNN weight attack methodology called Bit-Flip Attack (BFA) which can crush a neural network through maliciously flipping extremely small amount of bits within its weight storage memory system (i.e., DRAM). The bit-flip operations could be conducted through well-known Row-Hammer attack, while our main contribution is to develop an algorithm to identify the most vulnerable bits of DNN weight parameters (stored in memory as binary bits), that could maximize the accuracy degradation with a minimum number of bit-flips. Our proposed BFA utilizes a Progressive Bit Search (PBS) method which combines gradient ranking and progressive search to identify the most vulnerable bit to be flipped. With the aid of PBS, we can successfully attack a ResNet-18 fully malfunction (i.e., top-1 accuracy degrade from 69.8% to 0.1%) only through 13 bit-flips out of 93 million bits, while randomly flipping 100 bits merely degrades the accuracy by less than 1%. Code is released at: https://github.com/elliothe/Neural_Network_Weight_Attack
Discrimination of DNA Methylation Signal from Background Variation for Clinical Diagnostics

Advances in the study of human DNA methylation variation offer a new avenue for the translation of epigenetic research results to clinical applications. Although current approaches to methylome analysis have been helpful in revealing an epigenetic influence in major human diseases, this type of analysis has proven inadequate for the translation of these advances to clinical diagnostics. As in any clinical test, the use of a methylation signal for diagnostic purposes requires the estimation of an optimal cutoff value for the signal, which is necessary to discriminate a signal induced by a disease state from natural background variation. To address this issue, we propose the application of a fundamental signal detection theory and machine learning approaches. Simulation studies and tests of two available methylome datasets from autism and leukemia patients demonstrate the feasibility of this approach in clinical diagnostics, providing high discriminatory power for the methylation signal induced by disease, as well as high classification performance. Specifically, the analysis of whole biomarker genomic regions could suffice for a diagnostic, markedly decreasing its cost.
Power-Grid Controller Anomaly Detection with Enhanced Temporal Deep Learning

Controllers of security-critical cyber-physical systems, like the power grid, are a very important class of computer systems. Attacks against the control code of a power-grid system, especially zero-day attacks, can be catastrophic. Earlier detection of the anomalies can prevent further damage. However, detecting zero-day attacks is extremely challenging because they have no known code and have unknown behavior. Furthermore, if data collected from the controller is transferred to a server through networks for analysis and detection of anomalous behavior, this creates a very large attack surface and also delays detection. In order to address this problem, we propose Reconstruction Error Distribution (RED) of Hardware Performance Counters (HPCs), and a data-driven defense system based on it. Specifically, we first train a temporal deep learning model, using only normal HPC readings from legitimate processes that run daily in these power-grid systems, to model the normal behavior of the power-grid controller. Then, we run this model using real-time data from commonly available HPCs. We use the proposed RED to enhance the temporal deep learning detection of anomalous behavior, by estimating distribution deviations from the normal behavior with an effective statistical test. Experimental results on a real power-grid controller show that we can detect anomalous behavior with high accuracy (>99.9%), nearly zero false positives and short (<360ms) latency.
miR-29a is repressed by MYC in pancreatic cancer and its restoration drives tumor suppressive effects via downregulation of LOXL2

Pancreatic ductal adenocarcinoma (PDAC) is an intractable cancer with a dismal prognosis. miR-29a is commonly downregulated in PDAC; however, mechanisms for its loss and role still remain unclear. Here, we show that in PDAC, repression of miR-29a is directly mediated by MYC via promoter activity. RNA sequencing analysis, integrated with miRNA target prediction, identified global miR-29a downstream targets in PDAC. Target enrichment coupled with gene ontology and survival correlation analyses identified the top five miR-29a-downregulated target genes (LOXL2, MYBL2, CLDN1, HGK, and NRAS) that are known to promote tumorigenic mechanisms. Functional validation confirmed that upregulation of miR-29a is sufficient to ablate translational expression of these five genes in PDAC. We show that the most promising target among the identified genes, LOXL2, is repressed by miR-29a via 3'-untranslated region binding. Pancreatic tissues from a PDAC murine model and patient biopsies showed overall high LOXL2 expression with inverse correlations with miR-29a levels. Collectively, our data delineate an antitumorigenic, regulatory role of miR-29a and a novel MYC-miR-29a-LOXL2 regulatory axis in PDAC pathogenesis, indicating the potential of the molecule in therapeutic opportunities. IMPLICATIONS: This study unravels a novel functional role of miR-29a in PDAC pathogenesis and identifies an MYC-miR-29a-LOXL2 axis in regulation of the disease progression, implicating miR-29a as a potential therapeutic target for PDAC. VISUAL OVERVIEW: http://mcr.aacrjournals.org/content/molcanres/18/2/311/F1.large.jpg.
Injection molded open microfluidic well plate inserts for user-friendly coculture and microscopy

