Miracle-dz/UniFAR-FTUs huggingface dataset:
Query Adaptive Late Fusion for Image Retrieval

Feature fusion is a commonly used strategy in image retrieval tasks, which aggregates the matching responses of multiple visual features. Feasible sets of features can be either descriptors (SIFT, HSV) for an entire image or the same descriptor for different local parts (face, body). Ideally, the to-be-fused heterogeneous features are pre-assumed to be discriminative and complementary to each other. However, the effectiveness of different features varies dramatically according to different queries. That is to say, for some queries, a feature may be neither discriminative nor complementary to existing ones, while for other queries, the feature suffices. As a result, it is important to estimate the effectiveness of features in a query-adaptive manner. To this end, this article proposes a new late fusion scheme at the score level. We base our method on the observation that the sorted score curves contain patterns that describe their effectiveness. For example, an "L"-shaped curve indicates that the feature is discriminative while a gradually descending curve suggests a bad feature. As such, this paper introduces a query-adaptive late fusion pipeline. In the hand-crafted version, it can be an unsupervised approach to tasks like particular object retrieval. In the learning version, it can also be applied to supervised tasks like person recognition and pedestrian retrieval, based on a trainable neural module. Extensive experiments are conducted on two object retrieval datasets and one person recognition dataset. We show that our method is able to highlight the good features and suppress the bad ones, is resilient to distractor features, and achieves very competitive retrieval accuracy compared with the state of the art. In an additional person re-identification dataset, the application scope and limitation of the proposed method are studied.
Mobile Phone-Based Audio Announcement Detection and Recognition for People with Hearing Impairment

Automatic audio announcement systems are widely used in public places such as transportation vehicles and facilities, hospitals, and banks. However, these systems cannot be used by people with hearing impairment. That brings great inconvenience to their lives. In this paper, an approach of audio announcement detection and recognition for the hearing-impaired people based on the smart phone is proposed and a mobile phone application (app) is developed, taking the bank as a major applying scenario. Using the app, the users can sign up alerts for their numbers and then the system begins to detect audio announcements using the microphone on the smart phone. For each audio announcement detected, the speech within it is recognized and the text is displayed on the screen of the phone. When the number the user input is announced, alert will be given by vibration. For audio announcement detection, a method based on audio segment classification and postprocessing is proposed, which uses a SVM classifier trained on audio announcements and environment noise collected in banks. For announcement speech recognition, an ASR engine is developed using a GMM-HMM-based acoustic model and a finite state transducer (FST) based grammar. The acoustic model is trained on audio announcement speech collected in banks, and the grammar is human-defined according to the patterns used by the automatic audio announcement systems. Experimental results show that character error rates (CERs) around 5% can be achieved for the announcement speech, which shows feasibility of the proposed method and system.
High frequency somatosensory stimulation in dystonia: Evidence fordefective inhibitory plasticity

Apart from motor symptoms, multiple deficits of sensory processing have been demonstrated in dystonia. The most consistent behavioural measure of this is abnormal somatosensory temporal discrimination threshold, which has recently been associated with physiological measures of reduced inhibition within the primary somatosensory area. High‐frequency repetitive sensory stimulation is a patterned electric stimulation applied to the skin through surface electrodes that has been recently reported to shorten somatosensory temporal discrimination in healthy subjects and to increase the resting level of excitability in several different types of inhibitory interaction in the somatosensory and even motor areas.
Non-apoptotic Fas (CD95) Signaling on T Cells Regulates the Resolution of Th2-Mediated Inflammation

