Miracle-dz/UniFAR-FTUs huggingface dataset:
Dexterous manipulation through rolling

Nonholonomic constraints in robotic systems are the source of some difficulties in planning and control; however, they also introduce interesting properties that can be practically exploited. In this paper we consider the design of a robot hand that achieves dexterity (i.e. the ability to arbitrarily locate and reorient manipulated objects) through rolling. Some interesting issues arising in planning and controlling motions of such device are considered, including exact planning for a spherical object and approximate planning for general objects. An experimental prototype of a three-plus-one degree of freedom hand achieving dexterous manipulation capabilities is described along with experimental results from manipulation.
The Disintegrin and Metalloprotease ADAM12 Is Associated with TGF-β-Induced Epithelial to Mesenchymal Transition

The increased expression of the Disintegrin and Metalloprotease ADAM12 has been associated with human cancers, however its role remain unclear. We have previously reported that ADAM12 expression is induced by the transforming growth factor, TGF-β and promotes TGF-β-dependent signaling through interaction with the type II receptor of TGF-β. Here we explore the implication of ADAM12 in TGF-β-mediated epithelial to mesenchymal transition (EMT), a key process in cancer progression. We show that ADAM12 expression is correlated with EMT markers in human breast cancer cell lines and biopsies. Using a non-malignant breast epithelial cell line (MCF10A), we demonstrate that TGF-β-induced EMT increases expression of the membrane-anchored ADAM12L long form. Importantly, ADAM12L overexpression in MCF10A is sufficient to induce loss of cell-cell contact, reorganization of actin cytoskeleton, up-regulation of EMT markers and chemoresistance. These effects are independent of the proteolytic activity but require the cytoplasmic tail and are specific of ADAM12L since overexpression of ADAM12S failed to induce similar changes. We further demonstrate that ADAM12L-dependent EMT is associated with increased phosphorylation of Smad3, Akt and ERK proteins. Conversely, inhibition of TGF-β receptors or ERK activities reverses ADAM12L-induced mesenchymal phenotype. Together our data demonstrate that ADAM12L is associated with EMT and contributes to TGF-β-dependent EMT by favoring both Smad-dependent and Smad-independent pathways.
A nuclear localization domain in the hnRNP A1 protein

The heterogeneous nuclear RNP (hnRNP) A1 protein is one of the major pre-mRNA/mRNA binding proteins in eukaryotic cells and one of the most abundant proteins in the nucleus. It is localized to the nucleoplasm and it also shuttles between the nucleus and the cytoplasm. The amino acid sequence of A1 contains two RNP motif RNA-binding domains (RBDs) at the amino terminus and a glycine-rich domain at the carboxyl terminus. This configuration, designated 2x RBD-Gly, is representative of perhaps the largest family of hnRNP proteins. Unlike most nuclear proteins characterized so far, A1 (and most 2x RBD-Gly proteins) does not contain a recognizable nuclear localization signal (NLS). We have found that a segment of ca. 40 amino acids near the carboxyl end of the protein (designated M9) is necessary and sufficient for nuclear localization; attaching this segment to the bacterial protein beta- galactosidase or to pyruvate kinase completely localized these otherwise cytoplasmic proteins to the nucleus. The RBDs and another RNA binding motif found in the glycine-rich domain, the RGG box, are not required for A1 nuclear localization. M9 is a novel type of nuclear localization domain as it does not contain sequences similar to classical basic-type NLS. Interestingly, sequences similar to M9 are found in other nuclear RNA-binding proteins including hnRNP A2.
Personalized Recommendation System for Questions to Answer on SuperUser Group 44 :

We have built a personalized recommendation engine that is able to predict, at a given snapshot of time, the probability of a given user answering a particular SuperUser question. We use this in the application of generating personalized suggestions of questions that each user would be interested in answering. We have developed 2 components to recommend relevant questions to users: one which computes the match in a question’s topic to a potential answerer’s interests, and another which predicts the likelihood of an answer coming in for a question at a given point in time. We combine these results by multiplying and normalizing to generate an overall estimate of the probability a question is answered by a given user at a given time. We use community detection at the tag-similarity level to establish recommended questions for particular communities, and use this to diversity recommendations, particularly for users who have limited history on the site. Results show that our combined approach is more accurate at modeling question-answering events on a subset of the StackOverflow question-answering network than either of the individual components alone. Our final model is able to generate recommendations such that the actual question answered is among the 29 top recommendations .
Rapid selection of reliable templates for visual tracking

