Miracle-dz/UniFAR-FTUs huggingface dataset:
On the quantitative edge of feature and gestalt -based associative learning : learning-related ERP changes in the hippocampus and the prefontal cortex during a complex auditory discrimination task in rabbit NM-paradigm

In this study, a difficult unimodal sequential discrimination task, which could be solved rather simply by using abstract information, was performed in rabbit nictitating membrane paradigm to test the working hypothesis that animals are capable of using abstract information, at least to some extent. Two tones, A and B, formed four sequential, 500 ms stimulus onset asynchrony pairs with those being dissimilar receiving reinforcement (AB, BA). During the training, electroencephalography was measured via intracranial electrodes placed in the hippocampus and the prefrontal cortex, which both are believed to contribute to the learning of this task. Event-related potentials of the trials were analyzed in terms of learning-related changes and possible behavioural correlates of these changes. Discriminative changes were found only in the prefrontal cortex at the latency of 250 ms after the onset of the second tone. These changes were also behaviourally correlated, even though no discriminative behavioural responses were found in separate behavioural data analysis. Therefore these results, together with previous studies, support the view that rabbits 1) are able to learn a task of this level of difficulty and 2) may use abstract learning strategies. However, further studies with systematic modifications of the experimental procedure are needed before further conclusions. In addition, a new experimental paradigm to study abstract learning is presented and a possible cognitive mechanism for relational representations is proposed.
Parameter Sensitivity Visualization for DTI Fiber Tracking

Fiber tracking of diffusion tensor imaging (DTI) data offers a unique insight into the three-dimensional organisation of white matter structures in the living brain. However, fiber tracking algorithms require a number of user-defined input parameters that strongly affect the output results. Usually the fiber tracking parameters are set once and are then re-used for several patient datasets. However, the stability of the chosen parameters is not evaluated and a small change in the parameter values can give very different results. The user remains completely unaware of such effects. Furthermore, it is difficult to reproduce output results between different users. We propose a visualization tool that allows the user to visually explore how small variations in parameter values affect the output of fiber tracking. With this knowledge the user cannot only assess the stability of commonly used parameter values but also evaluate in a more reliable way the output results between different patients. Existing tools do not provide such information. A small user evaluation of our tool has been done to show the potential of the technique.
Testing the evolution of the DB white dwarf GD 358: first results of a new approach using asteroseismology

Aims. We present a new method that investigates the evolutionary history of the pulsating DB white dwarf GD 358 using asteroseismology. This is done considering the internal C/O profile, which describes the relative abundances of carbon and oxygen from the core of the star to its surface. Different evolutionary channels lead to the generation of different C/O profiles, and these affect the pulsation periods. Methods. We used the C/O profiles associated with white dwarfs that evolved through binary evolution channels where the progenitor experienced one or two episodes of mass loss during one or two common envelope (CE) phases, and two profiles from single-star evolution. We computed models using these different profiles and used a genetic algorithm (GA) to optimize the search in the parameter space for the best fit to the observed pulsation periods. We used three-parameter models, adjusting the stellar mass (M� ), the effective temperature (Teff), and the helium mass of the external layer (MHe). Results. Our results suggest that binary evolution profiles may provide a better match to the pulsation periods of GD 358. The best fit to the observations is obtained using a profile related to an evolutionary history where two episodes of mass loss happen during two CE phases, the first during the RGB (red giant branch) stage. The values obtained are Teff = 24 300 K, M� = 0.585 M� ,a nd log (MHe/M� ) = −5.66. The best-fit model has a mass close to the mean mass for DB white dwarfs found in various works and a temperature consistent with UV spectra obtained with the IUE satellite.
Analyzing Structures in the Semantic Vector Space: A Framework for Decomposing Word Embeddings

Word embeddings are rich word representations, which in combination with deep neural networks, lead to large performance gains for many NLP tasks. However, word embeddings are represented by dense, real-valued vectors and they are therefore not directly interpretable. Thus, computational operations based on them are also not well understood. In this paper, we present an approach for analyzing structures in the semantic vector space to get a better understanding of the underlying semantic encoding principles. We present a framework for decomposing word embeddings into smaller meaningful units which we call sub-vectors. The framework opens up a wide range of possibilities analyzing phenomena in vector space semantics, as well as solving concrete NLP problems: We introduce the category completion task and show that a sub-vector based approach is superior to supervised techniques; We present a sub-vector based method for solving the word analogy task, which substantially outperforms different variants of the traditional vector-offset method.
Appropriate control of wireless networks with flow level dynamics

