Miracle-dz/UniFAR-FTUs huggingface dataset:
Applying portfolio theory to prediction correction of train arrival times

The application of portfolio theory to the prediction of train arrival times is shown to improve prediction accuracy. The "portfolio" comprises two correction methods based on a Wiener process: one uses history data for the current day and the other uses data for previous days. The error between the predicted and actual time is assumed to have a normal distribution. Portfolio theory is used to determine the optimal application of the two methods to the correction process. Simulation using actual data showed that the average error in the predicted arrival time was reduced to 4 s from 12 s when the timetable was dense. This error reduction will, for example, improve the efficiency of regenerative braking systems, in which the kinetic energy of an arriving (braking) train is electrically transmitted to a departing (accelerating) train.
Automatic Classification of Single Facial Images

We propose a method for automatically classifying facial images based on labeled elastic graph matching, a 2D Gabor wavelet representation, and linear discriminant analysis. Results of tests with three image sets are presented for the classification of sex, "race", and expression. A visual interpretation of the discriminant vectors is provided.
SCHOOL VOUCHERS AND STUDENT ACHIEVEMENT : FIRST-YEAR EVIDENCE FROM THE LOUISIANA SCHOLARSHIP PROGRAM

We evaluate the Louisiana Scholarship Program (LSP), a prominent school voucher plan. The LSP provides public funds for disadvantaged students at low-performing Louisiana public schools to attend private schools of their choice. LSP vouchers are allocated by random lottery at schools with more eligible applicants than available seats. We estimate causal effects of voucher receipt by comparing outcomes for lottery winners and losers in the first year after the program expanded statewide. This comparison reveals that LSP participation substantially reduces academic achievement. Attendance at an LSP-eligible private school lowers math scores by 0.4 standard deviations and increases the likelihood of a failing score by 50 percent. Voucher effects for reading, science and social studies are also negative and large. The negative impacts of vouchers are consistent across income groups, geographic areas, and private school characteristics, and are larger for younger children. These effects are not explained by the quality of fallback public schools for LSP applicants: students lotteried out of the program attend public schools with scores below the Louisiana average. Survey data show that LSP-eligible private schools experience rapid enrollment declines prior to entering the program, indicating that the LSP may attract private schools struggling to maintain enrollment. These results suggest caution in the design of voucher systems aimed at expanding school choice for disadvantaged students. Atila Abdulkadiroglu Duke University Department of Economics Durham, NC 27708 atila.abdulkadiroglu@duke.edu Parag A. Pathak Department of Economics, E17-240 MIT 77 Massachusetts Avenue Cambridge, MA 02139 and NBER ppathak@mit.edu Christopher R. Walters Department of Economics University of California at Berkeley 530 Evans Hall #3880 Berkeley, CA 94720-3880 and NBER crwalters@econ.berkeley. edu
EEG-Based BCI System Using Adaptive Features Extraction and Classification Procedures

Motor imagery is a common control strategy in EEG-based brain-computer interfaces (BCIs). However, voluntary control of sensorimotor (SMR) rhythms by imagining a movement can be skilful and unintuitive and usually requires a varying amount of user training. To boost the training process, a whole class of BCI systems have been proposed, providing feedback as early as possible while continuously adapting the underlying classifier model. The present work describes a cue-paced, EEG-based BCI system using motor imagery that falls within the category of the previously mentioned ones. Specifically, our adaptive strategy includes a simple scheme based on a common spatial pattern (CSP) method and support vector machine (SVM) classification. The system's efficacy was proved by online testing on 10 healthy participants. In addition, we suggest some features we implemented to improve a system's “flexibility” and “customizability,” namely, (i) a flexible training session, (ii) an unbalancing in the training conditions, and (iii) the use of adaptive thresholds when giving feedback.
Using the semantic web to navigate conceptual spaces: an application for the philosophical domain

The semantic web offers new possibilities in the eLearning area, which are dependent at least on two factors: the increasing availability of resources on the web and the semantic power of their meta-descriptions. This paper looks at this second issue, and tries to determine the general framework and the important features of the metadata for a specific domain, philosophy. Thanks to an adequate conceptualization, in fact, it is possible to recollect resources and compose them into a novel narrative, in order to provide specific learning services.
Tübingen system in VarDial 2017 shared task: experiments with language identification and cross-lingual parsing