Fas (CD95/APO-1) and its ligand (FasL/CD95L) promote the resolution of type 2 lung inflammation and eosinophilia. We previously found that Fas-deficiency on T cells, but not eosinophils, delayed resolution of inflammation. However, Fas can signal both cell death and have a positive signaling function that can actually activate cells. In this study, we investigated whether Fas-induced death or Fas-activated signaling pathways promote resolution of allergic lung inflammation. By increasing T cell survival through two Fas-independent pathways, using Bim-deficient T cells or Bcl-xL overexpressing T cells, no differences in resolution of Th2-mediated inflammation was observed. Furthermore, Th2 cells were inherently resistant to Fas-mediated apoptosis and preferentially signaled through non-apoptotic pathways following FasL treatment. Utilizing Fas-mutant mice deficient in apoptotic but sufficient for non-apoptotic Fas signaling pathways, we demonstrate that non-apoptotic Fas signaling in T cells drives resolution of Th2-mediated airway inflammation. Our findings reveal a previously unknown role for non-apoptotic Fas signaling on Th2 cells in the induction of resolution of type 2 inflammation.
An environmental DNA sampling method for aye‐ayes from their feeding traces

Abstract Noninvasive sampling is an important development in population genetic monitoring of wild animals. Particularly, the collection of environmental DNA (eDNA) which can be collected without needing to encounter the target animal facilitates the genetic analysis of endangered species. One method that has been applied to these sample types is target capture and enrichment which overcomes the issue of high proportions of exogenous (nonhost) DNA from these lower quality samples. We tested whether target capture of mitochondrial DNA from sampled feeding traces of the aye‐aye, an endangered lemur species would yield mitochondrial DNA sequences for population genetic monitoring. We sampled gnawed wood where aye‐ayes excavate wood‐boring insect larvae from trees. We designed RNA probes complementary to the aye‐aye's mitochondrial genome and used these to isolate aye‐aye DNA from other nontarget DNA in these samples. We successfully retrieved six near‐complete mitochondrial genomes from two sites within the aye‐aye's geographic range that had not been sampled previously. Our method demonstrates the application of next‐generation molecular techniques to species of conservation concern. This method can likely be applied to alternative foraged remains to sample endangered species other than aye‐ayes.
Integrated Millimeter Wave and Sub-6 GHz Wireless Networks: A Roadmap for Joint Mobile Broadband and Ultra-Reliable Low-Latency Communications

Next-generation wireless networks must enable emerging technologies such as augmented reality and connected autonomous vehicles via a wide range of wireless services that span enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC). Existing wireless systems that solely rely on the scarce sub-6 GHz, mW frequency bands will be unable to meet such stringent and mixed service requirements for future wireless services due to spectrum scarcity. Meanwhile, operating at high-frequency mmWave bands is seen as an attractive solution, primarily due to the bandwidth availability and possibility of large-scale multi-antenna communication. However, even though leveraging the large bandwidth at mmWave frequencies can potentially boost the wireless capacity for eMBB services and reduce the transmission delay for low-latency applications, mmWave communication is inherently unreliable due to its susceptibility to blockage, high path loss, and channel uncertainty. Hence, to provide URLLC and high-speed wireless access, it is desirable to seamlessly integrate the reliability of mW networks with the high capacity of mmWave networks. To this end, in this article, the first comprehensive tutorial for integrated mmWave-mW communications is introduced. This envisioned integrated design will enable wireless networks to achieve URLLC along with eMBB by leveraging the best of two worlds: reliable, long-range communications at the mW bands and directional high-speed communications at the mmWave frequencies. To achieve this goal, key solution concepts are discussed that include new architectures for the radio interface, URLLC-aware frame structure and resource allocation methods along with mobility management, to realize the potential of integrated mmWave-mW communications. The opportunities and challenges of each proposed scheme are discussed and key results are presented to show the merits of t
Ubiquitin ligase COP1 coordinates transcriptional programs that control cell type specification in the developing mouse brain

