Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast dual minimization of the vectorial total variation norm and applications to color image processing

We propose a regularization algorithm for color/vectorial images which is fast, easy to code and mathematically well-posed. More precisely, the regularization model is based on the dual formulation of the vectorial Total Variation (VTV) norm and it may be regarded as the vectorial extension of the dual approach defined by Chambolle in [13] for gray-scale/scalar images. The proposed model offers several advantages. First, it minimizes the exact VTV norm whereas standard approaches use a regularized norm. Then, the numerical scheme of minimization is straightforward to implement and finally, the number of iterations to reach the solution is low, which gives a fast regularization algorithm. Finally, and maybe more importantly, the proposed VTV minimization scheme can be easily extended to many standard applications. We apply this $L^1$ vectorial regularization algorithm to the following problems: color inverse scale space, color denoising with the chromaticity-brightness color representation, color image inpainting, color wavelet shrinkage, color image decomposition, color image deblurring, and color denoising on manifolds. Generally speaking, this VTV minimization scheme can be used in problems that required vector field (color, other feature vector) regularization while preserving discontinuities.
Characterizing vulnerability to IP hijack attempts

The Border Gateway Protocol (BGP) is critical to the global internet infrastructure. Unfortunately BGP routing was designed with limited regard for security. As a result, IP route hijacking has been observed for more than 15 years. Well known incidents include a 2008 hijack of YouTube, loss of connectivity for Australia in 2012, and an event that partially crippled Google in November 2012. The extent of an IP Hijack can be localized to a small region or can propagate to become a world-wide event. Concern has been escalating as critical national infrastructure is reliant on a secure foundation for the internet. Disruptions to military, banking, utilities, industry, and commerce can be catastrophic. The paper addresses the question of why some autonomous systems are more prone to attack than others. We present a series of findings related to the manner in which IP hijacks propagate through the global routing infrastructure. We examine why some attacks extend widely while others remain isolated and explore topological patterns that enhance or inhibit attacks.
How molecular imaging is speeding up antiangiogenic drug development

Drug development is a long process that generally spans about 10 to 15 years. The shift in recent drug discovery to novel agents against specific molecular targets highlights the need for more robust molecular imaging platforms. Using molecular probes, molecular imaging can aid in many steps of the drug development process, such as providing whole body readout in an intact system, decreasing the workload and speeding up drug development/validation, and facilitating individualized anticancer treatment monitoring and dose optimization. The main focus of this review is the recent advances in tumor angiogenesis imaging, and the targets include vascular endothelial growth factor and vascular endothelial growth factor receptor, integrin αvβ3, matrix metalloproteinase, endoglin (CD105), and E-selectin. Through tumor angiogenesis imaging, it is expected that a robust platform for understanding the mechanisms of tumor angiogenesis and evaluating the efficacy of novel antiangiogenic therapies will be developed, which can help antiangiogenic drug development in both the preclinical stage and the clinical settings. Molecular imaging has enormous potential in improving the efficiency of the drug development process, including the specific area of antiangiogenic drugs. [Mol Cancer Ther 2006;5(11):2624–33]
12.7 A 0.85V 600nW all-CMOS temperature sensor with an inaccuracy of ±0.4°C (3σ) from −40 to 125°C

This paper describes an all-CMOS temperature sensor intended for RFID applications that achieves both sub-1V operation and high accuracy (±0.4°C) over a wide temperature range (-40 to 125°C). It is also an ultra-low-power design: drawing 700nA from a 0.85V supply. This is achieved by the use of dynamic threshold MOSTs (DTMOSTs) as temperature-sensing devices, which are then read out by an inverter-based 2nd-order zoom ADC. Circuit errors are mitigated by the use of dynamic error-correction techniques, while DTMOST spread is reduced by a single room temperature (RT) trim. The latter feature constitutes a significant advance over previous all-CMOS designs [5,6], which require two-point trimming to approach the same level of accuracy.
Biphasic changes in locomotor behavior and in expression of mRNA for NGFI-A and NGFI-B in rat striatum following acute caffeine administration

