Miracle-dz/UniFAR-FTUs huggingface dataset:
Collapsing non-idempotent intersection types

We proved recently that the extensional collapse of the relational model of linear logic coincides with its Scott model, whose objects are preorders and morphisms are downwards closed relations. This result is obtained by the construction of a new model whose objects can be understood as preorders equipped with a realizability predicate. We present this model, which features a new duality, and explain how to use it for reducing normalization results in idempotent intersection types (usually proved by reducibility) to purely combinatorial methods. We illustrate this approach in the case of the call-by-value lambda-calculus, for which we introduce a new resource calculus, but it can be applied in the same way to many different calculi.
SPEECH SIGNAL COMPRESSED SENSING BASED ON K-SVD ADAPTIVE DICTIONARY

This paper proposes a novel and successful method f or speech signal compressed sensing based on KSingular Value Decomposition (K-SVD) algorithm. K-S VD is an iterative method that alternates between sparse representation of the train samples based on the current dictionary and a process of updating t he dictionary atoms to better fit the speech data. The presented K-SVD algorithm is applied here for trai ning an adaptive overcomplete dictionary which can best sui a set of given speech signals. The sparse coefficients can be obtained by conducting the Ort hogonal Matching Pursuit(OMP)sparse decomposition algorithm. At last, the original signal can be reco nstructed by exploiting reconstruction algorithm. T he experimental results show that, compared with the t raditional basis function dictionary, the proposed method has a stronger adaptability which can be ava ilably used for speech signal sparse representiatio n. Moreover, the compressed sensing based on K-SVD alg orithm achieves higher reconstruction accuracy and better denoising efficiency.
Hidden Markov Models as a Support for Diagnosis: Formalization of the Problem and Synthesis of the Solution

In modern information infrastructures, diagnosis must be able to assess the status or the extent of the damage of individual components. Traditional one-shot diagnosis is not adequate, but streams of data on component behavior need to be collected and filtered over time as done by some existing heuristics. This paper proposes instead a general framework and a formalism to model such over-time diagnosis scenarios, and to find appropriate solutions. As such, it is very beneficial to system designers to support design choices. Taking advantage of the characteristics of the hidden Markov models formalism, widely used in pattern recognition, the paper proposes a formalization of the diagnosis process, addressing the complete chain constituted by monitored component, deviation detection and state diagnosis. Hidden Markov models are well suited to represent problems where the internal state of a certain entity is not known and can only be inferred from external observations of what this entity emits. Such over-time diagnosis is a first class representative of this category of problems. The accuracy of diagnosis carried out through the proposed formalization is then discussed, as well as how to concretely use it to perform state diagnosis and allow direct comparison of alternative solutions
Mean-Field Approximations to the Longest Common Subsequence Problem

The Longest Common Subsequence (LCS) problem is a fundamental problem of sequence comparison. A natural approximation to this problem is a model in which every pairs of letters of two “sequences” are matched independently of the other pairs with probability 1 /S , S representing the size of the alphabet. This model is analogous to a mean ﬁeld version of the LCS problem, which can be solved with a cavity approach [2]. We reﬁne here this approximation by incorporating in a systematic way correlations among the matches in the cavity calculation. We obtain a series of closer and closer approximations to the LCS problem, which we quantify in the large S limit, both with a perturbative approach and by Monte-Carlo simulations. We ﬁnd that, as it happens in the expansion around mean-ﬁeld for other disordered systems, the corrections to our approximations depend upon long-ranged correlation eﬀects which render the large S expansion non-perturbative.
Ventilatory baroreflex sensitivity in humans is not modulated by chemoreflex activation.

