Miracle-dz/UniFAR-FTUs huggingface dataset:
A Learning Algorithm with Compression-Based Regularization

This paper investigates, from information theoretic principles, a learning problem based on the principle that any regularity in a given dataset can be exploited to extract compact features from data, in order to build meaningful representations of a relevant content. We begin by introducing the fundamental tradeoff between the average risk and the model complexity. Interestingly, our formulation allows an information theoretic formulation of the multi-task learning (MTL) problem. Then, we present an iterative algorithm for computing the optimal tradeoffs. Remarkably, empirical results illustrate that there exists an optimal information rate minimizing the excess risk which depends on the nature and the amount of available training data. An application to hierarchical text categorization is also investigated, extending previous works.
Low Complexity Heart Rate Measurement from Wearable Wrist-Type Photoplethysmographic Sensors Robust to Motion Artifacts

This paper presents a low complexity while accurate Heart Rate (HR) estimation technique from signals captured by Photoplethysmographic (PPG) sensors worn on the wrist during intensive physical exercise. Wrist-type PPG signals experience severe Motion Artifacts (MA) that hinder efficient HR estimation especially during intensive physical exercises. To suppress the motion artifacts efficiently, simultaneous 3 dimensional acceleration signals are used as reference MAs. The proposed method achieves an Average Absolute Error (AAE) of 1.19 Beats Per Minute (BPM) on the 12 benchmark PPG recordings in which subjects run at speeds of up to 15 km/h. This method also achieves an AAE of 2.17 BPM on the whole benchmark database of 23 recordings that include both running and arm movement activities. This performance is comparable with state-of-the-art algorithms while at a significantly reduced computational cost which makes its standalone implementation on wearable devices feasible. The proposed algorithm achieves an average processing time of 32 milliseconds per input frames of length 8 seconds (2 channel PPG and 3D ACC signals) on a 3.2 GHz processor.
Nondata-Aided Doppler Frequency Estimation for OFDM Systems Over Doubly Selective Fading Channels

This paper introduces a time-domain nondata-aided maximum-likelihood (ML) estimation of Doppler frequency for orthogonal frequency-division multiplexing (OFDM) over doubly selective fading channels with the Clarke–Jakes–Doppler spectrum. The proposed approach adopts the repetitive signal structure of OFDM, such as the cyclic prefix, which can be used for synchronization purpose. The originally derived ML estimation results in a high computational cost; therefore, after gaining insights into the log-likelihood function, we further design an algorithm with reduced complexity. The proposed algorithm only has a complexity <inline-formula> <tex-math notation="LaTeX">$\sim O(QN_{d})$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$N_{d}$ </tex-math></inline-formula> denote the numbers of repetitive parts and samples in a repetitive part, respectively. In contrast to conventional works based on (data aided) channel estimates and/or assuming that channels are flat fading, the proposed method employs the cyclostationary feature of received signals and is not sensitive to channel estimation error and unknown channel phases. Furthermore, the Cramér–Rao lower bounds of the Doppler frequency estimate over nonline-of-sight (NLOS) and line-of-sight multipath fading channels are analyzed. Simulations confirm the advantages of the proposed estimator.
WeSamBE: A Weight-Sample-Based Method for Background Subtraction

Background subtraction techniques are often treated as fundamental and significant ways to analyze and understand video content. In this paper, we propose a weight-sample-based method for foreground detection. This method allows us to use a few samples with variable weights to achieve effective change detection. To rapidly adapt to changing scenarios, a minimum-weight update policy is first proposed to replace the most inefficient sample instead of the oldest sample or a random sample. In addition, a reward-and-penalty weighting strategy is put forward to reinforce active samples and punish others. In this way, the weights of relatively effective samples are increased and the false updating of effective samples with smaller weights is reduced. Moreover, some other strategies, such as spatial-diffusion policy and random time subsampling, are also incorporated to ensure the flexibility of the proposed method. Finally, in our experiments, an adaptive feedback technique is incorporated into our algorithm to adapt to more challenging videos, and the final results indicate that our method is superior to the state-of-the-art approaches on the challenging CDnet data set.
Exposure Control Using Bayesian Optimization Based on Entropy Weighted Image Gradient

