cssi/SciSciGPT-SciSciNet huggingface dataset:
Quantum fluctuations in a cavity-QED system with quantized center-of-mass motion

We investigate the quantum fluctuations of a single atom in a weakly driven cavity where the center-of-mass motion of the atom is quantized in one dimension. We present analytic results for the second-order intensity correlation function ${g}^{(2)}(\ensuremath{\tau})$ and the intensity-field correlation function ${h}_{\ensuremath{\theta}}(\ensuremath{\tau})$ for both transmitted and fluorescent light for weak driving fields. We find that the coupling of the center-of-mass motion to the intracavity field mode can be deleterious to nonclassical effects in photon statistics, but less so for the intensity-field correlations.
Analytic methods for uniform hypergraphs

Abstract This paper presents some analytic methods for studying uniform hypergraphs. Its starting point is the spectral theory of 2-graphs, in particular, the largest and the smallest eigenvalues λ and λ min of 2-graphs. First, these two parameters are extended to weighted uniform hypergraphs; second, the eigenvalues-numbers λ and λ min are extended to eigenvalues-functions λ ( p ) and λ min ( p ) , which also encompass other graph parameters like the Lagrangian and the number of edges. In this way the functions λ ( p ) and λ min ( p ) seamlessly join spectral and traditional results in hypergraphs. In particular, this new viewpoint helps to show that spectral extremal and edge extremal problems are asymptotically equivalent. Naturally, all results about λ ( p ) and λ min ( p ) also extend spectral hypergraph theory, but delve into deeper problems than before. In fact, the resulting theory is new even for 2-graphs, where some well-settled topics become research challenges again. The paper covers a multitude of topics, with more than a hundred concrete statements to underpin an analytic theory for hypergraphs. Essential among these topics are a Perron–Frobenius type theory and methods for extremal hypergraph problems. Many open problems are raised and directions for possible further research are outlined.
Predicting human-driving behavior to help driverless vehicles drive: random intercept Bayesian additive regression trees

The development of driverless vehicles has spurred the need to predict human driving behavior to facilitate interaction between driverless and human-driven vehicles. Predicting human driving movements can be challenging, and poor prediction models can lead to accidents between the driverless and human-driven vehicles. We used the vehicle speed obtained from a naturalistic driving dataset to predict whether a human-driven vehicle would stop before executing a left turn. In a preliminary analysis, we found that BART produced less variable and higher AUC values compared to a variety of other state-of-the-art binary predictor methods. However, BART assumes independent observations, but our dataset consists of multiple observations clustered by driver. Although methods extending BART to clustered or longitudinal data are available, they lack readily available software and can only be applied to clustered continuous outcomes. We extend BART to handle correlated binary observations by adding a random intercept and used a simulation study to determine bias, root mean squared error, 95% coverage, and average length of 95% credible interval in a correlated data setting. We then successfully implemented our random intercept BART model to our clustered dataset and found substantial improvements in prediction performance compared to BART and random intercept linear logistic regression.
Computable isomorphisms for certain classes of infinite graphs

We investigate (2,1):1 structures, which consist of a countable set A together with a function f:A→A such that for every element x in A, f maps either exactly one element or exactly two elements of...
Learning with Square Loss: Localization through Offset Rademacher Complexity

We consider regression with square loss and general classes of functions without the boundedness assumption. We introduce a notion of offset Rademacher complexity that provides a transparent way to study localization both in expectation and in high probability. For any (possibly non-convex) class, the excess loss of a two-step estimator is shown to be upper bounded by this offset complexity through a novel geometric inequality. In the convex case, the estimator reduces to an empirical risk minimizer. The method recovers the results of (Rakhlin et al., 2015) for the bounded case while also providing guarantees without the boundedness assumption.
Near-Optimal Straggler Mitigation for Distributed Gradient Methods

Modern learning algorithms use gradient descent updates to train inferential models that best explain data. Scaling these approaches to massive data sizes requires proper distributed gradient descent schemes where distributed worker nodes compute partial gradients based on their partial and local data sets, and send the results to a master node where all the computations are aggregated into a full gradient and the learning model is updated. However, a major performance bottleneck that arises is that some of the worker nodes may run slow. These nodes a.k.a. stragglers can significantly slow down computation as the slowest node may dictate the overall computational time. We propose a distributed computing scheme, called Batched Coupon's Collector (BCC) to alleviate the effect of stragglers in gradient methods. We prove that our BCC scheme is robust to a near optimal number of random stragglers. We also empirically demonstrate that our proposed BCC scheme reduces the run-time by up to 85.4% over Amazon EC2 clusters when compared with other straggler mitigation strategies. We also generalize the proposed BCC scheme to minimize the completion time when implementing gradient descent-based algorithms over heterogeneous worker nodes.
A Ride-Matching Strategy For Large Scale Dynamic Ridesharing Services Based on Polar Coordinates