Increasing arterial blood pressure (AP) decreases ventilation, whereas decreasing AP increases ventilation in experimental animals. To determine whether a "ventilatory baroreflex" exists in humans, we studied 12 healthy subjects aged 18-26 yr. Subjects underwent baroreflex unloading and reloading using intravenous bolus sodium nitroprusside (SNP) followed by phenylephrine ("Oxford maneuver") during the following "gas conditions:" room air, hypoxia (10% oxygen)-eucapnia, and 30% oxygen-hypercapnia to 55-60 Torr. Mean AP (MAP), heart rate (HR), cardiac output (CO), total peripheral resistance (TPR), expiratory minute ventilation (V(E)), respiratory rate (RR), and tidal volume were measured. After achieving a stable baseline for gas conditions, we performed the Oxford maneuver. V(E) increased from 8.8 ± 1.3 l/min in room air to 14.6 ± 0.8 l/min during hypoxia and to 20.1 ± 2.4 l/min during hypercapnia, primarily by increasing tidal volume. V(E) doubled during SNP. CO increased from 4.9 ± . 3 l/min in room air to 6.1 ± . 6 l/min during hypoxia and 6.4 ± . 4 l/min during hypercapnia with decreased TPR. HR increased for hypoxia and hypercapnia. Sigmoidal ventilatory baroreflex curves of V(E) versus MAP were prepared for each subject and each gas condition. Averaged curves for a given gas condition were obtained by averaging fits over all subjects. There were no significant differences in the average fitted slopes for different gas conditions, although the operating point varied with gas conditions. We conclude that rapid baroreflex unloading during the Oxford maneuver is a potent ventilatory stimulus in healthy volunteers. Tidal volume is primarily increased. Ventilatory baroreflex sensitivity is unaffected by chemoreflex activation, although the operating point is shifted with hypoxia and hypercapnia.
Quantification of the BOLD Response via Blood Gas Modulations

This thesis is intended to contribute to a quantitative understanding of the blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) signal in order to increase its clinical potential. Here, the vascular, neuronal and physical processes which combine to give a resulting BOLD signal are investigated using respiratory challenges. The effect of isocapnic hyperoxia on vascular responses is investigated at 7 Tesla. No significant change was found in resting-state cerebral blood flow (CBF), resting-state cerebral blood volume (CBV) and task-evoked CBF. This challenges a previously held idea that hyperoxia is vasoconstrictive. The effect of isocapnic hyperoxia on neuronal oscillations was assessed with magnetoencephalography (MEG). Whilst a significant reduction in oscillatory power is reported in the occipital lobe, the change is significantly smaller than the global reduction previously measured with hypercapnia. These findings suggest that hyperoxia is an ideal tool for calibrated BOLD fMRI. The relationship between the change in blood oxygenation and change in transverse relaxation plays a key role in calibrated BOLD fMRI. However, previous measurements have been confounded by a change in CBV. Here, the relationship was found to be sub-linear across 1.5, 3 and 7 Tesla. Previous results which suggest a supralinear relationship at 1.5/3 Tesla and a linear relationship at 7 Tesla, are attributed to the relative contribution of intravascular/extravascular signals and their dependence on field strength, blood oxygenation and echo time. Finally, a comparison of single and multiphase ASL is made at 7 Tesla, with a modified Look-locker EPI sequence presented which allows simultaneous measurement of CBF and transit time, whilst increasing the available BOLD signal. This could have important implications for hypercapnia calibrated BOLD fMRI, where choice of ASL sequence may affect the estimated change in CMRO2. Furthermore, it provides a framework for future cerebral haemodynamic studies where simultaneous measurements are required.
The Effects of In-Store Environment Cues on Purchase Intentions Across the Three Types of Restaurants in Korea

Abstract This study is to examine the relative effects of in-store environment cues on purchase intentions across the three types of restaurants in Korea. For this purpose, required data were collected from 594 respondents in the three types of restaurants: family (157 respondents), hotel (333 respondents), and up-scale restaurants (104 respondents), and were analyzed with SPSS/PC. The suggestions from the analysis are: First, restaurant marketers should notice the weighted importance of in-store environment cues and then, effectively allocate their limited resources into in-store environments. Second, it is suggested that the restaurant marketers can properly segment their target market according to their perceived levels of in-store environment cues. Finally, facilities and in-store promotions are found to be quite significant factors on purchase intentions regardless of restaurant types.
Pathological serosa and node-based classification accurately predicts gastric cancer recurrence risk and outcome, and determines potential and limitation of a Japanese-style extensive surgery for Western patients: A prospective with quality control 10-year follow-up study