Under- and oversaturation can cause severe image degradation in many vision-based robotic applications. To control camera exposure in dynamic lighting conditions, we introduce a novel metric for image information measure. Measuring an image gradient is typical when evaluating its level of image detail. However, emphasizing more informative pixels substantially improves the measure within an image. By using this entropy weighted image gradient, we introduce an optimal exposure value for vision-based approaches. Using this newly invented metric, we also propose an effective exposure control scheme that covers a wide range of light conditions. When evaluating the function (e.g., image frame grab) is expensive, the next best estimation needs to be carefully considered. Through Bayesian optimization, the algorithm can estimate the optimal exposure value with minimal cost. We validated the proposed image information measure and exposure control scheme via a series of thorough experiments using various exposure conditions.
Doppler ultrasound surveillance of TIPS-patency in the era of covered stents – retrospective analysis of a large single-center cohort

Abstract Purpose The purpose of this study was to determine accuracy and necessity of long-term Doppler ultrasound (DU) surveillance of transjugular intrahepatic portosystemic shunt (TIPS) patency after implantation of an ePTFE-covered stent-graft (Viatorr). Methods This single-center retrospective study includes 228 consecutive cirrhotic patients with TIPS implantation due to portal hypertensive complications. Standardized DU surveillance was scheduled 3 – 5 days, 3 months, and 6 months after TIPS implantation and every 6 months thereafter. Portal venography was performed in case of DU findings suspicious of TIPS dysfunction, clinical signs of recurrent portal hypertension, or refractory hepatic encephalopathy. Results During a mean follow-up of 16.6 ± 23.4 months, 866 DU examinations were performed. Twenty-two cases of TIPS dysfunction were observed in 16 patients with no first dysfunction more than 4 years after implantation. Routine DU in asymptomatic patients had little therapeutic impact (0.75 %). DU and venography were concordant in 39/46 (84.8 %) paired examinations, and 1-, 2-, and 5-year primary TIPS patency was 87.4 %, 83.7 %, and 79.97 %, respectively. Patients with TIPS dysfunction and subsequent successful revision during the first 2 years of follow-up had a significantly higher risk (p = 0.001) of new dysfunction compared to those without TIPS dysfunction. Cumulative 1-, 2-, and 5-year survival was 68.7 %, 61.3 %, and 42.7 %, respectively. Conclusions Despite acceptable accuracy, scheduled DU surveillance proved to have minor therapeutic impact. Thus, detailed DU surveillance is not useful in asymptomatic patients after 2 years of unremarkable follow-up. In contrast, long-term DU surveilleance should be performed in patients after successful revision of TIPS dysfunction and patients with prothrombotic states (e. g., portal vein thrombosis, Budd-Chiari syndrome).
A Novel Algorithm for Unbiased Learning to Rank

Although click data is widely used in search systems in practice, so far the inherent bias, most notably position bias, has prevented it from being used in training of a ranker for search, i.e., learning-to-rank. Recently, a number of authors have proposed new techniques referred to as 'unbiased learning-to-rank', which can reduce position bias and train a relatively high-performance ranker using click data. Most of the algorithms, based on the inverse propensity weighting (IPW) principle, first estimate the click bias at each position, and then train an unbiased ranker with the estimated biases using a learning-to-rank algorithm. However, there has not been a method for pairwise learning-to-rank that can jointly conduct debiasing of click data and training of a ranker using a pairwise loss function. In this paper, we propose a novel algorithm, which can jointly estimate the biases at click positions and the biases at unclick positions, and learn an unbiased ranker. Experiments on benchmark data show that our algorithm can significantly outperform existing algorithms. In addition, an online A/B Testing at a commercial search engine shows that our algorithm can effectively conduct debiasing of click data and enhance relevance ranking.
Sulforaphane regulates apoptosis- and proliferation-related signaling pathways and synergizes with cisplatin to suppress human ovarian cancer

