Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust hand tracking with refined CAMShift based on combination of Depth and image features

Hand tracking is essential for natural Human Robot/Computer Interaction (HRI/HCI), although efficient and robust hand tracking in complex environment is still a challenging issue. While most researchers simplify the issue by strictly controlling the environment with many restrictions on users' clothing, or the scene complexity, or hand motion, this paper focused on reducing these restrictions. As one major cause of the restrictions is the lack of depth information, this paper proposed a method combining depth cues with image features. Depth and motion cues were extracted through background subtraction and histogram based segmentation. Guided by the depth cues extracted, color image features were then extracted with skin-color region segmentation. Then different cues were fused adaptively to construct a probability map for the hand to be tracked. With this map, a refined CAMShift tracking scheme was developed. And based on hand direction constraints we conjectured empirically, a further refinement step was proposed to segment hand from forearm, which is usually avoided using restrictions on clothing for simplicity. A number of experiments were performed to demonstrate the method's effectiveness and robustness. Tracking rates in the experiments are around 85% for ordinary situations, and around 75% for complex situations, such as fast hand motion and distractors.
Phenotype of Striatofugal Medium Spiny Neurons in Parkinsonian and Dyskinetic Nonhuman Primates: A Call for a Reappraisal of the Functional Organization of the Basal Ganglia

The classic view of anatomofunctional organization of the basal ganglia is that striatopallidal neurons of the “indirect” pathway express D2 dopamine receptors and corelease enkephalin with GABA, whereas striatopallidal neurons of the “direct” pathway bear D1 dopamine receptors and corelease dynorphin and substance P with GABA. Although many studies have investigated the pathophysiology of the basal ganglia after dopamine denervation and subsequent chronic levodopa (l-dopa) treatment, none has ever considered the possibility of plastic changes leading to profound reorganization and/or biochemical phenotype modifications of medium spiny neurons. Therefore, we studied the phenotype of striatal neurons in four groups of nonhuman primates, including the following: normal, parkinsonian, parkinsonian chronically treated with l-dopa without exhibiting dyskinesia, and parkinsonian chronically treated with l-dopa exhibiting overt dyskinesia. To identify striatal cells projecting to external (indirect) or internal (direct) segments of the globus pallidus, the retrograde tracer cholera toxin subunit B (CTb) was injected stereotaxically into the terminal areas. Using immunohistochemistry techniques, brain sections were double labeled for CTb and dopamine receptors, opioid peptides, or the substance P receptor (NK1). We also used HPLC-RIA to assess opioid levels throughout structures of the basal ganglia. Our results suggest that medium spiny neurons retain their phenotype because no variations were observed in any experimental condition. Therefore, it appears unlikely that dyskinesia is related to a phenotype modification of the striatal neurons. However, this study supports the concept of axonal collateralization of striatofugal cells that project to both globus pallidus pars externa and globus pallidus pars interna. Striatofugal pathways are not as segregated in the primate as previously considered.
THE APPLICATION OF OBJECT TECHNOLOGY IN AASHTO ’ S NEW BRIDGE LOAD RATING AND DESIGN SYSTEMS

Funded by the contributions of over 30 states and the US Federal Highway Administration, the American Association of State Highway and Transportation Officials (AASHTO) is nearing completion of a three-year project to develop a pair of related systems for bridge load rating and design, known as VirtisTM and OpisTM , respectively. A consulting team led by Michael Baker Jr., Inc. is developing the systems. Bridge load rating and design are two very different business processes, yet they have much in common, including similar philosophical and mathematical approaches, and similar data. The challenge of the Virtis/Opis project has been to satisfy the complex requirements of each separate system, while saving AASHTO millions of dollars in development and maintenance costs by reusing substantial quantities of software between the two systems. An object-oriented design and development approach has been used to help in meeting these challenges.
Where Are We After Five Editions?: Robot Vision Challenge, a Competition that Evaluates Solutions for the Visual Place Classification Problem

