Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient multiclass classification with duet

In the upcoming era of edge computing, the capability to perform fast training and classification at the edge is an increasing need due to limited connectivity, hardware resources, privacy concerns, profitability, and more. Accordingly, we propose a new classifier termed Duet. Duet incorporates the advantages of bagging and boosting decision-tree-based ensemble methods (DTEMs) by using two classifiers instead of a monolithic one. A simple bagging model is trained using the entire training dataset and is responsible for capturing the easier concepts. Then, a boosting model is trained using only a fraction of the dataset representing the concepts the bagging model finds hard. To make the whole process resource efficient, we develop a new heuristic approach to rank data with respect to concepts that the bagging model finds hard. We use this approach, termed data instance predictability to determine the dataset fraction for the boosting model training. We implement Duet as a scikit-learn classifier. Evaluation using datasets from different domains and with different characteristics indicates that Duet offers a better tradeoff between classification accuracy and system performance than monolithic DTEMs. Moreover, in an evaluation over a resource-constrained Raspberry Pi 3 device Duet successfully completes all training tasks, where some monolithic models fail due to insufficient resources, indicating broader applicability of Duet to resource-constrained edge devices. Duet is a part of an effort for advancements in resource-efficient classification, and its scikit-learn implementation can be found in https://research.vmware. com/projects/efficient-machine-learning-classification.
DyFiP: explainable AI-based dynamic filter pruning of convolutional neural networks

Filter pruning is one of the most effective ways to accelerate Convolutional Neural Networks (CNNs). Most of the existing works are focused on the static pruning of CNN filters. In dynamic pruning of CNN filters, existing works are based on the idea of switching between different branches of a CNN or exiting early based on the hardness of a sample. These approaches can reduce the average latency of inference, but they cannot reduce the longest-path latency of inference. In contrast, we present a novel approach of dynamic filter pruning that utilizes explainable AI along with early coarse prediction in the intermediate layers of a CNN. This coarse prediction is performed using a simple branch that is trained to perform top-k classification. The branch either predicts the output class with high confidence, in which case the rest of the computations are left out. Alternatively, the branch predicts the output class to be within a subset of possible output classes. After this coarse prediction, only those filters that are important for this subset of classes are then evaluated. The importances of filters for each output class are obtained using explainable AI. Using this concept of dynamic pruning, we are able not only to reduce the average latency of inference, but also the longest-path latency of inference. Our proposed architecture for dynamic pruning can be deployed on different hardware platforms.
FlexHTTP: an intelligent and scalable HTTP version selection system

HTTP has been the primary protocol for web data transmission for decades. Since the late 1990s, HTTP/1.1 has been widely used. Recently, both HTTP/2 and HTTP/3 have been proposed to achieve a better experience on web browsing. However, it is still unclear which of them performs better in different scenarios. In this paper, we first leverage the controllable experimental environment of the Emulab testbed to conduct a series of measurements and find that under different network conditions and web page structures, neither HTTP/2 nor HTTP/3 can always perform better. Motivated by this finding, we propose FlexHTTP, an intelligent and scalable HTTP version selection system. FlexHTTP embeds a supervised machine learning-based classifier to select the appropriate HTTP version according to network conditions and web page structures. FlexHTTP adopts a set of distributed agent servers to ensure scalability and keep the classifier up-to-date under network dynamics. We implement a proof-of-concept prototype of FlexHTTP on the Emulab testbed. Experiments show that FlexHTTP achieves a reduction of Speed Index by up to 600ms.
Toward Reliable Non-Line-of-Sight Localization Using Multipath Reflections

The past decade's research in RF indoor localization has led to technologies with decimeter-level accuracy under controlled experimental settings. However, existing solutions are not reliable in challenging environments with rich multipath and various occlusions. The errors can be 3-5 times compared to settings with clear LoS paths. In addition, when the direct path is completely blocked, such approaches would generate wrong location estimates. In this paper, we present NLoc, a reliable non-line-of-sight localization system that overcomes the above limitations. The key innovation of NLoc is to convert multipath reflections to virtual direct paths to enhance the localization performance. To this end, NLoc first extracts reliable multi-dimensional parameters by characterizing phase variations. Then, it models the relation between the target location and the geometric features of multipath reflections to obtain virtual direct paths. Finally, it incorporates novel algorithms to remove random ToF offsets due to lack of synchronization and compensate target orientation that determines the geometric features, for accurate location estimates. We implement NLoc on commercial off-the-shelf WiFi devices. Our experiments in multipath challenged environments with dozens of obstacles and occlusions demonstrate that NLoc outperforms state-of-the-art approaches by 44% at the median and 200% at 90% percentile.
On the Feasibility of Securing Vehicle-Pavement Interaction

