Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-Stage Robust Network Flow and Design Under Demand Uncertainty

We describe a two-stage robust optimization approach for solving network flow and design problems with uncertain demand. In two-stage network optimization, one defers a subset of the flow decisions until after the realization of the uncertain demand. Availability of such a recourse action allows one to come up with less conservative solutions compared to single-stage optimization. However, this advantage often comes at a price: two-stage optimization is, in general, significantly harder than single-stage optimization. For network flow and design under demand uncertainty, we give a characterization of the first-stage robust decisions with an exponential number of constraints and prove that the corresponding separation problem is NP-hard even for a network flow problem on a bipartite graph. We show, however, that if the second-stage network topology is totally ordered or an arborescence, then the separation problem is tractable. Unlike single-stage robust optimization under demand uncertainty, two-stage robust optimization allows one to control conservatism of the solutions by means of an allowed “budget for demand uncertainty. ” Using a budget of uncertainty, we provide an upper bound on the probability of infeasibility of a robust solution for a random demand vector. We generalize the approach to multicommodity network flow and design, and give applications to lot-sizing and location-transportation problems. By projecting out second-stage flow variables, we define an upper bounding problem for the two-stage min-max-min optimization problem. Finally, we present computational results comparing the proposed two-stage robust optimization approach with single-stage robust optimization as well as scenario-based two-stage stochastic optimization.
A Hybrid Approach to Wide-Baseline Image Matching

Recent works such as DEEPDESC, DEEPCOMPARE have proposed the learning of robust local image descriptors using a Siamese convolutional neural network directly from images instead of handcrafting them like traditional descriptors such as SIFT and MROGH. Though these algorithms show the state-of-the-art results on the Multi-View Stereo (MVS) dataset, they fail to accomplish many challenging real world tasks such as stitching image panoramas, primarily due to the limited performance of finding correspondence. In this paper, we propose a novel hybrid algorithm with which we are able to harness the power of a learning based approach along with the discriminative advantages that traditional descriptors have to offer. We also propose the PhotoSynth dataset, with size of an order of magnitude more that the traditional MVS dataset in terms of the number of scenes, images, patches along with positive and negative correspondence. Our PhotoSynth dataset also has better coverage of the overall viewpoint, scale, and lighting challenges than the MVS dataset. We evaluate our approach on two data sets which provides images having high viewpoints difference and wide-baselines. One of them is Graffiti scene from the Oxford Affine Covariant Regions Dataset (ACRD) for matching images with 2D affine transformations. The other is the Fountain-P11 dataset for images with 3D projective transformations. We report, to the best of our knowledge, the best results till date on the ACRD Graffiti scene compared to descriptors such as SIFT, MROGH or any other learnt descriptors such as DEEPDESC.
Matrix Metrics : Network-Based Systemic Risk Scoring Sanjiv

Matrix Metrics: Network-Based Systemic Risk Scoring I propose a novel framework for network-based systemic risk measurement and management. I define a new systemic risk score that depends on the level of individual risk at each financial institution and the interconnectedness across institutions, and is generally applicable irrespective of how interconnectedness is defined. This risk metric is decomposable into risk contributions from each entity, forming a basis for taxing each entity appropriately. We may calculate risk increments to assess the potential risk of each entity on the overall financial system. The paper develops other subsidiary risk measures such as system fragility and entity criticality. An assessment using a measure of spillover risk is obtained to determine the scale of externalities that one bank might impose on the system; the metric is robust to this cross risk, and does not induce predatory spillovers. The analysis suggests that splitting up too-big-to-fail banks does not lower systemic risk.
Regulation of Acetate Kinase Isozymes and Its Importance for Mixed-Acid Fermentation in Lactococcus lactis

ABSTRACT Acetate kinase (ACK) converts acetyl phosphate to acetate along with the generation of ATP in the pathway for mixed-acid fermentation in Lactococcus lactis. The reverse reaction yields acetyl phosphate for assimilation purposes. Remarkably, L. lactis has two ACK isozymes, and the corresponding genes are present in an operon. We purified both enzymes (AckA1 and AckA2) from L. lactis MG1363 and determined their oligomeric state, specific activities, and allosteric regulation. Both proteins form homodimeric complexes, as shown by size exclusion chromatography and static light-scattering measurements. The turnover number of AckA1 is about an order of magnitude higher than that of AckA2 for the reaction in either direction. The Km values for acetyl phosphate, ATP, and ADP are similar for both enzymes. However, AckA2 has a higher affinity for acetate than does AckA1, suggesting an important role under acetate-limiting conditions despite the lower activity. Fructose-1,6-bisphosphate, glyceraldehyde-3-phosphate, and phospho-enol-pyruvate inhibit the activities of AckA1 and AckA2 to different extents. The allosteric regulation of AckA1 and AckA2 and the pool sizes of the glycolytic intermediates are consistent with a switch from homolactic to mixed-acid fermentation upon slowing of the growth rate.
Effects of Nintendo Wii Fit Plus training on ankle strength with functional ankle instability