The time course of expression of mRNA for NGFI-A and NGFI-B after a single intraperitoneal injection of saline or caffeine was examined using in situ hybridization. Administration of a high dose of caffeine (100 mg/kg) decreased locomotor behavior and increased NGFI-A and NGFI- B mRNA in the entire striatum. A lower dose of caffeine (50 mg/kg) caused a weak enhancement of both messages, which was confined to the lateral part of caudate-putamen. This dose increased horizontal, but not vertical, movement. In rats treated with the lowest dose of caffeine (25 mg/kg), the expression of both investigated genes tended to be lower than for saline-treated rats and both horizontal and vertical locomotor activity increased markedly. The reduction in the number of labeled neurons seemed to occur predominantly in enkephalin- containing neurons, which coexpress adenosine A2A receptors and dopamine D2 receptors. The decrease of mRNA for NGFI-A, NGFI-B, and jun B caused by caffeine (25 mg/kg) could be mimicked by the D2 agonist quinpirole (1 and 3 mg/kg). Moreover, caffeine could significantly decrease the expression seen following treatment with the D2 antagonist raclopride (2 mg/kg). In addition, in the parietal cortex, 25 mg/kg of caffeine caused a significant elevation of both examined immediate early genes. Thus, biphasic changes in locomotion induced by caffeine are paralleled by biphasic changes in mRNA for NGFI-A, NGFI-B, and jun B. The results also provide additional support for a functionally important interaction between adenosine and dopamine D2 receptors.
GSM Based Water level and Temperature Monitoring System

The system we have proposed is an extended approach to monitor a control industrial system. We can monitor the industrial system from any location, due to this it will save lots of time in this busy era. In this project, we have designed GSM based water level and temperature monitoring system (WLTMS). We detect the water level of the tank which is connected to the industry. We also monitor the temperature of the tank. For this purpose, we used LM35 sensor which defines the parameters of the temperature sensor. Analogue output of LM35 is amplified through a process of signal conditioning, where OP-741 is used to amplify the signal. Amplified signal is fed into an ADC for the sake of digital data. This digital data is transferred to an LCD for displaying result. PIC microcontroller is used for this procedure. Modem is also connected to this controller for the wireless communication of the data through GSM technology by receiving an alert through SMS.
A new clustering approach for group detection in scene-independent dense crowds

Despite significant progress in crowd behaviour analysis over the past few years, most of today's state of the art algorithms focus on analysing individual behaviour in a specific-scene. Recently, the widespread availability of cameras and a growing need for public safety have shifted the attention of researchers in video surveillance from individual behavior analysis to group and crowd behavior analysis. However, dangerous and illegal behaviours are mostly occurred from groups of people. Group detection is the main process to separate people in crowded scene into different group based on their interactions. Results of group detection can further to apply in analyze group and crowd behaviour. This paper present a study of the group detection and propose a novel approach for clustering group of people in different crowded scenes based on trajectories. For the clustering of group of people we propose novel formula to compute the weights based on the distance, the occurrence, and the speed correlations of two people in a tracklet cluster to infer the people relationship in a tracklet clusters with Expectation Maximization (EM) in order to overcome occlusion in crowded scenes.
Quantum codes from concatenated algebraic-geometric codes

We apply Steane's enlargement of the Calderbank-Shor-Steane (CSS) codes and additive codes over F/sub 4/ to concatenated algebraic-geometric codes to construct many good quantum codes with fewer restrictions on the parameters compared to some known quantum codes. Some of the quantum codes we have constructed are either optimal or have parameters as good as the best known codes, while some have parameters better than those obtained from other known constructions.
Classical multilevel and Bayesian approaches to population size estimation using multiple lists

One of the major objections to the standard multiple‐recapture approach to population estimation is the assumption of homogeneity of individual ‘capture’ probabilities. Modelling individual capture heterogeneity is complicated by the fact that it shows up as a restricted form of interaction among lists in the contingency table cross‐classifying list memberships for all individuals. Traditional log‐linear modelling approaches to capture–recapture problems are well suited to modelling interactions among lists but ignore the special dependence structure that individual heterogeneity induces. A random‐effects approach, based on the Rasch model from educational testing and introduced in this context by Darroch and co‐workers and Agresti, provides one way to introduce the dependence resulting from heterogeneity into the log‐linear model; however, previous efforts to combine the Rasch‐like heterogeneity terms additively with the usual log‐linear interaction terms suggest that a more flexible approach is required. In this paper we consider both classical multilevel approaches and fully Bayesian hierarchical approaches to modelling individual heterogeneity and list interactions. Our framework encompasses both the traditional log‐linear approach and various elements from the full Rasch model. We compare these approaches on two examples, the first arising from an epidemiological study of a population of diabetics in Italy, and the second a study intended to assess the ‘size’ of the World Wide Web. We also explore extensions allowing for interactions between the Rasch and log‐linear portions of the models in both the classical and the Bayesian contexts.
The Tower of Hanoi for Evaluating Dysexecutive Syndrome in Patients with Parkinson’s: Standardization Values

Objective: The Tower of Hanoi measures executive functions using non-verbal content and requires perception of position in space. The main objective of this study is to standardize the use of the TOH as a measurement tool in Parkinson’s disease. Patients and Methods: Of the Control Group subjects, 192 (59.6%) were women, 223 subjects (69.25%) were able to perform the TOH with 3 discs. In the Parkinson’s Group, there were 57 women (39.3%), and 66 subjects (45.5%) did not get past that level. Results: If we take the TOH (with 3 or 4 discs) as a tool for discriminating between those who have dysexecutive syndrome and those who do not, we find that the Parkinson’s Group presents dysexecutive syndrome significantly more frequently than the Control Group (p ≤ 0.0064). Conclusion: We can conclude that dysexecutive syndrome is frequent in Parkinson’s patients and it is more prevalent than in the general population.
Robot Programming by Demonstration with situated spatial language understanding

