Miracle-dz/UniFAR-FTUs huggingface dataset:
Recognizing Gait Pattern of Parkinson's Disease Patients Based on Fine-Grained Movement Function Features

Parkinson's Disease (PD) is one of the typical movement disorder diseases, which has a serious impact on the daily lives of elderly people. In this paper, we propose a novel framework for PD gait pattern recognition. The key idea of our approach is to distinguish PD gait patterns from healthy individuals by accurately extracting gait features that indicate three aspects of movement function, i.e., Stability, symmetry and harmony. Concretely, our framework contains three steps: gait phase discrimination, feature extraction and selection and pattern classification. In the first step, we put forward a key event based method to discriminate four gait phases from plantar pressure data. In the second step, based on the gait phases, we extract and select gait features that can indicate stability, symmetry and harmony of movement function. In the third step, we recognize PD gait pattern by employing BP neural network. We evaluate the framework using a real plantar pressure dataset that contains 93 PD patients and 72 healthy individuals. Experimental results demonstrate that our framework outperforms the baseline approach by 32.7% on average in terms of Precision, 42.2% on average in terms of Recall, and 24.0% on average in terms of AUC.
Efficient Partitioning of Memory Systems and Its Importance for Memory Consolidation

Long-term memories are likely stored in the synaptic weights of neuronal networks in the brain. The storage capacity of such networks depends on the degree of plasticity of their synapses. Highly plastic synapses allow for strong memories, but these are quickly overwritten. On the other hand, less labile synapses result in long-lasting but weak memories. Here we show that the trade-off between memory strength and memory lifetime can be overcome by partitioning the memory system into multiple regions characterized by different levels of synaptic plasticity and transferring memory information from the more to less plastic region. The improvement in memory lifetime is proportional to the number of memory regions, and the initial memory strength can be orders of magnitude larger than in a non-partitioned memory system. This model provides a fundamental computational reason for memory consolidation processes at the systems level.
Acoustic Segmentation for Audio Browsers

Online digital audio is a rapidly growing resource, which can be accessed in rich new ways not previously possible. For example, it is possible to listen to just those portions of a long discussion which involve a given subset of people, or to instantly skip ahead to the next speaker. Providing this capability to users, however, requires generation of necessary indices, as well as an interface which utilizes these indices to aid navigation. We describe algorithms which generate indices from automatic acoustic segmentation. These algorithms use hidden Markov models to segment audio into segments corresponding to di erent speakers or acoustics classes (e.g. music). Unsupervised model initialization using agglomerative clustering is described, and shown to work as well in most cases as supervised initialization. We also describe a user interface which displays the segmentation in the form of a timeline, with tracks for the di erent acoustic classes. The interface can be used for direct navigation through the audio.
Arc consistency and friends

A natural and established way to restrict the constraint satisfaction problem is to fix the relations that can be used to pose constraints; such a family of relations is called a constraint language. In this article, we study arc consistency, a heavily investigated inference method, and three extensions thereof from the perspective of constraint languages. We conduct a comparison of the studied methods on the basis of which constraint languages they solve, and we present new polynomial-time tractability results for singleton arc consistency, the most powerful method studied.
Efficient Probabilistic Inference for Dynamic Relational Models

Over the last couple of years, the interest in combining probability and logic has grown strongly. This led to the development of different software packages like PRISM, ProbLog and Alchemy, which offer a variety of exact and approximate algorithms to perform inference and learning. What is lacking, however, are algorithms to perform efficient inference in relational temporal models by systematically exploiting temporal and local structure. Since many real-world problems require temporal models, we argue that more research is necessary to use this structure to obtain more efficient inference and learning. While existing relational representations of dynamic domains focus rather on approximate inference techniques we propose an exact algorithm.
Association of Alcohol Craving With α‐Synuclein (SNCA)

Background: Studies have found that genomic variation in the gene SNCA, which encodes the protein a-synuclein, may contribute to the variation in alcohol consumption in an inbred rat model of alcohol preference. Studies in humans have provided support for an association between SNCA and craving for alcohol. Methods: To examine the role of this gene in alcohol dependence and related phenotypes, 30 single nucleotide polymorphisms (SNPs) were genotyped across the SNCA gene in a sample of 219 multiplex alcoholic families of European American descent. Two phenotypes, alcohol dependence and alcohol craving, were analyzed using the pedigree disequilibrium test. Results: There was no evidence of association between any of the SNCA SNPs and alcohol dependence (p � 0.13). In contrast, 8 SNPs provided evidence of association (po0.05) with the phenotype of alcohol craving. Haplotype analysis further supported evidence of an association with alcohol craving; a haplotype encompassing SNPs in intron 4 through the region downstream of the gene was overtransmitted to cravers and a second haplotype was overtransmitted to noncravers. Conclusions: These results suggest that variation in SNCA contributes to alcohol craving, a common, although not uniform, feature of alcohol dependence.
On Flow Authority Discovery in Social Networks

