Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective mosquito and arbovirus surveillance using metabarcoding

Effective vector and arbovirus surveillance requires timely and accurate screening techniques that can be easily upscaled. Next‐generation sequencing (NGS) is a high‐throughput technology that has the potential to modernize vector surveillance. When combined with DNA barcoding, it is termed ‘metabarcoding. ’ The aim of our study was to establish a metabarcoding protocol to characterize pools of mosquitoes and screen them for virus. Pools contained 100 morphologically identified individuals, including one Ross River virus (RRV) infected mosquito, with three species present at different proportions: 1, 5, 94%. Nucleic acid extracted from both crude homogenate and supernatant was used to amplify a 269‐bp section of the mitochondrial cytochrome c oxidase subunit I (COI) locus. Additionally, a 67‐bp region of the RRV E2 gene was amplified from synthesized cDNA to screen for RRV. Amplicon sequencing was performed using an Illumina MiSeq, and bioinformatic analysis was performed using a DNA barcode database of Victorian mosquitoes. Metabarcoding successfully detected all mosquito species and RRV in every positive sample tested. The limits of species detection were also examined by screening a pool of 1000 individuals, successfully identifying the species and RRV from a single mosquito. The primers used for amplification, number of PCR cycles and total number of individuals present all have effects on the quantification of species in mixed bulk samples. Based on the results, a number of recommendations for future metabarcoding studies are presented. Overall, metabarcoding shows great promise for providing a new alternative approach to screening large insect surveillance trap catches.
Inductive Framework for Multi-Aspect Streaming Tensor Completion with Side Information

Low rank tensor completion is a well studied problem and has applications in various fields. However, in many real world applications the data is dynamic, i.e., new data arrives at different time intervals. As a result, the tensors used to represent the data grow in size. Besides the tensors, in many real world scenarios, side information is also available in the form of matrices which also grow in size with time. The problem of predicting missing values in the dynamically growing tensor is called dynamic tensor completion. Most of the previous work in dynamic tensor completion make an assumption that the tensor grows only in one mode. To the best of our Knowledge, there is no previous work which incorporates side information with dynamic tensor completion. We bridge this gap in this paper by proposing a dynamic tensor completion framework called Side Information infused Incremental Tensor Analysis (SIITA), which incorporates side information and works for general incremental tensors. We also show how non-negative constraints can be incorporated with SIITA, which is essential for mining interpretable latent clusters. We carry out extensive experiments on multiple real world datasets to demonstrate the effectiveness of SIITA in various different settings.
A Demonstration of Tracking the Position of a Moving LEGO Train using the Cricket Indoor Location System

The Cricket indoor location system [1] was motivated by the importance of mobile and context-aware applications in sensor networks and pervasive computing environments, and the poor indoor performance of the Global Positioning System (GPS). A compelling attribute of applications in pervasive environments is context-awareness, being able to discover the external context and adapting accordingly. An important example of context is location: the position in some coordinate system of a device or user, the geographic space in which a device or user is (e.g., the room or portion of a room), or the orientation of a device within some coordinate system. Knowledge of location in the form of coordinate position, spatial resolution, and orientation (a.k.a. “directionality” or “heading”) enables a wide variety of pervasive computing applications such as resource discovery, pose-aware “point-and-use” interfaces, navigation, and augmented reality. Although location information in outdoor environments may be obtained via the Global Positioning System or using the cellular infrastructure (with the emerging E-911 services) augmented with a magnetic compass, such capa-
Efficient Training and Evaluation of Recurrent Neural Network Language Models for Automatic Speech Recognition

Recurrent neural network language models (RNNLMs) are becoming increasingly popular for a range of applications including automatic speech recognition. An important issue that limits their possible application areas is the computational cost incurred in training and evaluation. This paper describes a series of new efficiency improving approaches that allows RNNLMs to be more efficiently trained on graphics processing units (GPUs) and evaluated on CPUs. First, a modified RNNLM architecture with a nonclass-based, full output layer structure (F-RNNLM) is proposed. This modified architecture facilitates a novel spliced sentence bunch mode parallelization of F-RNNLM training using large quantities of data on a GPU. Second, two efficient RNNLM training criteria based on variance regularization and noise contrastive estimation are explored to specifically reduce the computation associated with the RNNLM output layer softmax normalisation term. Finally, a pipelined training algorithm utilizing multiple GPUs is also used to further improve the training speed. Initially, RNNLMs were trained on a moderate dataset with 20M words from a large vocabulary conversational telephone speech recognition task. The training time of RNNLM is reduced by up to a factor of 53 on a single GPU over the standard CPU-based RNNLM toolkit. A 56 times speed up in test time evaluation on a CPU was obtained over the baseline F-RNNLMs. Consistent improvements in both recognition accuracy and perplexity were also obtained over C-RNNLMs. Experiments on Google's one billion corpus also reveals that the training of RNNLM scales well.
Addressing reverse inference in psychiatric neuroimaging: Meta‐analyses of task‐related brain activation in common mental disorders