We consider the network control problem for wireless networks with flow level dynamics under the general k-hop interference model. In particular, we investigate the control problem in low load and high load regimes. In the low load regime, we show that the network can be stabilized by a regulated maximal scheduling policy considering flow level dynamics if the offered load satisfies a constraining bound condition. Because maximal matching is a general scheduling rule whose implementation is not specified, we propose a constant-time and distributed scheduling algorithm for a general k-hop interference model which can approximate the maximal scheduling policy within an arbitrarily small error. Under the stability condition, we show how to calculate transmission rates for different user classes such that the long-term (time average) network utility is maximized. Our results imply that congestion control is unnecessary when the offered load is low and optimal user rates can be determined to maximize users' long-term satisfaction. In the high load regime where the network can be unstable under the regulated maximal scheduling policy, we propose the cross-layer congestion control and scheduling algorithm which can stabilize the network under arbitrary network load. Through numerical analysis for some typical networks, we show that the proposed scheduling algorithm has much lower overhead than other existing queue-length-based constant-time scheduling schemes in the literature, and it achieves performance much better than the guaranteed bound. In addition, using congestion control in the low load condition results in much lower average utility compared to that due to the optimal transmission rate derived in the paper.
Robust Support Vector Machines for Speaker Verification Task

An important step in speaker verification is extracting features that best characterize the speaker voice. This paper investigates a front-end processing that aims at improving the performance of speaker verification based on the SVMs classifier, in text independent mode. This approach combines features based on conventional Mel-cepstral Coefficients (MFCCs) and Line Spectral Frequencies (LSFs) to constitute robust multivariate feature vectors. To reduce the high dimensionality required for training these feature vectors, we use a dimension reduction method called principal component analysis (PCA). In order to evaluate the robustness of these systems, different noisy environments have been used. The obtained results using TIMIT database showed that, using the paradigm that combines these spectral cues leads to a significant improvement in verification accuracy, especially with PCA reduction for low signal-to-noise ratio noisy environment.
Phosphorus dynamics in a tropical forest

Background and aims We hypothesized that successful early ecosystem and soil development in these Pdeficient soil materials will initially depend on effective re-establishment of P storage and cycling through organic matter. This hypothesis was tested in a 26-year chronosequence of seven lightly fertilized, oxidic soil materials restored to eucalypt forest communities after bauxite mining. Methods Total P (Pt) status, Hedley P fractions and partial chemical speciation (NaOH-EDTA extraction and analysed using solution P NMR spectroscopy) were determined in the restored soils. Results Concentrations of Pt and most Hedley fractions changed with restoration period, declined with depth and were strongly positively correlated with C and N concentrations. Biological P dominated the Labile and Intermediate P fractions while Long-term P was dominantly inorganic. Organic P concentrations in NaOH-EDTA extracts and their chemical natures were similar in restored and unburned native forest sites. Phosphomonoesters were the dominant class of organic P. Conclusions Surprisingly rapid P accretion and fractional changes occurred over 26 years, largely in the surface soils and closely associated with organic matter status. Alkaline hydrolysis products of phosphodiesters and pyrophosphate indicated the importance of microbial P cycling. The important consequences for longterm ecosystem development and biological diversity require further study.
The Fuzzy Vault for Fingerprints is Vulnerable to Brute Force Attack

The fuzzy vault approach is one of the best studied and well accepted ideas for binding cryptographic security into biometric authentication. We present in this paper a brute force attack which improves upon the one described by Clancy et. al. [CKL] in an implementation of the vault for fingerprints. On base of this attack, we show that three implementations of the fingerprint vault are vulnerable and show that the vulnerability cannot be avoided by mere parameter selection in the actual frame of the procedure. We also give several suggestions which can improve the fingerprint vault to a cryptographically secure algorithm. In particular, we introduce the idea of fuzzy vault with quiz which draws upon information resources unused by the current version of the vault. This is work in progress, bringing important security improvements and which can be adapted to the other biometric applications of the vault.
Data Balancing Based on Pre-Training Strategy for Liver Segmentation from CT Scans

Data imbalance is often encountered in deep learning process and is harmful to model training. The imbalance of hard and easy samples in training datasets often occurs in the segmentation tasks from Contrast Tomography (CT) scans. However, due to the strong similarity between adjacent slices in volumes and different segmentation tasks (the same slice may be classified as a hard sample in liver segmentation task, but an easy sample in the kidney or spleen segmentation task), it is hard to solve this imbalance of training dataset using traditional methods. In this work, we use a pre-training strategy to distinguish hard and easy samples, and then increase the proportion of hard slices in training dataset, which could mitigate imbalance of hard samples and easy samples in training dataset, and enhance the contribution of hard samples in training process. Our experiments on liver, kidney and spleen segmentation show that increasing the ratio of hard samples in the training dataset could enhance the prediction ability of model by improving its ability to deal with hard samples. The main contribution of this work is the application of pre-training strategy, which enables us to select training samples online according to different tasks and to ease data imbalance in the training dataset.
Gene Diversity of Five Human Immunodeficiency Virus Type 1 Subtypes : Evidence for Naturally Occurring Mutations That Contribute to Drug Resistance , Limited Recombination Patterns , and Common Ancestry for Subtypes B and D