A central characteristic of social networks is that it facilitates rapid dissemination of information between large groups of individuals. This paper will examine the problem of determination of information flow representatives, a small group of authoritative representatives to whom the dissemination of a piece of information leads to the maximum spread. Clearly, information flow is affected by a number of different structural factors such as the node degree, connectivity, intensity of information flow interaction and the global structural behavior of the underlying network. We will propose a stochastic information flow model, and use it to determine the authoritative representatives in the underlying social network. We will first design an accurate RankedReplace algorithm, and then use a Bayes probabilistic model in order to approximate the effectiveness of this algorithm with the use of a fast algorithm. We will examine the results on a number of real social network data sets, and show that the method is more effective than state-of-the-art methods.
Improved batch elimination: A fast algorithm to identify and remove harmful compiler optimizations

Modern compilers provide several optimizations that can be applied to the source code, in order to increase its performance. Due to the complex relationship between various optimizations, discovering harmful compiler optimizations is a problem in the context of compilers. Strategies based on iterative compilation try to solve this problem evaluating the performance of the compiled program using different sets. In this context, Combined Elimination is an efficient iterative compilation strategy. The purpose of Combined Elimination is to identify the harmful optimizations and remove them in an iterative compilation process. Combined Elimination provides good results, which are close to those founded by an exhaustive search approach. However, its drawback is the number of program runs. In this paper, we proposed an iterative compilation algorithm, named Improved Batch Elimination. This algorithm is based on the first step towards Combined Elimination, the Batch Elimination algorithm. The goal of Improved Batch Elimination is to produce results similar to Combined Elimination, with a complexity similar to Batch Elimination. In other words, the goal is to produce good results and to be faster than Combined Elimination. We evaluate our algorithm by measuring the performance of Spec Cpu2006, Polybench and cBench benchmarks under a set of llvm compiler optimizations. The results indicate that Improved Batch Elimination is a good strategy to remove harmful compiler optimizations, using few program runs.
Robust routing in ISP networks considering YouTube traffic demand fluctuations

Content Delivery Networks (CDN) are responsible for a large traffic share in today's ISP networks. CDN operators deploy load balancing between their data centers which may cause large traffic fluctuations within an ISP network. These traffic shifts in turn can lead to severe congestion situations if the traffic routing is not able to distribute the load properly. In this paper we first present a traffic demand model that includes the representation of YouTube traffic fluctuations caused by the dynamic data center selection mechanism applied in the YouTube CDN. This model is derived from measurements of the YouTube data center selection behavior and from traffic measurements in a large German ISP network. We then address the problem of determining a robust routing scheme using our traffic demand model. The target of the routing optimization is to minimize the maximum link utilization in an ISP network with given link capacities. Finally we apply our robust routing approach on different network topology and traffic demand scenarios and discuss the results.
Structural Determination of Three Different Series of Compounds as Hsp90 Inhibitors Using 3D-QSAR Modeling, Molecular Docking and Molecular Dynamics Methods

Hsp90 is involved in correcting, folding, maturation and activation of a diverse array of client proteins; it has also been implicated in the treatment of cancer in recent years. In this work, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), molecular docking and molecular dynamics were performed on three different series of Hsp90 inhibitors to build 3D-QSAR models, which were based on the ligand-based or receptor-based methods. The optimum 3D-QSAR models exhibited reasonable statistical characteristics with averaging internal q2 > 0.60 and external r2pred > 0.66 for Benzamide tetrahydro-4H-carbazol-4-one analogs (BT), AT13387 derivatives (AT) and Dihydroxylphenyl amides (DA). The results revealed that steric effects contributed the most to the BT model, whereas H-bonding was more important to AT, and electrostatic, hydrophobic, H-bond donor almost contributed equally to the DA model. The docking analysis showed that Asp93, Tyr139 and Thr184 in Hsp90 are important for the three series of inhibitors. Molecular dynamics simulation (MD) further indicated that the conformation derived from docking is basically consistent with the average structure extracted from MD simulation. These results not only lead to a better understanding of interactions between these inhibitors and Hsp90 receptor but also provide useful information for the design of new inhibitors with a specific activity.
A Spiking Neural Network Model of an Actor-Critic Learning Agent

