Miracle-dz/UniFAR-FTUs huggingface dataset:
High-resolution radar imaging of moving humans using doppler processing and compressed sensing

Frontal radar imaging of human activities may be useful in certain applications, such as through-wall surveillance, where cameras and x-ray sensors cannot be deployed. High-resolution radar images are currently obtained using electrically large antenna apertures operating at high frequencies. However, high frequencies are heavily attenuated by most walls. Also, the implementation of a radar with lots of array elements and associated data acquisition channels is costly and complex. In this paper, we propose methods to generate high-resolution Doppler-enhanced radar images of moving humans at low carrier frequencies with limited number of antenna elements. When a human moves, different body parts give rise to distinct Doppler returns. The key feature of our method is to dynamically resolve multiple body parts of the human across three dimensions: Doppler, azimuth, and elevation. The additional Doppler dimension allows us to relax the resolution requirements in terms of the carrier frequency and number of array elements across the other dimensions. We further reduce the number of array elements below the Nyquist limits by incorporating compressed sensing principles into two-dimensional beamforming because compressed sensing is particularly suited for solving certain types of under-resolved problems.We test our technique on simulated electromagnetic radar scattered data from a moving human for different radar configurations.We also study the robustness of the proposed technique to noise.
Analgesic effects of the COX-2 inhibitor parecoxib on surgical pain through suppression of spinal ERK signaling

Cyclooxygenase (COX)-2 inhibitors are widely used for postoperative pain control in clinical practice. However, it is unknown whether spinal sensitization is involved in the analgesic effects of COX-2 inhibitors on surgical pain. Extracellular signal-regulated kinase (ERK) in the spinal cord is implicated in various types of pain, including surgical pain. The present study investigated the role of spinal ERK signaling in the analgesic effect of the COX-2 inhibitor parecoxib on surgical pain. Surgical pain was produced in rats by surgical incision of the hind paw. Phosphorylated (p)-ERK1/2 expression was determined by immunohistochemistry. Pain hypersensitivity was evaluated by measuring the paw withdrawal threshold using the von Frey test. The selective COX-2 inhibitor parecoxib was delivered 20 min before or 20 min after the incision by intraperitoneal injection. Pretreatment with parecoxib markedly attenuated the pain hypersensitivity induced by incision. However, post-treatment with parecoxib produced minimal analgesic effects. Parecoxib inhibited the increase in spinal p-ERK expression following surgical incision. The present study thus suggests that the COX-2 inhibitor parecoxib exerts its analgesic effect on surgical pain through the inhibition of neuronal ERK activation in the spinal cord. COX-2 inhibitor delivery prior to surgery has more potent analgesic effects, suggesting the advantage of preventive analgesia for post-operative pain control.
Axonal Segregation and Role of the Vesicular Glutamate Transporter VGLUT3 in Serotonin Neurons

A subset of monoamine neurons releases glutamate as a cotransmitter due to presence of the vesicular glutamate transporters VGLUT2 or VGLUT3. In addition to mediating vesicular loading of glutamate, it has been proposed that VGLUT3 enhances serotonin (5-HT) vesicular loading by the vesicular monoamine transporter (VMAT2) in 5-HT neurons. In dopamine (DA) neurons, glutamate appears to be released from specialized subsets of terminals and it may play a developmental role, promoting neuronal growth and survival. The hypothesis of a similar developmental role and axonal localization of glutamate co-release in 5-HT neurons has not been directly examined. Using postnatal mouse raphe neurons in culture, we first observed that in contrast to 5-HT itself, other phenotypic markers of 5-HT axon terminals such as the 5-HT reuptake transporter (SERT) show a more restricted localization in the axonal arborization. Interestingly, only a subset of SERT- and 5-HT-positive axonal varicosities expressed VGLUT3, with SERT and VGLUT3 being mostly segregated. Using VGLUT3 knockout mice, we found that deletion of this transporter leads to reduced survival of 5-HT neurons in vitro and also decreased the density of 5-HT-immunoreactivity in terminals in the dorsal striatum and dorsal part of the hippocampus in the intact brain. Our results demonstrate that raphe 5-HT neurons express SERT and VGLUT3 mainly in segregated axon terminals and that VGLUT3 regulates the vulnerability of these neurons and the neurochemical identity of their axonal domain, offering new perspectives on the functional connectivity of a cell population involved in anxiety disorders and depression.
KAST: K-associative sector translation for NAND flash memory in real-time systems