Functional magnetic resonance imaging (fMRI) studies in psychiatry use various tasks to identify case‐control differences in the patterns of task‐related brain activation. Differently activated regions are often ascribed disorder‐specific functions in an attempt to link disease expression and brain function. We undertook a systematic meta‐analysis of data from task‐fMRI studies to examine the effect of diagnosis and study design on the spatial distribution and direction of case‐control differences on brain activation. We mapped to atlas regions coordinates of case‐control differences derived from 537 task‐fMRI studies in schizophrenia, bipolar disorder, major depressive disorder, anxiety disorders, and obsessive compulsive disorder comprising observations derived from 21,427 participants. The fMRI tasks were classified according to the Research Domain Criteria (RDoC). We investigated whether diagnosis, RDoC domain or construct and use of regions‐of‐interest or whole‐brain analyses influenced the neuroanatomical pattern of results. When considering all primary studies, we found an effect of diagnosis for the amygdala and caudate nucleus and an effect of RDoC domains and constructs for the amygdala, hippocampus, putamen and nucleus accumbens. In contrast, whole‐brain studies did not identify any significant effect of diagnosis or RDoC domain or construct. These results resonate with prior reports of common brain structural and genetic underpinnings across these disorders and caution against attributing undue specificity to brain functional changes when forming explanatory models of psychiatric disorders. Hum Brain Mapp 38:1846–1864, 2017. © 2017 Wiley Periodicals, Inc.
Optimal Single Sample Tests for Structured versus Unstructured Network Data

We study the problem of testing, using only a single sample, between mean field distributions (like Curie-Weiss, Erd\H{o}s-R\'enyi) and structured Gibbs distributions (like Ising model on sparse graphs and Exponential Random Graphs). Our goal is to test without knowing the parameter values of the underlying models: only the \emph{structure} of dependencies is known. We develop a new approach that applies to both the Ising and Exponential Random Graph settings based on a general and natural statistical test. The test can distinguish the hypotheses with high probability above a certain threshold in the (inverse) temperature parameter, and is optimal in that below the threshold no test can distinguish the hypotheses. The thresholds do not correspond to the presence of long-range order in the models. By aggregating information at a global scale, our test works even at very high temperatures. The proofs are based on distributional approximation and sharp concentration of quadratic forms, when restricted to Hamming spheres. The restriction to Hamming spheres is necessary, since otherwise any scalar statistic is useless without explicit knowledge of the temperature parameter. At the same time, this restriction radically changes the behavior of the functions under consideration, resulting in a much smaller variance than in the independent setting; this makes it hard to directly apply standard methods (i.e., Stein's method) for concentration of weakly dependent variables. Instead, we carry out an additional tensorization argument using a Markov chain that respects the symmetry of the Hamming sphere.
Cache-aware scheduling and analysis for multicores

The major obstacle to use multicores for real-time applications is that we may not predict and provide any guarantee on real-time properties of embedded software on such platforms; the way of handling the on-chip shared resources such as L2 cache may have a significant impact on the timing predictability. In this paper, we propose to use cache space isolation techniques to avoid cache contention for hard real-time tasks running on multicores with shared caches. We present a scheduling strategy for real-time tasks with both timing and cache space constraints, which allows each task to use a fixed number of cache partitions, and makes sure that at any time a cache partition is occupied by at most one running task. In this way, the cache spaces of tasks are isolated at run-time. As technical contributions, we have developed a sufficient schedulability test for non-preemptive fixed-priority scheduling for multicores with shared L2 cache, encoded as a linear programming problem. To improve the scalability of the test, we then present our second schedulability test of quadratic complexity, which is an over approximation of the first test. To evaluate the performance and scalability of our techniques, we use randomly generated task sets. Our experiments show that the first test which employs an LP solver can easily handle task sets with thousands of tasks in minutes using a desktop computer. It is also shown that the second test is comparable with the first one in terms of precision, but scales much better due to its low complexity, and is therefore a good candidate for efficient schedulability tests in the design loop for embedded systems or as an on-line test for admission control.
Facial Expression Recognition Using Salient Features and Convolutional Neural Network

