Miracle-dz/UniFAR-FTUs huggingface dataset:
Modulation of prefrontal connectivity in postherpetic neuralgia patients with chronic pain: a resting-state functional magnetic resonance-imaging study

Background Although the interaction between pain and cognition has been recognized for decades, the neural substrates underlying their association remain unclear. The prefrontal cortex (PFC) is known as a critical brain area for higher cognitive functions, as well as for pain perception and modulation. The objective of the present study was to explore the role of the PFC in the interaction between chronic pain and cognitive functions by examining the relationship between spontaneous activity in the frontal lobe and pain intensity reported by postherpetic neuralgia (PHN) patients. Methods Resting-state functional magnetic resonance imaging data from 16 PHN patients were collected, and regional homogeneity and related functional connectivity were analyzed. Results The results showed negative correlations between patients’ pain scores and regional homogeneity values in several prefrontal areas, including the left lateral PFC, left medial PFC, and right lateral orbitofrontal cortex (P<0.05, AlphaSim-corrected). Further analysis revealed that the functional connectivity of some of these prefrontal areas with other cortical regions was also modulated by pain intensity. Therefore, functional connections of the left lateral PFC with both the left parietal cortex and the left occipital cortex were correlated with patients’ pain ratings (P<0.05, AlphaSim-corrected). Similarly, functional connectivity between the right lateral orbitofrontal cortex and bilateral postcentral/precentral gyri was also correlated with pain intensity in the patients (P<0.05, AlphaSim-corrected). Conclusion Our findings indicate that activity in the PFC is modulated by chronic pain in PHN patients. The pain-related modulation of prefrontal activity may serve as the neural basis for interactions between chronic pain and cognitive functions, which may link to cognitive impairments observed in chronic pain patients.
RNN Encoder-Decoder for the inference of regular human mobility patterns

In this study, we proposed a deep learning model to infer the daily individual mobility pattern from static census data. Our work was inspired by Google Brain team work on machine learning system to automatically produce captions that accurately describe images using recurrent encoder-decoder model. They also use a convolutional neural network to exploit the strong spatially local correlation present in their structured data i.e., images. Unfortunately, survey data are generally heterogeneous with unknown local structure. Thus we have adapted their model using instead an appropriate mixed-variate version of restricted Boltzmann machine (MVRBM). This leads to estimation of daily regular mobility displacements in the form of variable length sequence given input individual attributes. The prime strength of our approach is that the resulting mobility flows inherit all the attributes contained in the input census which are typically missing in portable digital media data. Moreover, the model makes use of land use and point of interest data. optimized in this way, the model is scalable to apply to other places conditioned with censuses availability. Finally, it has been validated and showed its efficiency in a real context.
Formal and computational model of Adam Smith’s Invisible Hand

Since 1756 Adam Smith’s Invisible Hand phenomena is considered an unproven, even harmful myth. This paper proves that a computational model of this phenomena can be proposed on the basis of spontaneous, unconscious computations over a social structure. Such computations can be described with the help of molecular model of computations. For needs of Invisible Hand detection, an Extended Turing Test has been proposed. It compares two anonymous social structures, to detect if the Invisible Hand emerged in one without authority, allowing the social structure to perform better. This allows us to assemble together Smith’s, Hayek’s and Nozick’s thoughts on the Invisible Hand, into one consistent formal and computational model. From this a new control paradigm can be derived. This explains how ants build bridges with their bodies, to provide further optimization of their supply routes. As a result, practical applications can be proposed, on how to structure Collective Intelligence of team of ant-like robots, for rescue works after earthquake.
An Experimental Perspective on Sampling Methods for Imbalanced Learning From Financial Databases

