Miracle-dz/UniFAR-FTUs huggingface dataset:
Exponential Family Estimation via Adversarial Dynamics Embedding

We present an efficient algorithm for maximum likelihood estimation (MLE) of exponential family models, with a general parametrization of the energy function that includes neural networks. We exploit the primal-dual view of the MLE with a kinetics augmented model to obtain an estimate associated with an adversarial dual sampler. To represent this sampler, we introduce a novel neural architecture, dynamics embedding, that generalizes Hamiltonian Monte-Carlo (HMC). The proposed approach inherits the flexibility of HMC while enabling tractable entropy estimation for the augmented model. By learning both a dual sampler and the primal model simultaneously, and sharing parameters between them, we obviate the requirement to design a separate sampling procedure once the model has been trained, leading to more effective learning. We show that many existing estimators, such as contrastive divergence, pseudo/composite-likelihood, score matching, minimum Stein discrepancy estimator, non-local contrastive objectives, noise-contrastive estimation, and minimum probability flow, are special cases of the proposed approach, each expressed by a different (fixed) dual sampler. An empirical investigation shows that adapting the sampler during MLE can significantly improve on state-of-the-art estimators.
Protecting Privacy in Location-Based Services Using K-Anonymity without Cloaked Region

The emerging location-detection devices together with ubiquitous connectivity have enabled a large variety of location-based services (LBS). Unfortunately, LBS may threaten the users’ privacy. K-anonymity cloaking the user location to K-anonymizing spatial region (K-ASR) has been extensively studied to protect privacy in LBS. Traditional K-anonymity method needs complex query processing algorithms at the server side. SpaceTwist [8] rectifies the above shortcoming of traditional K-anonymity since it only requires incremental nearest neighbor (INN) queries processing techniques at the server side. However, Space Twist may fail since it cannot guarantee K-anonymity. In this paper, our proposed framework, called KAWCR (K-anonymity Without Cloaked Region), rectifies the shortcomings and retains the advantages of the above two techniques. KAWCR only needs the server to process INN queries and can guarantee that the users issuing the query is indistinguishable from at least K-1 other users. The extensive experimental results show that the communication cost of KAWCR for kNN queries is lower than that of both traditional K-anonymity and SpaceTwist.
Efficient Feature Subset Selection and Subset Size Optimization

A broad class of decision-making problems can be solved by learning approach. This can be a feasible alternative when neither an analytical solution exists nor the mathematical model can be constructed. In these cases the required knowledge can be gained from the past data which form the so-called learning or training set. Then the formal apparatus of statistical pattern recognition can be used to learn the decision-making. The first and essential step of statistical pattern recognition is to solve the problem of feature selection (FS) ormore generally dimensionality reduction (DR). The problem of feature selection in statistical pattern recognition will be of primary focus in this chapter. The problem fits in the wider context of dimensionality reduction (Section 2) which can be accomplished either by a linear or nonlinear mapping from the measurement space to a lower dimensional feature space, or by measurement subset selection. This chapter will focus on the latter (Section 3). The main aspects of the problem as well as the choice of the right feature selection tools will be discussed (Sections 3.1 to 3.3). Several optimization techniques will be reviewed, with emphasis put to the framework of sequential selection methods (Section 4). Related topics of recent interest will be also addressed, including the problem of subset size determination (Section 4.7), search acceleration through hybrid algorithms (Section 5), and the problem of feature selection stability and feature over-selection (Section 6).
Anxiety, Depression, and Adverse Clinical Outcomes in Patients With Atrial Fibrillation Starting Warfarin: Cardiovascular Research Network WAVE Study

Background Anxiety and depression are associated with worse outcomes in several cardiovascular conditions, but it is unclear whether they affect outcomes in atrial fibrillation (AF). In a large diverse population of adults with AF, we evaluated the association of diagnosed anxiety and/or depression with stroke and bleeding outcomes. Methods and Results The Cardiovascular Research Network WAVE (Community‐Based Control and Persistence of Warfarin Therapy and Associated Rates and Predictors of Adverse Clinical Events in Atrial Fibrillation and Venous Thromboembolism) Study included adults with AF newly starting warfarin between 2004 and 2007 within 5 health delivery systems in the United States. Diagnosed anxiety and depression and other patient characteristics were identified from electronic health records. We identified stroke and bleeding outcomes from hospitalization databases using validated International Classification of Diseases, Ninth Revision (ICD‐9), codes. We used multivariable Cox regression to assess the relation between anxiety and/or depression with outcomes after adjustment for stroke and bleeding risk factors. In 25 570 adults with AF initiating warfarin, 490 had an ischemic stroke or intracranial hemorrhage (1.52 events per 100 person‐years). In multivariable analyses, diagnosed anxiety was associated with a higher adjusted rate of combined ischemic stroke and intracranial hemorrhage (hazard ratio, 1.52; 95% confidence interval, 1.01–2.28). Results were not materially changed after additional adjustment for patient‐level percentage of time in therapeutic anticoagulation range on warfarin (hazard ratio, 1.56; 95% confidence interval, 1.03–2.36). In contrast, neither isolated depression nor combined depression and anxiety were significantly associated with outcomes. Conclusions Diagnosed anxiety was independently associated with increased risk of combined ischemic stroke and intracranial hemorrhage in adults with AF initiating warfarin that was not explained by differences in risk factors or achieved anticoagulation quality.
Patients’ perceived needs of osteoarthritis health information: A systematic scoping review