UICC classification accurately predicts overall survival but not recurrence-risk. We report here data of overall and first site-specific recurrence following curative surgery useful for the development of recurrence-oriented preventive target therapies. Patients who underwent resection for gastric cancer were stratified according to curability of surgery [curative (R0) vs non-curative resection], extent of surgery [limited (D1) vs extended (D2) node dissection] and pathological nodal/serosal status. The intent-to-treat principle, log-rank test and Cox regression analysis were used for statistical analysis of time-to-event (recurrence, death) endpoints. Curative resection only produced a chance of cure whereas survival was very poor following non-curative resection (P < 0.0001). For D2 R0 subgroup of patients, a pathological serosa and a node state-based classification into three groups, proved to be of clinical implication. Risk of recurrence after a median follow-up of 92 months was low among patients with both serosa and node-negative cancer (first group; 11%), moderate among those with either serosa or node-positive cancer (second group; 53%) and very high among those with both serosa and node-positive cancer (third group; 83%). In multivariate analysis, the relative risks of recurrence and death from gastric cancer among patients in the second and third groups, as compared to those in the first, were 7.07 (95% CI, 2.36–21.17; P = 0.0002) and 16.19 (95% CI, 5.76–45.54; P < 0.0001) respectively. First site-specific recurrence analysis revealed: low rate of loco-regional recurrence alone (12%), serosa state determinant factor of the site-recurrence (peritoneal for serosa-positive and haematogenous for serosa-negative cancers) and dramatic increase of all types of recurrence by the presence of nodal metastases. Our findings demonstrate that a pathological serosa- and node-based classification is very simple and predicts accurately site-specific recurrence-risks. Furthermore they reveal that risk of recurrence following curative D2 surgery alone is low for serosa- and node-negative cancers, but very high in serosa- and node-positive cancers suggesting the need for new therapeutic strategies in this subgroup of patients. © 2001 Cancer Research Campaign http://www.bjcancer. com
An overview of St14: A type II transmembrane serine protease

St14 is a type II transmembrane serine protease that contains a cytoplasm N-terminal domain, a stem domain, and a serine protease catalytic domain. It is primarily synthesized as an inactive zymogen and co-expressed with the cognate inhibitor hepatocyte growth factor inhibitor (HAI) in epithelial cells. Accumulating evidence suggests that it plays critical roles in pre-implant embryonic compaction, placental development, as well as epidermal barrier formation after activation. The homologous ablation of St14 in mouse underlies the neonatal death at 48h for severe deficiency of the epidermal barrier. It has also been considered as a prognostic marker of epithelial cancers as it is involved in cancer metastasis and invasion in multiple ways by activating several kinds of substrates, such as hepatocyte growth factor (HGF), urokinase-type Plasminogen activator (uPA). Notably, the strict regulation of St14 by HAIs is found to play a profound role in all of these activities. However, very little is currently understood about the mechanisms that downregulate St14 gene expression in early neonatal mortality. Herein we summarize the current knowledge of the regulation network and the understanding of the biological functions of St14. Published by www.inter-use.com. Available online March 5 th 2015, Vol. 3 Issue 2 Page 10-17.
Re-Annotator: Annotation Pipeline for Microarray Probe Sequences