The financial market is one of the major consumers of data mining techniques, and the main reason is their efficiency to analyze complex data. One important trait shared between most financial applications is class imbalance. Since traditional classification methods assume nearly balanced classes and equal misclassification costs, they usually fail to deal with imbalanced data. However, in financial contexts, problems are usually imbalanced, and instances from the minority class are known for deficits of millions of dollars every year, e.g., credit card frauds, money laundering transactions and so forth. Over the years, several techniques for dealing with class imbalance have been developed, such as sampling techniques and algorithm adaptations. In this study, we analyze how different sampling techniques impact the performance of different classification systems on financial applications. Results show that, for the given datasets, sampling techniques allow the improvement of prediction performance of the minority class while also improving overall classification rates. Nevertheless, their use often deteriorates the performance in predicting the majority class.
A Refined 3D Dataset for the Analysis of Player Actions in Exertion Games

Modeling and accurately analyzing human activities plays an important role, considering the rise of modern applications in human-computer interaction and, more recently, exertion games. Especially in serious exergames aimed at tutoring (e.g. sports) or rehabilitation and physiotherapy, the need for accurate detection of the human body and its motion is uncompromising. However, modern human skeleton tracking techniques suffer from a variety of issues, such as jittering and sensitivity to original conditions. In this study we show how a simple yet effective fairing pipeline on an inherently noisy dataset can produce data capable for precise experimentation with state-of-the-art human action modeling algorithms.
A Review of Near-Memory Computing Architectures: Opportunities and Challenges

The conventional approach of moving stored data to the CPU for computation has become a major performance bottleneck for emerging scale-out data-intensive applications due to their limited data reuse. At the same time, the advancement in integration technologies have made the decade-old concept of coupling compute units close to the memory (called Near-Memory Computing) more viable. Processing right at the "home" of data can completely diminish the data movement problem of data-intensive applications. This paper focuses on analyzing and organizing the extensive body of literature on near-memory computing across various dimensions: starting from the memory level where this paradigm is applied, to the granularity of the application that could be executed on the near-memory units. We highlight the challenges as well as the critical need of evaluation methodologies that can be employed in designing these special architectures. Using a case study, we present our methodology and also identify topics for future research to unlock the full potential of near-memory computing.
Receiver Design in Molecular Communications: An Approach Based on Artificial Neural Networks

The design of communication systems typically relies on the development of mathematical models that describe the underlying communication channel. In many communication systems, however, accurate channel models may not be known, or the models may not be accurate enough or even not available for efficient system design. In these scenarios, a completely new approach to communication system design and analysis is required. An example of such situations arises in the emerging research field of molecular communications, for which it is very difficult to develop accurate analytical models for several operating scenarios. In this context, the use of data-driven techniques based on artificial neural networks may provide an alternative and suitable solution towards the design and analysis of molecular communication systems. In this paper, we explore the potential of artificial neural networks for application to the design of robust receiver schemes. We study a molecular communication system in the presence of inter-symbol interference and show that a receiver based on artificial neural networks can be trained by using only empirical (raw) data and can provide the same performance as a receiver that has perfect knowledge of the underlaying channel model.
Heme oxygenase-1 protects airway epithelium against apoptosis by targeting the proinflammatory NLRP3–RXR axis in asthma

