Miracle-dz/UniFAR-FTUs huggingface dataset:
Transaction Management for Mobile Objects Using Optimistic Concurrency Control

We present computationally efficient error-correcting codes and holographic proofs. Our error-correcting codes are asymptotically good and can be encoded and decoded in linear time. Our construction of holographic proofs provide, for every proof of any theorem, a slightly larger ``holographic'''' proof whose accuracy can be probabilistically checked by an algorithm that only reads a constant number of the bits of the holographic proof and runs in poly-logarithmic time (such proofs have also been called ``transparent proofs'''' and ``probabilistically checkable proofs''''). We explain how these constructions are related and how improvements of these constructions should result in a strengthening of this relationship. For every constant $r$ such that $0 > r 0$ and a linear-time decoding algorithm for these codes that maps every word of relative distance at most $\epsilon $ from a codeword to that codeword. The encoding circuits have logarithmic depth. The decoding algorithm can be implemented as a circuit with $\O{n \log n}$ wires and logarithmic depth. These constructions make use of explicit constructions of expander graphs and superconcentrators. Our constructions of holographic proofs improve on the theorem $PCP(\log n, 1) = NP$, proved by Arora, Lund, Motwani, Sudan, and Szegedy, by providing, for every $\epsilon <0$, constant-query checkable proofs of size $\O{n^{1+\epsilon }}$. That is, we design a probabilistic poly-logarithmic time proof checking algorithm that takes two inputs: a theorem candidate and a proof candidate. After reading a constant number of bits from each input, the proof checker decides whether to accept or reject its inputs. For every rigorous proof of length $n$ of any theorem, there is an easily computable holographic proof of that theorem of size $\O{n^{1+\epsilon }}$ such that, with probability one, the proof checker will accept the holographic proof and an encoding of the theorem. Conversely, if the proof checker accepts a theorem candidate and a proof candidate with probability greater than one-half, then the theorem candidate is close to a unique encoding of a true theorem and the proof candidate constitutes a proof of that theorem.
Nonlinear model predictive control of joint ankle by electrical stimulation for drop foot correction

In this paper we investigate the use of optimal control techniques to improve Functional Electrical Stimulation (FES) for drop foot correction on hemiplegic patients. A model of the foot and the tibialis anterior muscle, the contraction of which is controlled by electrical stimulation has been established and is used in the optimal control problem. The novelty in this work is the use of the ankle accelerations and shank orientations (so-called external states) in the model, which have been measured on hemiplegic patients in a previous experiment using Inertial Measurement Units (IMUs). The optimal control problem minimizes the square of muscle excitations which serves the overall goal of reducing energy consumption in the muscle. In a first step, an offline optimal control problem is solved for test purposes and shows the efficiency of the FES optimal control for drop foot correction. In a second step, a Nonlinear Model Predictive Control (NMPC) problem - or online optimal control problem, is solved in a simulated environment. While the ulitmate goal is to use NMPC on the real system, i.e. directly on the patient, this test in simulation was meant to show the feasibility of NMPC for online drop foot correction. In the optimization problem, a set of fixed constraints of foot orientation was applied. Then, an original adaptive constraint taking into account the current ankle height, was introduced and tested. Comparisons between results under fixed and adaptive constraints highlight the advantage of the adaptive constraints in terms of energy consumption, where its quadratic sum of controls, obtained by NMPC, was three times lower than with the fixed constraint. This feasibility study was a first step in application of NMPC on real hemiplegic patients for online FES-based drop foot correction. The adaptive constraints method presents a new and efficient approach in terms of muscular energy consumption minimization.
An ASCA Study of the Heavy-Element Distribution in Clusters of Galaxies

We perform a spatially resolved X-ray spectroscopic study of a set of 11 relaxed clusters of galaxies observed by the ROSAT/PSPC and ASCA/SIS. Using a method, which corrects for the energy-dependent effects of the ASCA point-spread function based on ROSAT images, we constrain the spatial distribution of Ne, Si, S, and Fe in each cluster. Theoretical prescriptions for the chemical yields of Types Ia and II supernovae then allow determination of the Fe enrichment from both types of supernovae as a function of radius within each cluster. Using optical measurements from the literature, we also determine the iron mass-to-light ratio (IMLR) separately for Fe synthesized in both types of supernovae. For clusters with the best photon statistics, we find that the total Fe abundance decreases significantly with radius while the Si abundance is either flat or decreases less rapidly, resulting in an increasing Si/Fe ratio with radius. This result indicates a greater predominance of Type II supernova (SN) enrichment at large radii in clusters. On average, the IMLR synthesized within Type II SNe increases with radius within clusters, while the IMLR synthesized within Type Ia SNe decreases. At a fixed radius of 0.4 Rvirial there is also a factor of 5 increase in the IMLR synthesized by Type II SNe between groups and clusters. This suggests that groups expelled as much as 90% of the Fe synthesized within Type II SNe at early times. All of these results are consistent with a scenario in which the gas was initially heated and enriched by Type II SN-driven galactic winds. Because of the high entropy of the preheated gas, SN II products are only weakly captured in groups. Gravitationally bound gas was then enriched with elements synthesized by Type Ia supernovae as gas-rich galaxies accreted onto clusters and were stripped during passage through the cluster core via density-dependent mechanisms (e.g., ram pressure ablation, galaxy harassment, etc.). We suggest that the high Si/Fe ratios in the outskirts of rich clusters may arise from enrichment by Type II SNe released to the intracluster medium via galactic starburst-driven winds. Low S/Fe ratios observed in clusters suggest metal-poor galaxies as a major source of SN II products.
Cortical Transformation of Spatial Processing for Solving the Cocktail Party Problem: A Computational Model123

