Miracle-dz/UniFAR-FTUs huggingface dataset:
Appetitive and Aversive Information Coding in the Primate Dorsal Raphé Nucleus

Serotonin is known to play a key role in the regulation of emotional behavior. There have been conflicting hypotheses about whether the central serotonergic system is involved in positive or negative emotional information processing. To reveal whether and how such opposing information processing can be achieved by single neurons in the dorsal raphé nucleus (DRN), the major source of serotonin in the forebrain, we recorded the activity of DRN neurons while monkeys were conditioned in a Pavlovian procedure with two distinct contexts: an appetitive block where a reward was available; and an aversive one where an airpuff was delivered. We found that single DRN neurons were involved in several aspects of both appetitive and aversive information processing. First, more than half of the recorded DRN neurons discriminated between appetitive and aversive contexts by tonic changes in their activity. In the appetitive context, they then kept track of the expected reward value indicated by the conditioned stimuli. Some of them also encoded an error between the obtained and expected values. In the aversive context, the same neurons maintained tonic modulation in their activity throughout the block. However, modulation of their responses to aversive task events depending on airpuff probability was less common. Together, these results indicate that single DRN neurons encode both appetitive and aversive information, but over differing time scales: relatively shorter for appetitive, and longer for aversive. Such temporally distinct processes of value coding in the DRN may provide the neural basis of emotional information processing in different contexts.
Dopamine, Corticostriatal Connectivity, and Intertemporal Choice

Value-based decisions optimize behavioral outcomes. Because delayed rewards are discounted, an increased tendency to choose smaller, immediate rewards can lead to suboptimal choice. Steep discounting of delayed rewards (impulsivity) characterizes subjects with frontal lobe damage and behavioral disorders including substance abuse. Correspondingly, animal studies and indirect evidence in humans suggest that lower dopamine in the frontal cortex contributes to steeper discounting by impairing corticostriatal function. To test this hypothesis directly, we performed a randomized, double-blind, counterbalanced, placebo-controlled study in which we administered the brain penetrant catechol-O-methyltransferase inhibitor tolcapone or placebo to healthy subjects performing a delay discounting task. Tolcapone significantly increased choice of delayed monetary rewards, and this tolcapone-induced increase covaried with increased BOLD activity in the left ventral putamen and anterior insula. Tolcapone also changed corticostriatal connectivity: specifically, by inducing a decrease in the coherence between ventral putamen and pregenual cingulate cortex. These results indicate that raising cortical dopamine levels attenuates impulsive choice by changing corticostriatal function.
A Directional Antenna Medium Access Control Protocol for Wireless Ad Hoc Networks

In this paper we propose a Directional Antenna Medium Access (DAMA) protocol that takes advantage of the benefits offered by directional antennas and is best suited for sparsely distributed networks. A large portion of DAMA has been inspired by the IEEE 802.11 MAC, with major enhancements including a new neighbor discovery scheme, and an optimized circular directional transmission of RTS and CTS to prevent the hidden node problem, reduce collisions and decrease node deafness. In addition, a pair of communicating nodes using DAMA simultaneously transmits the circular directional RTS and CTS only to those sectors with neighbors, hence reducing overall communication delay and enhancing throughput. We have compared DAMA with IEEE 802.11 and two recently proposed directional MAC protocols, and results show that DAMA performs better than these protocols in the majority of the scenarios investigated while we point out that the performance depends on the network topology and traffic pattern.
Connectionist Classifier System Based on Accuracy in Autonomous Agent Control

In this paper a new connectionist classifier system based on accuracy is proposed, which uses a layer of competitive artificial neurons for decision-making. New algorithms of this system learning in multi-step problems are developed, where the correct result becomes known only at a certain system’s step. The connectionist system is employed to control an autonomous agent in discrete environments.
European Perspectives:

Urban transport systems are critical elements of the urban fabric. They ensure that people have access to goods, services, employment and recreation opportunities, that freight circulates efficiently and they enable local economies to flourish. However, if the high density of buildings is the first defining characteristic of towns and cities, then high volumes of traffic is now the second. Traffic has significant impacts on the environment and on the health of urban citizens, as well as on the overall quality of life in towns. Rising congestion levels are hampering mobility, with increasing costs for the economy (0.5% of Community GDP for road traffic congestion, rising to 1% by 2010).
Classification of normal and abnormal lung sounds using neural network and support vector machines