Microarray technologies are established approaches for high throughput gene expression, methylation and genotyping analysis. An accurate mapping of the array probes is essential to generate reliable biological findings. However, manufacturers of the microarray platforms typically provide incomplete and outdated annotation tables, which often rely on older genome and transcriptome versions that differ substantially from up-to-date sequence databases. Here, we present the Re-Annotator, a re-annotation pipeline for microarray probe sequences. It is primarily designed for gene expression microarrays but can also be adapted to other types of microarrays. The Re-Annotator uses a custom-built mRNA reference database to identify the positions of gene expression array probe sequences. We applied Re-Annotator to the Illumina Human-HT12 v4 microarray platform and found that about one quarter (25%) of the probes differed from the manufacturer’s annotation. In further computational experiments on experimental gene expression data, we compared Re-Annotator to another probe re-annotation tool, ReMOAT, and found that Re-Annotator provided an improved re-annotation of microarray probes. A thorough re-annotation of probe information is crucial to any microarray analysis. The Re-Annotator pipeline is freely available at http://sourceforge.net/projects/reannotator along with re-annotated files for Illumina microarrays HumanHT-12 v3/v4 and MouseRef-8 v2.
The use of the special semi-groups for solving differential equations

In general, there is no method for finding closed form first integrals or solutions of ordinary differential equations with non-constant coefficients. Thus, one usually performs heuristics, but this involves fastidious computations. The aim of this paper is to propose strategies that computerize such heuristics to help the analysis. In section 1, we formulate our questions in terms of differential algebra. Then, we are able to derive algebraic constructive criteria for the search for closed form solutions of differential equations of the type <italic>s(x,y, …y<supscrpt>(n–1</supscrpt>)y<supscrpt>(n)</supscrpt> + t(x,y,…,y<supscrpt>(n–1)</supscrpt>) = 0</italic> (sections 2 and 3). In particular, we focus on the so-called Special polynomials (or Darboux curves). In Section 4, we show how our tools link the expression of the solutions to that of the first integrals, and how it gives a strategy to compute them. Then, in section 5, we show how these techniques permit to derive algorithmic methods to find solutions of order <italic>n</italic>–1 for linear differential equations of order <italic>n</italic>; we specifically detail the second order case.
Sparse signal recovery in the presence of colored noise and rank-deficient noise covariance matrix: An SBL approach

In this work, we address the recovery of sparse and compressible vectors in the presence of colored noise possibly with a rank-deficient noise covariance matrix, from overcomplete noisy linear measurements. We exploit the structure of the noise covariance matrix in a Bayesian framework. In particular, we propose the CoNo-SBL algorithm based on the popular and efficient Sparse Bayesian Learning (SBL) technique. We also derive Bayesian and Marginalized Cramér Rao lower Bounds (CRB) for the problem of estimating compressible vectors. We consider an unknown compressible vector drawn from a Student-t prior distribution, and derive CRBs that encompass the random nature of the unknown compressible vector and the parameters of the prior distribution, in the presence of colored noise and rank-deficient noise covariance matrix. Using Monte Carlo simulations, we demonstrate the efficacy of the proposed CoNo-SBL algorithm as compared to compressed sensing and greedy techniques. Further, we demonstrate the mean squared error performance of the proposed estimator compared to the CRBs, for different ranks of the noise covariance matrix.
Virtual Werder 3 D RoboCup 2007 Team Description Paper

This paper describes the current status of the Virtual Werder 3D team. The recent changes in the 3D simulation league from the spheres-based simulation to the humanoid simulation forces the teams to work on basic skills first as it is not sensible to work on elaborated behavior if the agent does not yet feature a sufficient set of basic skills. Although the agent framework from the previous year remained intact, we had to develop a completely new skill level including a walking engine to allow the agent to move on the field, and a set of special actions, such as kicks and stand-up moves. The development of this basic skills is based on experiences and results of the Bremen humanoid team BHuman. Further, the plan recognition and other issues are addressed as mid-term-goals of our team beyond RoboCup 2007.
White Matter Atrophy and Cognitive Dysfunctions in Neuromyelitis Optica