In this paper, we study a challenging problem of how to pool multiple ride-share trip requests in real time under an uncertain environment. The goals are better performance metrics of efficiency and acceptable satisfaction of riders. To solve the problem effectively, an objective function that compromises the benefits and losses of dynamic ridesharing service is proposed. The Polar Coordinates based Ride-Matching strategy (PCRM) that can adapt to the satisfaction of riders on board is also addressed. In the experiment, large scale data sets from New York City (NYC) are applied. We do a case study to identify the best set of parameters of the dynamic ridesharing service with a training set of 135,252 trip requests. In addition, we also use a testing set containing 427,799 trip requests and two state-of-the-art approaches as baselines to estimate the effectiveness of our method. The experimental results show that on average 38% of traveling distance can be saved, nearly 100% of passengers can be served and each rider only spends an additional 3.8 minutes in ridesharing trips compared to single rider service.
Open Problem: The Oracle Complexity of Convex Optimization with Limited Memory

We note that known methods achieving the optimal oracle complexity for first order convex optimization require quadratic memory, and ask whether this is necessary, and more broadly seek to characterize the minimax number of first order queries required to optimize a convex Lipschitz function subject to a memory constraint.
Flexible Computational Pipelines for Robust Abstraction-Based Control Synthesis

Successfully synthesizing controllers for complex dynamical systems and specifications often requires leveraging domain knowledge as well as making difficult computational or mathematical tradeoffs. This paper presents a flexible and extensible framework for constructing robust control synthesis algorithms and applies this to the traditional abstraction-based control synthesis pipeline. It is grounded in the theory of relational interfaces and provides a principled methodology to seamlessly combine different techniques (such as dynamic precision grids, refining abstractions while synthesizing, or decomposed control predecessors) or create custom procedures to exploit an application’s intrinsic structural properties. A Dubins vehicle is used as a motivating example to showcase memory and runtime improvements.
Retrosynthesis Prediction with Conditional Graph Logic Network

Retrosynthesis is one of the fundamental problems in organic chemistry. The task is to identify reactants that can be used to synthesize a specified product molecule. Recently, computer-aided retrosynthesis is finding renewed interest from both chemistry and computer science communities. Most existing approaches rely on template-based models that define subgraph matching rules, but whether or not a chemical reaction can proceed is not defined by hard decision rules. In this work, we propose a new approach to this task using the Conditional Graph Logic Network, a conditional graphical model built upon graph neural networks that learns when rules from reaction templates should be applied, implicitly considering whether the resulting reaction would be both chemically feasible and strategic. We also propose an efficient hierarchical sampling to alleviate the computation cost. While achieving a significant improvement of 8.2% over current state-of-the-art methods on the benchmark dataset, our model also offers interpretations for the prediction.
Momentum-Based Variance Reduction in Non-Convex SGD

Variance reduction has emerged in recent years as a strong competitor to stochastic gradient descent in non-convex problems, providing the first algorithms to improve upon the converge rate of stochastic gradient descent for finding first-order critical points. However, variance reduction techniques typically require carefully tuned learning rates and willingness to use excessively large "mega-batches" in order to achieve their improved results. We present a new algorithm, STORM, that does not require any batches and makes use of adaptive learning rates, enabling simpler implementation and less hyperparameter tuning. Our technique for removing the batches uses a variant of momentum to achieve variance reduction in non-convex optimization. On smooth losses $F$, STORM finds a point $x$ with $\mathbb{E}[\|\nabla F(x)\|]\le O(1/\sqrt{T}+\sigma^{1/3}/T^{1/3})$ in $T$ iterations with $\sigma^2$ variance in the gradients, matching the best-known rate but without requiring knowledge of $\sigma$.
Robust Dynamic Resource Allocation via Probabilistic Task Pruning in Heterogeneous Computing Systems

In heterogeneous distributed computing (HC) systems, diversity can exist in both computational resources and arriving tasks. In an inconsistently heterogeneous computing system, task types have different execution times on heterogeneous machines. A method is required to map arriving tasks to machines based on machine availability and performance, maximizing the number of tasks meeting deadlines (defined as robustness). For tasks with hard deadlines (e.g., those in live video streaming), tasks that miss their deadlines are dropped. The problem investigated in this research is maximizing the robustness of an oversubscribed HC system. A way to maximize this robustness is to prune (i.e., defer or drop) tasks with low probability of meeting their deadlines to increase the probability of other tasks meeting their deadlines. In this paper, we first provide a mathematical model to estimate a task's probability of meeting its deadline in the presence of task dropping. We then investigate methods for engaging probabilistic dropping and we find thresholds for dropping and deferring. Next, we develop a pruning-aware mapping heuristic and extend it to engender fairness across various task types. We show the cost benefit of using probabilistic pruning in an HC system. Simulation results, harnessing a selection of mapping heuristics, show efficacy of the pruning mechanism in improving robustness (on average by around 25%) and cost in an oversubscribed HC system by up to around 40%.
Near-optimal Repair of Reed-Solomon Codes with Low Sub-packetization