Significance The ubiquitin ligase CRL4COP1/DET1 modifies specific transcription factor substrates with polyubiquitin so that they are degraded. However, the Ras–MEK–ERK signaling pathway can inactivate CRL4COP1/DET1 and thereby promote the rapid accumulation of these transcription factors. Here we show that constitutive photomorphogenesis 1 (COP1) has a critical role in mouse brain development because its deletion from neural stem cells stabilizes the transcription factors c-JUN, ETV1, ETV4, and ETV5, leading to perturbation of normal gene expression patterns; anatomic anomalies in cerebral cortex, hippocampus, and cerebellum; and perinatal lethality. The E3 ubiquitin ligase CRL4COP1/DET1 is active in the absence of ERK signaling, modifying the transcription factors ETV1, ETV4, ETV5, and c-JUN with polyubiquitin that targets them for proteasomal degradation. Here we show that this posttranslational regulatory mechanism is active in neurons, with ETV5 and c-JUN accumulating within minutes of ERK activation. Mice with constitutive photomorphogenesis 1 (Cop1) deleted in neural stem cells showed abnormally elevated expression of ETV1, ETV4, ETV5, and c-JUN in the developing brain and spinal cord. Expression of c-JUN target genes Vimentin and Gfap was increased, whereas ETV5 and c-JUN both contributed to an expanded number of cells expressing genes associated with gliogenesis, including Olig1, Olig2, and Sox10. The mice had subtle morphological abnormalities in the cerebral cortex, hippocampus, and cerebellum by embryonic day 18 and died soon after birth. Elevated c-JUN, ETV5, and ETV1 contributed to the perinatal lethality, as several Cop1-deficient mice also lacking c-Jun and Etv5, or lacking Etv5 and heterozygous for Etv1, were viable.
LncRNA SLC8A1‐AS1 protects against myocardial damage through activation of cGMP‐PKG signaling pathway by inhibiting SLC8A1 in mice models of myocardial infarction

Extensive investigations into long noncoding RNAs (lncRNAs) in various diseases and cancers, including acute myocardial infarction (AMI) have been conducted. The current study aimed to investigate the role of lncRNA solute carrier family 8 member A1 antisense RNA 1 (SLC8A1‐AS1) in myocardial damage by targeting solute carrier family 8 member A1 (SLC8A1) via cyclic guanosine 3′,5′‐monophosphate‐protein kinase G (cGMP‐PKG) signaling pathway in AMI mouse models. Differentially expressed lncRNA in AMI were initially screened and target relationship between lncRNA SLC8A1‐AS1 and SLC8A1 was then verified. Infarct size, levels of inflammatory factors, biochemical indicators, and the positive expression of the SLC8A1 protein in AMI were subsequently determined. The expression of SLC8A1‐AS1, SLC8A1, PKG1, PKG2, atrial natriuretic peptide, and brain natriuretic peptide was detected to assess the effect of SLC8A1‐AS1 on SLC8A1 and cGMP‐PKG. The respective contents of superoxide dismutase, lactate dehydrogenase (LDH), and malondialdehyde (MDA) were detected accordingly. Microarray data GSE66360 provided evidence indicating that SLC8A1‐AS1 was poorly expressed in AMI. SLC8A1 was verified to be a target gene of lncRNA SLC8A1‐AS1. SLC8A1‐AS1 upregulation decreased levels of left ventricular end‐systolic diameter, −dp/ dt max, interleukin 1β (IL‐1β), IL‐6, transforming growth factor α, nitric oxide, inducible nitric‐oxide synthase, endothelial nitric‐oxide synthase, infarct size, LDH activity and MDA content, and increased IL‐10, left ventricular end‐diastolic pressure and + dp/ dt max. Furthermore, the overexpression of SLC8A1‐AS1 was noted to elicit an inhibitory effect on the cGMP‐PKG signaling pathway via SLC8A1. In conclusion, lncRNA SLC8A1‐AS1, by downregulating SLC8A1 and activating the cGMP‐PKG signaling pathway, was observed to alleviate myocardial damage, inhibit the release of proinflammatory factors and reduce infarct size, ultimately protecting against myocardial damage.
Beyond weight loss: A qualitative exploration of participant expectations and strategies for measuring progress during a weight management program

