cssi/SciSciGPT-SciSciNet huggingface dataset:
Adding Approximate Counters

We describe a general framework for adding the values of two approximate counters to produce a new approximate counter value whose expected estimated value is equal to the sum of the expected estimated values of the given approximate counters. (To the best of our knowledge, this is the first published description of any algorithm for adding two approximate counters.) We then work out implementation details for five different kinds of approximate counter and provide optimized pseudocode. For three of them, we present proofs that the variance of a counter value produced by adding two counter values in this way is bounded, and in fact is no worse, or not much worse, than the variance of the value of a single counter to which the same total number of increment operations have been applied. Addition of approximate counters is useful in massively parallel divide-and-conquer algorithms that use a distributed representation for large arrays of counters. We describe two machine-learning algorithms for topic modeling that use millions of integer counters and confirm that replacing the integer counters with approximate counters is effective, speeding up a GPU-based implementation by over 65% and a CPU-based implementation by nearly 50%, as well as reducing memory requirements, without degrading their statistical effectiveness.
Local Noncollinear Spin Analysis.

In this work, we generalize the local spin analysis of Clark and Davidson [J. Chem. Phys. 2001 115 (16), 7382] for the partitioning of the expectation value of the molecular spin square operator, ⟨Ŝ2⟩, into atomic contributions, ⟨ŜA·ŜB⟩, to the noncollinear spin case in the framework of density functional theory (DFT). We derive the working equations, and we show applications to the analysis of the noncollinear spin solutions of typical spin-frustrated systems and to the calculation of magnetic exchange couplings. In the former case, we employ the triangular H3He3 test molecule and a Mn3 complex to show that the local spin analysis provides additional information that complements the standard one-particle spin population analysis. For the calculation of magnetic exchange couplings, JAB, we employ the local spin partitioning to extract ⟨ŜA·ŜB⟩ as a function of the interatomic spin orientation given by the angle θ. This, combined with the dependence of the electronic energy with θ, provides a methodology to...
Jari-Erik Nurmi (1956-2017)

Jari-Erik Nurmi (1956-2017). Professor and Chair of the Department of Psychology, University of Jyvaskyla. Born in a small village in Western Finland and reared in a union household, Jari was not o...
Optimal voltage control using singular value decomposition of fast decoupled load flow jacobian

The problem of regulating voltages within the required limits is complicated by the fact that power system supplies power to a vast number of loads and is fed from many generating units. As loads vary, reactive power requirements of the transmission system vary. Moreover, voltage magnitude is relatively less sensitive to active power compared to reactive power due to high X/R ratio of transmission lines. Therefore, separating voltage control from active power is not only justified but also the common and practical way in power transmission systems. Considering these facts, the fast decoupled power flow jacobian can be used to control voltage magnitudes by reactive power compensation. In this paper, an optimal voltage control is presented to obtain new voltage set-points for PV buses by maximizing the effect of input change on output change using the Fast Decoupled Load Flow (FDLF) jacobian matrix. The proposed algorithm was tested on three IEEE systems: 9 bus, 14 bus and 30 bus systems.
Performance Measurement Evaluation Framework and Co-Benefit\/Tradeoff Analysis for Connected and Automated Vehicles (CAV) Applications: A Survey

A number of Connected and/or Automated Vehicle (CAV) applications have recently been designed to improve the performance of our transportation system. Safety, mobility and environmental sustainability are three cornerstone performance metrics when evaluating the benefits of CAV applications. These metrics can be quantified by various measures of effectiveness (MOEs). Most of the existing CAV research assesses the benefits of CAV applications on only one (e.g., safety) or two (e.g., mobility and environment) aspects, without holistically evaluating the interactions among the three types of MOEs. This paper first proposes a broad classification of CAV applications, i.e., vehicle-centric, infrastructure-centric, and traveler-centric. Based on a comprehensive literature review, a number of typical CAV applications have been examined in great detail, where a categorized analysis in terms of MOEs is performed. Finally, several conclusions are drawn, including the identification of influential factors on system performance, and suggested approaches for obtaining co-benefits across different types of MOEs.
The Entrepreneurial Culture and the Mysteries of Economic Development