Minimum storage regenerating (MSR) codes are MDS codes which allow for recovery of any single erased symbol with optimal repair bandwidth, based on the smallest possible fraction of the contents downloaded from each of the other symbols. Recently, certain Reed- Solomon codes were constructed which are MSR. However, the sub-packetization of these codes is exponentially large, growing like nΩ(n) in the constant-rate regime. In this work, we study the relaxed notion of ϵ-MSR codes, which incur a factor of (1 + ϵ) higher than the optimal repair bandwidth, in the context of Reed-Solomon codes. We give constructions of constant-rate ϵ-MSR Reed-Solomon codes with polynomial sub-packetization of nO(l/ϵ) and thereby giving an explicit tradeoff between the repair bandwidth and sub-packetization.
Rotate intra block copy for still image coding

This paper proposes a method called rotate intra block copy, which extends the intra block copy technique by making the block matching process invariant to rotation. HEVC intra prediction plus rotate intra block copy gives an average of 20% reduction in residual energy (i.e. prediction error) compared to HEVC intra prediction plus intra block copy. As the motion vector correlation in rotate intra block copy is different from the intra block copy, a new method of motion vector coding is presented. The impact of angular resolution on residual energy reduction is also evaluated. In a full codec pipeline, this reduction in residual energy translates into a coding gain in BD-rate of 3.4% over HEVC intra prediction plus intra block copy for both screen content and camera-captured gray scale images.
Millimeter Wave Integrated Access and Backhaul in 5G: Performance Analysis and Design Insights

With the emergence of integrated access and backhaul (IAB) in the fifth generation (5G) of cellular networks, backhaul is no longer just a passive capacity constraint in cellular network design. In fact, this tight integration of access and backhaul is one of the key ways in which 5G millimeter wave (mm-wave) heterogeneous cellular networks (HetNets) differ from traditional settings where the backhaul network was designed independently from the radio access network (RAN). With the goal of elucidating key design trends for this new paradigm, we develop a stochastic geometry-based analytical framework for a millimeter wave (mm-wave) two-tier HetNet with IAB where only the macro BSs (MBSs) have fiber access to the core network and the small cell BSs (SBSs) are wirelessly backhauled by the MBSs over mm-wave links. For this network, we derive the downlink rate coverage probability for two types of resource allocations at the MBS: 1) integrated resource allocation (IRA): where the total bandwidth (BW) is dynamically split between access and backhaul, and 2) orthogonal resource allocation (ORA): where a static partition is defined for the access and backhaul communications. Our analysis concretely demonstrates that offloading users from the MBSs to SBSs may not provide similar rate improvements in an IAB setting as it would in a HetNet with fiber-backhauled SBS. Our analysis also shows that it is not possible to improve the user rate in an IAB setting by simply densifying the SBSs due to the bottleneck on the rate of wireless backhaul links between MBS and SBS.
Real-Time Hardware-In-the-Loop Emulation Framework for DSRC-Based Connected Vehicle Applications

The rapid growth of connected and automated vehicle (CAV) solutions have made a significant impact on the safety of intelligent transportation systems. However, similar to any other emerging technology, thorough testing and evaluation studies are of paramount importance for the effectiveness of these solutions. Due to the safety-critical nature of this problem, large-scale real-world field tests do not seem to be a feasible and practical option. Thus, employing simulation and emulation approaches are preferred in the development phase of the safety-related applications in CAVs. Such methodologies not only mitigate the high cost of deploying large number of real vehicles, but also enable researchers to exhaustively perform repeatable tests in various scenarios. Software simulation of very large-scale vehicular scenarios is mostly a time consuming task and as a matter of fact, any simulation environment would include abstractions in order to model the real-world system. In contrast to the simulation-based solutions, network emulators are able to produce more realistic test environments. In this work, we propose a high-fidelity hardware-in-the-loop remote vehicle emulator (RVE) framework in order to create testing environments for vehicle-to-vehicle (V2V) communication. We put particular emphasis on the real-time communication simulation module (RTCSim) within RVE. The proposed RTCSim is able to run accurately in real-time fashion in contrast to other existing systems, which can potentially boost the development and validation of V2V systems.
Investigation of Cognitive and affective Attitude of Teachers toward Research and their behavioral Intention to conduct Research in the Future