Flash memory is a good candidate for the storage device in real-time systems due to its non-fluctuating performance, low power consumption and high shock resistance. However, the garbage collection for invalid pages in flash memory can invoke a long blocking time. Moreover, the worst-case blocking time is significantly long compared to the best-case blocking time under the current flash management techniques. In this paper, we propose a novel flash translation layer (FTL), called KAST, where user can configure the maximum log block associativity to control the worst-case blocking time. Performance evaluation using simulations shows that the overall performance of KAST is better than the current FTL schemes as well as KAST guarantees the longest block time is shorter than the specified value.
Securing Mobile Devices with Biotelemetry

As the value of information placed on mobile devices increases, so does the risk that the information will be lost or stolen. In dire scenarios, such as soldiers on the battlefield, there is a tension between accessing critical information quickly and protecting that information from unauthorized viewers. Lightweight body sensors that detect and process physiological information can provide an unconventional means for simultaneously securing data on a mobile device and making pertinent health information available to authorized remote viewers. In this paper we present the design, implementation, and evaluation of our three-tier Secure Mobile Computing (SMC) system. Tier one consists of a physiological sensor (initially an electrocardiograph), microcontroller, and radio (initially Bluetooth) with the form factor of a bandage, collectively termed the "patch. " The patch prototype collects and processes electrocardiograph (ECG) data and transmits the processed information over the wireless channel either continuously or periodically. The primary processing functionality, the heartbeat detection algorithm, has an average accuracy of over 99.5%. Tier two is the mobile device (e.g., cell phone, PDA, or laptop). SMC makes the utility of the mobile device dependent upon receipt of the patch's telemetry signal. SMC supports a number of programmable security policies that can either lock (e.g., encrypt) or erase data if the user is incapacitated or the mobile device loses proximity to the patch. Tier three is a web service that allows authorized viewers to view the sensor information remotely. We explore how SMC manages the interfaces between the tiers to implement security policies on the mobile device.
The relation between metacognitive monitoring and control

Introspective observation is what we have to rely on first and foremost and always. William James ( Principles of psychology , p. 185) William James' (1890) quote foreshadowed the current focus in the field of metacognition, the relation between monitoring and control. Monitoring means the ability to judge successfully one's own cognitive processes, and control means the ability to use those judgments to alter behavior. This chapter, like much of current metacognition research, concerns how we apply our judgments to alter our behavior, both during learning and during remembering. About twenty years ago, Cavanaugh and Perlmutter (1982) wrote that the “present state of metamemory is not good …” (p. 22), and Flavell (1982) wrote that “none of us has yet come up with deeply insightful, detailed proposals about what metacognition is, how it operates, and how it develops” (p. 28). Some thought that metacognition was no longer a worthwhile topic (see Marshall and Morton, 1978; Wellman, 1983). However, starting with the seminal work of Thomas Nelson and his colleagues (e.g. Nelson, 1984; Nelson et al., 1982; Nelson et al., 1986), metacognition made a strong comeback. Whereas the research on metacognition prior to the 1980s was dominated by research directed at developmental processes (e.g. Flavell, 1979), research in the 1980s and 1990s was dominated by cognitive psychologists interested in already-developed processes in adults. It seems, though, that in order to maximize the knowledge gained from both lines of research, a marriage between the two is needed.
Assessing Sparse Coding Methods for Contextual Shape Indexing of Maya Hieroglyphs

Bag-of-visual-words or bag-of-visterms (bov) is a common technique used to index Multimedia information with the purposes of retrieval and classification. In this work we address the problem of constructing efficient bov representations of complex shapes as are the Maya syllabic hieroglyphs. Based on retrieval experiments, we assess and evaluate the performance of several variants of the recent sparse coding method KSVD, and compare it with the traditional k-means clustering algorithm. We investigate the effects of a thresholding procedure used to facilitate the sparse decomposition of signals that are potentially sparse, and we also assess the performance of different pooling techniques to construct bov representations. Although the bov's computed via Sparse Coding do not outperform the retrieval precision of those computed by k-means, they achieve competitive results after an adequate enforcement of the sparsity, which leads to more discriminative bag representations with respect to using the original non-sparse descriptors. Also, we propose a simplified formulation of the HOOSC descriptor that improves the retrieval performance.
A longitudinal social network analysis of the editorial boards of medical informatics and bioinformatics journals.

