Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcranial diffuse optical monitoring of microvascular cerebral hemodynamics after thrombolysis in ischemic stroke

Abstract. The ultimate goal of therapeutic strategies for ischemic stroke is to reestablish the blood flow to the ischemic region of the brain. However, currently, the local cerebral hemodynamics (microvascular) is almost entirely inaccessible for stroke clinicians at the patient bed-side, and the recanalization of the major cerebral arteries (macrovascular) is the only available measure to evaluate the therapy, which does not always reflect the local conditions. Here we report the case of an ischemic stroke patient whose microvascular cerebral blood flow and oxygenation were monitored by a compact hybrid diffuse optical monitor during thrombolytic therapy. This monitor combined diffuse correlation spectroscopy and near-infrared spectroscopy. The reperfusion assessed by hybrid diffuse optics temporally correlated with the recanalization of the middle cerebral artery (assessed by transcranial-Doppler) and was in agreement with the patient outcome. This study suggests that upon further investigation, diffuse optics might have a potential for bed-side acute stroke monitoring and therapy guidance by providing hemodynamics information at the microvascular level.
Performance Evaluation of 3D Correspondence Grouping Algorithms

This paper presents a thorough evaluation of several widely-used 3D correspondence grouping algorithms, motived by their significance in vision tasks relying on correct feature correspondences. A good correspondence grouping algorithm is desired to retrieve as many as inliers from initial feature matches, giving a rise in both precision and recall. Towards this rule, we deploy the experiments on three benchmarks respectively addressing shape retrieval, 3D object recognition and point cloud registration scenarios. The variety in application context brings a rich category of nuisances including noise, varying point densities, clutter, occlusion and partial overlaps. It also results to different ratios of inliers and correspondence distributions for comprehensive evaluation. Based on the quantitative outcomes, we give a summarization of the merits/demerits of the evaluated algorithms from both performance and efficiency perspectives.
NO is required for memory formation and expression of memory, and for minor behavioral changes during training with inedible food in Aplysia

A learning experience may lead to changes in behavior during the experience, and also to memory expressed at a later time. Are signals causing changes in behavior during the learning experience related to the formation and expression of memory? We examined this question, using learning that food is inedible in Aplysia. Treatment of an isolated buccal ganglia preparation with an NO donor elicited rejection-like motor programs. Rejection initiated by NO production is consistent with aspects of behavioral changes seen while animals learn, and with memory formation. Nonetheless, applying the NO donor during training had only minor effects on behavior during the training, and did not improve memory, indicating that the induction of rejection in the buccal ganglia is unlikely to be the means by which NO during training contributes to memory formation. Block of NO during memory retrieval prevented the expression of memory, as measured by a lack of savings in time to stop responding to food. Applying an NO donor to the cerebral ganglion while eliciting fictive feeding inhibited the expression of feeding activity, indicating that some NO effects on memory consolidation and on expression of memory may be via effects on the cerebral ganglion.
Towards High Performance Video Object Detection for Mobiles

Despite the recent success of video object detection on Desktop GPUs, its architecture is still far too heavy for mobiles. It is also unclear whether the key principles of sparse feature propagation and multi-frame feature aggregation apply at very limited computational resources. In this paper, we present a light weight network architecture for video object detection on mobiles. Light weight image object detector is applied on sparse key frames. A very small network, Light Flow, is designed for establishing correspondence across frames. A flow-guided GRU module is designed to effectively aggregate features on key frames. For non-key frames, sparse feature propagation is performed. The whole network can be trained end-to-end. The proposed system achieves 60.2% mAP score at speed of 25.6 fps on mobiles (e.g., HuaWei Mate 8).
Exploring the Relationship between User Activities and Profile Images on Twitter through Machine Learning Techniques