A depth camera-based novel method is proposed here for efficient facial expression recognition. For each pixel in a depth image, eight local directional strengths are obtained and ranked. Once the rank of all pixels is obtained, eight histograms are developed for the eight surrounding directions. The histograms are then concatenated to represent features for a depth image of a face. This approach is named local directional rank histogram pattern (LDRHP). To combine with LDRHP features, one more robust feature extraction technique named local directional strength pattern (LDSP) is proposed. Typical local directional pattern (LDP) considers only absolute values of edge strengths for a pixel. This generalization in LDP may generate the same patterns for two different kinds of edge pixels. LDSP can overcome this problem. It considers the binary values of the position with the directions representing the highest and lowest original strengths. The highest strength indicates the strongest direction on the bright side of a pixel and the lowest one indicates the strongest direction in the dark side of that pixel. Hence, combining binary positions of these two directions can generate more robust patterns than LDP. Besides, LDSP pattern of a pixel is of six bits, whereas traditional LDP-based patterns are of eight bits (e.g., local directional deviation-based pattern and local directional position pattern). Thus, LDSP reduces the dimension of features with the same time adding robustness. For a depth image in a depth video, LDSP features are augmented with LDRHP features followed by Kernel principal component analysis and generalized discriminant analysis to generate more robust features. At last, the features are trained with a deep learning approach and convolutional neural network for successful facial expression recognition. The proposed approach is compared and shown to outperform the traditional expression recognition methods.
SmartSkin: an infrastructure for freehand manipulation on interactive surfaces

This paper introduces a new sensor architecture for making interactive surfaces that are sensitive to human hand and finger gestures. This sensor recognizes multiple hand positions and shapes and calculates the distance between the hand and the surface by using capacitive sensing and a mesh-shaped antenna. In contrast to camera-based gesture recognition systems, all sensing elements can be integrated within the surface, and this method does not suffer from lighting and occlusion problems. This paper describes the sensor architecture, as well as two working prototype systems: a table-size system and a tablet-size system. It also describes several interaction techniques that would be difficult to perform without using this architecture
Numerical Evaluation of Image Contrast for Thicker and Thinner Objects among Current Intraoral Digital Imaging Systems

The purpose is to evaluate the performance of current intraoral digital detectors in detail using a precise phantom and new method. Two aluminum step wedges in 0.5 mm steps were exposed by two photostimulable phosphor plate (PSP) systems—one with automatic exposure compensation (AEC) and the other without AEC—and a CCD sensor. Images were obtained with 3 doses at 60 kV. The effect of metallic material also was evaluated. The contrast-to-noise ratio (CNR) for thinner steps and the low contrast value (LCV) for thicker steps were obtained. The CCD system was the best under all conditions (P < 0.001), although the Gray value was sensitive to the dose, and the Gray value-dose relation varied greatly. The PSP system with AEC was superior to that without AEC for the LCV (P < 0.001) but was inferior to it regarding the CNR (P < 0.001). CNR and LCV in the PSP system without AEC were not affected by the metallic plate. Intraoral digital imaging systems should be chosen according to their diagnostic purpose. PSP system with AEC may be the best for detecting molar proximal caries, whereas the PSP system without AEC may be better for evaluating small bone regeneration in periodontal disease. The CCD system provided the best performance.
Agoraphobia and panic disorder: 3.5 years after alprazolam and/or exposure treatment.

BACKGROUND Long-term follow-ups after controlled studies of exposure therapy for agoraphobia/panic are few. Most of these studies found that improvement during treatment persists to the end of follow-up. METHODS Out of 69 patients with panic disorder plus agoraphobia who had been in an 8-week controlled study of alprazolam and/or exposure, 31 were followed up at a mean of 3.5 years later (4 years after trial entry). The 31 patients followed up included more cases who had relapsed at week 43 than did the group which did not attend the 3.5-year follow-up. RESULTS As a group, followed-up cases maintained their gains over the 3.5 years, more so among ex-exposure than ex-relaxation cases. Ex-exposure patients did significantly better than relaxation patients on disability and survival time. Ex-alprazolam and ex-exposure patients did not differ significantly on any variable at the 3.5-year follow-up. No baseline variable predicted outcome at follow-up. CONCLUSIONS Present results modestly confirm those of previous studies finding lasting improvement years after exposure, though some residual symptoms were the norm.
The Resurrection Initiative: Storing Ancestral Genotypes to Capture Evolution in Action