Road surface information (e.g., smooth road or bumpy road with potholes or bumps) is important for safe driving (i.e., it's necessary to be aware of the road surface conditions during driving). However, the high-cost sensor (e.g., LiDAR and camera) based road surface sensing approaches cannot work properly in inclement weather conditions (e.g., fogging and snowing) due to the line-of-sight requirement. The low-cost and ubiquitous smartphone-based road surface sensing approach is not reliable and safe to use, since it relies on the vibration of the vehicle body to sense the road surface (i.e., the vehicle's tires need to touch the bumps on the road surface). Can we automate the contact-free road surface sensing with low-cost sensors for safe driving without requiring the vehicle's tires to touch the bumps on the road surface? In this paper, we propose Tago, a system that can achieve contact-free road surface sensing with commodity passive RFID tags. Instead of deploying RFID tags or readers along the road or lamp post (i.e., infrastructure-based deployment), we deploy the reader inside of the vehicle and attach the tag and the reader's antenna at the front end of the vehicle like the vehicle's headlights (i.e., infrastructure-free deployment). However, there is a great challenge to obtain the clean reflection from the road surface, since the reflection may be drown in the backscattered signals due to multipath effect. Moreover, it is not reliable to use the composite signals received at the reader to sense the road surface conditions. Therefore, we first comprehensively analyse the variation of composite signals received at the reader. Then, we propose a signal cancellation approach to extract the clean reflections from the road surface, such that we can accurately sense the road surface conditions for safe driving. Our experiments with different vehicles (e.g., Honda Civic Frankenfish, Folsom, Flutter and CR-V Warner) driven on different roadways (e.g., urban and residential area) show that Tago can effectively sense the road surface information.
EyeQoE

As virtual reality (VR) offers an unprecedented experience than any existing multimedia technologies, VR videos, or called 360-degree videos, have attracted considerable attention from academia and industry. How to quantify and model end users' perceived quality in watching 360-degree videos, or called QoE, resides the center for high-quality provisioning of these multimedia services. In this work, we present EyeQoE, a novel QoE assessment model for 360-degree videos using ocular behaviors. Unlike prior approaches, which mostly rely on objective factors, EyeQoE leverages the new ocular sensing modality to comprehensively capture both subjective and objective impact factors for QoE modeling. We propose a novel method that models eye-based cues into graphs and develop a GCN-based classifier to produce QoE assessment by extracting intrinsic features from graph-structured data. We further exploit the Siamese network to eliminate the impact from subjects and visual stimuli heterogeneity. A domain adaptation scheme named MADA is also devised to generalize our model to a vast range of unseen 360-degree videos. Extensive tests are carried out with our collected dataset. Results show that EyeQoE achieves the best prediction accuracy at 92.9%, which outperforms state-of-the-art approaches. As another contribution of this work, we have publicized our dataset on https://github.com/MobiSec-CSE-UTA/EyeQoE_Dataset. git.
Differences in homologous recombination and maintenance of heteropolyploidy between Haloferax volcanii and Haloferax mediterranei

Polyploidy, the phenomenon of having more than one copy of the genome in an organism, is common among haloarchaea. While providing short-term benefits for DNA repair, polyploidy is generally regarded as an ‘evolutionary trap’ that by the notion of the Muller’s ratchet will inevitably conclude in the species’ decline or even extinction due to a gradual reduction in fitness. In most reported cases of polyploidy in archaea, the genetic state of the organism is considered as homoploidy i.e. all copies of the genome are identical. Here we demonstrate that while this is indeed the prevalent genetic status in the halophilic archaeon H. volcanii, its close relative H. mediterranei maintains a prolonged heteroploidy state in a non-selective environment once a second allele is introduced. Moreover, a strong genetic linkage was observed between two distant loci in H. mediterranei indicating a low rate of homologous recombination while almost no such linkage was shown in H. volcanii indicating a high rate of recombination in the latter species. We suggest that H. volcanii escapes Muller’s ratchet by means of an effective chromosome-equalizing gene-conversion mechanism facilitated by highly active homologous recombination, whereas H. mediterranei must elude the ratchet via a different, yet to be elucidated mechanism.
Suboptimal foraging decisions and involvement of the ventral tegmental area in human opioid addiction

