Miracle-dz/UniFAR-FTUs huggingface dataset:
Delay‐Dependent Stability Criterion for Discrete‐Time Systems with Time‐Varying Delays

The stability analysis problem is considered for linear discrete‐time systems with time‐varying delays. A novel summation inequality is proposed, which takes the double summation information of the system state into consideration. The inequality relaxes the recently proposed discrete Wirtinger inequality and its improved version. Based on construction of a suitable Lyapunov‐Krasovskii functional and the novel summation inequality, an improved delay‐dependent stability criterion for asymptotic stability of the systems is derived in terms of linear matrix inequalities. Numerical examples are given to demonstrate the advantages of the proposed method.
NUCLEI AND NUCLEAR REACTIONS

The statistical analysis of the distributions of the 0 state energies in actinide nuclei is carried out, by using the Brody distribution function. It reveals an intermediate character of the experimental 0 spectra between order and chaos. This function is turned out to be different for the available experimental data and the calculated ones in the frame of the quasiparticle-phonon model. K e yw o r d s: 0 states, Brody distribution, quasiparticle-phonon model.
2 . 3 Non-conjugacy on Logistic-Normal Prior with Multinomial Likelihood

The Recurrent Chinese Restaurant Process (RCRP) is a powerful statistical method for modeling evolving clusters in large scale social media data. With the RCRP, one can allow both the number of clusters and the cluster parameters in a model to change over time. However, application of the RCRP has largely been limited due to the non-conjugacy between the cluster evolutionary priors and the Multinomial likelihood. This non-conjugacy makes inference di cult and restricts the scalability of models which use the RCRP, leading to the RCRP being applied only in simple problems, such as those that can be approximated by a single Gaussian emission. In this paper, we provide a novel solution for the non-conjugacy issues for the RCRP and an example of how to leverage our solution for one speci c problem the social event discovery problem. By utilizing Sequential Monte Carlo methods in inference, our approach can be massively paralleled and is highly scalable, to the extent it can work on tens of millions of documents. We are able to generate high quality topical and location distributions of the clusters that can be directly interpreted as real social events, and our experimental results suggest that the approaches proposed achieve much better predictive performance than techniques reported in prior work. We also demonstrate how the techniques we develop can be used in a much more general ways toward similar problems.
Quantifying non-classical teleportation

In a previous work [Phys. Rev. Lett. 119, 110501] we proved that every entangled quantum state can be used to implement a non-classical teleportation channel. Here we turn to the question of quantifying how non-classical these channels are. We define several quantifiers of non-classical teleportation which can be computed using semidefinite programming. Furthermore, we prove quantitative relations between these quantifiers and the amount of entanglement in the underlying states. Apart from providing further insight on the relation between teleportation and entanglement, our work introduces a method to estimate entanglement in a teleportation experiment.
Approximate Method for Solving Scheduling Problems With Minimax Criteria

We are given a set N = {1, . , n} of n jobs that must be processed on m machines M = {1, . ,m}. Preemption of the jobs is not allowed. Each machine can handle only one job at a time. For each job j we have: rj – release date; 0 ≤ pji ≤ +∞ – processing time job j on the machine i (if pji = +∞, then job j can not process on the machine i); dj – due date. Between jobs ratios of a precedence in the form of an acyclic oriented graph G ⊂ N × N are set. Through πi we will definete the schedule of the jobs processeded on the machine i, i = 1, . ,m. Naturally, admissible schedules without artificial idle times of the machines, satisfying the graph are considered only. In this paper, we consider the approach finding of the approximate solution with the guaranteed absolute error for the problems minimizing maximum lateness. The idea of the approach consists in construction to a initial instance A such instance B (with the same number of jobs) with minimum of estimation of absolute error that 0 ≤ Lmax(π) − Lmax(π) ≤ ρd(A,B) + ρr(A,B) + ρp(A,B), where
Adaptive Lambda Least-Squares Temporal Difference Learning

Temporal Difference learning or TD($\lambda$) is a fundamental algorithm in the field of reinforcement learning. However, setting TD's $\lambda$ parameter, which controls the timescale of TD updates, is generally left up to the practitioner. We formalize the $\lambda$ selection problem as a bias-variance trade-off where the solution is the value of $\lambda$ that leads to the smallest Mean Squared Value Error (MSVE). To solve this trade-off we suggest applying Leave-One-Trajectory-Out Cross-Validation (LOTO-CV) to search the space of $\lambda$ values. Unfortunately, this approach is too computationally expensive for most practical applications. For Least Squares TD (LSTD) we show that LOTO-CV can be implemented efficiently to automatically tune $\lambda$ and apply function optimization methods to efficiently search the space of $\lambda$ values. The resulting algorithm, ALLSTD, is parameter free and our experiments demonstrate that ALLSTD is significantly computationally faster than the na\"{i}ve LOTO-CV implementation while achieving similar performance.
Threshold Phenomena for Interference with Randomly Placed Sensors Rafał Kapelko