ABSTRACT In rare circumstances, scientists have been able to revive dormant propagules from ancestral populations and rear them with their descendants to make inferences about evolutionary responses to environmental change. Although this is a powerful approach to directly assess microevolution, it has previously depended entirely upon fortuitous conditions to preserve ancestral material. We propose a coordinated effort to collect, preserve, and archive genetic materials today for future studies of evolutionary change—a “resurrection paradigm. ” The availability of ancestral material that is systematically collected and intentionally stored using best practices will greatly expand our ability to illuminate microevolutionary patterns and processes and to predict ongoing responses of species to global change. In the workshop “Project Baseline,” evolutionary biologists and seed storage experts met to discuss establishing a coordinated effort to implement the resurrection paradigm.
Pragmatically Framed Cross-Situational Noun Learning Using Computational Reinforcement Models

Cross-situational learning and social pragmatic theories are prominent mechanisms for learning word meanings (i.e., word-object pairs). In this paper, the role of reinforcement is investigated for early word-learning by an artificial agent. When exposed to a group of speakers, the agent comes to understand an initial set of vocabulary items belonging to the language used by the group. Both cross-situational learning and social pragmatic theory are taken into account. As social cues, joint attention and prosodic cues in caregiver's speech are considered. During agent-caregiver interaction, the agent selects a word from the caregiver's utterance and learns the relations between that word and the objects in its visual environment. The “novel words to novel objects” language-specific constraint is assumed for computing rewards. The models are learned by maximizing the expected reward using reinforcement learning algorithms [i.e., table-based algorithms: Q-learning, SARSA, SARSA-λ, and neural network-based algorithms: Q-learning for neural network (Q-NN), neural-fitted Q-network (NFQ), and deep Q-network (DQN)]. Neural network-based reinforcement learning models are chosen over table-based models for better generalization and quicker convergence. Simulations are carried out using mother-infant interaction CHILDES dataset for learning word-object pairings. Reinforcement is modeled in two cross-situational learning cases: (1) with joint attention (Attentional models), and (2) with joint attention and prosodic cues (Attentional-prosodic models). Attentional-prosodic models manifest superior performance to Attentional ones for the task of word-learning. The Attentional-prosodic DQN outperforms existing word-learning models for the same task.
Study on astigmation of the virtual images formed by grating diffraction

Diffraction astigmation formed by the grating diffraction light was investigated. The grating diffraction astigmation varies with the incidence angle and the characteristics of the variation were discussed. The angles of incidence light and other conclusions corresponding to the minimum diffraction astigmation were obtained based on a systematic analysis.
Dual Learning for Cross-domain Image Captioning

Recent AI research has witnessed increasing interests in automatically generating image descriptions in text, which is coined as theimage captioning problem. Significant progresses have been made in domains where plenty of labeled training data (i.e. image-text pairs) are readily available or collected. However, obtaining rich annotated data is a time-consuming and expensive process, creating a substantial barrier for applying image captioning methods to a new domain. In this paper, we propose a cross-domain image captioning approach that uses a novel dual learning mechanism to overcome this barrier. First, we model the alignment between the neural representations of images and that of natural languages in the source domain where one can access sufficient labeled data. Second, we adjust the pre-trained model based on examining limited data (or unpaired data) in the target domain. In particular, we introduce a dual learning mechanism with a policy gradient method that generates highly rewarded captions. The mechanism simultaneously optimizes two coupled objectives: generating image descriptions in text and generating plausible images from text descriptions, with the hope that by explicitly exploiting their coupled relation, one can safeguard the performance of image captioning in the target domain. To verify the effectiveness of our model, we use MSCOCO dataset as the source domain and two other datasets (Oxford-102 and Flickr30k) as the target domains. The experimental results show that our model consistently outperforms previous methods for cross-domain image captioning.
Complete Genome Sequence of a Recombinant Porcine Epidemic Diarrhea Virus Strain, CH/JXJA/2017, Isolated in Jiangxi, China, in 2017

ABSTRACT The full-length genome sequence of a variant of porcine epidemic diarrhea virus (PEDV), that of strain CH/JXJA/2017, was highly homologous to CH/ZMDZY/11, a highly virulent Chinese PEDV strain. CH/JXJA/2017 had a distant relationship with the attenuated CV777 vaccine strain, but the insertion sites of the S1 gene were similar to those of the recombinant strain of CH/ZMDZY/11.