Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification and quantification of protein S-nitrosation by nitrite in the mouse heart during ischemia

Nitrate (NO3−) and nitrite (NO2−) are known to be cardioprotective and to alter energy metabolism in vivo. NO3− action results from its conversion to NO2− by salivary bacteria, but the mechanism(s) by which NO2− affects metabolism remains obscure. NO2− may act by S-nitrosating protein thiols, thereby altering protein activity. But how this occurs, and the functional importance of S-nitrosation sites across the mammalian proteome, remain largely uncharacterized. Here we analyzed protein thiols within mouse hearts in vivo using quantitative proteomics to determine S-nitrosation site occupancy. We extended the thiol-redox proteomic technique, isotope-coded affinity tag labeling, to quantify the extent of NO2−-dependent S-nitrosation of proteins thiols in vivo. Using this approach, called SNOxICAT (S-nitrosothiol redox isotope-coded affinity tag), we found that exposure to NO2− under normoxic conditions or exposure to ischemia alone results in minimal S-nitrosation of protein thiols. However, exposure to NO2− in conjunction with ischemia led to extensive S-nitrosation of protein thiols across all cellular compartments. Several mitochondrial protein thiols exposed to the mitochondrial matrix were selectively S-nitrosated under these conditions, potentially contributing to the beneficial effects of NO2− on mitochondrial metabolism. The permeability of the mitochondrial inner membrane to HNO2, but not to NO2−, combined with the lack of S-nitrosation during anoxia alone or by NO2− during normoxia places constraints on how S-nitrosation occurs in vivo and on its mechanisms of cardioprotection and modulation of energy metabolism. Quantifying S-nitrosated protein thiols now allows determination of modified cysteines across the proteome and identification of those most likely responsible for the functional consequences of NO2− exposure.
Privacy as a Currency: Un-regulated?

We are living in a time where many of the decisions that affect us are made by mathematical models. These models rely on data. Precision and relevance of the decisions made by these models is dependent on quality of the data being fed to them. Therefore, there is a rush to collect personal data. Majority of the organizations that provide online services are at the forefront of collecting user data. Users, either voluntarily or by coercion, divulge information about themselves in return of personalized service, for example. These organizations’ revenue model is based on advertisement where advertisers are paired with user profiles that are built on top of collected data. This data is being used for a variety of purposes apart from delivering targeted advertisements. Mathematical decision models are impartial to the data on which they operate. An error, omission or misrepresentation in data has an irrevocable consequence on our lives, at times, without corrective remedies. This paper touches upon various facets of information gathering; information bias, economics of privacy, information asymmetry – and their implications to our ecosystem if left unaddressed.
Analysis of the Bayesian multi-scale optimistic optimization on the CEC2016 and BBOB testbeds

This paper provides an empirical analysis of the recently proposed Bayesian Multi-Scale Optimistic Optimization (BaMSOO) algorithm for solving bound-constrained black-box global optimization problems under expensive as well as cheap budgets of function evaluations. The CEC2016 and BBOB benchmarks are used in assessing the algorithm. We also compare BaMSOO with the Simultaneous Optimistic Optimization (SOO) algorithm from which BaMSOO is derived. The results indicate that BaMSOO has a comparable performance with SOO under expensive-budget settings but outperforms it with increasing evaluation budgets. Finally, we provide useful insights regarding the algorithm's relative efficiency and future directions.
Reinforcement learning with internal-dynamics-based exploration using a chaotic neural network

In this paper, a novel concept is proposed where exploration, which is essential in reinforcement learning, is considered to be one aspect of the motions generated by the learner's internal dynamics and is expected to develop through learning towards more purposeful higher dynamic functions such as “thinking”. To realize such a concept, a chaotic neural network is introduced for generating motions with exploratory factors that are derived from the internal chaotic dynamics without adding external random noises. Effective exploration is expected based on the dynamics called “chaotic itinerancy”, which is also expected to be the key to learning higher dynamic functions more easily that require both stable and transitive dynamics. This paper also proposes a reinforcement learning method without any external random noise, using the temporal difference (TD) error of the state value and the contribution trace of each input to the output increase in each neuron. It was confirmed in a simple learning task that by using a chaotic neural network, an agent could explore in accordance with the internal chaotic dynamics and could learn goal-directed behaviors. The proposed framework seems promising to explain the emergence of higher intelligence in real lives and also to develop human-like intelligence though there are many remaining problems to be solved.
High-Frequency Oscillations in Distributed Neural Networks Reveal the Dynamics of Human Decision Making