Culture is easy to study but difficult to specify. This essay attempts to pin down this illusive subject by linking it to entrepreneurship—that is to specific efforts to combine land, labor, capital, and knowledge in the creation of economic activity that has some aspect of novelty. Entrepreneurship is important because of its central role in capitalism. Culture is important because it influences the willingness of individuals to take the risk of exploring possibilities for entrepreneurial ventures even though the most of them will be unsuccessful in the long-run. In search of entrepreneurial culture in America around 1800, this paper examimes immigration, agriculture, commerce, and the beginnings of the Industrial Revolution in the US. These insights are then employed in an examination of the post-WWII efforts of the World Bank—most of which failed—to promote economic growth in nations that had not yet experienced “modernization.”
Set cover in sub-linear time

We study the classic set cover problem from the perspective of sub-linear algorithms. Given access to a collection of m sets over n elements in the query model, we show that sub-linear algorithms derived from existing techniques have almost tight query complexities. On one hand, first we show an adaptation of the streaming algorithm presented in [17] to the sub-linear query model, that returns an α-approximate cover using O(m(n/k)1/(α−1) + nk) queries to the input, where k denotes the value of a minimum set cover. We then complement this upper bound by proving that for lower values of k, the required number of queries is [EQUATION], even for estimating the optimal cover size. Moreover, we prove that even checking whether a given collection of sets covers all the elements would require Ω(nk) queries. These two lower bounds provide strong evidence that the upper bound is almost tight for certain values of the parameter k. On the other hand, we show that this bound is not optimal for larger values of the parameter k, as there exists a (1 + e)-approximation algorithm with O(mn/ke2) queries. We show that this bound is essentially tight for sufficiently small constant ϵ, by establishing a lower bound of [EQUATION] query complexity. Our lower-bound results follow by carefully designing two distributions of instances that are hard to distinguish. In particular, our first lower bound involves a probabilistic construction of a certain set system with a minimum set cover of size αk, with the key property that a small number of "almost uniformly distributed" modifications can reduce the minimum set cover size down to k. Thus, these modifications are not detectable unless a large number of queries are asked. We believe that our probabilistic construction technique might find applications to lower bounds for other combinatorial optimization problems.
Understanding Excess Emissions from Industrial Facilities: Evidence from Texas.

We analyze excess emissions from industrial facilities in Texas using data from the Texas Commission on Environmental Quality. Emissions are characterized as excess if they are beyond a facility’s permitted levels and if they occur during startups, shutdowns, or malfunctions. We provide summary data on both the pollutants most often emitted as excess emissions and the industrial sectors and facilities responsible for those emissions. Excess emissions often represent a substantial share of a facility’s routine (or permitted) emissions. We find that while excess emissions events are frequent, the majority of excess emissions are emitted by the largest events. That is, the sum of emissions in the 96–100th percentile is often several orders of magnitude larger than the remaining excess emissions (i.e., the sum of emissions below the 95th percentile). Thus, the majority of events emit a small amount of pollution relative to the total amount emitted. In addition, a small group of high emitting facilities in the...
Security and Privacy in the IoT

Deploying existing data security solutions to the Internet of Things (IoT) is not straightforward because of device heterogeneity, highly dynamic and possibly unprotected environments, and large scale. In this paper, we first outline IoT security and privacy risks and critical related requirements in different application domains. We then discuss aspects of a roadmap for IoT security and privacy with focus on access control, software and firmware, and intrusion detection systems. We conclude the paper by outlining a few challenges.
On the Optimality of Gluing over Scales

We show that for every α>0, there exist n-point metric spaces (X,d) where every “scale” admits a Euclidean embedding with distortion at most α, but the whole space requires distortion at least $\varOmega (\sqrt {\alpha\log n})$. This shows that the scale-gluing lemma (Lee in SODA’05: Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 92–101, Society for Industrial and Applied Mathematics, Philadelphia, 2005) is tight and disproves a conjecture stated there. This matching upper bound was known to be tight at both endpoints, i.e., when α=Θ(1) and α=Θ(log n), but nowhere in between. More specifically, we exhibit n-point spaces with doubling constant λ requiring Euclidean distortion $\varOmega (\sqrt{\log\lambda\log n})$, which also shows that the technique of “measured descent” (Krauthgamer et al. in Geom. Funct. Anal. 15(4):839–858, 2005) is optimal. We extend this to L p spaces with p>1, where one requires distortion at least Ω((log n)1/q (log λ)1−1/q ) when q=max {p,2}, a result which is tight for every p>1.
Offshoring and occupational specificity of human capital