This paper investigates the problem of efficient displacement of random sensors where good communication within the network is provided, there is no interference between sensors and the movement cost is minimized in expectation. Fix d ∈ N \ {0}. Assume n sensors are initially placed in the hyperoctant [0,∞)d according to d identical and independent Poisson processes each with arrival rate n1/d. Let 0 < s ≤ v be given real numbers. We are allowed to move the sensors, so that every two consecutive sensors are placed at distance greater than or equal to s and less than or equal to v. When a sensor is displaced a distance equal to m, the cost of movement is proportional to some (fixed) power a > 0 of the m distance travelled. We consider a−total movement (see Definition 2) for one dimension and d−dimensional a−total movement (see Definition 3) for the higher dimension. Motivation for using this extended cost metric arises from the fact that the parameter a in the exponents represents various conditions on the barrier: lubrication, obstacles, etc. The main contribution of this paper can be summarized as follows. 1) If the sensors are displaced in the half-infinite interval [0,∞), the tradeoffs between the interference distances s, v and the expected minimum a−total movement are derived. We discover a sharp decline and a sharp increase in the expected minimum a−total movement around the interference distance s, v equal to 1 n . 2) For the sensors in the hyperoctant [0,∞)d (d > 1), the tradeoffs between the interference distances s, v and the expected minimum d−dimensional a−total movement are obtained. We also discover a sharp decline and a sharp increase in the expected minimum d−dimensional a−total movement around the interference distance s, v equal to 1 n1/d .
GRÖBNER BASES IN COCOA AND COCOALIB

We present a survey on the developments related to Gröbner bases, and show explicit examples in CoCoA. The CoCoA project dates back to 1987: its aim was to create a " mathematician "-friendly computational laboratory for studying Commutative Algebra, most especially Gröbner bases. Always maintaining this " friendly " tradition, the project has grown and evolved, and the software has been completely rewritten. CoCoA offers Gröbner bases for all levels of interest: from the basic, explicit call in the interactive system CoCoA-5 [5], to problem-specific optimized implementations, to the computer–computer communication with the open source C++ software library, Co-CoALib [4], or the prototype OpenMath-based server. The openness and clean design of CoCoALib and CoCoA-5 are intended to offer different levels of usage, and to encourage external contributions.
Polymorphisms of vitamin D receptor gene Taqi susceptibility of prostate cancer: a meta-analysis

Objective: Many studies have investigated the association of the vitamin D receptor gene TaqI polymorphism with prostate cancer (PCa) risk. However, the evidence is inadequate to draw robust conclusions. To shed light on these inconclusive findings, we conducted a meta-analysis. Materials and methods: We searched PubMed for eligible articles. The relevant data were abstracted by two independent reviewers with the Stata 11.0 software. Results: A total of 27 studies were included. The pooled outcomes indicated that the TaqI genetic polymorphisms were significantly associated with the risk of PCa (T vs t allele: odds ratio [OR] = 1.11, 95% confidence interval [CI] = 1.03–1.21, P = 0.008; TT vs tt: OR = 1.19, 95% CI = 1.01–1.42, P = 0.040; TT + Tt vs tt: OR = 1.18, 95% CI = 1.02–1.38, P = 0.031), especially in the Asian population (T vs t allele: OR = 1.11, 95% CI = 1.03–1.21, P = 0.008; TT/Tt vs tt: OR = 1.93, 95% CI = 1.02–3.66, P = 0.043). In the tumor stage stratified analyses, the pooled results showed no significant difference in genetic polymorphisms between the local tumor group and the control group or between the local tumor group and the advanced tumor group. However, the genotypes TT and TT/Tt were significantly higher in the advanced PCa group compared to the control group (T vs t allele: OR = 1.20, 95% CI = 1.01–1.42, P = 0.040; TT vs tt: OR = 1.34, 95% CI = 1.08–1.67, P = 0.009; TT/Tt vs tt: OR = 1.28, 95% CI = 1.05–1.56, P = 0.015). Conclusion: The vitamin D receptor gene TaqI allele polymorphism might be associated with a PCa risk, especially in Asians, which might provide new clues for the pathogenesis research and clinical diagnosis of PCa in the future.
Multivariate Variable Selection through Use of Null-Beamforming: Principle Variable Analysis