This paper describes our systems and results on VarDial 2017 shared tasks. Besides three language/dialect discrimination tasks, we also participated in the cross-lingual dependency parsing (CLP) task using a simple methodology which we also briefly describe in this paper. For all the discrimination tasks, we used linear SVMs with character and word features. The system achieves competitive results among other systems in the shared task. We also report additional experiments with neural network models. The performance of neural network models was close but always below the corresponding SVM classifiers in the discrimination tasks. For the cross-lingual parsing task, we experimented with an approach based on automatically translating the source treebank to the target language, and training a parser on the translated treebank. We used off-the-shelf tools for both translation and parsing. Despite achieving better-than-baseline results, our scores in CLP tasks were substantially lower than the scores of the other participants.
Chains , Discs and Drums : Ant Colony Optimization Dynamics on Diverse Construction Graphs Extended Abstract

The aim of this work is to provide an analytical framework for investigating the dynamics of the Ant Colony Optimization (ACO) metaheuristic, and to illustrate the applicability of this framework by comparing different encoding strategies for subset problems. ACO is an optimization technique inspired by observations on the behavior of biological ant colonies; it has been introduced by Dorigo, Maniezzo and Colorni [2] and developed later into a metaheuristic for combinatorial optimization problems [1]. From a technical point of view, ACO can be considered as reinforcement learning on a graph that encodes the problem instance, the so-called construction graph, as it has been formally defined in [3]. A solution to the given problem is encoded as a feasible walk in the construction graph. A feasible walk starts in a fixed initial node of the construction graph and has to satisfy the constraint that each node is visited at most once, i.e., already visited nodes are “tabu”. There may be additional, problem-specific rules defining certain nodes as tabu in certain cirumstances. When there is no neighbor anymore that is not tabu, the walk stops and is decoded as a solution to the given problem. The probability pkl to go from a node k to an allowed neighbor node l is chosen proportional to the so-called pheromone τkl, a memory value storing how good transition (k, l) has been in previous walks. Pheromone is initialized by a constant value. After the walk has finished, pheromone is updated. There are several possible ways to do that. The “classical” update rule, which we consider in this work, is the following: First, set τkl = (1 − ρ)τkl for each edge (k, l), where ρ ∈]0, 1[ is the so-called evaporation factor. Then, increase the pheromone on the edges of the chosen walk x by a factor proportional to the fitness f(x) of the walk. The process of random walk construction and pheromone update is iterated. Instead of a single walk (“1 ant”), also s > 1 walks may be constructed simultaneously or sequentially (“s ants”) in each iteration (“round”). In general, the procedure above needs not to converge. For an alternative pheromone update rule, where only the best walk seen so far is reinforced (“Global Best”), convergence results have been obtained ([3] – [5]). However, in theoretical research, the behavior of the ACO procedure under other update rules than Global Best is still widely unexplored. A remarkable exception is the article by Merkle and Middendorf [6] who analyze the ACO dynamics for a specific problem under an update rule where the best walk of each round is reinforced.
Composing features and resolving interactions

One of the accepted techniques for developing and maintaining feature-rich applications is to treat each feature as a separate concern. However, most features are not separate concerns because they override and extend the same basic service. That is, “independent” features are coupled to one another through the system's basic service. As a result, seemingly unrelated features subtly interfere with each other when trying to override the system behaviour in different directions. The problem is how to coordinate features' access to the service's shared variables. This paper proposes coordinating features via feature composition. We model each feature as a separate labelled-transition system and define a 1conflict-free (CF) composition operator that prevents enabled transitions from synchronizing if they interact: if several features' transitions are simultaneously enabled but have conflicting actions, a non-conflicting subset of the enabled transitions are synchronized in the composition. We also define a conflict- and violation-free (CVF) composition operator that prevents enabled transitions from executing if they violate features' invariants. Both composition operators use priorities among features to decide whether to synchronize transitions.
Municipal Debt and Marginal Tax Rates: is There a Tax Premium in Asset Prices?

We study the marginal tax rate incorporated into short-term tax-exempt municipal rates using a unique new data set from the municipal swap market. By applying an affine term-structure framework, we are able to identify both the marginal tax rate and the credit/liquidity spread in one-week tax-exempt rates. Furthermore, we obtain maximum likelihood estimates of the risk premia associated with these variables. The average marginal tax rate during the sample period is 41.6 percent. We find that the marginal tax rate is significantly positively related to returns in the stock and bond markets. The risk premium associated with the marginal tax rate is negative, consistent with the strong contracyclical nature of aftertax fixed-income cash flows which increase in bad states of the economy as personal income and the effective marginal tax rates applied to those cash flows decline.
Edge Weights Transformation in Graph Par tition Problem

Abstr act. In this paper we consider a problem of graph partition into two parts of equal sizes with minimal sum of edge weights between them. It is known that this problem is NP-complete and its solution reduces to minimization of a quadratic binary functional with constraints. In previous work it was shown that if we exponentiate connection matrix to some a power a significant increase of the basins of attraction of the deepest functional minima has place. Also the effectiveness of the minimization algorithm increases substantially. In this paper we show that in spite of the constraints we have in the graph bipartition problem the proposed approach of matrix transformation works very well with this problem too.
An Upper Bound of Soft Decode and Forward Relaying Over Rayleigh Fading Channels