Ovarian cancer is currently the most life-threatening type of gynecological malignancy with limited treatment options. Therefore, improved targeted therapies are required to combat ovarian cancer across the world. Sulforaphane is found in raw cruciferous vegetables. The chemotherapeutic and anti-carcinogenic properties of sulforaphane have been demonstrated, however, the underlying mechanisms remain to be fully elucidated, particularly in ovarian cancer. In the present study, the possibility of repurposing sulforaphane as an anti-ovarian cancer agent was examined. Cell viability and colony formation assay were used to test the anticancer efficiency of sulforaphane. Then wound healing assay, migration assay, cell cycle and apoptosis assays were used to detect how the drug worked on the cells. The mechanism of sulforaphane was investigated by western blot analysis. It was found that sulforaphane effectively suppressed the progression of human ovarian cancer cell proliferation, migration and cell cycle, and promoted apoptosis. Sulforaphane inhibited multiple cancer-associated signaling pathways, including B-cell lymphoma 2 (Bcl-2), Bcl-2-associated X protein, cytochrome c, Caspase-3, phosphorylated AKT, phosphorylated nuclear factor-κB, P53, P27, Cyclin-D1 and cMyc, and reduced the expression levels of human epidermal growth factor receptor 2 in human ovarian cancer cells. Sulforaphane synergized with cisplatin to suppress the cancer cell proliferation and enhance ovarian cancer cell apoptosis. Xenograft experiments in vivo confirmed that sulforaphane effectively suppressed tumor growth by inhibiting ovarian cancer cell proliferation through targeting tumor-related signals. The results indicated that sulforaphane may be repurposed as an effective anti-ovarian cancer agent, with further preclinical or clinical investigations required.
Joint I-Vector with End-to-End System for Short Duration Text-Independent Speaker Verification

Factor analysis based $i$-vector has been the state-of-the-art method for speaker verification. Recently, researchers propose to build DNN based end-to-end speaker verification systems and achieve comparable performance with $i$-vector. Since these two methods possess their own property and differ from each other significantly, we explore a framework to integrate these two paradigms together to utilize their complementarity. More specifically, in this paper we develop and compare four methodologies to integrate traditional $i$-vector into end-to-end systems, including score fusion, embeddings concatenation, transformed concatenation and joint learning. All these approaches achieve significant gains. Moreover, the hard trial selection is performed on the end-to-end architecture which further improves the performance. Experimental results on a text-independent short-duration dataset generated from SRE 2010 reveal that the newly proposed method reduces the EER by relative 31.0% and 28.2% compared to the $i$-vector and end-to-end baselines respectively.
ManifoldNet: A Deep Network Framework for Manifold-valued Data

Deep neural networks have become the main work horse for many tasks involving learning from data in a variety of applications in Science and Engineering. Traditionally, the input to these networks lie in a vector space and the operations employed within the network are well defined on vector-spaces. In the recent past, due to technological advances in sensing, it has become possible to acquire manifold-valued data sets either directly or indirectly. Examples include but are not limited to data from omnidirectional cameras on automobiles, drones etc., synthetic aperture radar imaging, diffusion magnetic resonance imaging, elastography and conductance imaging in the Medical Imaging domain and others. Thus, there is need to generalize the deep neural networks to cope with input data that reside on curved manifolds where vector space operations are not naturally admissible. In this paper, we present a novel theoretical framework to generalize the widely popular convolutional neural networks (CNNs) to high dimensional manifold-valued data inputs. We call these networks, ManifoldNets. In ManifoldNets, convolution operation on data residing on Riemannian manifolds is achieved via a provably convergent recursive computation of the weighted Frechet Mean (wFM) of the given data, where the weights makeup the convolution mask, to be learned. Further, we prove that the proposed wFM layer achieves a contraction mapping and hence ManifoldNet does not need the non-linear ReLU unit used in standard CNNs. We present experiments, using the ManifoldNet framework, to achieve dimensionality reduction by computing the principal linear subspaces that naturally reside on a Grassmannian. The experimental results demonstrate the efficacy of ManifoldNets in the context of classification and reconstruction accuracy.
Local Point Pair Feature Histogram for Accurate 3D Matching