Background Optimal management of osteoarthritis requires active patient participation. Understanding patients’ perceived health information needs is important in order to optimize health service delivery and health outcomes in osteoarthritis. We aimed to review the existing literature regarding patients’ perceived health information needs for OA. Methods A systematic scoping review was performed of publications in MEDLINE, EMBASE, CINAHL and PsycINFO (1990–2016). Descriptive data regarding study design and methodology were extracted and risk of bias assessed. Aggregates of patients’ perceived needs of osteoarthritis health information were categorized. Results 30 studies from 2876 were included: 16 qualitative, 11 quantitative and 3 mixed-methods studies. Three areas of perceived need emerged: (1) Need for clear communication: terms used were misunderstood or had unintended connotations. Patients wanted clear explanations. (2) Need for information from various sources: patients wanted accessible health professionals with specialist knowledge of arthritis. The Internet, whilst a source of information, was acknowledged to have dubious reliability. Print media, television, support groups, family and friends were utilised to fulfil diverse information needs. (3) Needs of information content: patients desired more information about diagnosis, prognosis, management and prevention. Conclusions Patients desire more information regarding the diagnosis of osteoarthritis, its impact on daily life and its long-term prognosis. They want more information not only about pharmacological management options, but also non-pharmacological options to help them manage their symptoms. Also, patients wanted this information to be delivered in a clear manner from multiple sources of health information. To address these gaps, more effective communication strategies are required. The use of a variety of sources and modes of delivery may enable the provision of complementary material to provide information more successfully, resulting in better patient adherence to guidelines and improved health outcomes.
Soft edit distance for differentiable comparison of symbolic sequences

Edit distance, also known as Levenshtein distance, is an essential way to compare two strings that proved to be particularly useful in the analysis of genetic sequences and natural language processing. However, edit distance is a discrete function that is known to be hard to optimize. This fact hampers the use of this metric in Machine Learning. Even as simple algorithm as K-means fails to cluster a set of sequences using edit distance if they are of variable length and abundance. In this paper we propose a novel metric - soft edit distance (SED), which is a smooth approximation of edit distance. It is differentiable and therefore it is possible to optimize it with gradient methods. Similar to original edit distance, SED as well as its derivatives can be calculated with recurrent formulas at polynomial time. We prove usefulness of the proposed metric on synthetic datasets and clustering of biological sequences.
Joint Analysis of Acoustic Event and Scene Based on Multitask Learning

Acoustic event detection and scene classification are major research tasks in environmental sound analysis, and many methods based on neural networks have been proposed. Conventional methods have addressed these tasks separately; however, acoustic events and scenes are closely related to each other. For example, in the acoustic scene `office', the acoustic events `mouse clicking' and `keyboard typing' are likely to occur. In this paper, we propose multitask learning for joint analysis of acoustic events and scenes, which shares the parts of the networks holding information on acoustic events and scenes in common. By integrating the two networks, we expect that information on acoustic scenes will improve the performance of acoustic event detection. Experimental results obtained using TUT Sound Events 2016/2017 and TUT Acoustic Scenes 2016 datasets indicate that the proposed method improves the performance of acoustic event detection by 10.66 percentage points in terms of the F-score, compared with a conventional method based on a convolutional recurrent neural network.
Result verification, code verification and computation of support values in phylogenetics

Verification in phylogenetics represents an extremely difficult subject. Phylogenetic analysis deals with the reconstruction of evolutionary histories of species, and as long as mankind is not able to travel in time, it will not be possible to verify deep evolutionary histories reconstructed with modern computational methods. Here, we focus on two more tangible issues that are related to verification in phylogenetics (i) the inference of support values on trees that provide some notion about the 'correctness' of the tree within narrow limits and, more importantly; (ii) issues pertaining to program verification, especially with respect to codes that rely heavily on floating-point arithmetics. Program verification represents a largely underestimated problem in computational science that can have fatal effects on scientific conclusions.
Clinical characteristics of peri‐implant mucositis and peri‐implantitis