OBJECTIVE The goal of this research is to learn how the editorial staffs of bioinformatics and medical informatics journals provide support for cross-community exposure. Models such as co-citation and co-author analysis measure the relationships between researchers; but they do not capture how environments that support knowledge transfer across communities are organized. METHODS In this paper, we propose a social network analysis model to study how editorial boards integrate researchers from disparate communities. We evaluate our model by building relational networks based on the editorial boards of approximately 40 journals that serve as research outlets in medical informatics and bioinformatics. We track the evolution of editorial relationships through a longitudinal investigation over the years 2000 through 2005. RESULTS Our findings suggest that there are research journals that support the collocation of editorial board members from the bioinformatics and medical informatics communities. Network centrality metrics indicate that editorial board members are located in the intersection of the communities and that the number of individuals in the intersection is growing with time. CONCLUSIONS Social network analysis methods provide insight into the relationships between the medical informatics and bioinformatics communities. The number of editorial board members facilitating the publication intersection of the communities has grown, but the intersection remains dependent on a small group of individuals and fragile.
CONSTRUCTION OF AN INDIVIDUALIZED VISUAL SPEECH-SYNTHESIZER FROM ORTHOGONAL 2 D-IMAGES

In speech communication, the visual information plays an important role. This information, such as lips movement and face expression, is perceived and processed by humans. They can enhance the speech intelligibility, especially in a noisy environment. It is therefore advantageous to incorporate them in a speech synthesis system. This audiovisual speech synthesizer is widely known as talking head. The visual speech synthesizer constructed here is a first step towards creating a complete talking head. The visual speech-synthesizer constructed in this work is an individualized 3D-face model consisting of 283 3D-points with 6 articulatory parameters, which control the movement of the speech-articulators (jaw, lips, and larynx). This model is created by a non-linear modification of an existing 3D-face model based on a set of the individual’s face features. For this purpose, two orthogonal images (i.e. the frontal and profile images) of the person are utilized. The facial features in the frontal and profile images are extracted manually. The texture image of the individual is then mapped to the constructed face-model, to give it a natural look. The articulatory parameters are determined using PCA (Principal Component Analysis) performed on the selected subsets of the 3D-model points.
An innovative local adaptive voltage scaling architecture for on-chip variability compensation

In this paper we propose a fine-grain Adaptive Voltage and Frequency Scaling (LAVFS) architecture to optimize energy efficiency in presence of in-die variability. For each System-on-Chip power domain, we use a Dynamic Voltage Scaling technique called ‘Vdd-Hopping’ as efficient as DC/DC power converters but much easier to integrate and control at fine-grain. Unlike previous AVS schemes, the supply voltages are fixed and the functional core frequencies are adjusted according to local variability. The hopping ratio is then dynamically determined to achieve an applicative performance target. The proposed Local AVFS architecture based on the dynamic hopping solution and its application to GALS architectures is proposed.
Enhancing stress management techniques using virtual reality

Chronic stress is one of the major problems in our current fast paced society. The body reacts to environmental stress with physiological changes (e.g. accelerated heart rate), increasing the activity of the sympathetic nervous system. Normally the parasympathetic nervous system should bring us back to a more balanced state after the stressful event is over. However, nowadays we are often under constant pressure, with a multitude of stressful events per day, which can result in us constantly being out of balance. This highlights the importance of effective stress management techniques that are readily accessible to a wide audience. In this paper we present an exploratory study investigating the potential use of immersive virtual reality for relaxation with the purpose of guiding further design decisions, especially about the visual content as well as the interactivity of virtual content. Specifically, we developed an underwater world for head-mounted display virtual reality. We performed an experiment to evaluate the effectiveness of the underwater world environment for relaxation, as well as to evaluate if the underwater world in combination with breathing techniques for relaxation was preferred to standard breathing techniques for stress management. The underwater world was rated as more fun and more likely to be used at home than a traditional breathing technique, while providing a similar degree of relaxation.
Towards Optimal Symbolization for Time Series Comparisons

The abundance and value of mining large time series data sets has long been acknowledged. Ubiquitous in fields ranging from astronomy, biology and web science the size and number of these datasets continues to increase, a situation exacerbated by the exponential growth of our digital footprints. The prevalence and potential utility of this data has led to a vast number of time-series data mining techniques, many of which require symbolization of the raw time series as a pre-processing step for which a number of well used, pre-existing approaches from the literature are typically employed. In this work we note that these standard approaches are sub-optimal in (at least) the broad application area of time series comparison leading to unnecessary data corruption and potential performance loss before any real data mining takes place. Addressing this we present a novel quantizer based upon optimization of comparison fidelity and a computationally tractable algorithm for its implementation on big datasets. We demonstrate empirically that our new approach provides a statistically significant reduction in the amount of error introduced by the symbolization process compared to current state-of-the-art. The approach therefore provides a more accurate input for the vast number of data mining techniques in the literature, providing the potential of increased real world performance across a wide range of existing data mining algorithms and applications.
Spin-Based Computing: Device Concepts, Current Status, and a Case Study on a High-Performance Microprocessor