We propose a method that rates the suitability of given templates for template-based tracking in real-time. This is important for applications with online template selection, such as SLAM, where it is essential to track a low number of preferably reliable templates. Our approach is based on simple image features specifically designed to identify texture properties which are problematic for tracking. During a training step, a support vector régresser is learned. It uses a tracking quality measure which considers both convergence rate and speed obtained by simulation of many tracking attempts. Finally, a minimum set of image features is identified to speedup the online selection process. In experiments on real-world video sequences our method improved the detection rate of an existing tracking-by-detection system by 8% on average.
Discriminating Environmental Conditions for Significant Warm Sector and Boundary Tornadoes in Parts of the Great Plains

AbstractUsing system-relative composites, based on a dataset of significant tornadoes and null supercell events, environmental conditions associated with occurrences of significant tornadoes near discernible surface boundaries were compared to nontornadic boundary supercells, and warm sector significant tornadoes to nontornadic warm sector supercells, for a portion of the Great Plains. Results indicated that significant boundary tornadoes were associated with the exit region of a 300-hPa jet maximum, while null boundary events were in closer proximity to the 300-hPa jet entrance region. The differences at 300 hPa led to significant differences at the surface, as the null composite indicated deformation and confluence into the surface boundary and enhanced frontogenesis, while this was not present in the boundary significant tornado composite. Significant synoptic differences also were noted between the warm sector tornadoes and the warm sector null events. The warm sector significant tornadoes were associ.
Secure Evaluation Protocol for Personalized Medicine

The increasing availability and use of genome data for applications like personalized medicine have created opportunities for the improved diagnosis and treatment of various medical conditions. However, it has a potential to be used for discrimination, thereby presenting a set of serious challenges in privacy and security. We propose a secure evaluation algorithm to compute genomic tests that are based on a linear combination of genome data values (we use the Warfarin dosing algorithm as a representative example). Our proposal relies on a combination of partially homomorphic Paillier encryption and private information retrieval. We implement a prototype system that includes the Paillier encryption part of our protocol. Our initial evaluation demonstrates a good potential for real time use in a physician-patient scenario, with a response time of around 200ms in a Wi-Fi communications environment.
Moving Destination Prediction Using Sparse Dataset

Moving destination prediction offers an important category of location-based applications and provides essential intelligence to business and governments. In existing studies, a common approach to destination prediction is to match the given query trajectory with massive recorded trajectories by similarity calculation. Unfortunately, due to privacy concerns, budget constraints, and many other factors, in most circumstances, we can only obtain a sparse trajectory dataset. In sparse dataset, the available moving trajectories are far from enough to cover all possible query trajectories; thus the predictability of the matching-based approach will decrease remarkably. Toward destination prediction with sparse dataset, instead of searching similar trajectories over the sparse records, we alternatively examine the changes of distances from sampling locations to final destination on query trajectory. The underlying idea is intuitive: It is directly motivated by travel purpose, people always get closer to the final destination during the movement. By borrowing the conception of gradient descent in optimization theory, we propose a novel moving destination prediction approach, namely MGDPre. Building upon the mobility gradient descent, MGDPre only investigates the behavior characteristics of query trajectory itself without matching historical trajectories, and thus is applicable for sparse dataset. We evaluate our approach based on extensive experiments, using GPS trajectories generated by a sample of taxis over a 10-day period in Shenzhen city, China. The results demonstrate that the effectiveness, efficiency, and scalability of our approach outperform state-of-the-art baseline methods.
Loss of β-Glucocerebrosidase Activity Does Not Affect Alpha-Synuclein Levels or Lysosomal Function in Neuronal Cells

To date, a plethora of studies have provided evidence favoring an association between Gaucher disease (GD) and Parkinson’s disease (PD). GD, the most common lysosomal storage disorder, results from the diminished activity of the lysosomal enzyme β-glucocerebrosidase (GCase), caused by mutations in the β-glucocerebrosidase gene (GBA). Alpha-synuclein (ASYN), a presynaptic protein, has been strongly implicated in PD pathogenesis. ASYN may in part be degraded by the lysosomes and may itself aberrantly impact lysosomal function. Therefore, a putative link between deficient GCase and ASYN, involving lysosomal dysfunction, has been proposed to be responsible for the risk for PD conferred by GBA mutations. In this current work, we aimed to investigate the effects of pharmacological inhibition of GCase on ASYN accumulation/aggregation, as well as on lysosomal function, in differentiated SH-SY5Y cells and in primary neuronal cultures. Following profound inhibition of the enzyme activity, we did not find significant alterations in ASYN levels, or any changes in the clearance or formation of its oligomeric species. We further observed no significant impairment of the lysosomal degradation machinery. These findings suggest that additional interaction pathways together with aberrant GCase and ASYN must govern this complex relation between GD and PD.
Detection of text captions in compressed domain video