This paper reports the discovery of a fast, yet highly discriminative local 3D descriptor for point cloud data. Local descriptors are popular and highly effective for various 3D tasks such as registration, pose estimation and object recognition. Good solutions for these tasks critically depend on the ability to make correct associations between two or more models, or the local features on these, even under the inﬂuence of disturbances such as noise, clutter and occlusions. Our descriptor formulation is inspired by the geometric relations employed by the well-known Point Pair Feature, used originally on a global scale for classiﬁcation and later on a semi-global scale for recognition. We have identi-ﬁed the most discriminative subset of relations for use in a local descriptor, resulting in a condensed representation of the local variation around a surface point. We compare against seven competing mesh and point cloud descriptors on eight different matching benchmarks with a well-deﬁned evaluation protocol. In all cases, our descriptor outperforms earlier works, providing relative gains in accuracy above 100% for two of the four real datasets considered. Finally, we subject all descriptors to RANSAC based pose estimation and object recognition evaluation on four real datasets. In all four cases, our descriptor matches or surpasses state of the art performances.
Proteomics and the microbiome: pitfalls and potential

ABSTRACT Introduction: Human symbiotic microbiota are now known to play important roles in human health and disease. Significant progress in our understanding of the human microbiome has been driven by recent technological advances in the fields of genomics, transcriptomics, and proteomics. As a complementary method to metagenomics, proteomics is enabling detailed protein profiling of the microbiome to decipher its structure and function and to analyze its relationship with the human body. Fecal proteomics is being increasingly applied to discover and validate potential health and disease biomarkers, and Therapeutic Goods Administration (TGA)-approved instrumentation and a range of clinical assays are being developed that will collectively play key roles in advancing personalized medicine. Areas covered: This review will introduce the complexity of the microbiome and its role in health and disease (in particular the gastrointestinal tract or gut microbiome), discuss current genomic and proteomic methods for studying this system, including the discovery of potential biomarkers, and outline the development of clinically accepted protocols leading to personalized medicine. Expert commentary: Recognition of the important role the microbiome plays in both health and disease is driving current research in this key area. A proteogenomics approach will be essential to unravel the biologies underlying this complex network.
Leveraging DRAM Refresh to Protect the Memory Timing Channel of Cloud Chip Multi-processors

The shared memory controllers of single-chip cloud computing (SCC) processors are vulnerable to timing-channel attacks. Existing protection strategies based on fixed memory bandwidth assignment degrade processor performance and severely harm the experience of cloud-users. This paper proposes a novel light-weight timing channel protection scheme against both side channel and covert channel attacks to the shared memory controllers of cloud chip multi-processors (CMPs). Instead of enforcing a fixed time-slot assignment to the applications, we go an entirely different technical route and utilize the background DRAM refresh as a free noise source to eliminate the time correlation between victim and attacker applications. The proposed protection framework, MemJam, relies on the emerging fine-grained refresh technology to achieve the effects of timing channel obfuscation. Multi-programmed workloads running in a cloud CMP were used to evaluate the protection method. The results show that the light-weight refresh-based noise can effectively block the timing-channel between user applications, and achieve up to 1.61~2.49X memory performance boost compared to prior solutions.
Adaptive Context-aware Reinforced Agent for Handwritten Text Recognition