We examine the relative timing of numerous brain regions involved in human decisions that are based on external criteria, learned information, personal preferences, or unconstrained internal considerations. Using magnetoencephalography (MEG) and advanced signal analysis techniques, we were able to non-invasively reconstruct oscillations of distributed neural networks in the high-gamma frequency band (60–150 Hz). The time course of the observed neural activity suggested that two-alternative forced choice tasks are processed in four overlapping stages: processing of sensory input, option evaluation, intention formation, and action execution. Visual areas are activated first, and show recurring activations throughout the entire decision process. The temporo-occipital junction and the intraparietal sulcus are active during evaluation of external values of the options, 250–500 ms after stimulus presentation. Simultaneously, personal preference is mediated by cortical midline structures. Subsequently, the posterior parietal and superior occipital cortices appear to encode intention, with different subregions being responsible for different types of choice. The cerebellum and inferior parietal cortex are recruited for internal generation of decisions and actions, when all options have the same value. Action execution was accompanied by activation peaks in the contralateral motor cortex. These results suggest that high-gamma oscillations as recorded by MEG allow a reliable reconstruction of decision processes with excellent spatiotemporal resolution.
Walk‐Through Illustrations: Frame‐Coherent Pen‐and‐Ink Style in a Game Engine

In this paper we show how a game engine designed to generate photorealistic images can be extended to produce non‐photorealistic and hybrid renditions. We introduce new hardware‐based methods to accomplish pen‐and‐ink illustrations. The combination of the highly optimized processing of a game engine and the use of hardware for NPR algorithms yields real‐time animation of pen‐and‐ink illustrations.
Cytotoxic Effects of Fucoidan Nanoparticles against Osteosarcoma

In this study, we analyzed the size-dependent bioactivities of fucoidan by comparing the cytotoxic effects of native fucoidan and fucoidan lipid nanoparticles on osteosarcoma in vitro and in vivo. In vitro experiments indicated that nanoparticle fucoidan induced apoptosis of an osteosarcoma cell line more efficiently than native fucoidan. The more potent effects of nanoparticle fucoidan, relative to native fucoidan, were confirmed in vivo using a xenograft osteosarcoma model. Caco-2 cell transport studies showed that permeation of nanoparticle fucoidan was higher than native fucoidan. The higher bioactivity and superior bioavailability of nanoparticle fucoidan could potentially be utilized to develop novel therapies for osteosarcoma.
Second-Order and Moderate Deviations Asymptotics for Successive Refinement

We derive the optimal second-order coding region and moderate deviations constant for successive refinement source coding with a joint excess-distortion probability constraint. We consider two scenarios: 1) a discrete memoryless source (DMS) and arbitrary distortion measures at the decoders and 2) a Gaussian memoryless source (GMS) and quadratic distortion measures at the decoders. For a DMS with arbitrary distortion measures, we prove an achievable second-order coding region, using type covering lemmas by Kanlis and Narayan and by No, Ingber, and Weissman. We prove the converse using the perturbation approach by Gu and Effros. When the DMS is successively refinable, the expressions for the second-order coding region and the moderate deviations constant are simplified and easily computable. For this case, we also obtain new insights on the second-order behavior compared with the scenario where separate excess-distortion proabilities are considered. For example, we describe a DMS, for which the optimal second-order region transitions from being characterizable by a bivariate Gaussian to a univariate Gaussian, as the distortion levels are varied. We then consider a GMS with quadratic distortion measures. To prove the direct part, we make use of the sphere covering theorem by Verger–Gaugry, together with appropriately-defined Gaussian type classes. To prove the converse, we generalize Kostina and Verdú’s one-shot converse bound for point-to-point lossy source coding. We remark that this proof is applicable to general successively refinable sources. In the proofs of the moderate deviations results for both scenarios, we follow a strategy similar to that for the second-order asymptotics and use the moderate deviations principle.
Quantifying Quality Operational Costs in a Multi-Agent System for Airline Operations Recovery

When recovering from operational problems, the Airline Operations Control Centre (AOCC) usually tries to minimize direct operational costs while satisfying all the required rules. In this paper we present the implementation of a Distributed Multi-Agent System (MAS) representing the existing real-life roles in an AOCC. This MAS includes software agents that cooperate through a distributed problem solving approach, to find the best solution for each problem. We propose a general approach to quantify quality operational costs, so that passengers' satisfaction can also be considered in the final decision. We present a real case study to introduce our approach to quantify the quality operational costs and solve several real unexpected crew problems. We show that our MAS with quality costs is able to reduce flight delays and increase passenger satisfaction without increasing significantly the direct operational costs. A comparison with two other methods is presented. Copyright © 2007 Praise Worthy Prize S.r. l. - All rights reserved.
Characteristic Polynomials of Skew-Adjacency Matrices of Oriented Graphs