In this paper, we propose a new method for detecting text captions in MPEG video stream. It is based on the observation that text captions in video frames typically possess high contrast against the background for them to be visible. The method operates on DCT coefficients in MPEG domain. The main contribution of this work is in developing a binarzied contrast feature domain in which the presence of text in video frames can be highlighted. A weighting function is defined to deduce the probability of a text frame. From the set of representative text frames located, further techniques are developed to outline, segment, and recognise text captions. The techniques have been tested on commercial and news videos from the MPEG7 data set and local TCS news video. The results demonstrate that our approach is effective in detecting and locating text captions in general video.
A Prototype Antibody Microarray Platform to Monitor Changes in Protein Tyrosine Phosphorylation*

Reversible protein phosphorylation is a key regulatory process in all living cells. Deregulation of modification control mechanisms, especially in the case of tyrosine, may lead to malignant transformation and disease. Phosphotyrosine (p-Tyr) accounts for only 0.05% of the total cellular phospho-amino acid content, yet plays an unusually prominent role in eukaryotic signaling, development, and growth. Tracking temporal and positional p-Tyr changes across the cellular proteome, i.e. tyrosine phosphoproteomics, is therefore tremendously valuable. Here, we describe and evaluate a prototype antibody (Ab) microarray platform to monitor changes in protein Tyr phosphorylation. Availability permitting, a virtually unlimited number of Abs, each recognizing a specific cellular protein, may be arrayed on a chip, incubated with total cell or tissue extracts or with biological fluids, and then probed with a fluorescently labeled p-Tyr-specific monoclonal Ab, PY-KD1, specifically generated for this assay as part of the current study. The optimized protocol allowed detection of changes in the Tyr phosphorylation state of selected proteins using submicrogram to low nanogram of total protein extract, amounts that may conceivably be obtained from a thousand to a hundred thousand cells, or less, depending on the cell or tissue type. The assay platform was evaluated by assessing changes in a rationally selected subset of the Tyr phosphoproteome of Bcr-Abl-expressing cells treated with a specific inhibitor, Gleevec, and of epidermal growth factor (EGF)-treated HeLa cells. The results, ratiometric rather than strictly quantitative in nature, conformed with previous identifications of several Bcr-Abl and EGF receptor targets, and associated proteins, as detected by exhaustive mass spectrometric analyses. The Ab microarray method described here offers advantages of low sample and reagent consumption, scalability, detection multiplexing, and potential compatibility with microfluidic devices and automation. The system may hold particular promise for dissecting signaling pathways, molecular classification of tumors, and profiling of novel target-cancer drugs.
S.O.S. On My Phone: An Analysis of Motives and Incentives for Participation in Smartphone‐Based Volunteering

This paper aimed to analyse the potential of new technologies to support volunteer engagement in emergency response. To examine prerequisites for the use of a smartphone-based application by medically trained volunteers in circumstances in which regular medical resources are exceeded, the influence of volunteering motives and user characteristics were assessed via an online survey. Results demonstrate that value-oriented individuals evaluated the system more positively and were more willing to participate. Career-oriented motivation and technical expertise had a partial influence. Different incentives emphasized in the system description did not make any difference, possibly because the overall evaluation was very positive. Among other implications, results point to the relevance of investigating ways to address value-oriented individuals and to effectively trigger their motivation.
Vision Dependent Viewpoint Selection Method in 3D Scene Observation

This paper proposed a novel method for selecting good views in 3D scene Observation. A lot of existing methods for computing viewpoint quality are based on the number of visible faces and the projected area only, so they miss the visual identity of the scene. Our method combines the notion of mesh saliency and fuzzy nearness. First, we construct a fuzzy set for a viewpoint. The elements of the set are faces in the scene, and membership function is based on the visibility of faces. Then, we define an ideal fuzzy set, and the membership degree is in proportion to the mesh saliency weighted actual area of the face. Finally, we take the fuzzy nearness of these two fuzzy sets as the measure of viewpoint quality. In addition, we design a CUDA method, which avoids data transfer from device memory to host memory. So this method improves the efficiency in the computing of the viewpoint quality. We give some experiment results, and these results show that our method works well.
Tensor field design in volumes

The design of 3D tensor fields is important in several graphics applications such as procedural noise, solid texturing, and geometry synthesis. Different fields can lead to different visual effects. The topology of a tensor field, such as degenerate tensors, can cause artifacts in these applications. Existing 2D tensor field design systems cannot handle the topology of 3D tensor fields. We present, to our best knowledge, the first 3D tensor field design system. At the core of our system is the ability to specify and control the type, number, location, shape, and connectivity of degenerate tensors. To enable such capability, we have made a number of observations of tensor field topology that were previously unreported. We demonstrate applications of our method in volumetric synthesis of solid and geometry texture as well as anisotropic Gabor noise.
Insolvency prediction model using artificial neural network for Malaysian general insurers

