Miracle-dz/UniFAR-FTUs huggingface dataset:
Sequencing, Annotation and Analysis of the Syrian Hamster (Mesocricetus auratus) Transcriptome

Background The Syrian hamster (golden hamster, Mesocricetus auratus) is gaining importance as a new experimental animal model for multiple pathogens, including emerging zoonotic diseases such as Ebola. Nevertheless there are currently no publicly available transcriptome reference sequences or genome for this species. Results A cDNA library derived from mRNA and snRNA isolated and pooled from the brains, lungs, spleens, kidneys, livers, and hearts of three adult female Syrian hamsters was sequenced. Sequence reads were assembled into 62,482 contigs and 111,796 reads remained unassembled (singletons). This combined contig/singleton dataset, designated as the Syrian hamster transcriptome, represents a total of 60,117,204 nucleotides. Our Mesocricetus auratus Syrian hamster transcriptome mapped to 11,648 mouse transcripts representing 9,562 distinct genes, and mapped to a similar number of transcripts and genes in the rat. We identified 214 quasi-complete transcripts based on mouse annotations. Canonical pathways involved in a broad spectrum of fundamental biological processes were significantly represented in the library. The Syrian hamster transcriptome was aligned to the current release of the Chinese hamster ovary (CHO) cell transcriptome and genome to improve the genomic annotation of this species. Finally, our Syrian hamster transcriptome was aligned against 14 other rodents, primate and laurasiatheria species to gain insights about the genetic relatedness and placement of this species. Conclusions This Syrian hamster transcriptome dataset significantly improves our knowledge of the Syrian hamster's transcriptome, especially towards its future use in infectious disease research. Moreover, this library is an important resource for the wider scientific community to help improve genome annotation of the Syrian hamster and other closely related species. Furthermore, these data provide the basis for development of expression microarrays that can be used in functional genomics studies.
A unified view for discriminative objective functions based on negative exponential of difference measure between strings

This paper presents a novel unified view of a wide variety of objective functions suitable for discriminative training applied to sequential pattern recognition problems, such as automatic speech recognition. Focusing on a central component of conventional objective functions, the sum of modified joint probabilities of observations and strings, the analysis generalizes these objective functions by weighting each term in the sum by an important function, the negative exponential of difference measure between strings. The interesting and valuable results of this investigation are highlighted in a comprehensive relationship chart that covers all of the common approaches (Maximum Mutual Information, Minimum Classification Error, Minimum Phone/Word Error), as well as corresponding novel generalizations and modifications of those approaches.
Validation of adaptive threshold spike detector for neural recording

We compare the performance of algorithms for automatic spike detection in neural recording applications. Each algorithm sets a threshold based on an estimate of the background noise level. The adaptive spike detection algorithm is suitable for implementation in analog VLSI; results from a proof-of-concept chip using neural data are presented. We also present simulation results of algorithm performance on neural data and compare it to other methods of threshold level adjustment based on the root-mean-square (rms) voltage measured over a finite window. We show that the adaptive spike detection algorithm measures the background noise level accurately despite the presence of large-amplitude action potentials and multi-unit hash. Simulation results enable us to optimize the algorithm parameters, leading to an improved spike detector circuit that is currently being developed.
Hardware Friendly Probabilistic Spiking Neural Network With Long-Term and Short-Term Plasticity

This paper proposes a probabilistic spiking neural network (PSNN) with unimodal weight distribution, possessing long- and short-term plasticity. The proposed algorithm is derived by both the arithmetic gradient decent calculation and bioinspired algorithms. The algorithm is benchmarked by the Iris and Wisconsin breast cancer (WBC) data sets. The network features fast convergence speed and high accuracy. In the experiment, the PSNN took not more than 40 epochs for convergence. The average testing accuracy for Iris and WBC data is 96.7% and 97.2%, respectively. To test the usefulness of the PSNN to real world application, the PSNN was also tested with the odor data, which was collected by our self-developed electronic nose (e-nose). Compared with the algorithm (K-nearest neighbor) that has the highest classification accuracy in the e-nose for the same odor data, the classification accuracy of the PSNN is only 1.3% less but the memory requirement can be reduced at least 40%. All the experiments suggest that the PSNN is hardware friendly. First, it requires only nine-bits weight resolution for training and testing. Second, the PSNN can learn complex data sets with a little number of neurons that in turn reduce the cost of VLSI implementation. In addition, the algorithm is insensitive to synaptic noise and the parameter variation induced by the VLSI fabrication. Therefore, the algorithm can be implemented by either software or hardware, making it suitable for wider application.
Robust Non-line-of-sight Imaging with Single Photon Detectors