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Effects of oestrogen deficiency on the alveolar bone of rats with experimental periodontitis

Periodontitis is an inflammatory disease characterized by loss of connective tissue and alveolar bone, and osteoporosis is a common disease characterized by a systemic impairment of bone mass and microarchitecture. To date, the association between periodontitis and osteoporosis has remained to be fully elucidated. In the present study, an experimental rat model of periodontitis was used to explore the effects of oestrogen deficiency-induced osteoporosis on the maxillary alveolar bone. Forty-four female, six-month-old Sprague-Dawley rats were randomly divided into four groups: Control, ligature, ovariectomized (OVX), and OVX + ligature. One month after ovariectomy, rats in the ligature and OVX + ligature groups received ligatures on their first and second maxillary molars for 1 month. Fluorescent labelling was performed prior to sacrificing the animals. At the end of the experiment, the maxillae and serum were collected and subjected to micro-computed tomography analysis, confocal laser-scanning microscopic observation, Van Gieson's fuchsin staining, tartrate-resistant acid phosphatase staining and ELISA. Ligatures slightly reduced the alveolar bone mineral density (BMD) and bone formation rate, but significantly reduced alveolar crest height (ACH). Ovariectomy reduced the alveolar BMD, impaired the trabecular structure, reduced the bone formation rate and increased the serum levels of bone resorption markers. Animals in the OVX + ligature group exhibited a lower alveolar BMD, a poorer trabecular structure, a reduced ACH, a lower bone formation rate and higher serum levels of bone resorption markers compared with those in the control group. The results of the present study showed that ovariectomy enhanced alveolar bone loss and reduced the ACH of rats with experimental periodontitis. Thus, post-menopausal osteoporosis may influence the progression of periodontitis.
How Coalitions Enhance Cooperation in the IPD over Complex Networks

Grouping agents into coalitions can be an important way of cooperation through which multi-agent systems(MAS) improve their performance, accomplish their assignments, increase their benefits, and achieve their goals. However, agents belonging to a coalition must decide how they behave to increase their gains. In this paper, we present a model that combines learning and coalition formation to enhance cooperation over the Iterated Prisoner's Dilemma (IPD). We focus on agents interacting over complex networks since they provide a realistic model of the nowadays interconnected world. Our results suggest that coalitions are a relevant contribution to achieve cooperation in the IPD.
Carbonic Anhydrase IX in Early-Stage Non–Small Cell Lung Cancer

Purpose: Tumor hypoxia is associated with poor prognosis and increased tumor aggressiveness. Carbonic anhydrase (CA) IX, an endogenous marker for tumor hypoxia, catalyzes the hydration of carbon dioxide into carbonic acid and contributes to the pH regulation of tumor cells. Therefore, CA IX might allow tumors to acclimate to a hypoxic microenvironment, promoting tumor cell proliferation. We hypothesized that CA IX expression is related to tumor cell proliferation and poor disease-free survival in patients with early-stage non–small-cell lung cancer (NSCLC). Experimental Design: CA IX expression was measured in 75 resected NSCLC tumors to assess prognostic implications for disease-free survival. The relationship of CA IX expression with microvessel density (MVD) and proliferation (Ki-67) index was assessed via colocalization analysis. Results: All patients had operable NSCLC (stage I, 58; stage II, 17). CA IX expression was present in 54 (72%) of 75 patients and was associated with tumor necrosis (P < 0.05). CA IX-positive tumor areas showed greater cell proliferation as measured by Ki-67 index (P < 0.05) and less MVD (P < 0.05) than did CA IX-negative areas in colocalization analysis. The percentage of CA IX-positive tumor cells was significantly related to postoperative recurrence and poor disease-free survival (P < 0.05). Ki-67 index and pathologic stage were also independent prognostic factors for worse disease-free survival (P < 0.05). Conclusions: CA IX expression of tumor cells may be an indicator for poor disease-free survival in early-stage NSCLC.
Performance analysis of energy aware routing schemes under various mobility models

In Mobile Ad hoc Networks (MANETs) nodes are free to move arbitrarily with different speeds, which makes the network topology change randomly and unpredictably. The mobility models give the movement pattern of the nodes w.r. t. their direction, location and speed over time. Some of these models depict real life scenarios. This paper analyses the energy consumption of our previous proposed energy efficient algorithms viz. energy aware AODV (EA-AODV) and Optimized Energy Aware Routing (OEAR) scheme, under various mobility models. The mobility models considered in the paper are random walk, random waypoint, column based, reference point group mobility model. Simulation results obtained in NS2.34 show that OEAR has better energy consumption.
A Behavior Based Approach to Humanoid Robot Manipulation