Visual Abstract In multisource, “cocktail party” sound environments, human and animal auditory systems can use spatial cues to effectively separate and follow one source of sound over competing sources. In multisource, “cocktail party” sound environments, human and animal auditory systems can use spatial cues to effectively separate and follow one source of sound over competing sources. While mechanisms to extract spatial cues such as interaural time differences (ITDs) are well understood in precortical areas, how such information is reused and transformed in higher cortical regions to represent segregated sound sources is not clear. We present a computational model describing a hypothesized neural network that spans spatial cue detection areas and the cortex. This network is based on recent physiological findings that cortical neurons selectively encode target stimuli in the presence of competing maskers based on source locations (Maddox et al., 2012). We demonstrate that key features of cortical responses can be generated by the model network, which exploits spatial interactions between inputs via lateral inhibition, enabling the spatial separation of target and interfering sources while allowing monitoring of a broader acoustic space when there is no competition. We present the model network along with testable experimental paradigms as a starting point for understanding the transformation and organization of spatial information from midbrain to cortex. This network is then extended to suggest engineering solutions that may be useful for hearing-assistive devices in solving the cocktail party problem.
The Receptor-Like Kinase SOL2 Mediates CLE Signaling in Arabidopsis

Arabidopsis sol2 mutants showed CLV3 peptide resistance. Twenty-six synthetic CLE peptides were examined in the clv1, clv2 and sol2 mutants. sol2 showed different levels of resistance to the various peptides, and the spectrum of peptide resistance was quite similar to that of clv2. SOL2 encoded a receptor-like kinase protein which is identical to CORYNE (CRN). GeneChip analysis revealed that the expression of several genes was altered in the sol2 root tip. Here, we suggest that SOL2, together with CLV2, plays an important role in the regulation of root meristem development through the CLE signaling pathway.
Traffic light control in non-stationary environments based on multi agent Q-learning

In many urban areas where traffic congestion does not have the peak pattern, conventional traffic signal timing methods does not result in an efficient control. One alternative is to let traffic signal controllers learn how to adjust the lights based on the traffic situation. However this creates a classical non-stationary environment since each controller is adapting to the changes caused by other controllers. In multi-agent learning this is likely to be inefficient and computationally challenging, i.e., the efficiency decreases with the increase in the number of agents (controllers). In this paper, we model a relatively large traffic network as a multi-agent system and use techniques from multi-agent reinforcement learning. In particular, Q-learning is employed, where the average queue length in approaching links is used to estimate states. A parametric representation of the action space has made the method extendable to different types of intersection. The simulation results demonstrate that the proposed Q-learning outperformed the fixed time method under different traffic demands.
The Enrollment Effect of Secondary School Fees in Post-War Germany

This study utilizes the heterogeneity of the fee abolition for West German secondary schools to identify its effect on enrollment and to obtain an estimate of the price elasticity of demand for education. The analysis is based on administrative school enrollment statistics as well as on representative individual-level data from three annual surveys of the German Mikrozensus. Estimates suggest that enrollment in Advanced Schools increased by about six percent due to the fee abolition, where the results are sensitive to the specification choice. The positive enrollment effect of fee abolition for women exceeds that for men. A fifty percent reduction in fees is associated with an overall change in enrollment rates by 3 percent, where the elasticity of the demand for females' education again exceeds that for males'.
Performance and Energy Impact of Instruction-Level Value Predictor Filtering

This work evaluates value predictor access filtering and its effects on performance and dynamic energy consumption in a wide-issue, high-frequency processor. New and previously proposed filtering strategies are analyzed with realistic predictor constraints, such as port restrictions and table access latency. Filters restrict access to the value predictor for instructions with unconsumed predictions, poorly predicted instructions, and quickly executing instructions. Read access filtering improves speedup due to value prediction from 16.1% to 23.6%, while reducing dynamic value predictor reads by 31.3%. Adding write filtering decreases update activity by 78.6%, while still providing 14.8% speedup. The overall reduction in activity leads to a value predictor energy consummption decrease
An auto-generated nonlinear MPC algorithm for real-time obstacle avoidance of ground vehicles