Objectives: Previous research with participants in weight management has primarily focused on participant weight-loss expectations. The purpose of this study was to explore participant expectations and strategies for measuring progress during a community-based weight management program. Methods: Semi-structured interviews were completed with 22 participants with overweight or obesity, who were currently enrolled in a 2-year weight management program in order to understand their expectations and strategies for measuring their progress. Results: Aside from weight loss, participants’ expectations included improved health, fitness, and mood, gaining support, and developing new habits. Participants discussed measuring their success by focusing on weight loss and changes to their body, improved health, mood, and fitness, creation of new habits, and gaining additional support. Conclusion: The results of this study suggest that participant’s in a weight management program expected benefits beyond just weight loss.
Cross-lingual Transfer Learning for Multilingual Task Oriented Dialog

One of the first steps in the utterance interpretation pipeline of many task-oriented conversational AI systems is to identify user intents and the corresponding slots. Since data collection for machine learning models for this task is time-consuming, it is desirable to make use of existing data in a high-resource language to train models in low-resource languages. However, development of such models has largely been hindered by the lack of multilingual training data. In this paper, we present a new data set of 57k annotated utterances in English (43k), Spanish (8.6k) and Thai (5k) across the domains weather, alarm, and reminder. We use this data set to evaluate three different cross-lingual transfer methods: (1) translating the training data, (2) using cross-lingual pre-trained embeddings, and (3) a novel method of using a multilingual machine translation encoder as contextual word representations. We find that given several hundred training examples in the the target language, the latter two methods outperform translating the training data. Further, in very low-resource settings, multilingual contextual word representations give better results than using cross-lingual static embeddings. We also compare the cross-lingual methods to using monolingual resources in the form of contextual ELMo representations and find that given just small amounts of target language data, this method outperforms all cross-lingual methods, which highlights the need for more sophisticated cross-lingual methods.
Feature Learning for Scene Flow Estimation from LIDAR

To perform tasks in dynamic environments, many mobile robots must estimate the motion in the surrounding world. Recently, techniques have been developed to estimate scene flow directly from LIDAR scans, relying on handdesigned features. In this work, we build an encoding network to learn features from an occupancy grid. The network is trained so that these features are discriminative in finding matching or non-matching locations between successive timesteps. This learned feature space is then leveraged to estimate scene flow. We evaluate our method on the KITTI dataset and demonstrate performance that improves upon the accuracy of the current state-of-the-art. We provide an implementation of our method at https://github.com/aushani/flsf.
Using accelerometers to develop time-energy budgets of wild fur seals from captive surrogates

Background Accurate time-energy budgets summarise an animal’s energy expenditure in a given environment, and are potentially a sensitive indicator of how an animal responds to changing resources. Deriving accurate time-energy budgets requires an estimate of time spent in different activities and of the energetic cost of that activity. Bio-loggers (e.g., accelerometers) may provide a solution for monitoring animals such as fur seals that make long-duration foraging trips. Using low resolution to record behaviour may aid in the transmission of data, negating the need to recover the device. Methods This study used controlled captive experiments and previous energetic research to derive time-energy budgets of juvenile Australian fur seals (Arctocephalus pusillus) equipped with tri-axial accelerometers. First, captive fur seals and sea lions were equipped with accelerometers recording at high (20 Hz) and low (1 Hz) resolutions, and their behaviour recorded. Using this data, machine learning models were trained to recognise four states—foraging, grooming, travelling and resting. Next, the energetic cost of each behaviour, as a function of location (land or water), season and digestive state (pre- or post-prandial) was estimated. Then, diving and movement data were collected from nine wild juvenile fur seals wearing accelerometers recording at high- and low- resolutions. Models developed from captive seals were applied to accelerometry data from wild juvenile Australian fur seals and, finally, their time-energy budgets were reconstructed. Results Behaviour classification models built with low resolution (1 Hz) data correctly classified captive seal behaviours with very high accuracy (up to 90%) and recorded without interruption. Therefore, time-energy budgets of wild fur seals were constructed with these data. The reconstructed time-energy budgets revealed that juvenile fur seals expended the same amount of energy as adults of similar species. No significant differences in daily energy expenditure (DEE) were found across sex or season (winter or summer), but fur seals rested more when their energy expenditure was expected to be higher. Juvenile fur seals used behavioural compensatory techniques to conserve energy during activities that were expected to have high energetic outputs (such as diving). Discussion As low resolution accelerometry (1 Hz) was able to classify behaviour with very high accuracy, future studies may be able to transmit more data at a lower rate, reducing the need for tag recovery. Reconstructed time-energy budgets demonstrated that juvenile fur seals appear to expend the same amount of energy as their adult counterparts. Through pairing estimates of energy expenditure with behaviour this study demonstrates the potential to understand how fur seals expend energy, and where and how behavioural compensations are made to retain constant energy expenditure over a short (dive) and long (season) period.
Individual differences in ERP measures of executive function in early childhood: Relation to low-risk preterm birth and parent-reported behavior