The study wants to determine the relationship between the cognitive and affective attitudes of teachers toward research and their intention to conduct research. To support and strengthen the study, theories on human attitude, human behavior, the relationship between attitude and behavior, the nature of academic research and related studies were discussed. The study used descriptive correlational method of research design and aided by fact finding inquiry. The population of the study were 150 teachers of Divine Word Colleges in Ilocos Region. Validated questionnaires were used to gather the data and to treat the data statistically, mean, and Pearson r were used. Mean was to measure the level of cognitive and affective attitude of teachers toward research and their behavioral intention to conduct research in the future and Pearson r was used to determine the correlation between cognitive and affective attitude toward research and intention to conduct research. The study found that teachers have high attitudes on positive cognition and affection toward research and moderate level on negative cognition and negative affection toward research. Both, cognitive and affective attitude toward research (positive and negative components) correlates to the behavioral intention to conduct research. Thus, the study recommends to strengthen cognitive and affective attitude toward research and minimize their negative attitude toward research through continuous training on research methodology.
The Eighty Five Percent Rule for optimal learning

Researchers and educators have long wrestled with the question of how best to teach their clients be they humans, non-human animals or machines. Here, we examine the role of a single variable, the difficulty of training, on the rate of learning. In many situations we find that there is a sweet spot in which training is neither too easy nor too hard, and where learning progresses most quickly. We derive conditions for this sweet spot for a broad class of learning algorithms in the context of binary classification tasks. For all of these stochastic gradient-descent based learning algorithms, we find that the optimal error rate for training is around 15.87% or, conversely, that the optimal training accuracy is about 85%. We demonstrate the efficacy of this ‘Eighty Five Percent Rule’ for artificial neural networks used in AI and biologically plausible neural networks thought to describe animal learning. Is there an optimum difficulty level for training? In this paper, the authors show that for the widely-used class of stochastic gradient-descent based learning algorithms, learning is fastest when the accuracy during training is 85%.
A sporadic low-velocity layer atop the 410-km discontinuity beneath the Pacific Ocean

Waveforms of SS precursors recorded by global stations are analyzed to investigate lateral heterogeneities of upper-mantle discontinuities on a global scale. A sporadic low-velocity layer immediately above the 410-km discontinuity (LVL-410) is observed worldwide, including East Asia, western North America, eastern South America, the Pacific Ocean, and possibly the Indian Ocean. Our best data coverage is for the Pacific Ocean, where the LVL-410 covers 33–50% of the resolved region. Lateral variations of our LVL-410 observations show no geographical correlation with 410-km discontinuity topography or tomographic models of seismic velocity, suggesting that the LVL-410 is not caused by regional thermal anomalies. We interpret the LVL-410 as partial melting due to dehydration of ascending mantle across the 410-km discontinuity, which is predicted by the transition zone water filter hypothesis. Given the low vertical resolution of SS precursors, it is possible that the regions without a clear LVL-410 detection also have a thin layer. Therefore, the strong lateral heterogeneity of the LVL-410 in our observations suggests partial melting with varying intensities across the Pacific, and further provides indirect evidence of a hydrous mantle transition zone with laterally varying water content.
Model-free Deep Reinforcement Learning for Urban Autonomous Driving

Urban autonomous driving decision making is challenging due to complex road geometry and multi-agent interactions. Current decision making methods are mostly manually designing the driving policy, which might result in suboptimal solutions and is expensive to develop, generalize and maintain at scale. On the other hand, with reinforcement learning (RL), a policy can be learned and improved automatically without any manual designs. However, current RL methods generally do not work well on complex urban scenarios. In this paper, we propose a framework to enable model-free deep reinforcement learning in challenging urban autonomous driving scenarios. We design a specific input representation and use visual encoding to capture the low-dimensional latent states. Several state-of-the-art model-free deep RL algorithms are implemented into our framework, with several tricks to improve their performance. We evaluate our method in a challenging roundabout task with dense surrounding vehicles in a high-definition driving simulator. The result shows that our method can solve the task well and is significantly better than the baseline.
Meta-Research: Releasing a preprint is associated with more attention and citations for the peer-reviewed article

Preprints in biology are becoming more popular, but only a small fraction of the articles published in peer-reviewed journals have previously been released as preprints. To examine whether releasing a preprint on bioRxiv was associated with the attention and citations received by the corresponding peer-reviewed article, we assembled a dataset of 74,239 articles, 5,405 of which had a preprint, published in 39 journals. Using log-linear regression and random-effects meta-analysis, we found that articles with a preprint had, on average, a 49% higher Altmetric Attention Score and 36% more citations than articles without a preprint. These associations were independent of several other article- and author-level variables (such as scientific subfield and number of authors), and were unrelated to journal-level variables such as access model and Impact Factor. This observational study can help researchers and publishers make informed decisions about how to incorporate preprints into their work.
Effect of chemical disorder on the electronic stopping of solid solution alloys