Abstract I document that workers in newly tradable service occupations possess more occupation-specific human capital and are more highly educated than workers in previously tradable occupations. Motivated by this observation, I develop a dynamic equilibrium model with labor market frictions and specific human capital to study the labor adjustment process after a trade shock. When calibrated to match the increase in U.S. trade between 1990 and 2010, the model suggests that (1) output increases immediately after a trade shock and converges quickly to the steady state; (2) labor market institutions likely play a larger role in the adjustment process than specific human capital; (3) the short run distributional effects are small if the labor market is flexible, even in the presence of specific human capital.
A Ternary Unification Framework for Optimizing TCAM-Based Packet Classification Systems

Packet classification is the key mechanism for enabling many networking and security services. Ternary content addressable memory (TCAM) has been the industrial standard for implementing high-speed packet classification because of its constant classification time. However, TCAM chips have small capacity, high power consumption, high heat generation, and large area-size. This paper focuses on the TCAM-based classifier compression problem: given a classifier $C$ , we want to construct the smallest possible list of TCAM entries $T$ that implement $C$ . In this paper, we propose the ternary unification framework (TUF) for this compression problem and three concrete compression algorithms within this framework. The framework allows us to find more optimization opportunities and design new TCAM-based classifier compression algorithms. Our experimental results show that the TUF can speed up the prior algorithm TCAM Razor by 20 times or more and leads to new algorithms that improve compression performance over prior algorithms by an average of 13.7% on our largest real-life classifiers. The experimental results show that our algorithms can improve both the runtime and the compression ratio over prior work.
Synaptic efficacy shapes resource limitations in working memory

Working memory (WM) is limited in its temporal length and capacity. Classic conceptions of WM capacity assume the system possesses a finite number of slots, but recent evidence suggests WM may be a continuous resource. Resource models typically assume there is no hard upper bound on the number of items that can be stored, but WM fidelity decreases with the number of items. We analyze a neural field model of multi-item WM that associates each item with the location of a bump in a finite spatial domain, considering items that span a one-dimensional continuous feature space. Our analysis relates the neural architecture of the network to accumulated errors and capacity limitations arising during the delay period of a multi-item WM task. Networks with stronger synapses support wider bumps that interact more, whereas networks with weaker synapses support narrower bumps that are more susceptible to noise perturbations. There is an optimal synaptic strength that both limits bump interaction events and the effects of noise perturbations. This optimum shifts to weaker synapses as the number of items stored in the network is increased. Our model not only provides a circuit-based explanation for WM capacity, but also speaks to how capacity relates to the arrangement of stored items in a feature space.
Impact of volunteering on cognitive decline of the elderly

Abstract Cognitive decline among the elderly imposes a large welfare and health care cost on the individual as well as society. Little however is known about factors that can mitigate cognitive decline. Using seven waves of the Health and Retirement Study and a fixed effects – instrumental variable methodology, this study estimates the effects of volunteering on old age cognitive decline. Although cognitive decline is an inevitable aspect of aging, our results suggest that volunteering participation significantly forestalls its progress among individuals aged 60 years and older.
Transcriptome Analysis of Four Arabidopsis thaliana Mediator Tail Mutants Reveals Overlapping and Unique Functions in Gene Regulation

The Mediator complex is a central component of transcriptional regulation in Eukaryotes. The complex is structurally divided into four modules known as the head, middle, tail and kinase modules, and in Arabidopsis thaliana, comprises 28-34 subunits. Here, we explore the functions of four Arabidopsis Mediator tail subunits, MED2, MED5a/b, MED16, and MED23, by comparing the impact of mutations in each on the Arabidopsis transcriptome. We find that these subunits affect both unique and overlapping sets of genes, providing insight into the functional and structural relationships between them. The mutants primarily exhibit changes in the expression of genes related to biotic and abiotic stress. We find evidence for a tissue specific role for MED23, as well as in the production of alternative transcripts. Together, our data help disentangle the individual contributions of these MED subunits to global gene expression and suggest new avenues for future research into their functions.
Rational homology manifolds and hypersurface normalizations