Imaging objects that are obscured by scattering and occlusion is an important challenge for many applications. For example, navigation and mapping capabilities of autonomous vehicles could be improved, vision in harsh weather conditions or under water could be facilitated, or search and rescue scenarios could become more effective. Unfortunately, conventional cameras cannot see around corners. Emerging, time-resolved computational imaging systems, however, have demonstrated first steps towards non-line-of-sight (NLOS) imaging. In this paper, we develop an algorithmic framework for NLOS imaging that is robust to partial occlusions within the hidden scenes. This is a common light transport effect, but not adequately handled by existing NLOS reconstruction algorithms, resulting in fundamental limitations in what types of scenes can be recovered. We demonstrate state-of-the-art NLOS reconstructions in simulation and with a prototype single photon avalanche diode (SPAD) based acquisition system.
Psychiatric context of Human Immunode fi ciency Virus infection among former plasma donors in rural China ☆

Psychiatric context of Human Immunodeficiency Virus infection among former plasma donors in rural China☆ J. Hampton Atkinson ⁎, Hua Jin , Chuan Shi , Xin Yu , Nichole A. Duarte , Corinna Young Casey , Donald R. Franklin Jr. , Ofilio Vigil , Lucette Cysique , Tanya Wolfson , P. Katie Riggs , Saurabh Gupta , Scott Letendre , Thomas D. Marcotte , Igor Grant , Zunyou Wu , Robert K. Heaton a and the HIV Neurobehavioral Research Center Group
Association of angiotensin‐converting enzyme gene polymorphism with schizophrenia and depressive symptom severity in a Chinese population

Depressive symptoms are frequently observed in schizophrenia patients. Angiotensin‐converting enzyme (ACE), a key enzyme of renin‐angiotensin system, can catalyze the degradation of neuropeptides and modulate dopaminergic and serotonergic neurotransmission. Previous studies have revealed the association of the ACE gene insertion/deletion polymorphism with depressive disorder and its treatment response but not with the depressive symptoms in schizophrenia.
Document-Level Machine Translation with Word Vector Models

In this paper we apply distributional semantic information to document-level machine translation. We train monolingual and bilingual word vector models on large corpora and we evaluate them first in a cross-lingual lexical substitution task and then on the final translation task. For translation, we incorporate the semantic information in a statistical document-level decoder (Docent), by enforcing translation choices that are semantically similar to the context. As expected, the bilingual word vector models are more appropriate for the purpose of translation. The final document-level translator incorporating the semantic model outperforms the basic Docent (without semantics) and also performs slightly over a standard sentence level SMT system in terms of ULC (the average of a set of standard automatic evaluation metrics for MT). Finally, we also present some manual analysis of the translations of some concrete documents
Combining feature sets with support vector machines: application to speaker recognition

In this paper, we describe a general technique for optimizing the relative weights of feature sets in a support vector machine (SVM) and show how it can be applied to the field of speaker recognition. Our training procedure uses an objective function that maps the relative weights of the feature sets directly to a classification metric (e.g. equal-error rate (EER)) measured on a set of training data. The objective function is optimized in an iterative fashion with respect to both the feature weights and the SVM parameters (i.e. the support vector weights and the bias values). In this paper, we use this procedure to optimize the relative weights of various subsets of features in two SVM-based speaker recognition systems: a system that uses transform coefficients obtained from maximum likelihood linear regression (MLLR) as features (A. Stolcke, et al., 2005) and another that uses relative frequencies of phone n-grams (W. M. Campbell, et al., 2003), (A. Hatch, et al., 2005). In all cases, the training procedure yields significant improvements in both EER and minimum DCF (i.e. decision cost function), as measured on various test corpora
Understanding and managing surface subsidence at New Gold's New Afton block cave operation

New Gold’s New Afton Mine is a 6 million tonne per year operating mine located 8 km outside of Kamloops, British Columbia, Canada. After ~17,700 m of decline access, underground ancillary and footprint development, the first drawbell was blasted in September 2011. The ore being mined is a copper–gold porphyry deposit situated within the Iron Mask batholith complex, bounded by two major subvertical fault structures, and plunges to the southwest. Determining the key components and driving mechanisms of surface subsidence is vital when considering potential impact on critical surface infrastructure. This paper will examine a number of fundamental learnings, starting from the initial feasibility study design assumptions through to actual cave behaviour and observed surface subsidence. As is the case in most start-up operations, early numerical modelling studies and inputs are often data poor and multiple assumptions need to be made. Furthermore, continuous and accurate calibration is required as subsidence evolves over time. This can be achieved through a better understanding of the regional geological model, and use of surface, subsurface and deep-seated instrumentation methods. At New Afton, we embarked upon an intensive instrumentation and drilling program to better understand the regional geological model and rock mass behaviour mechanisms, and their associated subsidence impacts.
An Effective Algorithm of Image Splicing Detection