We address the problem of real-time obstacle avoidance on low-friction road surfaces using spatial Nonlinear Model Predictive Control (NMPC). We use a nonlinear four-wheel vehicle dynamics model that includes load transfer. To overcome the computational difficulties we propose to use the ACADO Code Generation tool which generates NMPC algorithms based on the real-time iteration scheme for dynamic optimization. The exported plain C code is tailored to the model dynamics, resulting in faster run-times in effort for real-time feasibility. The advantages of the proposed method are shown through simulation.
Quantitative evaluation of effects of speech recognition errors on speech translation quality

This paper investigates the relationship between the quality of speech translation outputs and the errors in a speech recognition subsystem. In this study, we assume that a speech translation system is a sequential combination of speech recognition and automatic translation subsystems. We conducted speech translation experiments while changing parameters in the speech recognition subsystem to get different speech recognition results, which were then fed into the translation subsystem. We applied regression analysis to the interrelationship between the speech recognition outputs and the final translation results. We found that particular kinds of speech recognition errors, including deletion of punctuation marks and substitutions of nouns, cause severe semantic errors in speech translation. We also found that the final translation quality degrades logarithmically with respect to the number of speech recognition errors.
Reinfection results in accumulation of unintegrated viral DNA in cytopathic and persistent human immunodeficiency virus type 1 infection of CEM cells

High levels of unintegrated viral DNA accumulate during human immunodeficiency virus type 1 (HIV-1) infection of CEM T cells. Reinfection of already infected cells is required to attain these levels and reinfection also promotes the development of HIV-induced cytopathology. Rates of virus production, however, are independent of the accumulation of unintegrated viral DNA. Neutralizing antibody added soon after infection reduced viral DNA levels without appreciably affecting the production of cell-free viral p24 antigen or reverse transcriptase activity. Only 50 pM AZT were required to reduce the accumulation of unintegrated viral DNA by 50% in contrast to the 25 nM required to inhibit virus production by 50%. Cytopathology, as measured by number of syncytia in infected cell cultures, was correlated with highly elevated levels of unintegrated viral DNA. The minimal levels of unintegrated viral DNA present constitutively in the persistently infected HCEM cell line were consonant with the absence of cytopathic effects in these cells. These data demonstrate that inhibiting the reinfection of already infected cells modulates cytopathic HIV-1 infection to a form that is persistent and noncytopathic.
SketchMapia : a Framework for Qualitative Alignment of Sketch Maps and Metric Maps

The SketchMapia aims at relaxing technical constraints on Geographic Information Systems (GISs) by developing a framework taking freehand drawn sketch maps as input. This paper describes the framework that is designed to provide non-expert users of geospatial information with alternative methods for contributing data to and querying geographic information systems. The architecture of the framework consists of three components: (a) object recognizer, (b) qualitative qualifier, and (c) qualitative matcher. The web-interface of the project integrates these components and synchronizes the processes involve in the qualitative alignment of spatial objects.
Automatic Patient Search Using Bernoulli Model

Objective: Develop algorithms to automatically identify qualified patients for breast cancer clinical trials from free-text medical reports. Design: The Bernoulli model was trained to search for a qualified patient based on criterion. Measurement: The performance of the Bernoulli model was evaluated by the Precision-Recall curve and F-score. Results: The Single-word Bernoulli model trained in a two-class mode has greater performance than the model trained in a one-class mode. The performance of the model was also compared with some other techniques. Conclusions: The Bernoulli model method is easier to implement and performs better than several competing techniques.
GMiner: Rule-Based Fuzzy Clustering for Google Drive Behavioral Type Mining

Due to more and more on-premises services are migrating onto cloud, user behavioral analysis then gets popular as a data-driven way to administer lots accounts of on-cloud services. This paper proposes a novel rule-based approach, GMiner, for mining different types of Google cloud drive usages as an unsupervised account-management approach. Experiment results show that GMiner provides accurate, inter-pretable, and visualized clustering results which are helpful for highlighting inactive, quasi-insider accounts, or other potential cyber-security risks from real-environment dataset.
Analysis of biologically inspired model for object recognition