Asthma is thought to be caused by malfunction of type 2 T helper cell (Th2)–mediated immunity, causing excessive inflammation, mucus overproduction, and apoptosis of airway epithelial cells. Heme oxygenase-1 (HO-1) functions in heme catabolism and is both cytoprotective and anti-inflammatory. We hypothesized that this dual function may be related to asthma's etiology. Using primary airway epithelial cells (pAECs) and an asthma mouse model, we demonstrate that severe lung inflammation is associated with rapid pAEC apoptosis. Surprisingly, NOD-like receptor protein 3 (NLRP3) inhibition, retinoid X receptor (RXR) deficiency, and HO-1 induction were associated with abrogated apoptosis. MCC950, a selective small-molecule inhibitor of canonical and noncanonical NLRP3 activation, reduced RXR expression, leading to decreased pAEC apoptosis that was reversed by the RXR agonist adapalene. Of note, HO-1 induction in a mouse model of ovalbumin-induced eosinophilic asthma suppressed Th2 responses and reduced apoptosis of pulmonary pAECs. In vitro, HO-1 induction desensitized cultured pAECs to ovalbumin-induced apoptosis, confirming the in vivo observations. Critically, the HO-1 products carbon monoxide and bilirubin suppressed the NLRP3–RXR axis in pAECs. Furthermore, HO-1 impaired production of NLRP3–RXR–induced cytokines (interleukin [IL]-25, IL-33, thymic stromal lymphopoietin, and granulocyte-macrophage colony-stimulating factor) in pAECs and lungs. Finally, we demonstrate that HO-1 binds to the NACHT domain of NLRP3 and the RXRα and RXRβ subunits and that this binding is not reversed by Sn-protoporphyrin. Our findings indicate that HO-1 and its products are essential for pAEC survival to maintain airway epithelium homeostasis during NLRP3–RXR–mediated apoptosis and inflammation.
D-SET Mitigation Using Common Clock Tree Insertion Techniques for Triple-Clock TMR Flip-Flop

The paper presents a strategy for mitigation of SETs on a data path using three-clock input TMR (Triple Modular Redundancy) flip-flop cells (ϕTMR approach) as an alternative to the widely used TMR approach with local delay filtering (ΔTMR). The proposed flow enables the use of the common clock tree skewing techniques and is fully compatible with the standard digital design flow. The ϕTMR architecture is presented, and a shift-register test circuit is implemented in 130nm BiCMOS technology to compare the ϕTMR with the ΔTMR. Obtained results have shown that the ϕTMR approach results in power savings of 25% compared to ΔTMR. Furthermore, the robustness is increased by reducing the peak currents about 60% for the clock network, and 20% for the total current when ϕTMR is selected.
Multi ROI and Multi Map Networks for Accurate and Efficient Pedestrian Detection

Pedestrian detection as a key problem of computer vision has many comprehensive applications such as smart transportation, video surveillance and self-driving car. In this work, we explore the application of Faster R-CNN for pedestrian detection. We found that Faster R-CNN has not shown competitive results as we expected. The main challenges for Faster R-CNN may be small objects and hard negative examples. More interestingly, we found that when the RPN and the proposal classifier are trained separately and simply combined, the detection results can be significantly improved. Based on these observations, we designed a multi-map network and a multi-ROI network to solve these two problems. The multi-map network not only utilizes the convolutional feature maps output by last convolutional layer but also exploits the previous feature maps which have higher resolution but less semantic information. The multi-ROI network converts one bounding box to three bounding boxes including enlarged one, shrinking one and original one. Then, the network fuses the features of these three bounding boxes into one robust and discriminative feature. In addition to comparing these two networks with Faster R-CNN, we also merged them into one unified and end to end network called multi-ROI-map (MRM) network. Finally, we trained the RPN and MRM separately and fused their detection results. Overall, our method achieves a competitive result compared with some leading methods on the Caltech dataset and executes 2 times faster than these methods.
Logistic Regression of Point Matches for Accurate Transformation Estimation

Feature extraction and matching (FEM) has been widely used for the registration of partially overlapping 3D shapes. Due to various factors such as imaging noise, simple geometry, or clutter, it usually introduces false positive ones. To reliably estimate the underlying transformation that brings one partial shape into the best possible alignment with another, it is critical to estimate the extent to which the established point matches are correct. To this end, we propose to use the logit function for the regression of the errors of these point matches. The novel method includes three steps: (i) normalization of the errors of the point matches, (ii) logistic regression of the point matches for the estimation of their reliabilities/weights, and (iii) estimation of the underlying transformation in the weighted least squares sense. These steps are repeated until either the maximum number of iterations has been reached or the weighted average of the errors of the point matches has been below the scanning resolution. A comparative study using real data captured by different range sensors shows that the proposed method outperforms two state-of-the-art ones for more accurate estimation of the underlying transformation.
Maximal strength training: the impact of eccentric overload.