To implement image splicing detection a blind, passive and effective splicing detection scheme was proposed in this paper. The model was based on moment features extracted from the multi-size block discrete cosine transform (MBDCT) and some image quality metrics (IQMs) extracted from the given test image, which are sensitive to spliced image. This model can measure statistical differences between original image and spliced image. Experimental results demonstrate that this new splicing detection algorithm is effective and reliable; indicating that the proposed approach has a broad application prospect.
Journal of Modern Applied Statistical Methods within Groups Anova When Using a Robust Multivariate Measure of Location within Groups Anova When Using a Robust Multivariate Measure of Location

For robust measures of location associated with J dependent groups, various methods have been proposed that are aimed at testing the global hypothesis of a common measure of location applied to the marginal distributions. A criticism of these methods is that they do not deal with outliers in a manner that takes into account the overall structure of the data. Location estimators have been derived that deal with outliers in this manner, but evidently there are no simulation results regarding how well they perform when the goal is to test the some global hypothesis. The paper compares four bootstrap methods in terms of their ability to control the Type I error probability when the sample size is small, two of which were found to perform poorly. The choice of location estimator was found to be important as well. Indeed, for several of the estimators considered here, control over the Type I error probability was very poor. Only one estimator performed well when using the first of two general approaches that might be used. It is based on a variation of the (affine equivariant) Donoho-Gasko trimmed mean. For the second general approach, only a skipped estimator performed reasonably well. (It removes outliers via a projection method and averages the remaining data.) Only one bootstrap method was found to perform well when using the first approach. A different bootstrap method is recommended when using the second approach.
Vertical Distribution of Sediment Concentration in Offshore Waters

ABSTRACT Li, C. ; Fu, X. ; Xiao, Q. ; Pan, X. , and Cao, H. , 2018. Vertical distribution of sediment concentration in offshore waters. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 167–171. Coconut Creek (Florida), ISSN 0749-0208. The existing formulae of vertical distribution of sand concentration are difficult to integrate. Based on the formula of vertical distribution of the flow velocity obtained in the previous study and the suspended sediment diffusion equation in equilibrium, a formula of the distribution of suspended sediment is deduced. It can effectively avoid the phenomena that the Rouse formula equals zero at the water surface. The field measurement data of Jiangsu Sea is used to verify and analyze the deuced formula. And results show that it has a high accuracy and it solve the difficulties caused by lack of data in engineering, such as only the sediment concentration on the surface is given.
Bluepass: An indoor Bluetooth-based localization system for mobile applications

Location-Based Services have become quite popular in mobile computing. However, these services are often not effective when devices are in indoor environments. This paper presents an indoor room-level Bluetooth-based localization system. Fixed stations distributed throughout the environment get the RSSI from user devices. With these data, we compute the distance between the device and the stations, based on the signal propagation. In this paper, we propose a room localization method, based on a voting scheme, that eliminates cells close to a base station detecting devices with a weak signal. Another contribution of this paper is the empirical evaluation of the methods within a real environment.
Privacy tradeoffs in predictive analytics

Online services routinely mine user data to predict user preferences, make recommendations, and place targeted ads. Recent research has demonstrated that several private user attributes (such as political affiliation, sexual orientation, and gender) can be inferred from such data. Can a privacy-conscious user benefit from personalization while simultaneously protecting her private attributes? We study this question in the context of a rating prediction service based on matrix factorization. We construct a protocol of interactions between the service and users that has remarkable optimality properties: it is privacy-preserving, in that no inference algorithm can succeed in inferring a user's private attribute with a probability better than random guessing; it has maximal accuracy, in that no other privacy-preserving protocol improves rating prediction; and, finally, it involves a minimal disclosure, as the prediction accuracy strictly decreases when the service reveals less information. We extensively evaluate our protocol using several rating datasets, demonstrating that it successfully blocks the inference of gender, age and political affiliation, while incurring less than 5% decrease in the accuracy of rating prediction.
Learning to Run with Actor-Critic Ensemble

We introduce an Actor-Critic Ensemble(ACE) method for improving the performance of Deep Deterministic Policy Gradient(DDPG) algorithm. At inference time, our method uses a critic ensemble to select the best action from proposals of multiple actors running in parallel. By having a larger candidate set, our method can avoid actions that have fatal consequences, while staying deterministic. Using ACE, we have won the 2nd place in NIPS'17 Learning to Run competition, under the name of "Megvii-hzwer".
Transient structural distortion of metal-free Cu/Zn superoxide dismutase triggers aberrant oligomerization