ABSTRACT Although behavioral studies have demonstrated that executive function (EF) develops rapidly during early childhood, few studies have investigated neural systems supporting EF during the preschool years. These systems are sensitive to variations in children’s early life experiences, including preterm birth. The current study collected behavioral and event related potential (ERP) data during an EF task (directional Stroop) in a sample of 150 full-term and low-risk preterm children aged 4-years. Children’s IQ and processing speed (WPPSI-III), and parent report of EF (BRIEF-P), were also measured. Forty-nine children born full-term and 43 low-risk preterm children provided useable ERP data. Similar to prior studies with adults and older children, preschool-aged children showed modulation of ERP components (N2, P3) by cognitive conflict. Effects of trial type were also present for early attentional components (N1 and P2). Exploratory analyses demonstrated that ERP measures of EF were correlated with individual differences in cognitive and behavioral functioning in both full-term and low-risk preterm populations. Future research investigating the neural correlates of early measures of EF in low-risk preterm children and other at-risk groups is warranted to better understand how trajectories of EF development are altered in the first years of life.
MPNA: A Massively-Parallel Neural Array Accelerator with Dataflow Optimization for Convolutional Neural Networks

The state-of-the-art accelerators for Convolutional Neural Networks (CNNs) typically focus on accelerating only the convolutional layers, but do not prioritize the fully-connected layers much. Hence, they lack a synergistic optimization of the hardware architecture and diverse dataflows for the complete CNN design, which can provide a higher potential for performance/energy efficiency. Towards this, we propose a novel Massively-Parallel Neural Array (MPNA) accelerator that integrates two heterogeneous systolic arrays and respective highly-optimized dataflow patterns to jointly accelerate both the convolutional (CONV) and the fully-connected (FC) layers. Besides fully-exploiting the available off-chip memory bandwidth, these optimized dataflows enable high data-reuse of all the data types (i.e., weights, input and output activations), and thereby enable our MPNA to achieve high energy savings. We synthesized our MPNA architecture using the ASIC design flow for a 28nm technology, and performed functional and timing validation using multiple real-world complex CNNs. MPNA achieves 149.7GOPS/W at 280MHz and consumes 239mW. Experimental results show that our MPNA architecture provides 1.7x overall performance improvement compared to state-of-the-art accelerator, and 51% energy saving compared to the baseline architecture.
Characterization of 39 microsatellite markers from Nuphar shimadai (Nymphaeaceae) and cross‐amplification in two related taxa

Premise of the Study Microsatellite loci were developed for Nuphar shimadai (Nymphaeaceae) to evaluate the population genetic dynamics for conservation purposes. The species is an endemic aquatic species in Taiwan that is endangered by anthropogenic activities. Methods and Results A magnetic bead enrichment protocol was used to identify 72 potential microsatellite loci and develop 39 microsatellite markers from N. shimadai. The number of alleles per locus ranged from one to 10 per locus, with levels of observed heterozygosity ranging from 0 to 1.0 within populations. As a result of inbreeding within isolated populations, 65% of loci significantly deviated from Hardy–Weinberg equilibrium within populations. Conclusions These novel markers should be valuable tools to evaluate the genetic diversity within the endangered aquatic taxon N. shimadai for conservation and reintroduction purposes in Taiwan.