Neuromyelitis optica (NMO) is an inflammatory disease of central nervous system characterized by optic neuritis and longitudinally extensive acute transverse myelitis. NMO patients have cognitive dysfunctions but other clinical symptoms of brain origin are rare. In the present study, we aimed to investigate cognitive functions and brain volume in NMO. The study population consisted of 28 patients with NMO and 28 healthy control subjects matched for age, sex and educational level. We applied a French translation of the Brief Repeatable Battery (BRB-N) to the NMO patients. Using SIENAx for global brain volume (Grey Matter, GM; White Matter, WM; and whole brain) and VBM for focal brain volume (GM and WM), NMO patients and controls were compared. Voxel-level correlations between diminished brain concentration and cognitive performance for each tests were performed. Focal and global brain volume of NMO patients with and without cognitive impairment were also compared. Fifteen NMO patients (54%) had cognitive impairment with memory, executive function, attention and speed of information processing deficits. Global and focal brain atrophy of WM but not Grey Matter (GM) was found in the NMO patients group. The focal WM atrophy included the optic chiasm, pons, cerebellum, the corpus callosum and parts of the frontal, temporal and parietal lobes, including superior longitudinal fascicle. Visual memory, verbal memory, speed of information processing, short-term memory and executive functions were correlated to focal WM volumes. The comparison of patients with, to patients without cognitive impairment showed a clear decrease of global and focal WM, including brainstem, corticospinal tracts, corpus callosum but also superior and inferior longitudinal fascicles. Cognitive impairment in NMO patients is correlated to the decreased of global and focal WM volume of the brain. Further studies are needed to better understand the precise origin of cognitive impairment in NMO patients, particularly in the WM.
Solving Geometric Analogy Problems Through Two-Stage Analogical Mapping

Evans' 1968 ANALOGY system was the first computer model of analogy. This paper demonstrates that the structure mapping model of analogy, when combined with high-level visual processing and qualitative representations, can solve the same kinds of geometric analogy problems as were solved by ANALOGY. Importantly, the bulk of the computations are not particular to the model of this task but are general purpose: We use our existing sketch understanding system, CogSketch, to compute visual structure that is used by our existing analogical matcher, Structure Mapping Engine (SME). We show how SME can be used to facilitate high-level visual matching, proposing a role for structural alignment in mental rotation. We show how second-order analogies over differences computed via analogies between pictures provide a more elegant model of the geometric analogy task. We compare our model against human data on a set of problems, showing that the model aligns well with both the answers chosen by people and the reaction times required to choose the answers.
Quantitative Autoradiography of Dihydrorotenone Binding to Complex I of the Electron Transport Chain

Abstract: Defective complex I activity has been linked to Parkinson's disease and Huntington's disease, but little is known of the regional distribution of this enzyme in the brain. We have developed a quantitative autoradiographic assay using [3H]dihydrorotenone ([3H]DHR) to label and localize complex I in brain tissue sections. Binding was specific and saturable and in the cerebellar molecular layer had a KD of 11.5 ± 1.3 nM and a Bmax of 11.0 ± 0.4 nCi/mg of tissue. Unlabeled rotenone and 1‐methyl‐4‐phenylpyridinium ion competed effectively for DHR binding sites. Binding was markedly enhanced by 100 μM NADH. The distribution of complex 1 in brain, as revealed by DHR autoradiography, is unique but somewhat similar to that of cytochrome oxidase (complex IV). This assay may provide new insight into the roles of complex I in brain function and neurodegeneration.
Scalable Team Oriented Reliable Multicast routing protocol for tactical mobile ad hoc networks

This paper presents the scalable team oriented reliable multicast (STORM) routing protocol, designed to provide reliability in a tactical mobile ad hoc network (MANET) under a wide range of scenarios and network conditions. STORM organizes individual nodes with similar mobility patterns and speeds into teams, and builds a hierarchy-based multicast mesh structure among elected team nodes. A unicast acknowledgement scheme (UAS) is developed to build the routing structure in an efficient manner. To improve the reliability of STORM, a modified version of reliable adaptive congestion controlled multicast (ReACT) is used as a reliable transport protocol. The simulated performance results indicate that STORM is scalable with respect to number of multicast groups, multicast sources, and network load. Furthermore, the adoption of ReACT as a transport protocol improves the performance of STORM in conditions of heavy network traffic.