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease linked to the misfolding of Cu/Zn superoxide dismutase (SOD1). ALS-related defects in SOD1 result in a gain of toxic function that coincides with aberrant oligomerization. The structural events triggering oligomerization have remained enigmatic, however, as is the case in other protein-misfolding diseases. Here, we target the critical conformational change that defines the earliest step toward aggregation. Using nuclear spin relaxation dispersion experiments, we identified a short-lived (0.4 ms) and weakly populated (0.7%) conformation of metal-depleted SOD1 that triggers aberrant oligomerization. This excited state emanates from the folded ground state and is suppressed by metal binding, but is present in both the disulfide-oxidized and disulfide-reduced forms of the protein. Our results pinpoint a perturbed region of the excited-state structure that forms intermolecular contacts in the earliest nonnative dimer/oligomer. The conformational transition that triggers oligomerization is a common feature of WT SOD1 and ALS-associated mutants that have widely different physicochemical properties. But compared with WT SOD1, the mutants have enhanced structural distortions in their excited states, and in some cases slightly higher excited-state populations and lower kinetic barriers, implying increased susceptibility to oligomerization. Our results provide a unified picture that highlights both (i) a common denominator among different SOD1 variants that may explain why diverse mutations cause the same disease, and (ii) a structural basis that may aid in understanding how different mutations affect disease propensity and progression.
Equality of Opportunity: Theory and Evidence

Building on earlier work by political philosophers, economists have recently sought to define a concept of equity that accommodates the fairness of reward to individual responsibility and effort, while allowing for the existence of some inequalities which are unfair and should be compensated. This paper -- commissioned as a chapter for the Oxford Handbook of Well Being and Public Policy -- provides a critical review of the economic literature on equality and inequality of opportunity. A simple 'canonical model' of equal opportunity is proposed, and used to explore the two fundamental concepts in this (relatively) new theory of social justice: the principles of compensation and reward. Ex-ante and ex-post versions of the compensation principle are presented, and the tensions between them are discussed. Different approaches to the measurement of inequality of opportunity -- and empirical applications -- are reviewed, and implications for the measurement of poverty and of the rate of economic development are discussed.
Coding theorems for 'turbo-like' codes

In this paper we discuss AWGN coding theorems for ensembles of coding systems which are built from fixed convolutional codes interconnected with random interleavers. We call these systems “turbo-like” codes and they include as special cases both the classical turbo codes [1,2,3] and the serial concatentation of interleaved convolutional codes [4]. We offer a general conjecture about the behavior of the ensemble (maximum-likelihood decoder) word error probability as the word length approches infinity. We prove this conjecture for a simple class of rate 1/q serially concatenated codes where the outer code is a q-fold repetition code and the inner code is a rate 1 convolutional code with transfer function 1/(1 + D). We believe this represents the first rigorous proof of a coding theorem for turbo-like codes.
Trusted language runtime (TLR): enabling trusted applications on smartphones

Despite their popularity, today's smartphones do not yet offer environments for building and running trusted applications. At the same time, current systems designed for traditional desktop or server machines to enable trusted applications are either too heavyweight for smartphones or too difficult to program. This paper presents the Trusted Language Runtime (TLR), a system for developing and running trusted applications on a smartphone. The TLR is lightweight because 1) it makes use of ARM TrustZone, hardware support that offers rich trusted computing primitives, and 2) it leverages the . NET MicroFramework, a language runtime for embedded and resource-constrained devices. The TLR is easy to program because . NET offers the productivity benefits of modern high-level languages, such as strong typing and garbage collection, to application developers.
AROMA: automatic generation of radio maps for localization systems

Current methods for building radio maps for wireless localization systems require a tedious, manual and error-prone calibration of the area of interest. Each time the layout of the environment is changed or different hardware is used, the whole process of location fingerprinting and constructing the radio map has to be repeated. The process gets more complicated in the case of localizing multiple entities in a device-free scenario, since the radio map needs to take all possible combinations of the location of the entities into account. In this demo, we present a novel system (AROMA) that is capable of generating accurate radio maps for a given site of interest. AROMA constructs radio maps for both deterministic and probabilistic wireless localization systems. According to our knowledge, AROMA is the first system to generate radio maps for device-free localization. It uses 3D ray tracing enhanced with the uniform theory of diffraction (UTD) to model the electric field behavior and the human shadowing effect. AROMA also automates a number of routine tasks, such as importing building models and automatic sampling of the area of interest.