This work proposes feature extraction of lung sounds using wavelet coefficients and their classification by neural network and support vector machines. The lung sounds were classified into 6 classes. The results stated the advantages of a support vector machines for the classification of normal and abnormal lung sounds, and indicated that SVMs are a highly successful classifier with accuracy about 93.51-100 for classification of lung sounds.
Memory corruption vulnerabilities detection for Android binary software

The vulnerabilities exist in Android binary software bring critical threat to Mobile Internet security. In this paper, we put forward a novel method to detect memory corruption vulnerabilities for Android binary software which builds upon memory accession security rules and selective symbolic execution. We also implemented our prototype system and the evaluation results show that our method can detect the memory corruption vulnerabilities in Android binary software effectively and accurately.
JumpCut

We introduce JumpCut, a new mask transfer and interpolation method for interactive video cutout. Given a source frame for which a foreground mask is already available, we compute an estimate of the foreground mask at another, typically non-successive, target frame. Observing that the background and foreground regions typically exhibit different motions, we leverage these differences by computing two separate nearest-neighbor fields (split-NNF) from the target to the source frame. These NNFs are then used to jointly predict a coherent labeling of the pixels in the target frame. The same split-NNF is also used to aid a novel edge classifier in detecting silhouette edges (S-edges) that separate the foreground from the background. A modified level set method is then applied to produce a clean mask, based on the pixel labels and the S-edges computed by the previous two steps. The resulting mask transfer method may also be used for coherently interpolating the foreground masks between two distant source frames. Our results demonstrate that the proposed method is significantly more accurate than the existing state-of-the-art on a wide variety of video sequences. Thus, it reduces the required amount of user effort, and provides a basis for an effective interactive video object cutout tool.
Lightweight verification of control flow policies on Java bytecode

This paper presents the enforcement of control flow policies for Java bytecode devoted to open and constrained devices. On-device enforcement of security policies mostly relies on run-time monitoring or inline checking code, which is not appropriate for strongly constrained devices such as mobile phones and smart-cards. We present a proof-carrying code approach with on-device lightweight verification of control flow policies statically at loading- time. Our approach is suitable for evolving, open and constrained Java-based systems as it is compositional, to avoid re-verification of already verified bytecode upon loading of new bytecode, and it is regressive, to cleanly support bytecode unloading.
Opinion Mining and Analysis: A survey

The current research is focusing on the area of Opinion Mining also called as sentiment analysis due to sheer volume of opinion rich web resources such as discussion forums, review sites and blogs are available in digital form. One important problem in sentiment analysis of product reviews is to produce summary of opinions based on product features. We have surveyed and analyzed in this paper, various techniques that have been developed for the key tasks of opinion mining. We have provided an overall picture of what is involved in developing a software system for opinion mining on the basis of our survey and analysis.
Automatic classiﬁcation of endothelial and ﬁbroblast cell culture images

In the paper we searched for the subset of features suitable for the automated classiﬁcation of endothelial and ﬁbroblast cell culture images. The overall image classiﬁcation is composed of the two main steps. First, each of the cell culture is classiﬁed by its conﬂuence character. Secondly, conﬂuent endothelial cell cultures and ﬁbroblast cell cultures are separated from each other. Classiﬁcation due to the conﬂu-ence character is performed through edge detection, statistical moments and Haralick texture coeﬃcients. The method based on the Haralick texture coeﬃcients delivered the best results. The separation between the cell types is performed through Fourier descriptors, Gabor ﬁltering and through the so-called multiresolution segmentation method. The latter method delivered the highest separability value evaluated with the Fisher’s discriminant criterion.
From leeches to personalized medicine: evolving concepts in the management of polycythemia vera

Polycythemia vera is a clonal disorder of hematopoietic stem/progenitor cells. It manifests as an expansion of red cell mass. It is the most common chronic myeloproliferative neoplasm. In virtually all cases, it is characterized by a V617F point mutation in JAK2 exon 14 or less common mutations in exon 12. The landmark discovery of the autonomously activated JAK/STAT signaling pathway paved the way for the clinical development of the first target drug, the JAK1 and JAK2 inhibitor ruxolitinib. This is now approved for patients with resistance or intolerance to hydroxyurea. Phlebotomies and hydroxyurea are still the cornerstone of treatment, and aim to prevent the first appearance or recurrence of cardiovascular events that, together with progression to post-polycythemia vera myelofibrosis and leukemia, represent the main causes of death. Interferon-α is an alternative drug and has been shown to induce molecular remissions. It is currently undergoing phase III trials that might eventually lead to its approval for clinical use. The last few years have witnessed important advances towards an accurate early diagnosis of polycythemia vera, greater understanding of its pathogenesis, and improved patient management. This review will focus on the most recent achievements and will aim to unify the different concepts involved in a personalized approach to the patient with polycythemia vera. In spite of many recent advances in the understanding of its pathogenesis and improved disease management, polycythemia vera remains a life-threatening myeloproliferative neoplasm for which there is no cure. This review will present a critical overview of evolving concepts in diagnosis and treatment of this disease.
Natural Language Specification Improvement with Ontologies