Abstract The electronic stopping power of nickel-based equiatomic solid solutions alloys NiCr, NiFe and NiCo for protons and alpha projectiles is investigated in detail using real-time time-dependent density functional theory over a wide range of velocities. Recently developed numerical electronic structure methods are used to probe fundamental aspects of electron-ion coupling non-perturbatively and in a fully atomistic context, capturing the effect of the atomic scale disorder. The effects of particular electronic band structures and density of states reflect in the low velocity limit behavior. We compare our results for the alloys with those of a pure nickel target to understand how alloying affects the electronic stopping. We discover that NiCo and NiFe have similar stopping behavior as Ni while NiCr has an asymptotic stopping power that is more than a factor of two larger than its counterparts for velocities below 0.1 a.u.. We show that the low-velocity limit of electronic stopping power can be manipulated by controlling the broadening of the d-band through the chemical disorder. In this regime, the Bragg’s additive rule for the stopping of composite materials also fails for NiCr.
Medicaid Managed Care: Efficiency, Medical Loss Ratio, and Quality of Care

The recent final rule on Medicaid managed care establishes the minimum medical loss ratio (MLR) requirement for Medicaid managed care and contains several provisions to strengthen delivery and paym...
Semiclassical Explanation of the Matteucci–Pozzi and Aharonov–Bohm Phase Shifts

Classical electromagnetic forces can account for the experimentally observed phase shifts seen in an electron interference pattern when a line of electric dipoles or a line of magnetic dipoles (a solenoid) is placed between the electron beams forming the interference pattern.
FourierSAT: A Fourier Expansion-Based Algebraic Framework for Solving Hybrid Boolean Constraints

The Boolean SATisfiability problem (SAT) is of central importance in computer science. Although SAT is known to be NP-complete, progress on the engineering side—especially that of Conflict-Driven Clause Learning (CDCL) and Local Search SAT solvers—has been remarkable. Yet, while SAT solvers, aimed at solving industrial-scale benchmarks in Conjunctive Normal Form (CNF), have become quite mature, SAT solvers that are effective on other types of constraints (e.g., cardinality constraints and XORs) are less well-studied; a general approach to handling non-CNF constraints is still lacking. In addition, previous work indicated that for specific classes of benchmarks, the running time of extant SAT solvers depends heavily on properties of the formula and details of encoding, instead of the scale of the benchmarks, which adds uncertainty to expectations of running time.To address the issues above, we design FourierSAT, an incomplete SAT solver based on Fourier analysis of Boolean functions, a technique to represent Boolean functions by multilinear polynomials. By such a reduction to continuous optimization, we propose an algebraic framework for solving systems consisting of different types of constraints. The idea is to leverage gradient information to guide the search process in the direction of local improvements. Empirical results demonstrate that FourierSAT is more robust than other solvers on certain classes of benchmarks.
Generation of unstructured grids suitable for RANS calculations

The present Conference was intended to provide a forum to discuss future trends and developments that may affect the way Computational Aerosciences are conducted. One way to venture statements about the future is via exptrapolations from historical trends. In each of the individual disciplines that comprise the aerosciences - aerodynamics, structures, propulsion, control, stealth, etc. - one observes the typical bottom-up trend. Starting from sufficiently simple geometries and equations to have an impact and be identified as `computational’ (e.g., potential flow in 2-D for aerodynamics), more and more realism is added at the geometrical and PDE level. While the engineering process (Figure 1) follows the line: project, objectives, optimization goals, discipline, problem definition, gridding, solution of the PDE and evaluation, the developments in the Computational Sciences tend to run in the opposite direction: solvers, mesh generators, preprocessors, multi-disciplinary links, complete database. At present (1998), for commercial software we are at the threshold mesh generators/ preprocessors, so we can estimate that sometime during the next decade we will see the widespread use of multi-disciplinary links and towards the end of the decade the appearance of integrated analysis and simulation databases. At the research level, we are already entering the complete database approach.
Accelerating the discovery of multilayer nanostructures with analytic differentiation of the transfer matrix equations

The authors describe a novel theoretical methodology for the design of multilayer nanostructures that is based upon analytical differentiation of the transfer matrix equations, and demonstrate this methodology provides an efficient route to optimizing the geometries of structures for applications including incandescent light sources, anti-reflective solar coatings, and light-harvesting structures coupled to molecular chromophores.
Directory-based composite routing and scheduling policies for dynamic multimedia environments

In this paper we present and evaluate algorithms to address combined path and server selection (CPSS) problems in highly dynamic multimedia environments. Our goal is to ensure effective utilization of network and server resources while tolerating imprecision in system state information. Components within the framework implement the optimized scheduling policies as well as collect/update the network and server parameters using a directory service. We present and analyze multiple policies to solve the CPSS problem. In addition, we study multiple techniques for updating the directory service with system state information. We further evaluate the performance of the CPSS policies under different update mechanisms and study the implications of the CPSS policies on directory service management.
On Assessing Driver Awareness of Situational Criticalities: Multi-modal Bio-Sensing and Vision-Based Analysis, Evaluations, and Insights.