Addiction is marked by a tendency to exploit sources of reward despite diminishing returns. This behavior is aptly captured by animal patch-foraging models that have recently been extended to humans. Dopamine and norepinephrine centrally mediate addictive behavior and activity in both catecholaminergic systems is proposed to reflect the computations necessary for optimal foraging. However, the specific neural bases of excessive foraging and their role in human addiction are largely unknown. To address this gap, we studied the behavior of people with and without opioid use disorder (OUD) on a patch-foraging task in which they made serial decisions to “harvest” a depleting resource (“patch”) for reward or incur a varying cost to “travel” to a replenished patch. In a subset of participants, we used high-resolution neuromelanin-sensitive MRI to image neuromelanin concentration, a proxy for long-term catecholaminergic function, in distinct dopaminergic nuclei (ventral tegmental area, substantia nigra subregions) and the noradrenergic locus coeruleus. While all participants were sensitive to the long-run reward rates of different patch-foraging environments, OUD participants stayed in reward patches longer than optimal—markedly overharvesting a source of reward despite its declining value—and this correlated with more chronic drug use. Overharvesting was selectively associated with lower neuromelanin signal in the ventral tegmental area but not other dopaminergic nuclei, nor the locus coeruleus. Our findings suggest that foraging decisions relevant to addiction involve a ventral-tegmental-area circuit that may signal reward rates in dynamic environments and implicate this circuit in maladaptive reward pursuit in human addiction to opioids. Significance statement Patch-foraging provides a potentially important translational framework for understanding addictive behavior by revealing how maladaptive reward pursuit emerges in more ecologically valid decision contexts. Here, we show that the tendency to exploit sources of reward despite diminishing returns is associated with chronic drug use in people with opioid use disorder, a particularly devastating form of addiction. We further use neuromelanin-sensitive MRI, a neuroimaging measure of the long-term function of dopamine neurons, to reveal that variation in ventral tegmental area neuromelanin signal selectively underlies individual differences in this overharvesting bias. These findings establish a role for specific dopaminergic circuits in patch-foraging decisions and advance understanding of the neurobiology of human addiction to opioids that has so far eluded the field.
Physiological and molecular mechanisms regulated mesophyll conductance under severe drought in water-saving drought-resistant rice

Water-saving and drought-resistant rice (WDR) is a new type of rice varieties. It plays an important role in responding to drought with high yield and has been widely planted in central China at present. High photosynthetic production potential caused by high mesophyll conductance (gm) is the main factor promoted high yield formation in drought for WDR. But little is known about physiological and molecular mechanisms regulated gm in drought for WDR. Therefore, WDR cultivar HY73 and drought-sensitive cultivar HLY898 were used for comparative studies with three irrigation regimes before applying severe drought treatment at heading to create different differential individuals of photosynthetic potential and gm. The results showed that cultivar HY73 had lower up-regulation different expression genes (DEGs) than cultivar HLY898 in drought at transcriptional level. Conversely, DEGs of down-regulation was higher in cultivar HY73 than cultivar HLY898. In addition, 3071 DEGs were clustered in 3 modules named Midnightblue (734 DEGs), Blue (921 DEGs), and Turquoise (1416 DEGs) in severe drought merged three irrigation regimes and both cultivars, which the modules had significant correlational relationship with gm based on weighted gene co-expression network analysis (P<0.05). Only DEGs in midnightblue module were enriched in photosynthesis process and positively regulated gm (P<0.05). The main biological process were photosynthesis (GO:0015979), light harvesting in photosystem I (GO:0009768), reductive pentose-phosphate cycle (GO:0019253), protein-chromophore linkage (GO:0018298), photosynthetic electron transport in photosystem I (GO:0009773), and photosystem II repair (GO:0010206). These results indicate that gm and energy distribution in PSI and PSII systems could synergistic effect photosynthetic production potential in severe drought for rice plants. In the modules, the 18 most highly connected hub genes were screened using co-expression networks method. RT-PCR analysis indicated that CSP41B, PGLP1A, LHCA5, and GSTU6 genes had a similar variation trend with gm among treatments for both cultivar. LHCA5 and CSP41B genes were significantly up-regulated in HY73 compared with HLY898 in drought (P<0.05). And the both genes locates in thylakoid membrane in photosystems. Therefore, LHCA5 and CSP41B genes could be key genes to synergistically manage gm and energy distribution in photosystems. Our results provide some new physiological and molecular mechanisms regulated gm in severe drought for WDR.
Versican binds collagen via its G3 domain and regulates the organization and mechanics of collagenous matrices