Robot Programming by Demonstration (PbD) allows users to program a robot by demonstrating the desired behavior. Providing these demonstrations typically involves moving the robot through a sequence of states, often by physically manipulating it. This requires users to be co-located with the robot and have the physical ability to manipulate it. In this paper, we present a natural language based interface for PbD that removes these requirements and enables hands-free programming. We focus on programming object manipulation actions-our key insight is that such actions can be decomposed into known types of manipulator movements that are naturally described using spatial language; e.g., object reference expressions and prepositions. Our method takes a natural language command and the current world state to infer the intended movement command and its parametrization. We implement this method on a two-armed mobile manipulator and demonstrate the different types of manipulation actions that can be programmed with it. We compare it to a kinesthetic PbD interface and we demonstrate our method's ability to deal with incomplete language.
MOODY : A WEB-BASED MUSIC MOOD CLASSIFICATION AND RECOMMENDATION SYSTEM

Research in music psychology has disclosed that the affective aspects of music are important in defining “being musically similar” [1]. Moody 1 is a Web-based system that classifies and recommends songs according to the mood they express. Moody classifies songs into five major mood categories (passionate, cheerful, bittersweet, silly/quirky and aggressive) [3], and builds a mood profile for each song. When a user searches a song, Moody recommends songs with similar mood profiles to the searched song. The first version of Moody uses a SVM (SMO) classifier [4] built on spectral features extracted from the audio files of the songs [2]. Currently, two new Naive Bayes classifiers built on text features are added into Moody, including song lyrics and Web tags. The three classifiers will be combined to give the final classification results. Figure 1 shows a snapshot of the Moody interface. Besides song recommendation, Moody also helps users classify their own music in real time when users upload mp3 files 2 (Figure 2). Thus it can grow automatically and quickly by adding user uploaded songs. Another nice feature of Moody is to allow users to change songs’ mood labels (Figure 1). By aggregating users' feedbacks, Moody can continuously improve its classification accuracy.
Curse and Blessing of Uncertainty in Evolutionary Algorithm Using Approximation

Evolutionary frameworks that employ approximation models or surrogates for solving optimization problems with computationally expensive fitness functions may be referred as surrogate-assisted evolutionary algorithms (SAEA). In this paper, we present a study on the effects of uncertainty in the surrogate on SAEA. In particular, we focus on both the 'curse of uncertainty' and 'blessing of uncertainty' on evolutionary search, a notion borrowed from 'curse and blessing of dimensionality' in the work by Donoho (2000). Here, the 'curse of uncertainty' refers to impairments due to the errors in the approximation. The 'blessing of uncertainty' is less explicitly discussed in the literature, but refers to the benefits of approximation errors on evolutionary search. Empirical studies suggest that approximation errors lead to convergence at false global optima, but prove to be beneficial in some cases.
Pilots' eye movement patterns during performing air-to-air mission

Eye movement patterns are linked closely with the cognitive process of encoding information that pilots seek and perceive in the cockpit. A total of 30 qualified mission-ready military pilots participated in this research. The ages of participants are between 26 and 51 years old (M=29, SD=6); and total flying hours between 310 and 2920 hours (M=844, SD=720). Eye movement data were collected by a head-mounted ASL (Applied Science Laboratory) Mobile Eye combined with a jet fighter simulator which is a dynamic high fidelity trainer that replicates actual aircraft performance, navigation and weapon systems. The scenario was an air-to-air (AA) task. To complete the task, pilots have to search for the target with eye contact, pursue for aiming at the target, and lock-on for pick-off. The results showed there were significant differences in pilots’ fixations among the five different areas of interest (AOIs), p<. 001. Also, there were significant differences in pupil size (p<.001) for three different operating phases between experienced and less-experienced pilots; however, the average saccade velocity showed no difference. Understanding the pilot’s visual scan pattern for attention distribution whilst pursuing a dynamic target will facilitate aviation professionals in developing an effective training syllabus to improve safety of flight operation.
Entropy signature of the running cosmological constant