Requirements engineering can solve and cause many problems in a software development cycle. The difficulties in understanding and eliciting the desired functionality from the customer and then delivering the correct implementation of a software system lead to delays, mistakes, and high costs. Working with requirements means handling incomplete or faulty textual specifications. It is vital for a project to fully understand the purpose and the functionality of a software. The earlier specifications are corrected and improved, the better. We created a tool called RESI to support requirement analysts working with textual specifications. RESI checks for linguistic defects [1, 2] in specifications and offers a dialog-system which makes suggestions to improve the text. It points out to the user which parts of the specification are ambiguous, faulty or inaccurate and therefore need to be changed. For this task, RESI needs additional semantic information which is inherent to natural language specifications. It receives this information by utilizing ontologies.
Modeling and control of VTOL UAVs interacting with the environment

In this paper we focus on the problem of modeling and controlling a certain configuration of UAV (Unmanned Aerial Vehicle) considering explicitly the interaction with the environment. This innovative problem is particularly interesting in order to employ unmanned aircrafts in tasks and operations which may require explicitly or implicitly contacts between the UAV itself and the environment such as manipulation of remote objects or indoor flight. For a class of VTOL (Vertical Take-Off and Landing) aircrafts we start studying the problem of safe take-off from hostile terrains and the control of the vehicle when in contact with vertical fixed surfaces.
Special Techniques for Constituent Parsing of Morphologically Rich Languages

We introduce three techniques for improving constituent parsing for morphologically rich languages. We propose a novel approach to automatically find an optimal preterminal set by clustering morphological feature values and we conduct experiments with enhanced lexical models and feature engineering for rerankers. These techniques are specially designed for morphologically rich languages (but they are language-agnostic). We report empirical results on the treebanks of five morphologically rich languages and show a considerable improvement in accuracy and in parsing speed as well.
Probabilistic trajectory prediction with Gaussian mixture models

In the context of driver assistance, an accurate and reliable prediction of the vehicle's trajectory is beneficial. This can be useful either to increase the flexibility of comfort systems or, in the more interesting case, to detect potentially dangerous situations as early as possible. In this contribution, a novel approach for trajectory prediction is proposed which has the capability to predict the vehicle's trajectory several seconds in advance, the so called long-term prediction. To achieve this, previously observed motion patterns are used to infer a joint probability distribution as motion model. Using this distribution, a trajectory can be predicted by calculating the probability for the future motion, conditioned on the current observed history motion pattern. The advantage of the probabilistic modeling is that the result is not only a prediction, but rather a whole distribution over the future trajectories and a specific prediction can be made by the evaluation of the statistical properties, e.g. the mean of this conditioned distribution. Additionally, an evaluation of the variance can be used to examine the reliability of the prediction.
Steady-State Oscillations in Resonant Electrostatic Vibration Energy Harvesters

In this paper, we present a formal analysis and description of the steady-state behavior of an electrostatic vibration energy harvester operating in constant-charge mode and using different types of electromechanical transducers. The method predicts parameter values required to start oscillations, allows a study of the dynamics of the transient process, and provides a rigorous description of the system, necessary for further investigation of the related nonlinear phenomena and for the optimisation of converted power. We show how the system can be presented as a nonlinear oscillator and be analysed by the multiple scales method, a type of perturbation technique. We analyse two the most common cases of the transducer geometry and find the amplitude and the phase of steady-state oscillations as functions of parameters. The analytical predictions are shown to be in good agreement with the results obtained by behavioral modeling.
Geometrical considerations and nomenclature for reflectance

Report issued by the U.S. National Bureau of Standards discussing specifications of reflectance and proposed nomenclature. As stated in the introduction, "this monograph presents a unified approach to the specification of reflectance in relation to the beam geometry of both the incident and the reflected flux in any reflectometer or in any application of measured reflectance data" (p. 1). This report includes illustrations.
A Real-Time Network Traffic Anomaly Detection System Based on Storm