Social media profile images are one of many visual components of users. Moreover, user activities such as posting or chatting are regarded as self-expression behaviors. In this study, we examine Japanese Twitter users to explore the relationship between user activities and profile images. Logistic regression analysis is used to statistically identify and quantify relationships, leading us to conclude that several profile image categories significantly correlate with user activities. Furthermore, we use machine learning techniques (logistic regression, random forest, and support vector machine) to predict whether or not a user belongs to a specific profile image category. Each model's performance is evaluated and compared for all profile image categories. Primary results show that users whose profile image includes others' faces are more likely to use a replying function but less likely to add url links to their tweets, and that it is the easiest for machine learning models to find their category from their user activities. In short, our findings indicate that visual expression correlates with social media user behavior.
Regularized Singular Value Decomposition and Application to Recommender System

Singular value decomposition (SVD) is the mathematical basis of principal component analysis (PCA). Together, SVD and PCA are one of the most widely used mathematical formalism/decomposition in machine learning, data mining, pattern recognition, artificial intelligence, computer vision, signal processing, etc. In recent applications, regularization becomes an increasing trend. In this paper, we present a regularized SVD (RSVD), present an efficient computational algorithm, and provide several theoretical analysis. We show that although RSVD is non-convex, it has a closed-form global optimal solution. Finally, we apply RSVD to the application of recommender system and experimental result show that RSVD outperforms SVD significantly.
Integrating Traffic Datasets for Evaluating Road Networks

Improving the safety of roads has traditionally been approached by governmental agencies including the National Highway Traffic Safety Administration and State Departments of Transportation. In past literature, automobile crash data is analyzed using time-series prediction techniques to identify road segments and/or intersections likely to experience future crashes. After dangerous zones have been identified road modifications can be implemented improving public safety. This project introduces a historical safety metric for evaluating the relative danger of roads in a road network. The historical safety metric can be used to update routing choices of individual drivers improving public safety by avoiding historically more dangerous routes. The metric is constructed using crash frequency, severity, location and traffic information. An analysis of publicly available crash and traffic data in Allegheny County, Pennsylvania is used to generate the historical safety metric for a specific road network. Applications of this metric include comparison of routes based on the safety metric that begins with summing the danger of each accident on each street.
Cryohemolysis, erythrocyte osmotic fragility, and supplementary hematimetric indices in the diagnosis of hereditary spherocytosis

Background Hereditary spherocytosis (HS) is a chronic hemolytic anemia characterized by microspherocytes in the peripheral blood and increased erythrocyte osmotic fragility (EOF). This study evaluated the cryohemolysis test (CHT); initial hemolysis (IH); immediate and incubated hemolysis percentage in 5.5 g/L NaCl (H5.5); mean corpuscular hemoglobin concentration (MCHC); red blood cell distribution width (RDW); and Hb/MCHC, Hb/RDW, and MCHC/RDW ratios for the diagnosis of HS. Methods Data from 13 patients with HS were evaluated at the Instituto de Bioquímica Aplicada and compared with data from 14 unaffected individuals and 11 patients with anemia due to another etiology. Total blood and reticulocyte counts, CHT, and immediate and incubated EOF were performed in all subjects; sensitivity, specificity, efficiency, and Youden index (YI) were calculated. Results Eight patients with HS had MCHC ≥345 g/L, 10 had RDW ≥14.5%, 12 had IH >5.0 g/L, 11 had immediate H5.5 ≥5%, and 13 had incubated H5.5 ≥50% (the cut-off value to consider HS). The efficiency and YI were: immediate H5.5 (0.94–0.85), incubated H5.5 (0.89–0.82), IH (0.89–0.78), MCHC (0.87–0.62), CHT (0.84–0.54), and Hb/MCHC (0.71–0.56), respectively. The calculated ratios could distinguish subjects with HS from unaffected individuals (P<0.05), but not those with anemia of another etiology (P>0.05). Conclusion Although the CHT and supplementary hematimetric indexes were useful to differentiate individuals with SH from healthy controls, they cannot distinguish from anemias of other etiology. CHT and MCHC, in addition to EOF, are recommended for diagnosing HS patients because of their low cost and efficiency.
Analyse par intervalles pour la détection de boucles dans la trajectoire d'un robot mobile. (Interval analysis for loop detection in the trajectory of a mobile robot)