Automobiles for our roadways are increasingly using advanced driver assistance systems. The adoption of such new technologies requires us to develop novel perception systems not only for accurately understanding the situational context of these vehicles, but also to infer the driver’s awareness in differentiating between safe and critical situations. This manuscript focuses on the specific problem of inferring driver awareness in the context of attention analysis and hazardous incident activity. Even after the development of wearable and compact multi-modal bio-sensing systems in recent years, their application in driver awareness context has been scarcely explored. The capability of simultaneously recording different kinds of bio-sensing data in addition to traditionally employed computer vision systems provides exciting opportunities to explore the limitations of these sensor modalities. In this work, we explore the applications of three different bio-sensing modalities namely electroencephalogram (EEG), photoplethysmogram (PPG) and galvanic skin response (GSR) along with a camera-based vision system in driver awareness context. We assess the information from these sensors independently and together using both signal processing- and deep learning-based tools. We show that our methods outperform previously reported studies to classify driver attention and detecting hazardous/non-hazardous situations for short time scales of two seconds. We use EEG and vision data for high resolution temporal classification (two seconds) while additionally also employing PPG and GSR over longer time periods. We evaluate our methods by collecting user data on twelve subjects for two real-world driving datasets among which one is publicly available (KITTI dataset) while the other was collected by us (LISA dataset) with the vehicle being driven in an autonomous mode. This work presents an exhaustive evaluation of multiple sensor modalities on two different datasets for attention monitoring and hazardous events classification.
Evolutionary Dynamics of Abundant 7-bp Satellites in the Genome of Drosophila virilis.

The factors that drive the rapid changes in abundance of tandem arrays of highly repetitive sequences, known as satellite DNA, are not well understood. Drosophila virilis has one of the highest relative amounts of simple satellites of any organism that has been studied, with an estimated >40% of its genome composed of a few related 7-bp satellites. Here, we use D. virilis as a model to understand technical biases affecting satellite sequencing and the evolutionary processes that drive satellite composition. By analyzing sequencing data from Illumina, PacBio, and Nanopore platforms, we identify platform-specific biases and suggest best practices for accurate characterization of satellites by sequencing. We use comparative genomics and cytogenetics to demonstrate that the highly abundant AAACTAC satellite family arose from a related satellite in the branch leading to the virilis phylad 4.5-11 Ma before exploding in abundance in some species of the clade. The most abundant satellite is conserved in sequence and location in the pericentromeric region but has diverged widely in abundance among species, whereas the satellites nearest the centromere are rapidly turning over in sequence composition. By analyzing multiple strains of D. virilis, we saw that the abundances of two centromere-proximal satellites are anticorrelated along a geographical gradient, which we suggest could be caused by ongoing conflicts at the centromere. In conclusion, we illuminate several key attributes of satellite evolutionary dynamics that we hypothesize to be driven by processes including selection, meiotic drive, and constraints on satellite sequence and abundance.
An Experimental Study on Shear and Flexural Strengthening of Concrete Beams Using GFRP Composites

Maintaining and restoring buildings has nowadays gained special importance due to its high cost. Various different methods have been presented regarding this matter due to the above-mentioned reason and also the ever-growing demand for the engineers and the building-industry specialists for strengthening, restoring, and improving the concrete structures. The effect of FRP sheets on shear and flexural strengthening of simple concrete beams has been examined in this research. Four concrete beams containing concrete were constructed with a similar cross section and length and a common resistance level. They underwent the two-point flexural test and their exploitation conditions were studied. The results showed that if the FRP sheets are used appropriately and with proper bracing in strengthening the concrete beam samples, there will be a significant increase in their shear and flexural strength. Moreover, it was observed that relative deformation measured on the concrete surface and the GFRP sheet is proportional.
Some Research Problems in Biometrics: The Future Beckons

The need for reliably determining the identity of a person is critical in a number of different domains ranging from personal smartphones to border security; from autonomous vehicles to e-voting; from tracking child vaccinations to preventing human trafficking; from crime scene investigation to personalization of customer service. Biometrics, which entails the use of biological attributes such as face, fingerprints and voice for recognizing a person, is being increasingly used in several such applications. While biometric technology has made rapid strides over the past decade, there are several fundamental issues that are yet to be satisfactorily resolved. In this article, we will discuss some of these issues and enumerate some of the exciting challenges in this field.
How Media Covered “Arab Spring” Movement: Comparison Between the American Fox News and the Middle Eastern Al Jazeera