Type I collagen is the most abundant structural protein in the body and, with other fibrillar collagens, forms the fibrous network of the extracellular matrix (ECM). Another group of ECM polymers, the glycosaminoglycans (GAGs) and GAG-modified proteoglycans, play important roles in regulating collagen behaviors and contribute to the compositional, structural and mechanical complexity of the ECM. While the binding between collagen and small leucine-rich proteoglycans (SLRPs) has been studied in detail, the interactions between collagen and the large bottlebrush proteoglycan versican are not well understood. Here, we report that versican binds collagen directly and regulates collagen structure and mechanics. Versican colocalizes with collagen fibers in vivo and binds to collagen via its C-terminal G3 domain (a non-GAG-modified domain present in all known versican isoforms) in vitro; it promotes the deposition of a highly-aligned collagen-rich matrix by fibroblasts. Versican also shows an unexpected effect on the rheology of collagen gels in vitro, causing decreased stiffness and attenuated shear strain stiffening, and the cleavage of versican in liver results in reduced tissue compression stiffening. Thus, versican is an important collagen binding partner, playing a role in modulating collagen organization and mechanics.
Environmental response in gene expression and DNA methylation reveals factors influencing the adaptive potential of Arabidopsis lyrata

Understanding what factors influence plastic and genetic variation is valuable for predicting how organisms respond to changes in the selective environment. Here, using gene expression and DNA methylation as molecular phenotypes, we study environment-induced variation among Arabidopsis lyrata plants grown at lowland and alpine field sites. Our results show that gene expression is highly plastic, as many more genes are differentially expressed between the field sites than between populations. The environmentally responsive genes evolve under strong selective constraint – the strength of purifying selection on the coding sequence is high, while the rate of adaptive evolution is low. We find, however, that positive selection on regulatory variants has likely contributed to genetically variable environment-responses, but such variants segregate only between distantly related populations. In contrast to gene expression, DNA methylation at genic regions is largely insensitive to the environment, and plastic methylation changes are not associated with differential gene expression. Besides genes, we detect environmental effects at transposable elements (TEs): TEs at the high-altitude field site have higher expression and methylation levels, suggestive of a broad-scale TE activation. Compared to the lowland population, plants native to the alpine environment harbor an excess of recent TE insertions, and we observe that specific TE families are enriched within environmentally responsive genes. In sum, although our results suggest that strong evolutionary constraint at environmentally responsive genes may limit the species’ adaptive potential, plastic responses at TEs could rapidly create novel heritable variation that is primed towards environmental adaptation.
Multiplexed microfluidic chip for cell co-culture

Paracrine signaling is challenging to study in vitro, as conventional culture tools dilute soluble factors and offer little to no spatiotemporal control over signaling. Microfluidic chips offer potential to address both of these issues. However, few solutions offer both control over onset and duration of cell-cell communication, and high throughput. We have developed a microfluidic chip designed to culture cells in adjacent chambers, separated by valves to selectively allow or prevent exchange of paracrine signals. The chip features 16 fluidic inputs and 128 individuallyaddressable chambers arranged in 32 sets of 4 chambers. Media can be continuously perfused or delivered by diffusion, which we model under different culture conditions to ensure normal cell viability. Immunocytochemistry assays can be performed in the chip, which we modeled and fine-tuned to reduce total assay time to 1h. Finally, we validate the use of the chip for co-culture studies by showing that HEK293Ta cells respond to signals secreted by RAW 264.7 immune cells in adjacent chambers, only when the valve between the chambers is opened.
Sudden unexpected death in epilepsy is prevented by blocking postictal hypoxia

Epilepsy is at times a fatal disease. Sudden unexpected death in epilepsy (SUDEP) is the leading cause of mortality in people with intractable epilepsy and is defined by exclusion; non-accidental, non-toxicologic, and non-anatomic causes of death. While SUDEP often follows a bilateral tonic-clonic seizure, the mechanisms that ultimately lead to terminal apnea and then asystole remain elusive and there is a lack preventative treatments. Based on the observation that discrete seizures lead to local vasoconstriction, resulting in hypoperfusion, hypoxia and behavioural disturbances in the forebrain (Farrell et al., 2016), we reasoned that similar mechanisms may play a role in SUDEP when seizures invade the brainstem. Here we tested this neurovascular-based hypothesis of SUDEP in awake non-anesthetized mice by pharmacologically preventing seizure-induced vasoconstriction, with cyclooxygenase-2 or L-type calcium channel antagonists. In both acute and chronic mouse models of SUDEP, ibuprofen and nicardipine extended life. We also examined the potential role of spreading depolarization in the acute model of SUDEP. These data provide a proof of principle for the neurovascular hypothesis of SUDEP and the use of currently available treatments to prevent it.