An oriented graph $\overleftarrow{G}$ is a simple undirected graph $G$ with an orientation, which assigns to each edge a direction so that $\overleftarrow{G}$ becomes a directed graph. $G$ is called the underlying graph of $\overleftarrow{G}$ and we denote by $S(\overleftarrow{G})$ the skew-adjacency matrix of $\overleftarrow{G}$ and its spectrum $Sp(\overleftarrow{G})$ is called the skew-spectrum of $\overleftarrow{G}$. In this paper, the coefficients of the characteristic polynomial of the skew-adjacency matrix $S(\overleftarrow{G}) $ are given in terms of $\overleftarrow{G}$ and as its applications, new combinatorial proofs of known results are obtained and new families of oriented bipartite graphs $\overleftarrow{G}$ with $Sp(\overleftarrow{G})={\bf i} Sp(G) $ are given.
Developmental expression of glutamate transporters and glutamate dehydrogenase in astrocytes of the postnatal rat hippocampus

Glutamate is the major excitatory transmitter in the CNS and plays distinct roles in a number of developmental events. Its extracellular concentration, which mediates these activities, is regulated by glutamate transporters in glial cells and neurons. In the present study, we have used nonradioactive in situ hybridization, immunocytochemistry, and immunoblotting to show the cellular and regional expression of the high‐affinity glutamate transporters GLAST (EAAT1) and generic GLT1 (EAAT2; glial form of GLT1) in the rat hippocampus during postnatal development (P1–60). The results of transporter expression were compared with the localization and activity pattern of glutamate dehydrogenase (GDH), an important glutamate‐metabolizing enzyme. The study showed that both transporters and GDH were demonstrable at P1 (day of birth). The expression of GLAST (detected by in situ hybridization and immunocytochemistry) in the early postnatal development was higher than GLT1. Thereafter, the expression of both transporters increased, showing adult levels at between P20 and P30 (detected by in situ hybridization and immunoblotting). At these time points, the expression of GLT1 appeared to be significantly higher than the GLAST expression. GLT1 and GLAST proteins were demonstrable only in astrocytes. The increase of GDH activities (steepest increase from P5–P8), which were localized preferentially in astrocytes, was in agreement with the increase of transporter expression, preferentially with that of GLT1. These observations suggest that the extent of glutamate transporter expression and of glutamate‐metabolizing GDH activity in astrocytes is intimately correlated with the formation of glutamatergic synapses in the developing hippocampus. © 2004 Wiley‐Liss, Inc.
Decision Procedures for String Constraints

String-related defects are among the most prevalent and costly in modern software development. For example, in terms of frequency, cross-site scripting vulnerabilities have long surpassed traditional exploits like buffer overruns. The state of this problem is particularly disconcerting because it does not just affect legacy code: developing web applications today — even when adhering to best practices and using modern library support — remains error-prone. A number of program analysis approaches aim to prevent or mitigate string-related defects; examples include static bug detectors and automated testcase generators. Traditionally, this work has relied on built-in algorithms to reason about string-manipulating code. This arrangement is suboptimal for two reasons: first, it forces researchers to re-invent the wheel for each new analysis; and second, it does not encourage the independent improvement of domain-specific algorithms for handling strings. In this dissertation, we present research on specialized decision algorithms for string constraints. Our high-level approach is to provide a constraint solving interface; a client analysis can use that interface to reason about strings in the same way it might use a SAT solver to reason about binary state. To this end, we identify a set of string constraints that captures common programming language constructs, and permits efficient solving algorithms. We provide a core solving algorithm together with a machine-checkable proof of its correctness. Next, we focus on performance. We evaluate a variety of datastructures and algorithms in a controlled setting to inform our choice of each. Our final approach is based on two insights: (1) string constraints can be cast as an explicit search problem, and (2) to solve these constraints, we can
Multi-view Non-rigid Refinement and Normal Selection for High Quality 3D Reconstruction

In recent years, there have been a variety of proposals for high quality 3D reconstruction by fusion of depth and normal maps that contain good low and high frequency information respectively. Typically, these methods create an initial mesh representation of the complete object or scene being scanned. Subsequently, normal estimates are assigned to each mesh vertex and a mesh-normal fusion step is carried out. In this paper, we present a complete pipeline for such depth-normal fusion. The key innovations in our pipeline are twofold. Firstly, we introduce a global multi-view non-rigid refinement step that corrects for the non-rigid misalignment present in the depth and normal maps. We demonstrate that such a correction is crucial for preserving fine-scale 3D features in the final reconstruction. Secondly, despite adequate care, the averaging of multiple normals invariably results in blurring of3D detail. To mitigate this problem, we propose an approach that selects one out of many available normals. Our global cost for normal selection incorporates a variety of desirable properties and can be efficiently solved using graph cuts. We demonstrate the efficacy of our approach in generating high quality 3D reconstructions of both synthetic and real 3D models and compare with existing methods in the literature.
Ontology-Based Search of Genomic Metadata