As the end draws near for Moore's law, the search for low-power alternatives to complementary metal-oxide-semiconductor (CMOS) technology is intensifying. Among the various post-CMOS candidates, spintronic devices have gained special attention for their potential to overcome the power and performance limitations of CMOS. In particular, all spin logic (ASL) technology, which performs Boolean operations and transfers the output in the spin domain, has been proposed for enabling new capabilities-such as high density, low device count, and nonvolatility-that were previously impossible with CMOS technology. In this paper, first we provide an overview of the history and the current status of the various spintronic devices being pursued by the research community. Then, we describe how spin-based components are integrated into a computing system and the advantages that result. We use a hypothetical spintronic-based Intel Core i7 as a test vehicle to compare the system-level power requirements of ASL- and CMOS-based systems, taking into consideration the unique demands of spin-based interconnects. We conclude with a brief analysis of current limitations and future directions of spintronic research.
Detection of climate change‐driven trends in phytoplankton phenology

The timing of the annual phytoplankton spring bloom is likely to be altered in response to climate change. Quantifying that response has, however, been limited by the typically coarse temporal resolution (monthly) of global climate models. Here, we use higher resolution model output (maximum 5 days) to investigate how phytoplankton bloom timing changes in response to projected 21st century climate change, and how the temporal resolution of data influences the detection of long‐term trends. We find that bloom timing generally shifts later at mid‐latitudes and earlier at high and low latitudes by ~5 days per decade to 2100. The spatial patterns of bloom timing are similar in both low (monthly) and high (5 day) resolution data, although initiation dates are later at low resolution. The magnitude of the trends in bloom timing from 2006 to 2100 is very similar at high and low resolution, with the result that the number of years of data needed to detect a trend in phytoplankton phenology is relatively insensitive to data temporal resolution. We also investigate the influence of spatial scales on bloom timing and find that trends are generally more rapidly detectable after spatial averaging of data. Our results suggest that, if pinpointing the start date of the spring bloom is the priority, the highest possible temporal resolution data should be used. However, if the priority is detecting long‐term trends in bloom timing, data at a temporal resolution of 20 days are likely to be sufficient. Furthermore, our results suggest that data sources which allow for spatial averaging will promote more rapid trend detection.
Device analyzer: large-scale mobile data collection

We collected usage information from 12,500 Android devices in the wild over the course of nearly 2 years. Our dataset contains 53 billion data points from 894 models of devices running 687 versions of Android. Processing the collected data presents a number of challenges ranging from scalability to consistency and privacy considerations. We present our system architecture for collection and analysis of this highly-distributed dataset, discuss how our system can reliably collect time-series data in the presence of unreliable timing information, and discuss issues and lessons learned that we believe apply to many other big data collection projects.
High‐Resolution Volumetric Computation of Offset Surfaces with Feature Preservation

We present a new algorithm for the efficient and reliable generation of offset surfaces for polygonal meshes. The algorithm is robust with respect to degenerate configurations and computes (self‐)intersection free offsets that do not miss small and thin components. The results are correct within a prescribed ε‐tolerance. This is achieved by using a volumetric approach where the offset surface is defined as the union of a set of spheres, cylinders, and prisms instead of surface‐based approaches that generally construct an offset surface by shifting the input mesh in normal direction. Since we are using the unsigned distance field, we can handle any type of topological inconsistencies including non‐manifold configurations and degenerate triangles. A simple but effective mesh operation allows us to detect and include sharp features (shocks) into the output mesh and to preserve them during post‐processing (decimation and smoothing). We discretize the distance function by an efficient multi‐level scheme on an adaptive octree data structure. The problem of limited voxel resolutions inherent to every volumetric approach is avoided by breaking the bounding volume into smaller tiles and processing them independently. This allows for almost arbitrarily high voxel resolutions on a commodity PC while keeping the output mesh complexity low. The quality and performance of our algorithm is demonstrated for a number of challenging examples.
Belief-based nonlinear rescoring in Thai speech understanding

This paper proposes an approach to improve speech understanding based on rescoring of N-best semantic hypotheses. In rescoring, probabilities produced by an understanding component are combined with additional probabilities derived from system beliefs. While a normal rescoring approach is to multiply or linearly interpolate with belief probabilities, this paper shows that probabilities from various sources are better combined using a nonlinear estimator. Using the proposed model together with a dialogue-state dependent semantic model shows a significant improvement when applying to a Thai interactive hotel reservation agent (TIRA), the first spoken dialogue system in Thai language.