In this paper, we investigate a distributed turbo code (DTC) scheme dedicated to L-relay channels operating in the soft-decode-and-forward (soft-DF) mode. At the destination, the received replicas are combined using the maximal ratio combining (MRC). Then, we define the explicit upper bound for error rate assuming Binary phase shift keying (BPSK) transmission for fully interleaved channels with channel state information (CSI). We use the Rayleigh fading channels with independent fading. By means of the analytical given bound and the simulation results, we show the performance of the proposed scheme in terms of bit error rate (BER).
Traps, events, emulation, and enforcement: managing the yin and yang of virtualization-based security

We question current trends that attempt to leverage virtualization techniques to achieve security goals. We suggest that the security role of a virtual machine centers on being a policy interpreter rather than a resource provider. These two roles (security reference monitor and resource emulator) are currently conflated within the context of virtual machines and VMMs. We believe that this ``double-duty'' leads to both a significant performance impact as well as a bloated virtualization layer. Increased complexity reduces confidence that the code is elementary enough to verify or trust from a security perspective. Ironically, as more security-related functionality is shoved into a VM platform, the system becomes less trustworthy as it becomes increasingly trusted. We argue that a principle reason for such an unfortunate situation is the lack of efficient hardware trapping mechanisms. We propose an architecture to help ameliorate this problem by transferring the security enforcement and program analysis roles from the virtualization component to a policy-directed FPGA.
AENEID: A generic lithography-friendly detailed router based on post-RET data learning and hotspot detection

In the era of deep sub-wavelength lithography for nanometer VLSI designs, manufacturability and yield issues are critical and need to be addressed during the key physical design implementation stage, in particular detailed routing. However, most existing studies for lithography-friendly routing suffer from either huge run-time due to the intensive lithographic computations involved, or severe loss of quality of results because of the inaccurate predictive models. In this paper, we propose AENEID - a fast, generic and high performance lithography-friendly detailed router for enhanced manufacturability. AENEID combines novel hotspot detection and routing path prediction techniques through modern data learning methods and applies them at the detailed routing stage to drive high delity lithography-friendly routing. Compared with existing litho-friendly routing works, AENEID demonstrates 26% to 66% (avg. 50%) of lithography hotspot reduction at the cost of only 18%–38% (avg. 30%) of run-time overhead
A dynamic routing strategy for the traffic control of AGVs in automatic warehouses

In this paper we propose a novel algorithm for the dynamic routing of a group of Autonomous Guided Vehicles (AGVs) used for transporting goods in automatic warehouses. Our strategy allows to improve the efficiency of a fleet of AGVs in terms of delivery time and its computational burden is sufficiently small to be embedded in standard industrial traffic management system. The algorithm is validated in a small-scale automatic warehouse with real AGVs.
Frequency-Domain Volterra Filter Based on Data-Driven Soft Decision for Nonlinear Acoustic Echo Suppression

In this letter, we propose a novel frequency-domain second-order Volterra filter based on soft decision for nonlinear acoustic echo suppression. This letter offers an efficient algorithm for nonlinear echo power estimation using the second-order Volterra filter and an AES algorithm using the estimated nonlinear echo power spectrum within a soft decision baseline by incorporating the ratio of the a priori probability of near-end speech presence and absence, which is obtained by using a data-driven training method.
Blind PSNR estimation of video sequences using quantized DCT coefficient data

This paper proposes a no-reference PSNR estimation method for video sequences subject to lossy DCT-based encoding, such as MPEG-2 encoding. The proposed method is based on DCT coefficient statistics, which are modeled by Laplace probability density functions, with parameter λ. The distribution’s parameter is computed from the received quantized data, by combining maximum-likelihood with linear prediction estimates. The resulting coefficient distributions are then used for estimating the local error due to lossy encoding. Since no knowledge about the original (reference) sequences is required, the proposed method can be used as a no-reference metric for evaluating the quality of the encoded video sequences.
Output sensitive discretization for genetic algorithm with migration

Approximation of unknown input-output mappings by optimizing approximating functions is important for a number of practical applications. A straightforward method involves dividing the whole input space into small regions and finding the optimal approximating value within each. For such an method to work well the way the input region is divided is very important. In this paper we derive an algorithm that takes into account both the distribution of the input points and how rapidly the mapping is changing. The method is demonstrated on a simple function approximation problem.
Distributed joint power and admission control for ad-hoc and cognitive underlay networks