The renormalization group (RG) improved cosmologies based upon a RG trajectory of quantum Einstein gravity (QEG) with realistic parameter values are investigated using a system of cosmological evolution equations which allows for an unrestricted energy exchange between the vacuum and the matter sector. It is demonstrated that the scale dependence of the gravitational parameters, the cosmological constant in particular, leads to an entropy production in the matter system. The picture emerges that the Universe started out from a state of vanishing entropy, and that the radiation entropy observed today is essentially due to the coarse graining (RG flow) in the quantum gravity sector which is related to the expansion of the Universe. Furthermore, the RG improved field equations are shown to possess solutions with an epoch of power law inflation immediately after the initial singularity. The inflation is driven by the cosmological constant and ends automatically once the RG running has reduced the vacuum energy to the level of the matter energy density.
A motivational health companion in the home as part of an intelligent health monitoring sensor network

This paper describes our work in progress to develop a personal monitoring system that can monitor the physical and emotional condition of a patient by using contextual information from a sensor network, provide the patient with feedback concerning their health status and motivate the patient to adopt behavior with a positive health impact (such as exercising or taking medication at the right moment). We will extend the capabilities of an existing robotic health buddy with a (DBN based) sensor network. Then we will carry out a series of controlled, long-term field experiments where we identify and evaluate the effects of various agent social communicative behaviors on the user's adoption of health improving lifestyle patterns. The findings of the experiments will inform the final design of the health buddy and its behaviors. We will also realize system adaptivity of the data processing and data fusion methods as well as the health buddy adaptivity to the user's emotional state. The project will limit itself to monitoring and motivating people who suffer from cardiovascular chronic conditions and to the home environment.
Feature Enhancement With Joint Use of Consecutive Corrupted and Noise Feature Vectors With Discriminative Region Weighting

This paper proposes a feature enhancement method that can achieve high speech recognition performance in a variety of noise environments with feasible computational cost. As the well-known Stereo-based Piecewise Linear Compensation for Environments (SPLICE) algorithm, the proposed method learns piecewise linear transformation to map corrupted feature vectors to the corresponding clean features, which enables efficient operation. To make the feature enhancement process adaptive to changes in noise, the piecewise linear transformation is performed by using a subspace of the joint space of corrupted and noise feature vectors, where the subspace is chosen such that classes (i.e., Gaussian mixture components) of underlying clean feature vectors can be best predicted. In addition, we propose utilizing temporally adjacent frames of corrupted and noise features in order to leverage dynamic characteristics of feature vectors. To prevent overfitting caused by the high dimensionality of the extended feature vectors covering the neighboring frames, we introduce regularized weighted minimum mean square error criterion. The proposed method achieved relative improvements of 34.2% and 22.2% over SPLICE under the clean and multi-style conditions, respectively, on the Aurora 2 task.
Learning co-occurrence strokes for scene character recognition based on spatiality embedded dictionary

Robust scene-text-extraction system can be used in lots of areas. In this work, we propose to learn co-occurrence of local strokes for robust character recognition by using a spatiality embedded dictionary (SED). Different from spatial pyramid partitioning images into grids to incorporate spatial information, our SED associates every codeword with a particular response region and introduces more precise spatial information for character recognition. After localized soft coding and max pooling of the first layer, a sparse dictionary is learned to model co-occurrence of several local strokes, which further improves classification performance. Experiment on benchmark datasets demonstrates the effectiveness of our method and the results outperform state-of-the-art algorithms.
Energy-efficient Ambient Sound Sensing and Classification Using Smart Phones

The sensing and classification for indoor and outdoor environment based on ambient sound, which is practical for the research and application of mobile computing, have gradually attracted the attention of researchers. At present, GPS and Wi-Fi are often used for location, however, the former can only be used for outdoor and the availability of the latter cannot be guaranteed at all times and all places. Hence we propose to take advantage of microphone of smart phone to sense and classify indoor and outdoor environment and try to find the tradeoff between classification accuracy and energy consumption through numerous experiments. This paper focuses on carrying on large number of experiment through using Samsung Nexus and collecting multiple sound samples of indoor and outdoor environment, then analyze and evaluate current advanced feature extraction methods for ambient sound and diversified classifiers, and get their performance of accuracy and energy consumption at different environment and different time slots in a day. After extensive experiment, we find that MFCC plus MP feature extraction method is best among others, and Naïve Bayes classifier can produce best classification accuracy while it is the most energy-intensive classifier. We summarize the most suitable value of the parameters, including 15s sample length and 256 frame length. In order to save more energy, except for reducing the frequency of use of Naïve Bayes classifier, we also can accelerate the speed of file I/O.
S3: similarity search in CAD database systems

S3 is the prototype of a database system supporting the management and similarity retrieval of industrial CAD parts. The major goal of the system is to reduce the cost for developing and producing new parts by maximizing the reuse of existing parts. S3 supports the following three types of similarity queries: query by example (of an existing part in the database), query by sketch and thematic similarity query. S3 is an object-oriented system offering an adequate graphical user interface. On top of providing various state-of-the-art algorithms and index structures for geometry-based similarity retrieval, it is an excellent testbed for developing and testing new similarity algorithms and index structures.