The Encyclopedia of DNA Elements (ENCODE) is a huge and still expanding public repository of more than 4,000 experiments and 25,000 data files, assembled by a large international consortium since 2007; unknown biological knowledge can be extracted from these huge and largely unexplored data, leading to data-driven genomic, transcriptomic, and epigenomic discoveries. Yet, search of relevant datasets for knowledge discovery is limitedly supported: metadata describing ENCODE datasets are quite simple and incomplete, and not described by a coherent underlying ontology. Here, we show how to overcome this limitation, by adopting an ENCODE metadata searching approach which uses high-quality ontological knowledge and state-of-the-art indexing technologies. Specifically, we developed S.O. S. GeM(http://www.bioinformatics.deib.polimi.it/SOSGeM/), a system supporting effective semantic search and retrieval of ENCODE datasets. First, we constructed a Semantic Knowledge Base by starting with concepts extracted from ENCODE metadata, matched to and expanded on biomedical ontologies integrated in the well-established Unified Medical Language System. We prove that this inference method is sound and complete. Then, we leveraged the Semantic Knowledge Base to semantically search ENCODE data from arbitrary biologists' queries. This allows correctly finding more datasets than those extracted by a purely syntactic search, as supported by the other available systems. We empirically show the relevance of found datasets to the biologists' queries.
Adaptive ghosted views for Augmented Reality

In Augmented Reality (AR), ghosted views allow a viewer to explore hidden structure within the real-world environment. A body of previous work has explored which features are suitable to support the structural interplay between occluding and occluded elements. However, the dynamics of AR environments pose serious challenges to the presentation of ghosted views. While a model of the real world may help determine distinctive structural features, changes in appearance or illumination detriment the composition of occluding and occluded structure. In this paper, we present an approach that considers the information value of the scene before and after generating the ghosted view. Hereby, a contrast adjustment of preserved occluding features is calculated, which adaptively varies their visual saliency within the ghosted view visualization. This allows us to not only preserve important features, but to also support their prominence after revealing occluded structure, thus achieving a positive effect on the perception of ghosted views.
The 2012 KIT and KIT-NAIST English ASR systems for the IWSLT evaluation

This paper describes our English Speech-to-Text (STT) systems for the 2012 IWSLT TED ASR track evaluation. The systems consist of 10 subsystems that are combinations of different front-ends, e.g. MVDR based and MFCC based ones, and two different phone sets. The outputs of the subsystems are combined via confusion network combination. Decoding is done in two stages, where the systems of the second stage are adapted in an unsupervised manner on the combination of the first stage outputs using VTLN, MLLR, and cMLLR. Index Terms: speech recognition, IWSLT, TED talks, evaluation system, system development
PDE-Based Random-Valued Impulse Noise Removal Based on New Class of Controlling Functions

This paper is concerned with partial differential equation (PDE)-based image denoising for random-valued impulse noise. We introduce the notion of ENI (the abbreviation for “edge pixels, noisy pixels, and interior pixels”) that denotes the number of homogeneous pixels in a local neighborhood and is significantly different for edge pixels, noisy pixels, and interior pixels. We redefine the controlling speed function and the controlling fidelity function to depend on ENI. According to our two controlling functions, the diffusion and fidelity process at edge pixels, noisy pixels, and interior pixels can be selectively carried out. Furthermore, a class of second-order improved and edge-preserving PDE denoising models is proposed based on the two new controlling functions in order to deal with random-valued impulse noise reliably. We demonstrate the performance of the proposed PDEs via application to five standard test images, corrupted by random-valued impulse noise with various noise levels and comparison with the related second-order PDE models and the other special filtering methods for random-valued impulse noise. Our two controlling functions are extended to automatically other PDE models.
Checking Computations of Formal Method Tools - A Secondary Toolchain for ProB

We present the implementation of pyB, a predicate - and expression - checker for the B language. The tool is to be used for a secondary tool chain for data validation and data generation, with ProB being used in the primary tool chain. Indeed, pyB is an independent cleanroom-implementation which is used to double-check solutions generated by ProB, an animator and model-checker for B specifications. One of the major goals is to use ProB together with pyB to generate reliable outputs for high-integrity safety critical applications. Although pyB is still work in progress, the ProB/pyB toolchain has already been successfully tested on various industrial B machines and data validation tasks.
KUL-Eval: A Combinatory Categorial Grammar Approach for Improving Semantic Parsing of Robot Commands using Spatial Context

When executing commands, a robot has a certain level of contextual knowledge about the environment in which it operates. Taking this knowledge into account can be beneficial to disambiguate commands with multiple interpretations. We present an approach that uses combinatory categorial grammars for improving the semantic parsing of robot commands that takes into account the spatial context of the robot. The results indicate a clear improvement over non-contextual semantic parsing. This work was done in the context of the SemEval-2014 task on supervised semantic parsing of spatial robot commands.