Human visual system can categorize objects rapidly and effortlessly despite the complexity and objective ambiguities of natural images. Despite the ease with which we see, visual categorization is an extremely difficult task for computers due to the variability of objects, such as scale, rotation, illumination, position and occlusion. This paper presents a biologically inspired model which gives a promising solution to object categorization in color space. Here, the biologically inspired features were extracted by log-polar Gabor Transform, aided by maximum operation and convolution with Prototype patches based on the saliency of the image. The extracted features are classified by SVM classifier. The framework has been applied to the image dataset taken from the Amsterdam Library of Object Images (ALOI) and the results are presented.
Implementation and Sensorless Vector-Control Design and Tuning Strategy for SMPM Machines in Fan-Type Applications

This paper presents a complete design methodology for the sensorless vector control of permanent magnet synchronous machine (PMSM) motor drives in fan-type applications. The proposed strategy is built over a linear asymptotic state observer used to estimate the PMSM back-EMF, and a novel tracking controller based on a phase-locked loop (PLL) system, which by synchronizing the estimated and actual d-q frames estimates the rotor speed and position. The paper presents the complete derivation of all associated control-loops, namely state observer, tracking controller, d-q axes current regulator, speed controller, an anti-saturation control loop - which provides inherent operation in the flux-weakening region, and all corresponding anti-windup loops. Detailed design rules are provided for each of these loops, respectively verified through time-domain simulations, frequency-response analysis, and experimental results using a 300 Vdc 3.5 kW PMSM PWM motor-drive, validating both the design methodology and the excellent performance attained by the proposed control strategy
Trust-based grouping for cloud datacenters: Improving security in shared infrastructures

Cloud computing can offer virtually unlimited resources without any upfront capital investment through a payper-use pricing model. However, the shared nature of multitenant cloud datacenter networks enables unfair or malicious use of the intra-cloud network by tenants, allowing attacks against the privacy and integrity of data and the availability of resources. In this paper, we introduce a resource allocation strategy that increases the security of network resource sharing among tenant applications. The key idea behind the strategy is to group applications of mutually trusting users into virtual infrastructures (logically isolated domains composed of a set of virtual machines as well as the virtual network interconnecting them). This provides some level of isolation and higher security. However, the use of groups may lead to fragmentation and negatively affect resource utilization. We study the associated trade-off and feasibility of the proposed approach. Evaluation results show the benefits of our strategy, which is able to offer better network resource protection against attacks with low extra cost.
Stochastic volatility models with possible extremal clustering

In this paper we consider a heavy-tailed stochastic volatility model, $X_t=\sigma_tZ_t$, $t\in\mathbb{Z}$, where the volatility sequence $(\sigma_t)$ and the i.i. d. noise sequence $(Z_t)$ are assumed independent, $(\sigma_t)$ is regularly varying with index $\alpha>0$, and the $Z_t$'s have moments of order larger than $\alpha$. In the literature (see Ann. Appl. Probab. 8 (1998) 664-675, J. Appl. Probab. 38A (2001) 93-104, In Handbook of Financial Time Series (2009) 355-364 Springer), it is typically assumed that $(\log\sigma_t)$ is a Gaussian stationary sequence and the $Z_t$'s are regularly varying with some index $\alpha$ (i.e., $(\sigma_t)$ has lighter tails than the $Z_t$'s), or that $(Z_t)$ is i.i. d. centered Gaussian. In these cases, we see that the sequence $(X_t)$ does not exhibit extremal clustering. In contrast to this situation, under the conditions of this paper, both situations are possible; $(X_t)$ may or may not have extremal clustering, depending on the clustering behavior of the $\sigma$-sequence.
AN ANT COLONY OPTIMIZATION APPROACH FOR THE MIXED VEHICLE ROUTING PROBLEM WITH BACKHAULS

The Vehicle Routing Problem with Backhauls (VRPB) is a variant of the Vehicle Routing Problem where the vehicles are not only required to deliver goods but also to pick up some goods from the customers. In the mixed VRPB (MVRPB) each customer has either a delivery or a pick-up demand to be satisfied and the customers can be visited in any order along the route. Given a fleet of vehicles and a set of customers with known pick-up or delivery demands MVRPB determines a set of vehicle routes originating and ending at a single depot and visiting all customers exactly once. The objective is to minimize the total distance traversed with the least number of vehicles. A maximum route length restriction may also be imposed on the vehicles. From a practical point of view MVRPB models situations such as distribution of bottled drinks, chemicals, LPG tanks, etc. In the case of the bottled drinks for instance, full bottles are delivered to customers and empty ones are brought back either for re-use or for recycling. In the chemicals case, some hazardous materials may need to be returned for safe disposal. Regulations or environmental issues may also force companies to take responsibility for their products throughout their lifetime. For this problem, we propose an Ant Colony Optimization (ACO) approach utilizing a new visibility function which attempts to capture the “delivery and pick-up” nature of the problem. We perform an extensive experimental study to compare the performance of the proposed approach with those of the well-known benchmark problems from the literature. Our numerical tests show that the proposed approach provides encouraging results.