Open microfluidic cell culture systems are powerful tools for interrogating biological mechanisms. We have previously presented a microscale cell culture system, based on spontaneous capillary flow of biocompatible hydrogels, that is integrated into a standard cell culture well plate, with flexible cell compartment geometries and easy pipet access. Here, we present two new injection molded open microfluidic devices that also easily insert into standard cell culture well plates and standard culture workflows, allowing seamless adoption by biomedical researchers. These platforms allow culture and study of soluble factor communication among multiple cell types, and the microscale dimensions are well-suited for rare primary cells. Unique advances include optimized evaporation control within the well, manufacture with reproducible and cost-effective rapid injection molding, and compatibility with sample preparation workflows for high resolution microscopy (following well-established coverslip mounting procedures). In this work, we present several use cases that highlight the usability and widespread utility of our platform including culture of limited primary testis cells from surgical patients, microscopy readouts including immunocytochemistry and single molecule fluorescence in situ hybridization (smFISH), and coculture to study interactions between adipocytes and prostate cancer cells.
The regularity lemma is false over small fields

The regularity lemma is a stringent condition of the possible ranks of tensor blow-ups of linear subspaces of matrices. It was proved by Ivanyos, Qiao and Subrahmanyam in [5] when the underlying fi...
TESTING GENERAL RELATIVITY: PROGRESS, PROBLEMS, AND PROSPECTS.

The results from ground-based experimental testing are presented. Prospects for improving these experiments are discussed. Radar echo time delays, perihelion advance and solar oblateness, time variation of the gravitational constant, and radio wave deflection are considered. Ground-based and spacecraft techniques are compared on an accuracy vs. cost basis.
I-SAFE: instant suspicious activity identification at the edge using fuzzy decision making

Urban imagery usually serves as forensic analysis and by design is available for incident mitigation. As more imagery collected, it is harder to narrow down to certain frames among thousands of video clips to a specific incident. A real-time, proactive surveillance system is desirable, which could instantly detect dubious personnel, identify suspicious activities, or raise momentous alerts. The recent proliferation of the edge computing paradigm allows more data-intensive tasks to be accomplished by smart edge devices with lightweight but powerful algorithms. This paper presents a forensic surveillance strategy by introducing an Instant Suspicious Activity identiFication at the Edge (I-SAFE) using fuzzy decision making. A fuzzy control system is proposed to mimic the decision-making process of a security officer. Decisions are made based on video features extracted by a lightweight Deep Machine Learning (DML) model. Based on the requirements from the first-line law enforcement officers, several features are selected and fuzzified to cope with the state of uncertainty that exists in the officers' decision-making process. Using features in the edge hierarchy minimizes the communication delay such that instant alerting is achieved. Additionally, leveraging the Microservices architecture, the I-SAFE scheme possesses good scalability given the increasing complexities at the network edge. Implemented as an edge-based application and tested using exemplary and various labeled dataset surveillance videos, the I-SAFE scheme raises alerts by identifying the suspicious activity in an average of 0.002 seconds. Compared to four other state-of-the-art methods over two other data sets, the experimental study verified the superiority of the I-SAFE decentralized method.
Dense Depth Estimation in Monocular Endoscopy With Self-Supervised Learning Methods

We present a self-supervised approach to training convolutional neural networks for dense depth estimation from monocular endoscopy data without a priori modeling of anatomy or shading. Our method only requires monocular endoscopic videos and a multi-view stereo method, e.g., structure from motion, to supervise learning in a sparse manner. Consequently, our method requires neither manual labeling nor patient computed tomography (CT) scan in the training and application phases. In a cross-patient experiment using CT scans as groundtruth, the proposed method achieved submillimeter mean residual error. In a comparison study to recent self-supervised depth estimation methods designed for natural video on in vivo sinus endoscopy data, we demonstrate that the proposed approach outperforms the previous methods by a large margin. The source code for this work is publicly available online at https://github.com/lppllppl920/EndoscopyDepthEstimation-Pytorch .