This article describes the Robot Vision challenge, a competition that evaluates solutions for the visual place classification problem. Since its origin, this challenge has been proposed as a common benchmark where worldwide proposals are measured using a common overall score. Each new edition of the competition introduced novelties, both for the type of input data and subobjectives of the challenge. All the techniques used by the participants have been gathered up and published to make it accessible for future developments. The legacy of the Robot Vision challenge includes data sets, benchmarking techniques, and a wide experience in the place classification research that is reflected in this article.
A Comprehensive Appraisal of Style-Integration Methods

The paper provides a comprehensive appraisal of style-integration methods in equity index, fixed income, currency, and commodity futures markets. We confront the naive equal-weight integration (EWI) method with a host of ‘sophisticated’ style-integrations that derive the style exposures using past data according to utility maximization, style rotation, volatility timing, cross-sectional pricing, style momentum or principal components criteria. The analysis, conducted separately per futures market and cross-markets, reveals that the EWI portfolio is unrivalled in terms of risk-adjusted performance while it sustains a relatively low turnover. The findings are robust to analyses that entertain variants of the sophisticated integrations, longer estimation windows, several asset scoring schemes, data snooping tests, sub-periods evaluation and equities in place of futures.
RadLoco: A rapid and low cost indoor location-sensing system

Ubiquitous location-aware systems and services are becoming a reality as made evident by the widely known Global Position System. However, indoor location-aware sensing systems are not yet commercially viable since the indoor signal propagation is complex and difficult to model. In this paper, we present RadLoco, a location-sensing system using IEEE 802.11 wireless LAN radio received signal strength (RSS) maps. The system makes use of a rapid and cost-effective survey data collection tool using an radio/ultrasonic sensory network. To provide highly accurate location estimation, we make use of a kernel-window estimation algorithm. Our experimental results indicate that a 2000 m2 office building is surveyed in eight hours by a single technician, and that the mobile terminal is located with a mean error of less than 2.5 m.
Auditory category knowledge in experts and novices

What do listeners know about sounds that have a systematic organization? Research suggests that listeners store absolute pitch information as part of their representations for specific auditory experiences. It is unclear however, if such knowledge is abstracted beyond these experiences. In two studies we examined this question via a tone adjustment task in which listeners heard one of several target tones to be matched by adjusting the frequency of a subsequent starting tone. In the first experiment listeners estimated tones from one of three distributions differing in frequency range. The effect of tone matching in the three different distributions was then modeled using randomly generated data (RGD) to ascertain the degree to which individuals' estimates are affected by generalized note knowledge. Results showed that while listeners' estimates were similar to the RGD, indicating a central tendency effect reflective of the target tone distribution, listeners were more accurate than the RGD indicating that their estimates were affected by generalized note knowledge. The second experiment tested three groups of listeners who vary in the nature of their note knowledge. Specifically, absolute pitch (AP) possessors, non-AP listeners matched in musical expertise (ME), and non-AP musical novices (MN) adjusted tones from a micro-scale that included only two in-tune notes (B4 and C5). While tone estimates for all groups showed a central tendency effect reflective of the target tone distribution, each groups' estimates were more accurate than the RGD, indicating all listeners' estimates were guided by generalized note knowledge. Further, there was evidence that explicit note knowledge additionally influenced AP possessors' tone estimates, as tones closer to C5 had less error. Results indicate that everyday listeners possess generalized note knowledge that influences the perception of isolated tones and that this effect is made more evident with additional musical experience.
Fast Resource Co-provisioning for Time Critical Applications Based on Networked Infrastructures