Current approaches to humanoid robot manipulation typically rely upon detailed models of the manipulator and the object being manipulated. For robots to gain general utility in areas such as space exploration, small-parts assembly, agriculture, end even in our homes, they must be able to intelligently manipulate unknown objects in unstructured environments. A behavior based approach to manipulation can provide the tight visual and sensorimotor coupling to the world that is required during engagements in unknown and unstructured environments. To this end, we are developing an approach to humanoid robot manipulation which integrates force sensing and compliant manipulators into a behavior based control decomposition. In this paper we demonstrate work in these areas as applied to a new 29 degree-of-freedom force controlled humanoid robot named Domo.
Lmo Mutants Reveal a Novel Role for Circadian Pacemaker Neurons in Cocaine-Induced Behaviors

Drosophila has been developed recently as a model system to investigate the molecular and neural mechanisms underlying responses to drugs of abuse. Genetic screens for mutants with altered drug-induced behaviors thus provide an unbiased approach to define novel molecules involved in the process. We identified mutations in the Drosophila LIM-only (LMO) gene, encoding a regulator of LIM-homeodomain proteins, in a genetic screen for mutants with altered cocaine sensitivity. Reduced Lmo function increases behavioral responses to cocaine, while Lmo overexpression causes the opposite effect, reduced cocaine responsiveness. Expression of Lmo in the principal Drosophila circadian pacemaker cells, the PDF-expressing ventral lateral neurons (LNvs), is sufficient to confer normal cocaine sensitivity. Consistent with a role for Lmo in LNv function, Lmo mutants also show defects in circadian rhythms of behavior. However, the role for LNvs in modulating cocaine responses is separable from their role as pacemaker neurons: ablation or functional silencing of the LNvs reduces cocaine sensitivity, while loss of the principal circadian neurotransmitter PDF has no effect. Together, these results reveal a novel role for Lmo in modulating acute cocaine sensitivity and circadian locomotor rhythmicity, and add to growing evidence that these behaviors are regulated by shared molecular mechanisms. The finding that the degree of cocaine responsiveness is controlled by the Drosophila pacemaker neurons provides a neuroanatomical basis for this overlap. We propose that Lmo controls the responsiveness of LNvs to cocaine, which in turn regulate the flies' behavioral sensitivity to the drug.
Building Shared Mental Models between Humans and AI for Effective Collaboration

Intelligent systems have become increasingly common in settings ranging from performing everyday tasks more easily to decision-making for complex domains (e.g., healthcare, autonomous driving, criminal justice). Given this rising ubiquity of artificial intelligence (AI), both researchers and industry practitioners are exploring ways to better integrate AI agents in tasks that people do at home or work. However, these systems are currently limited because of gaps in the understanding between humans and their AI counterparts. In this paper, we propose methods for building shared mental models between humans and AI to enable human-AI collaboration at a level where both can be equal partners working on a shared task. We ground our approach in existing literature from CSCW and UX design.
A Class of Stochastic Variance Reduced Methods with an Adaptive Stepsize

Stochastic variance reduced methods have recently surged into prominence for solving large scale optimization problems in the context of machine learning. Tan, Ma and Dai et al. first proposed the new stochastic variance reduced gradient (SVRG) method with the Barzilai-Borwein (BB) method to compute step sizes automatically, which performs well in practice. On this basis, we propose a class of stochastic variance reduced methods with an adaptive stepsize which is based on local estimation of Lipschitz constant. Specifically, we adapt this stepsize to SVRG and stochastic recursive gradient algorithm (SARAH), which leads to two algorithms: SVRG-AS and SARAH-AS. We prove that both SVRG-AS and SARAH-AS converge linearly for strongly convex objective function. Numerical experiments on standard datasets indicate that our algorithms are effective and robust. The performance of SVRG-AS is better than SVRG-BB, and SARAH-AS is comparable to SARAH with best-tuned stepsizes. And our proposed stepsize is suitable for some other stochastic variance reduced methods. This work was supported by the National Natural Science Foundation of China (11731013,11571014,11331012) Corresponding author:Congying Han School of Mathematical Sciences, Key Laboratory of Big Data Mining and Knowledge Management, UCAS, Beijing, 100049, China Tel. : +86-010-88256908 E-mail: hancy@ucas.ac. cn Yan Liu School of Mathematical Sciences, University of Chinese Academy of Sciences (UCAS), Beijing, 100049, China E-mail: liuyan23ucas@outlook.com Tiande Guo School of Mathematical Sciences, Key Laboratory of Big Data Mining and Knowledge Management, UCAS, Beijing, 100049, China E-mail: tdguo@ucas.ac. cn