[Purpose] The objective of this study was to examine the effects of a training program using the Nintendo Wii Fit Plus on the ankle muscle strengths of subjects with functional ankle instability. [Subjects and Methods] This study was conducted using subjects in their 20s who had functional ankle instability. They were randomized to a strengthening training group and a balance training group with 10 subjects in each, and they performed an exercise using Nintendo Wii Fit Plus for 20 minutes. In addition, every participant completed preparation and finishing exercises for 5 minutes, respectively. [Results] The muscle strengths after conducting plantar flexion and dorsiflexion significantly increased at the angular velocities of 60° and 120° in the strengthening training group. Furthermore, the muscle strengths after conducting plantar flexion, dorsiflexion, eversion, and inversion significantly increased at the angular velocities of 60° and 120° in the balance training group. [Conclusion] The balance training group using Nintendo Wii Fit Plus showed better results than the strengthening training group. Consequently, it is recommended to add the balance training program of the Nintendo Wii Fit Plus to conventional exercise programs to improve ankle muscle strength in functional ankle instability at a low cost.
Context-Aware Object Connection Discovery in Large Graphs

Given a large graph and a set of objects, the task of object connection discovery is to find a subgraph that retains the best connection between the objects. Object connection discovery is useful to many important applications such as discovering the connection between different terrorist groups for counter-terrorism operations. Existing work considers only the connection between individual objects; however, in many real problems the objects usually have a context (e.g., a terrorist belongs to a terrorist group). We identify the context for the nodes in a large graph. We partition the graph into a set of communities based on the concept of modularity, where each community becomes naturally the context of the nodes within the community. By considering the context we also significantly improve the efficiency of object connection discovery, since we break down the big graph into much smaller communities. We first compute the best intra-community connection by maximizing the amount of information flow in the answer graph. Then, we extend the connection to the inter-community level by utilizing the community hierarchy relation, while the quality of the inter-community connection is also ensured by modularity. Our experiments show that our algorithm is three orders of magnitude faster than the state-of-the-art algorithm, while the quality of the query answer is comparable.
Judgmental Reasoning for Expert Systems

We present a new system for plausible reasoning in expert systems. It is an extension of the Horn clause subset of first order logic, and is distinguished by its use of non-numeric certainties and the assignment of certainties to logical formula rather than events. Model theoretic and fixpoint semantics are sketched for this language. An implementation in the logic programming language Prolog is described and the advantages of the system are discussed.
Towards Accurate and Efficient Chinese Part-of-Speech Tagging

From the perspective of structural linguistics, we explore paradigmatic and syntagmatic lexical relations for Chinese POS tagging, an important and challenging task for Chinese language processing. Paradigmatic lexical relations are explicitly captured by word clustering on large-scale unlabeled data and are used to design new features to enhance a discriminative tagger. Syntagmatic lexical relations are implicitly captured by syntactic parsing in the constituency formalism, and are utilized via system combination. Experiments on the Penn Chinese Treebank demonstrate the importance of both paradigmatic and syntagmatic relations. Our linguistically motivated, hybrid approaches yield a relative error reduction of 18% in total over state-of-the-art baselines. Despite the effectiveness to boost accuracy, computationally expensive parsers make hybrid systems inappropriate for many realistic NLP applications. In this article, we are also concerned with improving tagging efficiency at test time. In particular, we explore unlabeled data to transfer the predictive power of hybrid models to simple sequence models. Specifically, hybrid systems are utilized to create large-scale pseudo training data for cheap models. Experimental results illustrate that the re-compiled models not only achieve high accuracy with respect to per token classification, but also serve as a front-end to a parser well.
Solid-solution strengthening in ordered In x Ga1 −  x P alloys

Ordered In x Ga1 − x P alloys have been grown on to GaAs(001) substrates by metal–organic vapour-phase epitaxy. Lattice-matched compositions of alloys (In x Ga1 − x P with x ≈ 0.5) were used in order to produce epitaxial layers free of structural defects (threading dislocations). Growth temperature and substrate orientation were adjusted to control the degree η, of order, of the alloys in the range 0.3–0.5. Nanoindentation tests were carried out to measure the mechanical response of the heteroepitaxial layers. Transmission electron microscopy was used to characterize the structure as well as the plastic zones generated into the specimen by nanoindentation. Comparison of the alloys with binary references (InP and GaP bare substrates) showed strengthening, with the hardness and flow-stress values that are much larger than those of InP and of the same order of magnitude as those of GaP.
Local Versus Centralized Charging Strategies for Electric Vehicles in Low Voltage Distribution Systems