Le travail de these presente dans ce memoire a permis le developpement d’une nouvelle methode de detection de boucles dans la trajectoire d’un robot mobile. Celle-ci se base, contrairement a celles existantes, sur l’utilisation de donnees proprioceptives et est ainsi totalement decouplee des problematiques de localisation et cartographie auxquelles elle est generalement associee. L’utilisation de l’analyse par intervalles a permis, dans notre contexte de mesures a erreurs bornees, de calculer deux temps pour lesquels la position du robot est identique alors qu’un mouvement significatif a pu etre observe entre ces deux instants. La methode est automatique et ne necessite que la connaissance des equations d’etat du robot ainsi que les mesures de vitesse effectuees au cours de sa mission. Les resultats de l’algorithme sur donnees reelles, issues d’une experience effectuee en milieu sous-marin, sont tres satisfaisants et ont permis de prouver l’existence et l’unicite de plus de la moitie des boucles observees dans la trajectoire du robot.
Baseline VIPT Cache Parameters ( d ) VESPA ( VIPT enhancements for superpage accesses ) Cache Parameters

Hardware caches balance fast lookup, high hit rates, energy efficiency, and simplicity of implementation. For L1 caches however, achieving this balance is difficult because of constraints imposed by virtual memory. L1 caches are usually virtually-indexed and physically tagged (VIPT), but this means that they must be highly associative to achieve good capacity. Unfortunately, excessive associativity compromises performance by degrading access times without significantly boosting hit rates, and increases access energy. We propose SEESAW to overcome this problem. SEESAW leverages the increasing ubiquity of superpages1 – since superpages have more page offset bits, they can accommodate VIPT caches with more sets than what is traditionally possible with only base page sizes. SEESAW dynamically reduces the number of ways that are looked up based on the page size, improving performance and energy. SEESAW requires modest hardware and no OS or application changes. Keywords-Virtual Memory; L1 Caches; Memory systems; Superpages.
Cutting Edge: Blockade of Inhibitor of Apoptosis Proteins Sensitizes Neutrophils to TNF- but Not Lipopolysaccharide-Mediated Cell Death and IL-1β Secretion

The mammalian inhibitor of apoptosis proteins (IAPs) are key regulators of cell death and inflammation. A major function of IAPs is to block the formation of a cell death–inducing complex, termed the ripoptosome, which can trigger caspase-8–dependent apoptosis or caspase-independent necroptosis. Recent studies report that upon TLR4 or TNF receptor 1 (TNFR1) signaling in macrophages, the ripoptosome can also induce NLRP3 inflammasome formation and IL-1β maturation. Whether neutrophils have the capacity to assemble a ripoptosome to induce cell death and inflammasome activation during TLR4 and TNFR1 signaling is unclear. In this study, we demonstrate that murine neutrophils can signal via TNFR1-driven ripoptosome assembly to induce both cell death and IL-1β maturation. However, unlike macrophages, neutrophils suppress TLR4-dependent cell death and NLRP3 inflammasome activation during IAP inhibition via deficiencies in the CD14/TRIF arm of TLR4 signaling.
DEVELOPMENT OF A COMPUTER PLATFORMFOR OBJECT 3D RECONSTRUCTIONUSING COMPUTER VISION TECHNIQUES

In this paper we describe the development of a Computer Platform, whose goal is to recover the threedimensional (3D) structure of a scene or the shape of an object, using Structure From Motion (SFM) techniques. SFM is an Active Computer Vision technique, which doesn’t need contact or energy projection. The main objective of this project is to recover the 3D shape of an object or scene using the camera(s)’s or object’s movement, without imposing any kind of restrictions to it. Starting with an uncalibrated sequence of images, the referred movement is extracted, as well as the camera(s) parameters, and finally, the 3D geometry of the object or scene is inferred. Shortly, in the first section of this paper the goals are defined; in the second, the computer platform is presented, as well as some experimental results; in the third and last section, the conclusions relative to the study and work done are drawn and, finally, some perspectives of future work are given.
Incremental generative models for syntactic and semantic natural language processing