The ability to adapt behavior to maximize reward as a result of interactions with the environment is crucial for the survival of any higher organism. In the framework of reinforcement learning, temporal-difference learning algorithms provide an effective strategy for such goal-directed adaptation, but it is unclear to what extent these algorithms are compatible with neural computation. In this article, we present a spiking neural network model that implements actor-critic temporal-difference learning by combining local plasticity rules with a global reward signal. The network is capable of solving a nontrivial gridworld task with sparse rewards. We derive a quantitative mapping of plasticity parameters and synaptic weights to the corresponding variables in the standard algorithmic formulation and demonstrate that the network learns with a similar speed to its discrete time counterpart and attains the same equilibrium performance.
Effect of Terminal Electron Accepting Processes on Acetate Thresholds in Contaminated Sediments

Title of Thesis: EFFECT OF TERMINAL ELECTRON ACCEPTING PROCESSES ON ACETATE THRESHOLDS IN CONTAMINATED SEDIMENTS Gayle E. Davis, Master of Science, 2006 Directed By: Associate Professor, Eric A. Seagren Civil and Environmental Engineering Department Assistant Professor, Jennifer G. Becker Biological Resources Engineering Department Demonstrating the success of in situ bioremediation is challenging, requiring multiple lines of evidence, e.g., “footprints” of microbially-mediated processes that transform contaminants, like metabolic intermediates. This project evaluated the hypothesis that characteristic threshold concentrations of acetate, a key intermediate under anaerobic conditions, occur in contaminant plume regions dominated by different terminal electron accepting processes (TEAPs). The evaluation included characterizing the initially predominant TEAP(s) in sediment-slurry microcosms, and then measuring acetate thresholds in the sediment-slurries when dominated by different TEAPs. Based on the characterization, the sediments were dominated by methanogenesis, consistent with field observations. Interestingly, in the threshold experiments, similar acetate thresholds were observed under methanogenic conditions and in chlorinated-aliphatic degrading microcosms, although thermodynamics predict lower thresholds under the latter conditions. Therefore, this study demonstrated some of the potential, as well as complications, in using and interpreting acetate thresholds as an indicator of the dominant TEAP.
Overlapping community detection combining content and link

In classic community detection, it is assumed that communities are exclusive, in the sense of either soft clustering or hard clustering. It has come to attention in the recent literature that many real-world problems violate this assumption, and thus overlapping community detection has become a hot research topic. The existing work on this topic uses either content or link information, but not both of them. In this paper, we deal with the issue of overlapping community detection by combining content and link information. We develop an effective solution called subgraph overlapping clustering (SOC) and evaluate this new approach in comparison with several peer methods in the literature that use either content or link information. The evaluations demonstrate the effectiveness and promise of SOC in dealing with large scale real datasets.
On realizing distributed topology control in low-power IoT platforms

As current trends in distributed computing are enabling the vision of the Internet of Things (IoT), the necessity intensifies for addressing real-life aspects in massive scales. Energy efficiency remains a key design requirement for low-power IoT platforms, and distributed topology control techniques can yield the guidelines for optimizing the transmission power-connectivity nexus. In this work, we exploit previous theoretical results on necessary and sufficient conditions for establishing end-to-end connectivity, by introducing the notion of relative Delaunay neighbourhoods to computationally constrained hardware platforms. We implement the proposed approach on Contiki OS and we offer extensive emulation results, which highlight the scalability of our approach. Comparisons with benchmark solutions are offered to evaluate the performance of our framework in terms of achieved connectivity, memory demands, and energy efficiency.
Myelin Gene Expression after Experimental Contusive Spinal Cord Injury

After incomplete traumatic spinal cord injury (SCI), the spared tissue exhibits abnormal myelination that is associated with reduced or blocked axonal conductance. To examine the molecular basis of the abnormal myelination, we used a standardized rat model of incomplete SCI and compared normal uninjured tissue with that after contusion injury. We evaluated expression of mRNA for myelin proteins usingin situ hybridization with oligonucleotide probes to proteolipid protein (PLP), the major protein in central myelin; myelin basic protein (MBP), a major component of central myelin and a minor component of peripheral myelin; and protein zero (P0), the major structural protein of peripheral myelin, as well as myelin transcription factor 1 (MYT1). We found reduced expression of PLP and MBP chronically after SCI in the dorsal, lateral, and ventral white matter both rostral and caudal to the injury epicenter. Detailed studies of PLP at 2 months after injury indicated that the density of expressing cells was normal but mRNA per cell was reduced. In addition, P0, normally restricted to the peripheral nervous system, was expressed both at the epicenter and in lesioned areas at least 4 mm rostral and caudal to it. Thus, after SCI, abnormal myelination of residual axons may be caused, at least in part, by changes in the transcriptional regulation of genes for myelin proteins and by altered distribution of myelin-producing cells. In addition, the expression of MYT1 mRNA and protein seemed to be upregulated after SCI in a pattern suggesting the presence of undifferentiated progenitor cells in the chronically injured cord.
Capturing episodes: may the frame be with you