Resource provisioning is a key step in the deployment of applications onto clouds. When some datacenter is not accessible or some part of the infrastructure is crashed, the provisioning mechanism is therefore essential for these applications to recover quickly from sudden failures, especially for time critical applications. However, most current solutions focus on the cloud provider's hardware to achieve the fast provisioning of cloud resources. This paper proposes a co-provisioning mechanism to partition the customer's cloud resource requests while preserving their connectivity. This mechanism uses a brokering approach that is totally transparent to both the customer and the cloud provider, specifically considering the network topology. We carry out experiments on an NIaaS (networked infrastructure-as-a-service) platform, called ExoGENI. Experimental results and data analysis show that this mechanism is feasible and can dramatically improve the speed of resource provisioning.
Mouse Models of Liver Fibrosis

Autotaxin (ATX) is a secreted glycoprotein that converts lysophosphatidylcholine (LPC) to the bioactive phospholipid lysophosphatidic acid (LPA) and is the major enzyme generating circulating LPA. Inhibition of LPA signaling has profound anti-fibrotic effects in multiple organ systems including the lung, kidney, skin and peritoneum. However, other LPA-generating pathways exist and the role of ATX in localized tissue LPA production and fibrosis remains unclear and controversial. In this study, we describe the preclinical pharmacological, pharmacokinetic and pharmacodynamic properties of a novel small-molecule ATX inhibitor [3((6-chloro-2-cyclopropyl-1-(1-ethyl-1H-pyrazol-4-yl)-7-fluoro-1H-indol-3-yl) thio)-2fluorobenzoic acid sodium salt] (PAT-505). PAT-505 is a potent, selective, non-competitive inhibitor that displays significant inhibition of ATX activity in plasma and liver tissue after oral administration. When dosed therapeutically in a STAMTM model of non-alcoholic steatohepatitis (NASH), PAT-505 treatment resulted in a small, but significant, improvement in fibrosis with only minor improvements in hepatocellular ballooning and hepatic inflammation. In a cholinedeficient high-fat diet model of NASH, therapeutic treatment with PAT-505 robustly reduced liver fibrosis with no significant effect on steatosis, hepatocellular ballooning and inflammation. These data demonstrate that inhibiting autotaxin is anti-fibrotic and may represent a novel therapeutic approach for the treatment of multiple fibrotic liver diseases, including NASH. This article has not been copyedited and formatted. The final version may differ from this version. JPET Fast Forward. Published on October 17, 2016 as DOI: 10.1124/jpet.116. 237156 at A PE T Jornals on O cber 1, 2016 jpet.asjournals. org D ow nladed from
Movement Type Prediction before Its Onset Using Signals from Prefrontal Area: An Electrocorticography Study

Power changes in specific frequency bands are typical brain responses during motor planning or preparation. Many studies have demonstrated that, in addition to the premotor, supplementary motor, and primary sensorimotor areas, the prefrontal area contributes to generating such responses. However, most brain-computer interface (BCI) studies have focused on the primary sensorimotor area and have estimated movements using postonset period brain signals. Our aim was to determine whether the prefrontal area could contribute to the prediction of voluntary movement types before movement onset. In our study, electrocorticography (ECoG) was recorded from six epilepsy patients while performing two self-paced tasks: hand grasping and elbow flexion. The prefrontal area was sufficient to allow classification of different movements through the area's premovement signals (−2.0 s to 0 s) in four subjects. The most pronounced power difference frequency band was the beta band (13–30 Hz). The movement prediction rate during single trial estimation averaged 74% across the six subjects. Our results suggest that premovement signals in the prefrontal area are useful in distinguishing different movement tasks and that the beta band is the most informative for prediction of movement type before movement onset.
Multi-Touch Manipulation of Magic Lenses for Information Visualization

We introduce touch-enabled magic lenses that can be manipulated and parametrized through fluent interactions. Interaction with lenses for information visualization and data exploration has mostly been limited to single-user, single-function lenses. In this work, we present our prototype on lenses where lens function, parameters and combination of functions can be manipulated using fluent touch interaction. To achieve this, our tool consists of a widget-based approach for novice users as well as a set of continuous gestures for expert users. Additionally, we support the combination of lenses and thereby create a multi-purpose lens tool.
Learning Model-Based Sparsity via Projected Gradient Descent