This thesis investigates the role of linguistically-motivated generative models of syntax and semantic structure in natural language processing (NLP). Syntactic well-formedness is crucial in language generation, but most statistical models do not account for the hierarchical structure of sentences. Many applications exhibiting natural language understanding rely on structured semantic representations to enable querying, inference and reasoning. Yet most semantic parsers produce domain-specific or inadequately expressive representations. We propose a series of generative transition-based models for dependency syntax which can be applied as both parsers and language models while being amenable to supervised or unsupervised learning. Two models are based on Markov assumptions commonly made in NLP: The first is a Bayesian model with hierarchical smoothing, the second is parameterised by feed-forward neural networks. The Bayesian model enables careful analysis of the structure of the conditioning contexts required for generative parsers, but the neural network is more accurate. As a language model the syntactic neural model outperforms both the Bayesian model and n-gram neural networks, pointing to the complementary nature of distributed and structured representations for syntactic prediction. We propose approximate inference methods based on particle filtering. The third model is parameterised by recurrent neural networks (RNNs), dropping the Markov assumptions. Exact inference with dynamic programming is made tractable here by simplifying the structure of the conditioning contexts. We then shift the focus to semantics and propose models for parsing sentences to labelled semantic graphs. We introduce a transition-based parser which incrementally predicts graph nodes (predicates) and edges (arguments). This approach is contrasted against predicting top-down graph traversals. RNNs and pointer networks are key components in approaching graph parsing as an incremental prediction problem. The RNN architecture is augmented to condition the model explicitly on the transition system configuration. We develop a robust parser for Minimal Recursion Semantics, a linguistically-expressive framework for compositional semantics which has previously been parsed only with grammar-based approaches. Our parser is much faster than the grammar-based model, while the same approach improves the accuracy of neural Abstract Meaning Representation parsing.
EOG feature relevance determination for microsleep detection

Abstract Automatic relevance determination (ARD) was applied to two-channel EOG recordings for microsleep event (MSE) recognition. 10 s immediately before MSE and also before counterexamples of fatigued, but attentive driving were analysed. Two type of signal features were extracted: the maximum cross correlation (MaxCC) and logarithmic power spectral densities (PSD) averaged in spectral bands of 0.5 Hz width ranging between 0 and 8 Hz. Generalised learn-ing vector quantisation (GRLVQ) was used as ARD method to show the potential of feature reduction. This is compared to support-vector machines (SVM), in which the feature reduction plays a much smaller role. Cross validation yielded mean normalised relevancies of PSD features in the range of 1.6 – 4.9 % and 1.9 – 10.4 % for horizontal and vertical EOG, respectively. MaxCC relevancies were 0.002 – 0.006 % and 0.002 – 0.06 %, respectively. This shows that PSD features of vertical EOG are indispensable, whereas MaxCC can be neglected. Mean classification accuracies were estimated at 86.6±b 1.3 % and 92.3±b 0.2 % for GRLVQ and SVM, respectively. GRLVQ permits objective feature reduction by inclusion of all processing stages, but is not as accurate as SVM.
Orientation-Aware RFID Tracking with Centimeter-Level Accuracy