In recent years, with more and more people shopping, chatting and video online, the Internet is playing a more and more important role in human's daily life. Since the Internet is so close to our lives, it contains so much personal information that will cause a lot of troubles or even losses when divulged. So it's necessary and urgent to find a efficient way to detect the abnormal network behavior. In this paper, we present a new detection method based on compound session. In contrast to previous methods, our approach is based on the cloud computing platform and the cluster system, using Hadoop Distributed File System (HDFS) to analysis and using Twitter Storm to make real-time network anomaly detection come true.
The Research and Application about the Information Extraction in Chinese Domain

A specific prototype information service system was proposed by this paper, which can send interesting information to user with database search way from unstructured text. In order to achieve this goal, two fundamental issues were studied by using maximum entropy (ME) algorithm, which is named entity recognition and relation extraction. Our named entity recognition approach is distinguished from most of the previous approaches. Where, probabilistic feature functions are used instead of binary feature functions, it is one of the several differences between this model and the most of the previous ME based model. We also explore several new features in our model, which includes confidence functions, position of features etc. Like those in some previous works, we use sub-models to model Chinese person names, foreign names respectively, but we bring some new techniques in these sub-models. The experimental result is promising. Moreover, ME algorithm is the first time to be used to extract relations between entities from Chinese texts. Twelve features have been designed, which includes morphology, grammar and semantic feature. The experimental result is satisfied. Therefore, two research results were used into my information extraction system, the goal of information service came from unstructured text is achieved
Optimal transmission range and node degree for multi-hop routing in wireless sensor networks

The energy consumed by radio transceivers is a significant factor limiting the lifetime of wireless sensor networks (WSNs). Maximizing WSN lifetime is the overall aim of this work, which proposes that optimal routing be defined with a network design that minimizes the energy consumed by transceivers. This definition of optimal routing is greatly influenced by transmission range, which is adjustable and therefore a candidate predictor or independent variable that affects the criterion, which is the need to minimize energy consumption and thereby maximize lifetime. The effects of transmission range depend on the physical dimensions and node density of the WSN being observed. This dependency binds the result to a particular design and therefore, to be more general, it is noted that transmission range directly controls neighborhood size - also known as node degree - which forms a more generic predictor of optimal routing as defined in the empirical experiments of this work. Increasing node degree saves energy by reducing hop-count. This benefit, however, is vitiated in a complicated tradeoff that is understood through a broad view of the routing behavior observed at multiple network layers. A final investigation into the primary causes of wasted energy identifies future work that will enable WSNs to reap the energy savings made possible by the improved transmission ranges available with new transceivers.
The 3D positioning of ANT2 and ANT3 genes within female X chromosome territories correlates with gene activity.

The three-dimensional positioning of the X-chromosomal adenine nucleotide translocase genes, ANT2 and ANT3, were compared in the active and inactive X chromosome territories (Xa and Xi) of female human amniotic fluid cell nuclei. ANT2 is located in Xq24-q25 and is transcriptionally active on Xa, but inactive on Xi. ANT3 is located in the pseudoautosomal region Xp22.3 and escapes X-inactivation. Three-color fluorescence in situ hybridization, confocal laser scanning microscopy, and three-dimensional image analysis revealed that transcriptionally active ANT2 and ANT3 genes were positioned more peripheral within their chromosome territory than the inactive ANT2 gene. The position of the latter was significantly more interior in the Xi territory. Although the volumes of both X territories were similar, 3D distances between ANT2 and ANT3 were significantly smaller in Xi compared to Xa territories reflecting different territory shapes. Our data show a correlation between 3D positioning and transcriptional activity of these X-specific genes.
Multiple Equilibria in a Growth Model with Habit Persistence

This paper uses an otherwise standard, competitive growth model without externality and distortions to establish multiple balanced growth paths. Our model is based on the standard one-sector, endogenous growth model of Romer (1986), with a twist that households’ preference depends partly upon how his/her consumption compares to a habit stock formed by his/her own past consumption. This model establishes multiple equilibria because habit persistence in preference induces an intertemporal complementarity effect among consumption flows, with current consumption reinforcing future consumption. As a result, there exist two balanced-growth paths, with one path exhibiting low consumption and habits and high economic growth, and the other exhibiting high consumption and habits and low growth, and thus a development trap. Both steady states are saddle points, but an initial condition cannot pin down the steady state to which an economy converges. Both steady states cannot be pareto-ranked because of no market failure.
Context-aware taxi demand hotspots prediction

In an urban area, the demand for taxis is not always matched up with the supply. This paper proposes mining historical data to predict demand distributions with respect to contexts of time, weather, and taxi location. The four-step process consists of data filtering, clustering, semantic annotation, and hotness calculation. The results of three clustering algorithms are compared and demonstrated in a web mash-up application to show that context-aware demand prediction can help improve the management of taxi fleets.