Insolvency of insurance companies has been a concern to the community due to the need to protect the general public from the aftermath of insurer insolvency and to try to minimize the costs associated to this difficulty such as the insurance guaranty funds. The artificial neural network is utilized in this study to create an insolvency predictive model that could predict any future failure of general insurance company in Malaysia. The neural networks results show high predictability, suggesting the usefulness of this method for predicting future insurer insolvency in Malaysia.
Succinct Matrix Approximation and Efficient k-NN Classification

This work reveals that instead of the polynomial bounds in previous literatures there exists a sharper bound of exponential form for the L2 norm of an arbitrary shaped random matrix. Based on the newly elaborated bound, a nonuniform sampling method is presented to succinctly approximate a matrix with a sparse binary one, and thus relieves the computation loads of k-NN classifier in both time and storage. The method is also pass-efficient because sampling and quantizing are combined together in a single step and the whole process can be completed within one pass over the input matrix. In the evaluations on compression ratio and reconstruction error, the sampling method exhibits impressive capability in providing succinct and tight approximations for the input matrices. The most significant finding in the classification experiment is that the k-NN classifier based on the approximation can even outperform the standard one. This provides another strong evidence for the claim that our method is especially capable in capturing intrinsic characteristics.
An energy efficient cache design using Spin Torque Transfer (STT) RAM

The on-chip memory is a dominant source of power and energy consumption in modern and future processors. This paper explores the use of a new emerging non-volatile memory technology as a replacement for SRAM based lower level caches — Spin Torque Transfer(STT) RAM. While STTRAM achieves a reduction in leakage energy of 90% compared to SRAM, the dynamic energy for a write operation is 2X that of SRAM. Consequently, we propose additional microarchitectural optimizations to reduce overall dynamic energy which achieve an average reduction in dynamic energy over the base case of 30% with a range of 16% to 60% across 10 benchmarks.
Synchrony in Excitatory Neural Networks

Synchronization properties of fully connected networks of identical oscillatory neurons are studied, assuming purely excitatory interactions. We analyze their dependence on the time course of the synaptic interaction and on the response of the neurons to small depolarizations. Two types of responses are distinguished. In the first type, neurons always respond to small depolarization by advancing the next spike. In the second type, an excitatory postsynaptic potential (EPSP) received after the refractory period delays the firing of the next spike, while an EPSP received at a later time advances the firing. For these two types of responses we derive general conditions under which excitation destabilizes in-phase synchrony. We show that excitation is generally desynchronizing for neurons with a response of type I but can be synchronizing for responses of type II when the synaptic interactions are fast. These results are illustrated on three models of neurons: the Lapicque integrate-and-fire model, the model of Connor et al., and the Hodgkin-Huxley model. The latter exhibits a type II response, at variance with the first two models, that have type I responses. We then examine the consequences of these results for large networks, focusing on the states of partial coherence that emerge. Finally, we study the Lapicque model and the model of Connor et al. at large coupling and show that excitation can be desynchronizing even beyond the weak coupling regime.
Modeling Retinal Ganglion Cell Population Activity with Restricted Boltzmann Machines

The retina is a complex nervous system which encodes visual stimuli before higher order processing occurs in the visual cortex. In this study we evaluated whether information about the stimuli received by the retina can be retrieved from the firing rate distribution of Retinal Ganglion Cells (RGCs), exploiting High-Density 64x64 MEA technology. To this end, we modeled the RGC population activity using mean-covariance Restricted Boltzmann Machines, latent variable models capable of learning the joint distribution of a set of continuous observed random variables and a set of binary unobserved random units. The idea was to figure out if binary latent states encode the regularities associated to different visual stimuli, as modes in the joint distribution. We measured the goodness of mcRBM encoding by calculating the Mutual Information between the latent states and the stimuli shown to the retina. Results show that binary states can encode the regularities associated to different stimuli, using both gratings and natural scenes as stimuli. We also discovered that hidden variables encode interesting properties of retinal activity, interpreted as population receptive fields. We further investigated the ability of the model to learn different modes in population activity by comparing results associated to a retina in normal conditions and after pharmacologically blocking GABA receptors (GABAC at first, and then also GABAA and GABAB). As expected, Mutual Information tends to decrease if we pharmacologically block receptors. We finally stress that the computational method described in this work could potentially be applied to any kind of neural data obtained through MEA technology, though different techniques should be applied to interpret the results.