RFID tracking attracts a lot of research efforts in recent years. Most of the existing approaches, however, adopt an orientation-oblivious model. When tracking a target whose orientation changes, those approaches suffer from serious accuracy degradation. In order to achieve target tracking with pervasive applicability in various scenarios, we in this paper propose OmniTrack, an orientation-aware RFID tracking approach. Our study discovers the linear relationship between the tag orientation and the phase change of the backscattered signals. Based on this finding, we propose an orientation-aware phase model to explicitly quantify the respective impact of the read-tag distance and the tags orientation. OmniTrack addresses practical challenges in tracking the location and orientation of a mobile tag. Our experimental results demonstrate that OmniTrack achieves centimeterlevel location accuracy and has significant advantages in tracking targets with varing orientations, compared to the state-of-the-art approaches.
Novel coumarin derivatives as potent acetylcholinesterase inhibitors: insight into efficacy, mode and site of inhibition

The inhibitory efficacy of two substituted coumarin derivatives on the activity of neurodegenerative enzyme acetylcholinesterase (AChE) was assessed in aqueous buffer as well as in the presence of human serum albumin (HSA) and compared against standard cholinergic AD drug, Donepezil (DON). The experimental data revealed the inhibition to be of non-competitive type with both the systems showing substantial inhibitory activity on AChE. In fact, one of the tested compounds Chromenyl Coumarate (CC) was found to be better inhibitor (IC50 = 48.49 ± 5.6 nM) than the reference drug DON (IC50 = 74.13 ± 8.3 nM), unequivocally amplifying its importance. The structure of the compound was found to play a vital role in the inhibitory efficiency, validating previous Structure Activity Relationship (SAR) reviews for coumarin. The mechanism of inhibition remained impervious when the experimental medium was switched from aqueous buffer to HSA, albeit noticeable change in the inhibition potency of the compound 3, 3′- Methylene-bis (4-hydroxy coumarin) (MHC) (38%) and CC (35%). Both the coumarin derivatives were observed to bind to the peripheral anionic site (PAS) of AChE and also found to displace the fluorescence marker thioflavinT (ThT) from AChE binding pocket. All experimental observations were seconded by molecular docking and MD simulation results. The inferences drawn in this study form a foundation for further investigation on these compounds; magnifying the probability of their usage as AD drugs and re-emphasizes the significance of drug delivery media while considering the inhibition potencies of targeted drugs.
Appropriate kernels for Divisive Normalization explained by Wilson-Cowan equations

The interaction between wavelet-like sensors in Divisive Normalization is classically described through Gaussian kernels that decay with spatial distance, angular distance and frequency distance. However, simultaneous explanation of (a) distortion perception in natural image databases and (b) contrast perception of artificial stimuli requires very specific modifications in classical Divisive Normalization. First, the wavelet response has to be high-pass filtered before the Gaussian interaction is applied. Then, distinct weights per subband are also required after the Gaussian interaction. In summary, the classical Gaussian kernel has to be left- and right-multiplied by two extra diagonal matrices. In this paper we provide a lower-level justification for this specific empirical modification required in the Gaussian kernel of Divisive Normalization. Here we assume that the psychophysical behavior described by Divisive Normalization comes from neural interactions following the Wilson-Cowan equations. In particular, we identify the Divisive Normalization response with the stationary regime of a Wilson-Cowan model. From this identification we derive an expression for the Divisive Normalization kernel in terms of the interaction kernel of the Wilson-Cowan equations. It turns out that the Wilson-Cowan kernel is left- and-right multiplied by diagonal matrices with high-pass structure. In conclusion, symmetric Gaussian inhibitory relations between wavelet-like sensors wired in the lower-level Wilson-Cowan model lead to the appropriate non-symmetric kernel that has to be empirically included in Divisive Normalization to explain a wider range of phenomena.
Web spam challenge proposal for filtering in archives

In this paper we propose new tasks for a possible future Web Spam Challenge motivated by the needs of the archival community. The Web archival community consists of several relatively small institutions that operate independently and possibly over different top level domains (TLDs). Each of them may have a large set of historic crawls. Efficient filtering would hence require (1) enhanced use of the time series of domain snapshots and (2) collaboration by transferring models across different TLDs. Corresponding Challenge tasks could hence include the distribution of crawl snapshot data for feature generation as well as classification of unlabeled new crawls of the same or even different TLDs.