We are interested in detecting episodes in a data stream that are characterized by a period of time over which a condition holds, usually with a minimum duration. For example, we might want to know whenever any router has a packet-drop rate above 0.3% continuously for more than two minutes. Such episodes can be interesting in their own right for monitoring purposes, but they can also specify target regions for examination over the original or other stream. For instance, for each router-drop episode we detect, we might want to count the number of control messages the router received. We assert the key requirements are to detect the episodes, detect them accurately, and detect them promptly. Current capabilities for data-stream management systems (DSMSs) include functionality, such as pattern-matching and windowed aggregates, that can help with detecting some kinds of episodes. We offer a third alternative, frames, which generalizes the other two. Frames are intervals that segment a data stream into regions of interest. In contrast to windows, frame boundaries can be data dependent, such as when a predicate holds for a given duration, or the maximum and minimum values of an attribute diverge more than a certain amount. We introduce frames and their theory, plus their implementation in the NiagaraST DSMS. We then demonstrate some advantages of frames versus windows, such as better characterization of episodes, on real data sets and explore an extension, fragments, to deal with long episodes.
Prediction of drug combination chemosensitivity in human bladder cancer

The choice of therapy for metastatic cancer is largely empirical because of a lack of chemosensitivity prediction for available combination chemotherapeutic regimens. Here, we identify molecular models of bladder carcinoma chemosensitivity based on gene expression for three widely used chemotherapeutic agents: cisplatin, paclitaxel, and gemcitabine. We measured the growth inhibition elicited by these three agents in a series of 40 human urothelial cancer cell lines and correlated the GI50 (50% of growth inhibition) values with quantitative measures of global gene expression to derive models of chemosensitivity using a misclassification-penalized posterior approach. The misclassification-penalized posterior–derived models predicted the growth response of human bladder cancer cell lines to each of the three agents with sensitivities of between 0.93 and 0.96. We then developed an in silico approach to predict the cellular growth responses for each of these agents in the clinically relevant two-agent combinations. These predictions were prospectively evaluated on a series of 15 randomly chosen bladder carcinoma cell lines. Overall, 80% of the predicted combinations were correct (P = 0.0002). Together, our results suggest that chemosensitivity to drug combinations can be predicted based on molecular models and provide the framework for evaluation of such models in patients undergoing combination chemotherapy for cancer. If validated in vivo, such predictive models have the potential to guide therapeutic choice at the level of an individual's tumor. [Mol Cancer Ther 2007;6(2):578–86]
Study of possibility of on-pen matching for biometric handwriting verification

In this article the possibility of a biometric on-pen matching for dynamic handwriting-based user authentication is proposed. The approach is based on a digitizer pen originally designed to sample handwritten notes. In a first step the general ability with respect to recognition accuracy is studied to use the digitizer pen (also de-noted as smart pen) for biometric dynamic handwriting acquisition. The evaluation show that the digitizer pen results in similar outcomes as special online signature tablets regarding the chosen measurement, the equal error rate (EER). The second step considers a conceptional study of embedding an authentication algorithm within smart cards, which could be integrated into the pen hardware, thus allowing for on-card matching. In this second analysis, the focus is to study the feasibility of implementing a reference verification algorithm on-card with respect to its computational complexity. The suggested approach will further allow future authentication scenarios, whereby biometrics, secret knowledge and personal possession can be combined. While the possession aspect is maintained due to the fact that the entire authentication system is integrated in the smart pen and remains in ownership of users, the factor of secret knowledge can be integrated by alternative handwritten contents such as personal identification numbers, generally called semantics.
An improved two-dimensional numerical modeling method for E-core transformers

A new 2D modeling method for E-core transformers adds an extended flux return path to the model of the section perpendicular to the core. Averaged with a section parallel to the core modified to have the same reluctance as the 3D model, the finite-element simulation results for total magnetic energy and magnetic energy in the windings are within 0.2-5.6% error relative to the 3D model in fifteen of sixteen cases, a large improvement over existing methods. A refinement of fringing-flux calculations for gapped core legs is also presented.
Developing Parallel Programs

Parallel programming is an extension of sequential programming; today, it is becoming the mainstream paradigm in day-to-day information processing. Its aim is to build the fastest programs on parallel computers. The methodologies for developing a parallel program can be put into integrated frameworks. Development focuses on algorithm, languages, and how the program is deployed on the parallel computer.