Power control is important in interference-limited cellular, ad-hoc, and cognitive wireless networks, when the objective is to ensure a certain quality of service to each connection. Power control has been extensively studied in this context, including distributed algorithms that are particularly appealing in ad-hoc and cognitive settings. A long-standing issue is that the power control problem may be infeasible, thus requiring appropriate admission control. The power and admission control parts of the problem are tightly coupled, but the joint problem is NP-hard. In recent work, we developed a convex relaxation-based deflation approach to the joint problem, which was shown to outperform the prior art, and yield close to optimal solutions at moderate computational cost. In this paper, we derive a distributed version of our joint power and admission control algorithm. The algorithm alternates between distributed approximation and distributed deflation - reaching consensus on a user to drop, when needed. Both phases require only local communication and computation, yielding a relatively lightweight distributed algorithm which also attains close to optimal performance.
Robust algorithm to generate a diverse class of dense disordered and ordered sphere packings via linear programming.

We have formulated the problem of generating dense packings of nonoverlapping, nontiling nonspherical particles within an adaptive fundamental cell subject to periodic boundary conditions as an optimization problem called the adaptive-shrinking cell (ASC) formulation [S. Torquato and Y. Jiao, Phys. Rev. E 80, 041104 (2009)]. Because the objective function and impenetrability constraints can be exactly linearized for sphere packings with a size distribution in d-dimensional Euclidean space R(d), it is most suitable and natural to solve the corresponding ASC optimization problem using sequential-linear-programming (SLP) techniques. We implement an SLP solution to produce robustly a wide spectrum of jammed sphere packings in R(d) for d=2, 3, 4, 5, and 6 with a diversity of disorder and densities up to the respective maximal densities. A novel feature of this deterministic algorithm is that it can produce a broad range of inherent structures (locally maximally dense and mechanically stable packings), besides the usual disordered ones (such as the maximally random jammed state), with very small computational cost compared to that of the best known packing algorithms by tuning the radius of the influence sphere. For example, in three dimensions, we show that it can produce with high probability a variety of strictly jammed packings with a packing density anywhere in the wide range [0.6, 0.7408. ], where π/√18 = 0.7408. corresponds to the density of the densest packing. We also apply the algorithm to generate various disordered packings as well as the maximally dense packings for d=2, 4, 5, and 6. Our jammed sphere packings are characterized and compared to the corresponding packings generated by the well-known Lubachevsky-Stillinger (LS) molecular-dynamics packing algorithm. Compared to the LS procedure, our SLP protocol is able to ensure that the final packings are truly jammed, produces disordered jammed packings with anomalously low densities, and is appreciably more robust and computationally faster at generating maximally dense packings, especially as the space dimension increases.
Allocation of Repairable and Replaceable Components for a System Availability Using Selective Maintenance with Probabilistic Maintenance Time Constraints

In this paper, we obtain optimum allocation of replaceable and repairable components in a system design. When repair and replace time are considered as random in the constraints. We convert probabilistic constraint into an equivalent deterministic constraint by using chance constrained programming. We have used the selective maintenance policy to determine how many components to be replaced & repaired within the limited maintenance time interval and cost. A Numerical example is presented to illustrate the computational procedure and problem is solved by using LINGO Software.
Enhancement of esophageal speech using formant synthesis

The feasibility of using the formant analysis-synthesis approach to replace the voicing sources of esophageal speech was explored. The voicing sources were generated by using inverse-filtered signals extracted from normal speakers. Pitch extraction was tested with various pitch extraction methods, then a simple auto-correlation method was chosen. A special hardware unit was designed to perform the analysis-synthesis process in real-time. Results of a subjective test showed that the synthesized speech was significantly improved.
Understanding emotion processing in schizophrenia

Summary Impaired emotional functioning is a prominent feature of schizophrenia. Although positive symptoms have traditionally attracted more attention and targeted treatment, negative symptoms, including flat affect, are increasingly recognized as the more debilitating and resistant to intervention. We describe studies examining affect processing in schizophrenia, focusing on facial affect with initial findings in vocal affect, or prosody. Deficits in schizophrenia are pronounced, and studies with functional neuroimaging indicate that the neural substrates for these deficits center on the amygdala and its projections. The abnormalities are highly correlated with symptom severity and functional outcome. While there is quite extensive work on affect recognition abnormalities, deficits have also been documented in the ability to express affect on the face and in voice, and perhaps to a lesser extent in the experience of emotion. These abnormalities can be better studied when methods for quantitative analysis of emotional expression are available. Recognizing the existence of such deficits and their neural substrates will lead to improved approaches to pharmacological and behavioral treatment.