Several convex formulation methods have been proposed previously for statistical estimation with structured sparsity as the prior. These methods often require a carefully tuned regularization parameter, often a cumbersome or heuristic exercise. Furthermore, the estimate that these methods produce might not belong to the desired sparsity model, albeit accurately approximating the true parameter. Therefore, greedy-type algorithms could often be more desirable in estimating structured-sparse parameters. So far, these greedy methods have mostly focused on linear statistical models. In this paper, we study the projected gradient descent with a non-convex structured-sparse parameter model as the constraint set. Should the cost function have a stable model-restricted Hessian, the algorithm produces an approximation for the desired minimizer. As an example, we elaborate on application of the main results to estimation in generalized linear models.
In vitro characterization of a miR-122-sensitive double-helical switch element in the 5′ region of hepatitis C virus RNA

It has been proposed that the hepatitis C virus (HCV) internal ribosome entry site (IRES) resides within a locked conformation, owing to annealing of its immediate flanking sequences. In this study, structure probing using Escherichia coli dsRNA-specific RNase III and other classical tools showed that this region switches to an open conformation triggered by the liver-specific microRNA, miR-122. This structural transition, observed in vitro, may be the mechanistic basis for the involvement of downstream IRES structural domain VI in translation, as well as providing a role of liver-specific miR-122 in HCV infection. In addition, the induced RNA switching at the 5′ untranslated region could ultimately represent a new mechanism of action of micro-RNAs.
Curve tracking with improved chain code algorithm

Robot curve tracking is used in many aspects of industry, such as automatic welding and incising. In this paper, the technique of structure light triangulation is used for camera calibration of curve tracking system at first, and then an improved curve detection algorithm is presented. This algorithm adopts an extended chain code template. The discrete sampling point sequence of curve can be obtained by using this one, and the burr on the curve can be wiped off effectively by adjusting the size of chain code template automatically. The experiment results show that the improved chain code algorithm can measure and identify the curve more quickly and accurately, and implement the objective of curve tracking.
Augmenting the organizational memory: a field study of answer garden

A growing concern for organizations and groups has been to augment their knowledge and expertise. One such augmentation is to provide an organizational memory, some record of the organization's knowledge. However, relatively little is known about how computer systems might enhance organizational, group, or community memory. This paper presents findings from a field study of one such organizational memory system, the Answer Garden. The paper discusses the usage data and qualitative evaluations from the field study, and then draws a set of lessons for next-generation organizational memory systems.
MobSpiro: Mobile based spirometry for detecting COPD

Chronic respiratory diseases are diseases of the airways and other structures of the lung, usually resulting in difficulty in breathing and other symptoms. Chronic obstructive pulmonary disease (COPD) is considered to be one of the most common of respiratory diseases. Nowadays, spirometry remains the golden standard for diagnosing and staging COPD. By taking into consideration the possibility of this disease worsening over time and its negative impact on patient's life, sufferers should conduct regular spirometry checks at medical centers or buy expensive and portable devices to monitor and manage the disease. Therefore, COPD spirometry is costly in terms of both, money and time. In this work and due to the pervasiveness and advancement of smartphones, we attempt to make use of their built-in sensors and ever increasing computational capabilities to provide patients with a mobile based spirometer capable of diagnosing and managing COPD in a reliable and cost effective manner. We develop a model that allows the computation of the two critical parameters FVC and FEV1 by establishing a relationship between the frequency response of human exhalation recorded by mobile microphone and the actual flow rate. These two parameters are critical in diagnosing COPD. Preliminary results show that the mean percent error between FVC, FEV1, and FEV1/FVC ratio data as computed using MobSpiro application and the clinical spirometer is 4.6%, 3.1%, and 3.5% respectively. These results prove the effectiveness of the proposed system when compared to the clinical spirometer, and confirm that smartphones can play an important role in healthcare in the coming future.
Nakagami-Based AdaBoost Learning Framework for Detection of Anatomical Landmarks in 2D Fetal Neurosonograms