Controlled charging of electric vehicles offers a potential solution to accommodating large numbers of such vehicles on existing distribution networks without the need for widespread upgrading of network infrastructure. Here, a local control technique is proposed whereby individual electric vehicle charging units attempt to maximise their own charging rate for their vehicle while maintaining local network conditions within acceptable limits. Simulations are performed to demonstrate the benefits of the technique on a test distribution network. The results of the method are also compared to those from a centralized control method whereby EV charging is controlled by a central controller. The paper outlines the advantages and disadvantages of both strategies in terms of capacity utilization and total energy delivered to charging EVs.
Comparative In Vitro and In Silico Analyses of Variants in Splicing Regions of BRCA1 and BRCA2 Genes and Characterization of Novel Pathogenic Mutations

Several unclassified variants (UVs) have been identified in splicing regions of disease-associated genes and their characterization as pathogenic mutations or benign polymorphisms is crucial for the understanding of their role in disease development. In this study, 24 UVs located at BRCA1 and BRCA2 splice sites were characterized by transcripts analysis. These results were used to evaluate the ability of nine bioinformatics programs in predicting genetic variants causing aberrant splicing (spliceogenic variants) and the nature of aberrant transcripts. Eleven variants in BRCA1 and 8 in BRCA2, including 8 not previously characterized at transcript level, were ascertained to affect mRNA splicing. Of these, 16 led to the synthesis of aberrant transcripts containing premature termination codons (PTCs), 2 to the up-regulation of naturally occurring alternative transcripts containing PTCs, and one to an in-frame deletion within the region coding for the DNA binding domain of BRCA2, causing the loss of the ability to bind the partner protein DSS1 and ssDNA. For each computational program, we evaluated the rate of non-informative analyses, i.e. those that did not recognize the natural splice sites in the wild-type sequence, and the rate of false positive predictions, i.e., variants incorrectly classified as spliceogenic, as a measure of their specificity, under conditions setting sensitivity of predictions to 100%. The programs that performed better were Human Splicing Finder and Automated Splice Site Analyses, both exhibiting 100% informativeness and specificity. For 10 mutations the activation of cryptic splice sites was observed, but we were unable to derive simple criteria to select, among the different cryptic sites predicted by the bioinformatics analyses, those actually used. Consistent with previous reports, our study provides evidences that in silico tools can be used for selecting splice site variants for in vitro analyses. However, the latter remain mandatory for the characterization of the nature of aberrant transcripts.
A micropower low-voltage multiplier with reduced spurious switching

We describe a micropower 16times16-bit multiplier (18.8 muW/MHz @1.1 V) for low-voltage power-critical low speed (les5 MHz) applications including hearing aids. We achieve the micropower operation by substantially reducing (by ~62% and ~79% compared to conventional 16times16-bit and 32times32-bit designs respectively) the spurious switching in the Adder Block in the multiplier. The approach taken is to use latches to synchronize the inputs to the adders in the Adder Block in a predetermined chronological sequence. The hardware penalty of the latches is small because the latches are integrated (as opposed to external latches) into the adder, termed the latch adder (LA). By means of the LAs and timing, the number of switchings (spurious and that for computation) is reduced from ~5.6 and ~10 per adder in the adder block in conventional 16times16-bit and 32times32-bit designs respectively to ~2 in our designs. Based on simulations and measurements on prototype ICs (0.35 mum three metal dual poly CMOS process), we show that our 16times16-bit design dissipates ~32% less power, is ~20% slower but has ~20% better energy-delay-product (EDP) than conventional 16times16-bit multipliers. Our 32times32-bit design is estimated to dissipate ~53% less power, ~29% slower but is ~39% better EDP than the conventional general multiplier
Blind Separation of Superimposed Moving Images Using Image Statistics

We address the problem of blind separation of multiple source layers from their linear mixtures with unknown mixing coefficients and unknown layer motions. Such mixtures can occur when one takes photos through a transparent medium, like a window glass, and the camera or the medium moves between snapshots. To understand how to achieve correct separation, we study the statistics of natural images in the Labelme data set. We not only confirm the well-known sparsity of image gradients, but also discover new joint behavior patterns of image gradients. Based on these statistical properties, we develop a sparse blind separation algorithm to estimate both layer motions and linear mixing coefficients and then recover all layers. This method can handle general parameterized motions, including translations, scalings, rotations, and other transformations. In addition, the number of layers is automatically identified, and all layers can be recovered, even in the underdetermined case where mixtures are fewer than layers. The effectiveness of this technology is shown in experiments on both simulated and real superimposed images.
An NFSConfiguration Management System and its Underlying Object-Oriented Model