The purpose of the research paper is the comparison of two different Media with strong political power all over the world, i.e. Fox News and Al Jazeera. The comparison will be based via a specific event; more specifically the author will investigate the way each of these media covered the Arab Spring Movement, which was initiated in Tunisia on December 17, 2010. Since this particular study is of a historical type, it will be focused on the timeline of the events and the way each media covered each of those events. In addition, the analysis section of the comparison will be based upon the actual timeline of the Arab Spring. Finally, the whole research body will be supported via the presentation of the historical background of both media, while the events themselves took place. It has to be noted that the coverage of both networks presented in the present study is based on independent sources criticizing the way this broadcasting was actually performed. The findings regarding AL Jazeera were more extensive by the ones of FOX News, always according to the research conducted on the Internet and other academic sources.
Mechanisms of chromosome biorientation and bipolar spindle assembly analyzed by computational modeling

The essential functions required for mitotic spindle assembly and chromosome biorientation and segregation are not fully understood, despite extensive study. To illuminate the combinations of ingredients most important to align and segregate chromosomes and simultaneously assemble a bipolar spindle, we developed a computational model of fission-yeast mitosis. Robust chromosome biorientation requires progressive restriction of attachment geometry, destabilization of misaligned attachments, and attachment force dependence. Large spindle length fluctuations can occur when the kinetochore-microtubule attachment lifetime is long. The primary spindle force generators are kinesin-5 motors and crosslinkers in early mitosis, while interkinetochore stretch becomes important after biorientation. The same mechanisms that contribute to persistent biorientation lead to segregation of chromosomes to the poles after anaphase onset. This model therefore provides a framework to interrogate key requirements for robust chromosome biorientation, spindle length regulation, and force generation in the spindle.
Causes and consequences of mitochondrial proteome size variation in animals

Despite a conserved set of core mitochondrial functions, animal mitochondrial proteomes show a large variation in size. We analyzed putative mechanisms behind and functional significance of this variation by performing comparative analysis of the experimentally-verified mitochondrial proteomes of four bilaterian animals (human, mouse, Caenorhabditis elegans, and Drosophila melanogaster) and two non-animal outgroups (Acanthamoeba castellanii and Saccharomyces cerevisiae). We found that of several factors affecting mitochondrial proteome size, evolution of novel mitochondrial proteins in mammals and loss of ancestral proteins in protostomes were the main contributors. Interestingly, the gain and loss of the N-terminal mitochondrial targeting signal was not a major factor in the proteome size evolution.
Low Precision Floating Point Arithmetic for High Performance FPGA-based CNN Acceleration

Low precision data representation is important to reduce storage size and memory access for convolutional neural networks (CNNs). Yet, existing methods have two major limitations: (1) requiring re-training to maintain accuracy for deep CNNs, and (2) needing 16-bit floating point or 8-bit fixed point for a good accuracy. In this paper, we propose a low precision (8-bit) floating point (LPFP) quantization method for FPGA-based acceleration to overcome the above limitations. Without any re-training, LPFP finds an optimal 8-bit data representation with negligible top-1/top-5 accuracy loss (within 0.5%/0.3% in our experiments, respectively, and significantly better than existing methods for deep CNNs). Furthermore, we implement one 8-bit LPFP multiplication by one 4-bit multiply-adder (MAC) and one 3-bit adder, and therefore implement four 8-bit LPFP multiplications using one DSP slice of Xilinx Kintex-7 family (KC705 in this paper) while one DSP can implement only two 8-bit fixed point multiplications. Experiments on six typical CNNs for inference show that on average, we improve throughput by 64.5× over Intel i9 CPU and by 1.5× over existing FPGA accelerators. Particularly for VGG16 and YOLO, compared to six recent FPGA accelerators, we improve average throughput by 3.5× and 27.5× and improve average throughput per DSP by 4.1× and 5×, respectively. To the best of our knowledge, this is the first in-depth study to simplify one multiplication for CNN inference to one 4-bit MAC and implement four multiplications within one DSP while maintaining comparable accuracy without any re-training.
Demand Side Management for Homes in Smart Grids

Electricity usage is a major portion of utility bills and the best place to start lowering them. An effective home energy management approach is introduced to decrease customers' electricity bills by determining the optimal appliance scheduling under hourly pricing based demand response (DR) strategies. The proposed approach specifically addresses consumer comfort through acceptable appliance deferral times, within which their consumption can be shifted from the normal schedules. Electric vehicles (EVs), with vehicle-to-grid (V2G) and vehicle-to-home (V2H) abilities, Energy storage systems (ESS), and Renewable Energy Resource as a distributed generation are considered at the end-user premises to improve the energy management efficiency. A mixed-integer linear programming (MILP) framework is implemented to solve the proposed problem. Numerical results show that the proposed approach can reduce customers' electricity bill without sacrificing their comfort and fully exploit EVs and ESS capacities.
Expanding across time to deliver bandwidth efficiency and low latency