This article extends the idea of principal component analysis to multivariate variable selection. The basic premise behind the proposal is to scan through a predictor variable space with a series of filters called null-beamformers; each is tailored to a particular region in the space and resistant to interference effects originating from other regions. This gives rise to a predictive power map for predictor selection. The new approach attempts to explore the maximum amount of variation in the data with a small number of principal variables. Applying the proposal to simulated data and real cancer drug data, we show that it outperforms the existing methods in terms of sensitivity and specificity.
Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption ( Supplementary Material )

We are interested in leveraging ‘private’ images, which are not shared publicly but just saved on a personal storage privately, for visual learning. In this section, we would like to provide some statistics that motivate our work. Based on the recent report from Kleiner Perkins Caufield & Byers [14], the number of photos shared publicly on several social networking services (Snapchat, Instagram, WhatsApp, Facebook Messenger, and Facebook) per day has reached almost 3.5 billion in 2015. It also shows that the number of smartphone users in the world was about 2.5 billion in the same year. From these statistics, if everyone takes three photos per day on average, about four billion photos in total would be stored privately everyday. Some prior work [9, 10] has shown that such private photos still contained meaningful information including people, faces, and written texts, as well as some sensitive information like a computer screen and a bedroom. Our privacy-preserving framework is designed to learn visual classifiers by leveraging this vast amount of private images while preserving the privacy of the owners.
Transformer Nets : Learning Single-View 3 D Object Reconstruction without 3 D Supervision Supplementary Materials

Similar to the spatial transformer network introduced in [1], we propose a 2-step procedure: (1) performing dense sampling from input volume (in 3D world coordinates) to output volume (in screen coordinates), and (2) flattening the 3D spatial output across disparity dimension. In the experiment, we assume that transformation matrix is always given as input, parametrized by the viewpoint α. Again, the 3D point (xi , y s i , z s i ) in input volume V ∈ RH×W×D and corresponding point (xi, y i , di) in output volume U ∈ RH×W ′×D′ is linked by perspective transformation matrix Θ4×4. Here, (W,H,D) and (W ′, H ′, D′) are the width, height and depth of input and output volume, respectively. x s i y i z i 1  = θ11 θ12 θ13 θ14 θ21 θ22 θ23 θ24 θ31 θ32 θ33 θ34 θ41 θ42 θ43 θ44  x̃i t ỹi t z̃i t 1  (2)
Controllability for some integrodifferential equations driven by vector measures

In this work, we consider the question of controllability of a class of integrodifferential equations on Hilbert space with measures as controls. We assume that the linear part has a resolvent operator in the sense given by R. Grimmer. We generalize the original work of N. Ahmed on vector measures, and we use it to develop necessary and sufficient conditions for weak and the exact controllability of the integrodifferential equation. Using the latter, we prove that exact controllability of the integrodifferential equation implies exact controllability of a perturbed integrodifferential equation. Controllability problem for the perturbed system is formulated fixed point problem in the space of vector measures. Our results cover impulsive controls as well as regular controls. Copyright © 2016 John Wiley & Sons, Ltd.
Documents de Travail du Centre d ’ Economie de la Sorbonne The Declining Barriers to Foreign Direct Investments and How to See Them Christian GORMSEN

Although Foreign Direct Investments (FDI) are as important to the world economy as exports, the extensive literature on trade costs has no strong parallel for FDI. Data are hard to come by, and many of the barriers to FDI are unobservable. This paper circumvents the problem by inferring the barriers to FDI that are consistent with observed FDI data. I describe the distribution and evolution of these barriers to FDI between pairs of 28 OECD countries from 1985 to 2008. On average, barriers to FDI were halved every 4.8 years. Geography is a key determinant, but GDP per capita also plays a leading role. Decomposing the growth in FDI, I show that it has mainly been driven by lower bilateral barriers (75%), not by economic growth, and that bilateral FDI stocks will tend to crowd each other out, lowering their yearly growth by -3%.
Including the excluded: lessons learned from the poverty-targeting strategies used by microfinance providers.

This article examines the strategies employed to target the poor and provides an analysis of how they differ depending on the mandate and the development paradigm of the provider. Examining the context of microfinance service provision is useful for understanding trends in poverty targeting especially the current pressures for organisations to demonstrate financial self-sufficiency. On the one hand the goal of achieving a broader outreach to the poor seems to strain against the goal of financial self-sufficiency. On the other hand the achievement of such self-sufficiency is seen by others as essential for sustainable and cost-effective poverty reduction. The relevance of this debate on poverty targeting is analysed by examining the research conducted for the compendium. The contributions illustrate how different targeting methods combine to form particular strategies that are in tune with the mandate and rationale of each microfinance provider. These contributions imply that a narrow view of the costs and effects of targeting is of limited usefulness when considered in isolation from the added-value of targeting and the non-financial services that may accompany microfinance service delivery. (excerpt)
Information-Theoretic Characterization of Sparse Recovery