This paper describes an NFS configuration and management system for large and heterogeneous computer environments. It also shows how this system can be extended to address other services in the network. The solution is composed of a process that describes service configuration and management life-cycle, a modular architecture and an objected oriented model. The system supports multiple features, including: automatic host and service installation, service dependency inference and analysis, performance analysis, configuration optimization as well as service functioning monitoring and problem correction.
Effect of Cadmium Chloride on Metallothionein Levels in Carp

Due to anthropogenic activities, heavy metals still represent a threat for various trophic levels. If aquatic animals are exposed to heavy metals, we can obviously observe considerable toxicity. It is well known that organisms treated with heavy metals synthesize low molecular mass compounds rich in cysteine. In this work the effects of cadmium chloride (2.5, 5, 7.5, 10 and 12.5 mg/L) on common carp (Cyprinus carpio) was investigated. We determined cadmium content in tissue of muscle, liver and kidney by atomic absorption spectrometry with electrothermal atomization and content of metallothionein (MT) in the same tissues by the Brdicka reaction. Electrochemical methods can be considered as suitable and sensitive tools for MT determination in carp tissues. Results of our study showed a gradually enhancing of cadmium content in muscle with time and dose of cadmium chloride in water. MT levels in liver reached both high levels (above 130 ng/g) in fish exposed to 2.5, 5 and 7.5 mg/L and low level (to 50 ng/g) in fish exposed to 10 and 12.5 mg/L of cadmium chloride. This finding confirms that the synthesis of metallothioneins and binding capacity of these proteins is restricted.
Missing-Feature Approaches in Speech Recognition [ Improving recognition accuracy in noise by using partial spectrographic information ]

D espite decades of focused research on the problem, the accuracy of automatic speech recognition (ASR) systems is still adversely affected by noise and other sources of acoustical variability. For example, there are presently dozens, if not hundreds, of algorithms that have been developed to cope with the effects of quasistationary additive noise and linear filtering of the channel [16], [23]. While these approaches are reasonably effective in the context of their intended purposes, they are generally ineffective in improving recognition accuracy in many more difficult environments. Some of these more challenging conditions, which are frequently encountered in some of the most important potential speech recognition applications today, include speech in the presence of transient and nonstationary disturbances (as in many factory, military, and telematic environments), speech in the presence of background music or background speech (as in the automatic transcription of broadcast program material as well as in many natural environments), and speech at very low signal-to-noise ratios (SNRs). Conventional environmental compensation provides only limited benefit for these problems even today. For example, Raj et al. [29] showed that while codeword-dependent cepstral normalization is highly effective in dramatically reducing the impact of additive broadband noise on speech recognition accuracy, it is relatively ineffective when speech is presented to the system in the presence of background music, for reasons that are believed to be a consequence of the nonstationary nature of the acoustical degradation. [Bhiksha Raj and Richard M. Stern]
Condensation of the plasma membrane at the site of T lymphocyte activation

After activation, T lymphocytes restructure their cell surface to form membrane domains at T cell receptor (TCR)–signaling foci and immunological synapses (ISs). To address whether these rearrangements involve alteration in the structure of the plasma membrane bilayer, we used the fluorescent probe Laurdan to visualize its lipid order. We observed a condensation of the plasma membrane at TCR activation sites. The formation of ordered domains depends on the presence of the transmembrane protein linker for the activation of T cells and Src kinase activity. Moreover, these ordered domains are stabilized by the actin cytoskeleton. Membrane condensation occurs upon TCR stimulation alone but is prolonged by CD28 costimulation with TCR. In ISs, which are formed by conjugates of TCR transgenic T lymphocytes and cognate antigen-presenting cells, similar condensed membrane phases form first in central regions and later at the periphery of synapses. The formation of condensed membrane domains at T cell activation sites biophysically reflects membrane raft accumulation, which has potential implications for signaling at ISs.
Similarity Search over the Cloud Based on Image Descriptors' Dimensions Value Cardinalities

In recognition that in modern applications billions of images are stored into distributed databases in different logical or physical locations, we propose a similarity search strategy over the cloud based on the dimensions value cardinalities of image descriptors. Our strategy has low preprocessing requirements by dividing the computational cost of the preprocessing steps into several nodes over the cloud and locating the descriptors with similar dimensions value cardinalities logically close. New images are inserted into the distributed databases over the cloud efficiently, by supporting dynamical update in real-time. The proposed insertion algorithm has low computational complexity, depending exclusively on the dimensionality of descriptors and a small subset of descriptors with similar dimensions value cardinalities. Finally, an efficient query processing algorithm is proposed, where the dimensions of image descriptors are prioritized in the searching strategy, assuming that dimensions of high value cardinalities have more discriminative power than the dimensions of low ones. The computation effort of the query processing algorithm is divided into several nodes over the cloud infrastructure. In our experiments with seven publicly available datasets of image descriptors, we show that the proposed similarity search strategy outperforms competitive methods of single node, parallel and cloud-based architectures, in terms of preprocessing cost, search time and accuracy.