Datacenters need networks that support both low-latency and high-bandwidth packet delivery to meet the stringent requirements of modern applications. We present Opera, a dynamic network that delivers latency-sensitive traffic quickly by relying on multi-hop forwarding in the same way as expander-graph-based approaches, but provides near-optimal bandwidth for bulk flows through direct forwarding over time-varying source-to-destination circuits. The key to Opera's design is the rapid and deterministic reconfiguration of the network, piece-by-piece, such that at any moment in time the network implements an expander graph, yet, integrated across time, the network provides bandwidth-efficient single-hop paths between all racks. We show that Opera supports low-latency traffic with flow completion times comparable to cost-equivalent static topologies, while delivering up to 4x the bandwidth for all-to-all traffic and supporting 60% higher load for published datacenter workloads.
Unconventional ideas for ionization cooling of muons

Small muon beams increase the luminosity of a muon collider. Reducing the momentum and position spreads of muons reduces emittance and leads to small, cool beams. Ionization cooling has been observed at the Muon Ionization Cooling Experiment. 6D emittance reduction by a factor of 100, 000 has been achieved in simulation. Another factor of 5 in cooling would meet the basic requirements of a high luminosity muon collider. In this paper we compare, for the first time, the amount of RF needed in a cooling channel to previous linacs. We also outline three methods aimed to help achieve a final factor of 5 in 6D cooling.
What is the point of fairness?: disability, AI and the complexity of justice

Work integrating conversations around AI and Disability is vital and valued, particularly when done through a lens of fairness. Yet at the same time, analysing the ethical implications of AI for disabled people solely through the lens of a singular idea of "fairness" risks reinforcing existing power dynamics, either through reinforcing the position of existing medical gatekeepers, or promoting tools and techniques that benefit otherwise-privileged disabled people while harming those who are rendered outliers in multiple ways. In this paper we present two case studies from within computer vision - a subdiscipline of AI focused on training algorithms that can "see" - of technologies putatively intended to help disabled people but, through failures to consider structural injustices in their design, are likely to result in harms not addressed by a "fairness" framing of ethics. Drawing on disability studies and critical data science, we call on researchers into AI ethics and disability to move beyond simplistic notions of fairness, and towards notions of justice.
Integrity Constraints Revisited: From Exact to Approximate Implication

Integrity constraints such as functional dependencies (FD), and multi-valued dependencies (MVD) are fundamental in database schema design. Likewise, probabilistic conditional independences (CI) are crucial for reasoning about multivariate probability distributions. The implication problem studies whether a set of constraints (antecedents) implies another constraint (consequent), and has been investigated in both the database and the AI literature, under the assumption that all constraints hold exactly. However, many applications today consider constraints that hold only approximately. In this paper we define an approximate implication as a linear inequality between the degree of satisfaction of the antecedents and consequent, and we study the relaxation problem: when does an exact implication relax to an approximate implication? We use information theory to define the degree of satisfaction, and prove several results. First, we show that any implication from a set of data dependencies (MVDs+FDs) can be relaxed to a simple linear inequality with a factor at most quadratic in the number of variables; when the consequent is an FD, the factor can be reduced to 1. Second, we prove that there exists an implication between CIs that does not admit any relaxation; however, we prove that every implication between CIs relaxes "in the limit". Finally, we show that the implication problem for differential constraints in market basket analysis also admits a relaxation with a factor equal to 1. Our results recover, and sometimes extend, several previously known results about the implication problem: implication of MVDs can be checked by considering only 2-tuple relations, and the implication of differential constraints for frequent item sets can be checked by considering only databases containing a single transaction.
Effect of Mo in Combination with Nb on Austenite Grain Size Control in Vacuum Carburizing Steels

Vacuum carburizing with high-pressure gas quenching is increasingly employed to reduce near-surface intergranular oxidation and reduce quenching distortion. Vacuum carburizing can be conducted at higher operating temperatures, as high as 1100 °C, to reduce the processing times and increase furnace productivity. However, processing at elevated temperatures may result in excessive austenite grain coarsening, leading to the degradation of fatigue performance. Microalloying to form small carbo-nitride precipitates is one effective method to limit austenite grain growth during carburizing. In this study, the effects of microalloying a carburizing steel with molybdenum (Mo) and niobium (Nb) on microstructural grain refinement in the core have been investigated. Additions of Nb alone are found to provide some control of abnormal austenite grain growth. Additions of Mo in combination with Nb provide enhanced resistance to austenite grain growth, especially at high carburizing temperatures up to 1050 °C. The enhanced control is attributed to solute and precipitation effects.