In obstetrical ultrasound examinations, the appropriate fetal biometry plane is identified by the presence and absence of key anatomical landmarks within the image. In the 20 to 22 weeks of gestation, the transthalamic plane becomes of particular importance, as it is the stage at which early signs of trisomy 18 can be detected by the presence of cysts in the choroid plexus (CP). We propose a method to detect the presence of the CP in 2D ultrasound images of the fetal brain using an AdaBoost learning algorithm combined with statistical image representations estimated by the Nakagami distribution. We compared the performance of the detection using four different feature sets: intensity-based (I), empirically-fit Nakagami (Nm and Nw parametric image representations, and a combination of all feature sets, NImw. We found that the Nakagami scale feature, Nw, yielded the highest CP detection accuracy (87.49%).
Self-Similarity Metric for Index Pruning in Conceptual Vector Space Models

One of the critical issues in search engines is the size of search indexes: as the number of documents handled by an engine increases, the search must preserve its efficiency, despite the growth of indexing structures. A widely agreed solution to this problem is the adoption of smaller, or pruned, indexes that allow increasing the retrieval speed while keeping the search quality as high as possible. This paper extends the notion of pruned index to semantic search systems based on conceptual vector space models and proposes a new self-similarity metric for index pruning. A conceptual vector space model represents documents as vectors in a n-dimensional space where each dimension corresponds to an ontology concept. The pruning algorithm proposed in this paper acts on the basis of document self-similarity, preserving only the most significant components of a document conceptual vector. Unlike many already proposed algorithms, the self-similarity metric is only based on local information and does not require to recompute the whole pruned index when new documents are added, i.e., it can be used on-line, possibly combined with other off-line pruning policies. The proposed metric is tested against two benchmark sets respectively related to Siderurgy (250 documents annotated with respect to the e-Class ontology) and Disability (2500 documents annotated with respect to the Passepartout ontology). Results show that the compression ratio achieved by this technique is satisfying (50%), while ranking similarity with results coming from non-pruned indexes remains sufficiently high (80%), thus preserving the quality of provided results.
E-Cadherin Destabilization Accounts for the Pathogenicity of Missense Mutations in Hereditary Diffuse Gastric Cancer

E-cadherin is critical for the maintenance of tissue architecture due to its role in cell-cell adhesion. E-cadherin mutations are the genetic cause of Hereditary Diffuse Gastric Cancer (HDGC) and missense mutations represent a clinical burden, due to the uncertainty of their pathogenic role. In vitro and in vivo, most mutations lead to loss-of-function, although the causal factor is unknown for the majority. We hypothesized that destabilization could account for the pathogenicity of E-cadherin missense mutations in HDGC, and tested our hypothesis using in silico and in vitro tools. FoldX algorithm was used to calculate the impact of each mutation in E-cadherin native-state stability, and the analysis was complemented with evolutionary conservation, by SIFT. Interestingly, HDGC patients harbouring germline E-cadherin destabilizing mutants present a younger age at diagnosis or death, suggesting that the loss of native-state stability of E-cadherin accounts for the disease phenotype. To elucidate the biological relevance of E-cadherin destabilization in HDGC, we investigated a group of newly identified HDGC-associated mutations (E185V, S232C and L583R), of which L583R is predicted to be destabilizing. We show that this mutation is not functional in vitro, exhibits shorter half-life and is unable to mature, due to premature proteasome-dependent degradation, a phenotype reverted by stabilization with the artificial mutation L583I (structurally tolerated). Herein we report E-cadherin structural models suitable to predict the impact of the majority of cancer-associated missense mutations and we show that E-cadherin destabilization leads to loss-of-function in vitro and increased pathogenicity in vivo.