We use lower-case p(Y |XS) notation for the conditional outcome distribution given the true subset of variables averaged over the latent variable βS . In some cases when we would like to distinguish between the outcome distribution conditioned on different sets of variables we use pω( · | · ) notation, to emphasize that the conditional distribution is conditioned on the given variables, assuming the true set S is Sω. W.l. o.g. we assume the true set is S1 for below proofs. Define I = {1, . , ( D K ) } as the collection of sets ω of size K.
Calculate Word Similarity GraphLearning Word Vectors Word Sense Induction Text Corpus Word Vectors Word Similarity Graph Pooling of Word Vectors Sense Inventory Sense Vectors

We present a simple yet effective approach for learning word sense embeddings. In contrast to existing techniques, which either directly learn sense representations from corpora or rely on sense inventories from lexical resources, our approach can induce a sense inventory from existing word embeddings via clustering of ego-networks of related words. An integrated WSD mechanism enables labeling of words in context with learned sense vectors, which gives rise to downstream applications. Experiments show that the performance of our method is comparable to state-of-the-art unsupervised WSD systems.
Cosmos: The Infographic Book of Space

In writing 'Cosmos: The Infographic Book of Space' [1], we had the task of creating 100 infographic spreads spanning all of astronomy and space. From the solar system to cosmology and from space exploration to time dilation. The coverage is very broad. The same is true of the designs used through the book. With such a wide range of information, and to stop it getting repetitive, we used a huge variety of visual techniques. We also wanted to keep the book approachable. Some graphics are very simple, while others require more careful study. For each graphic we started from a topic. We then collated information or processed catalogues and databases. In some cases we did our own manual classification. We even asked for the help of the crowd! Once we had identified the key story we wanted to tell, we would create an initial draft. For data-driven designs, we did this with scripts written in perl, python or even javascript. We then worked with graphic designer Mark McCormick (Founded) to evolve each design to a final version. Working with both a designer and a publisher without physics backgrounds was invaluable in ensuring we presented the right message. We had several constraints in creating the book. One was the 'compressed' colour palette provided by the design aestheic. It is visually appealing but sometimes limited the information that we were able be convey. This, along with other constraints, led us to try different approaches. We learned a lot about producing infographics and presenting data to non-specialists. Our experience of data visualisation, together with Mark's eye for design and the latest scientific results, has created a book that has been well received by readers and critics. In this talk we will explain the methodology used to create the graphics. We will describe the lessons we learned in how to create successful infographics - as well as how not to! [1] S Lowe & C North (2015), Aurum Press Ltd, ISBN 1781314500
Climate Change Impact on Wheat Production in the Southern Great Plains of the US Using Downscaled Climate Data

Gradually developing climatic and weather anomalies due to increasing concentration of atmospheric greenhouse gases can pose threat to farmers and resource managers. There is a growing need to quantify the effects of rising temperature and changing climates on crop yield and assess impact at a finer scale so that specific adaptation strategies pertinent to that location can be developed. Our work aims to quantify and evaluate the influence of future climate anomalies on winter wheat (Triticum aestivum L.) yield under the Representative Concentration Pathways 6.0 and 8.5 using downscaled climate projections from different General Circulation Models (GCMs) and their ensemble. Marksim downscaled daily data of maximum (TMax) and minimum (TMin) air temperature, rainfall, and solar radiation (SRAD) from different Coupled Model Intercomparison Project GCMs (CMIP5 GCMs) were used to simulate the wheat yield in water and nitrogen limiting and non-limiting conditions for the future period of 2040-2060. The potential impact of climate changes on winter wheat production across Oklahoma was investigated. Climate change predictions by the downscaled GCMs suggested increase in air temperature and decrease in total annual rainfall. This will be really critical in a rainfed and semi-arid agro-ecological region of Oklahoma. Predicted average wheat yield during 2040-2060 increased under projected climate change, compared with the baseline years 1980-2014. Our results indicate that downscaled GCMs can be applied for climate projection scenarios for future regional crop yield assessment.
Stochastic Programming: Optimization When Uncertainty Matters

Stochastic Programming (SP) was first introduced by George Dantzig in the 1950's. Since that time, tremendous progress toward an understanding of properties of SP models and the design of algorithmic approaches for solving them has been made. As a result, SP is gaining recognition as a viable approach for large scale models of decisions under uncertainty. In this paper, we present an introduction to stochastic programming models and methodology at a level that is intended to be accessible to the breadth of members within the INFORMS community.