Handwritten text recognition has been a ubiquitous research problem in the ﬁeld of computer vision. Most existing approaches focus on the recognition of handwritten words without considering the cursive nature and signiﬁcant differences in the writing of individuals. In this paper, we address these problems by leveraging an adaptive context-aware reinforced agent which learns the actions to determine the scales of context regions during inference. We formulate our approach in a reinforcement learning framework. Speciﬁcally, we construct the action set with a number of context lengths. Given an image feature sequence, our model is trained to adaptively choose the optimal context length according to the observed state. An attention mechanism is then used to selectively attend the context region. Our model can generalize well from recognizing isolated words to recognizing individual lines of text while remain low computation overheads. We conduct extensive experiments on three large-scale handwritten text recognition datasets. The experimental results show that our proposed model is superior to the state-of-the-art alternatives.
Extended Targeted and Non-Targeted Strategies for the Analysis of Marine Toxins in Mussels and Oysters by (LC-HRMS)

When considering the geographical expansion of marine toxins, the emergence of new toxins and the associated risk for human health, there is urgent need for versatile and efficient analytical methods that are able to detect a range, as wide as possible, of known or emerging toxins. Current detection methods for marine toxins rely on a priori defined target lists of toxins and are generally inappropriate for the detection and identification of emerging compounds. The authors describe the implementation of a recent approach for the non-targeted analysis of marine toxins in shellfish with a focus on a comprehensive workflow for the acquisition and treatment of the data generated after liquid chromatography coupled with high resolution mass spectrometry (LC-HRMS) analysis. First, the study was carried out in targeted mode to assess the performance of the method for known toxins with an extended range of polarities, including lipophilic toxins (okadaic acid, dinophysistoxins, azaspiracids, pectenotoxins, yessotoxins, cyclic imines, brevetoxins) and domoic acid. The targeted method, assessed for 14 toxins, shows good performance both in mussel and oyster extracts. The non-target potential of the method was then challenged via suspects and without a priori screening by blind analyzing mussel and oyster samples spiked with marine toxins. The data processing was optimized and successfully identified the toxins that were spiked in the blind samples.
Multi-Scale Deep Compressive Sensing Network

With joint learning of sampling and recovery, the deep learning-based compressive sensing (DCS) has shown significant improvement in performance and running time reduction. Its reconstructed image, however, losses high-frequency content especially at low subrates. This happens similarly in the multi-scale sampling scheme which also samples more low-frequency components. In this paper, we propose a multi-scale DCS convolutional neural network (MS-DCSNet) in which we convert image signal using multiple scale-based wavelet transform, then capture it through convolution block by block across scales. The initial reconstructed image is directly recovered from multi-scale measurements. Multi-scale wavelet convolution is utilized to enhance the final reconstruction quality. The network is able to learn both multi-scale sampling and multi-scale reconstruction, thus results in better reconstruction quality.
Personality Recognition Based on User Generated Content

Personality recognition has lots of applications, like business recommendation systems, human resource management, author identification and psychological treatment. Traditionally, identifying one's personality traits depends on questionnaire scales. With the development of the Internet, people can express their opinions, emotions, attitudes, etc., on social networks. From the viewpoint of psychology, people's behaviors and decisions are influenced by their personality characteristics, so it's convenient for us to recognize one's personality based on his social network content. In this paper, we build a new personality recognition model based on a standard dataset, i.e., the Facebook update status content. Firstly, we extract the language features via the Linguistic Inquiry and Word Count (LIWC) dictionary tool. Then all the words in the status content are trained by Google news pre-trained word2vec embedding with 300 dimensions. Next, we construct the personality recognition model by using the Convolutional Neural Network (CNN) method with the LIWC features. We conduct five binary classifications on the most widely used personality model, i.e., the big five personality that contains five traits: extroversion, neuroticism, agreeableness, conscientiousness, and openness. Finally, our experimental results demonstrate that the method we present in this paper achieves very good performances, especially on the openness trait.