The search for the most potent strength training intervention is continuous. Maximal strength training (MST) yields large improvements in force-generating capacity (FGC), largely attributed to efferent neural drive enhancement. However, it remains elusive whether eccentric overload, before the concentric phase, may augment training-induced neuromuscular adaptations. A total of 53 23 ± 3 (SD)-yr-old untrained males were randomized to either a nontraining control group (CG) or one of two training groups performing leg press strength training with linear progression, three times per week for 8 wk. The first training group carried out MST with four sets of four repetitions at ~90% one-repetition maximum (1RM) in both action phases. The second group performed MST with an augmented eccentric load of 150% 1RM (eMST). Measurements were taken of 1RM and rate of force development (RFD), countermovement jump (CMJ) performance, and evoked potentials recordings [V-wave (V) and H-reflex (H) normalized to M-wave (M) in musculus soleus]. 1RM increased from 133 ± 16 to 157 ± 23 kg and 123 ± 18 to 149 ± 22 kg and CMJ by 2.3 ± 3.6 and 2.2 ± 3.7cm for MST and eMST, respectively (all P < 0.05). Early, late, and maximal RFD increased in both groups [634-1,501 N/s (MST); 644-2,111 N/s (eMST); P < 0.05]. These functional improvements were accompanied by increased V/M-ratio (MST: 0.34 ± 0.11 to 0.42 ± 14; eMST: .36 ± 0.14 to 0.43 ± 13; P < 0.05). Resting H/M-ratio remained unchanged. Training-induced improvements did not differ. All increases, except for CMJ, were different from the CG. MST is an enterprise for large gains in FGC and functional performance. Eccentric overload did not induce additional improvements, suggesting firing frequency and motor unit recruitment during MST may be maximal. NEW & NOTEWORTHY This is the first study to apply evoked potential recordings to investigate effects on efferent neural drive following high-intensity strength training with and without eccentric overload in a functionally relevant lower extremity exercise. We document that eccentric overload does not augment improvements in efferent neural drive or muscle force-generating capacity, suggesting that high-intensity concentric loads may maximally tax firing frequency and motor unit recruitment.
ASAP: Fast, Approximate Graph Pattern Mining at Scale

While there has been a tremendous interest in processing data that has an underlying graph structure, existing distributed graph processing systems take several minutes or even hours to mine simple patterns on graphs. This paper presents ASAP, a fast, approximate computation engine for graph pattern mining. ASAP leverages state-of-the-art results in graph approximation theory, and extends it to general graph patterns in distributed settings. To enable the users to navigate the tradeoff between the result accuracy and latency, we propose a novel approach to build the Error-Latency Profile (ELP) for a given computation. We have implementedASAP on a general-purpose distributed dataflow platform and evaluated it extensively on several graph patterns. Our experimental results show that ASAP outperforms existing exact pattern mining solutions by up to 77×. Further, ASAP can scale to graphs with billions of edges without the need for large clusters.
Write-Optimized and High-Performance Hashing Index Scheme for Persistent Memory

Non-volatile memory (NVM) as persistent memory is expected to substitute or complement DRAM in memory hierarchy, due to the strengths of non-volatility, high density, and near-zero standby power. However, due to the requirement of data consistency and hardware limitations of NVM, traditional indexing techniques originally designed for DRAM become inefficient in persistent memory. To efficiently index the data in persistent memory, this paper proposes a write-optimized and high-performance hashing index scheme, called level hashing, with low-overhead consistency guarantee and cost-efficient resizing. Level hashing provides a sharing-based two-level hash table, which achieves a constant-scale search/insertion/deletion/update time complexity in the worst case and rarely incurs extra NVM writes. To guarantee the consistency with low overhead, level hashing leverages log-free consistency schemes for insertion, deletion, and resizing operations, and an opportunistic log-free scheme for update operation. To cost-efficiently resize this hash table, level hashing leverages an inplace resizing scheme that only needs to rehash 1/3 of buckets instead of the entire table, thus significantly reducing the number of rehashed buckets and improving the resizing performance. Experimental results demonstrate that level hashing achieves 1:4×-3:0× speedup for insertions, 1:2×-2:1× speedup for updates, and over 4:3× speedup for resizing, while maintaining high search and deletion performance, compared with state-of-the-art hashing schemes.
Augmenting The Size of EEG datasets Using Generative Adversarial Networks