We prove a criterion for determining whether the normalization of a complex analytic space on which the constant sheaf is perverse is a rational homology manifold, using a perverse sheaf known as the multiple-point complex. This perverse sheaf is naturally associated to any "parameterized space", and has several interesting connections with the Milnor monodromy and mixed Hodge Modules.
Binary Classification with Karmic, Threshold-Quasi-Concave Metrics

Complex performance measures, beyond the popular measure of accuracy, are increasingly being used in the context of binary classification. These complex performance measures are typically not even decomposable, that is, the loss evaluated on a batch of samples cannot typically be expressed as a sum or average of losses evaluated at individual samples, which in turn requires new theoretical and methodological developments beyond standard treatments of supervised learning. In this paper, we advance this understanding of binary classification for complex performance measures by identifying two key properties: a so-called Karmic property, and a more technical threshold-quasi-concavity property, which we show is milder than existing structural assumptions imposed on performance measures. Under these properties, we show that the Bayes optimal classifier is a threshold function of the conditional probability of positive class. We then leverage this result to come up with a computationally practical plug-in classifier, via a novel threshold estimator, and further, provide a novel statistical analysis of classification error with respect to complex performance measures.
Acceleration through Optimistic No-Regret Dynamics

We consider the problem of minimizing a smooth convex function by reducing the optimization to computing the Nash equilibrium of a particular zero-sum convex-concave game. Zero-sum games can be solved using online learning dynamics, where a classical technique involves simulating two no-regret algorithms that play against each other and, after $T$ rounds, the average iterate is guaranteed to solve the original optimization problem with error decaying as $O(\log T/T)$. In this paper we show that the technique can be enhanced to a rate of $O(1/T^2)$ by extending recent work \cite{RS13,SALS15} that leverages \textit{optimistic learning} to speed up equilibrium computation. The resulting optimization algorithm derived from this analysis coincides \textit{exactly} with the well-known \NA \cite{N83a} method, and indeed the same story allows us to recover several variants of the Nesterov's algorithm via small tweaks. We are also able to establish the accelerated linear rate for a function which is both strongly-convex and smooth. This methodology unifies a number of different iterative optimization methods: we show that the \HB algorithm is precisely the non-optimistic variant of \NA, and recent prior work already established a similar perspective on \FW \cite{AW17,ALLW18}.
Concealment of risk and regulation of bank risk taking

This paper analyzes the effectiveness of banking regulation when risk can be concealed from the regulator. Three banking regimes are considered: regulation with direct controls, incentives-based regulation, and laissez-faire banking. The relative performance of the three regimes depends on the effectiveness of monitoring. Regulation with direct controls is superior when monitoring effectiveness is low, while incentives-based regulation is superior when monitoring effectiveness is high. Laissez-faire banking is equivalent to incentives-based regulation if market analysts and the regulator have access to the same information. When monitoring effectiveness is low, a regulator with direct controls can better restrain banks' risk taking than can the market; this result applies when banks can conceal much of their risk from the regulator.
Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer

Autoencoders provide a powerful framework for learning compressed representations by encoding all of the information needed to reconstruct a data point in a latent code. In some cases, autoencoders can "interpolate": By decoding the convex combination of the latent codes for two datapoints, the autoencoder can produce an output which semantically mixes characteristics from the datapoints. In this paper, we propose a regularization procedure which encourages interpolated outputs to appear more realistic by fooling a critic network which has been trained to recover the mixing coefficient from interpolated data. We then develop a simple benchmark task where we can quantitatively measure the extent to which various autoencoders can interpolate and show that our regularizer dramatically improves interpolation in this setting. We also demonstrate empirically that our regularizer produces latent codes which are more effective on downstream tasks, suggesting a possible link between interpolation abilities and learning useful representations.