Electroencephalography (EEG) is one of the most promising methods in the field of Brain-Computer Interfaces (BCIs) due to its rich time-domain resolution and the availability of advanced and portable sensor technology. One of the major challenges for EEG signal analysis is the small size of its datasets as it is usually demanding for human subjects to perform lengthy experiments. Consequently, this challenge can limit the performance of EEG signal classification models. In this paper, we propose a novel generative adversarial network (GAN) model that can learn the statistical characteristics of the EEG signal and augment its datasets size to enhance the performance of classification models. Results show that the proposed model significantly outperforms other generative models on the utilized EEG dataset. Furthermore, it significantly enhances the performance of classification models working on small size EEG datasets after augmenting them with generated samples.
Improving Instance Selection via Metric Learning

The k-Nearest Neighbor (k-NN) rule is widely used for classification tasks because of its simplicity and efficiency. However, a well-known drawback of k-NN is its dependence on the quality of the training set, since the k-NN makes no assumption about the importance of each instance. In fact, the existence of noisy and superfluous instances in the training set tends to increase the classification error rate. Thus, instance selection methods are useful to identify which instances belonging to the training set will be considered in the k-NN classifier. Our proposal shows a simple and effective way to improve instance selection methods using metric learning. The idea of our proposal relies on a pure geometric intuition that metric learning transforms the input space where points in the same class are simultaneously near each other and far from points in the other classes. In a more “organised” space, we show that instance selection methods can benefit from this transformed space. We carried out an experimental evaluation to compare the instance selection with and without metric learning on UCI benchmark data sets. The results reveals that the combination of metric and instance selection is very welcome. All tested instance selection methods improved significantly.
A High Refractive Index Plasmonic Sensor Based on D-Shaped Photonic Crystal Fiber With Laterally Accessible Hollow-Core

A unique surface plasmon resonance (SPR) sensor based on photonic crystal fiber (PCF) is proposed in this paper, which consists of a D-shaped profile where a gold film is coated on its flat surface and a laterally accessible hollow core in which the analyte is infiltrated. The spectral sensitivity and the amplitude sensitivity of the proposed sensor are investigated by the finite element method when it is completely and partly immersed into the analyte solution. Simulation results indicate that the proposed sensor can detect the refractive index of the analyte higher than that of the PCF background, and shows higher sensitivity when its core portion is immersed into the analyte. In this way, this sensor can provide the ability of anti-interference from the metal coating. The proposed sensor can be conveniently coated with metal films and provides a possibility for real time sensing, which are not possible with traditional PCF-based SPR sensors.
Trends in On-chip Dynamic Resource Management

The Complexity of emerging multi/many-core architectures and diversity of modern workloads demands coordinated dynamic resource management methods. We introduce a classification for these methods capturing the utilized resources and metrics. In this work, we use this classification to survey the key efforts in dynamic resource management. We first cover heuristic and optimization methods used to manage resources such as power, energy, temperature, Quality-of-Service (QoS) and reliability of the system. We then identify some of the machine learning based methods used in tuning architectural parameters in computer systems. In many cases, resource managers need to enforce design constraints during runtime with a certain level of guarantee. Hence, we also study the trend in deploying formal control theoretic approaches in order to achieve efficient and robust dynamic resource management.