OBJECTIVES To evaluate and correlate clinical parameters associated with peri-implant diseases based on established case definitions. MATERIALS AND METHODS A total of 75 patients exhibiting 269 implants (healthy: 77; peri-implant mucositis: 77; peri-implantitis: 115) were included in this observational study. Clinical parameters included bleeding on probing (BOP), probing depths (PDs), and suppuration (Supp). RESULTS Healthy sites were associated with the absence of BOP, while mean BOP in peri-implant mucositis and peri-implantitis patients amounted to 20.83% and 71.33%, corresponding to 43% and 86% at the implant level (p < .001), respectively. Peri-implantitis patients exhibited significantly higher mean PD values (4.46 mm) when compared with the peri-implant mucositis group (2.70 mm, p < .001). Supp was limited to peri-implantitis cases and detected in 30.16% of the patients (implant level: 17.39%). The regression model revealed a significant linear association between the number of BOP-positive sites around the implant (minimum 0, maximum 6) and mean PD values at peri-implant mucositis and peri-implantitis sites at both patient and implant levels. CONCLUSIONS The clinical parameters investigated were shown to be associated with the severity of peri-implant diseases.
Smart Diffusion of Government Contents

A nonaqueous electrolyte secondary battery according to the present invention has a power-generating element and a collector. The power-generating element is provided with a portion where a negative electrode plate and a positive electrode plate are not opposed to each other. In the power-generating element, the electrode plates are wound or laminated through a separation body so that the side edge portion of one of the electrode plates protrudes from that of the other. The collector is connected to the side edge portions. The collector has a plurality of grooves bonded to the side edges of the electrode plates. The bonding is made by at least one of a welding method such as ultrasonic welding method, laser welding method, electric welding method, arc welding method and plasma arc welding method, and a mechanical joint using a rivet, pin or eyelet, or by deforming under pressure the collector to crimp.
Collage Inference: Tolerating Stragglers in Distributed Neural Network Inference using Coding

MLaaS (ML-as-a-Service) offerings by cloud computing platforms are becoming increasingly popular these days. Pre-trained machine learning models are deployed on the cloud to support prediction based applications and services. For achieving higher throughput, incoming requests are served by running multiple replicas of the model on different machines concurrently. Incidence of straggler nodes in distributed inference is a significant concern since it can increase inference latency, violate SLOs of the service. In this paper, we propose a novel coded inference model to deal with stragglers in distributed image classification. We propose modified single shot object detection models, Collage-CNN models, to provide necessary resilience efficiently. A Collage-CNN model takes collage images formed by combining multiple images as its input and performs multi-image classification in one shot. We generate custom training collages using images from standard image classification datasets and train the model to achieve high classification accuracy. Deploying the Collage-CNN models in the cloud, we demonstrate that the 99th percentile latency can be reduced by 1.45X to 2.46X compared to replication based approaches and without compromising prediction accuracy.
Sentiment Classification using N-gram IDF and Automated Machine Learning

We propose a sentiment classification method with a general machine learning framework. For feature representation, n-gram IDF is used to extract software-engineering-related, dataset-specific, positive, neutral, and negative n-gram expressions. For classifiers, an automated machine learning tool is used. In the comparison using publicly available datasets, our method achieved the highest F1 values in positive and negative sentences on all datasets.
Scalable Supervised Discrete Hashing for Large-Scale Search

Supervised hashing methods have attracted much attention in these years. However, most existing supervised hashing algorithms have some of the following problems. First, most of them leverage the pairwise similarity matrix, whose size is quadratic to the number of training samples, to supervise the learning of hash codes. Thus, they are not scalable when dealing with large data. Second, most of them relax the discrete constraints for easy optimization and then quantize the learnt real-valued solution to binary hash codes. Therefore, the quantization error caused by the relaxation may lead to a decline of retrieval performance. To address these issues and make the supervised method scalable to large datasets, we present a novel hashing method, named Scalable Supervised Discrete Hashing (SSDH). Specifically, based on a new loss function, SSDH bypasses the direct optimization on the n by n pairwise similarity matrix. In addition, SSDH adopts no relaxation optimization scheme in the learning procedure and avoids the large quantization error problem. Moreover, during learning, it leverages both the pairwise similarity matrix and label matrix; thus, more semantic information can be embedded to the learning of hash codes. Extensive experiments are conducted on six benchmark datasets including two large-scale datasets, i.e., NUS-WIDE and ImageNet. The results show that SSDH can outperform state-of-the-art baselines on these datasets, demonstrating its effectiveness and efficiency.
The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning

Image partitioning, or segmentation without semantics, is the task of decomposing an image into distinct segments, or equivalently to detect closed contours. Most prior work either requires seeds, one per segment; or a threshold; or formulates the task as multicut / correlation clustering, an NP-hard problem. Here, we propose an efficient algorithm for graph partitioning, the “Mutex Watershed”. Unlike seeded watershed, the algorithm can accommodate not only attractive but also repulsive cues, allowing it to find a previously unspecified number of segments without the need for explicit seeds or a tunable threshold. We also prove that this simple algorithm solves to global optimality an objective function that is intimately related to the multicut / correlation clustering integer linear programming formulation. The algorithm is deterministic, very simple to implement, and has empirically linearithmic complexity. When presented with short-range attractive and long-range repulsive cues from a deep neural network, the Mutex Watershed gives the best results currently known for the competitive ISBI 2012 EM segmentation benchmark.
Bone Graft Substitutes for Bone Defect Regeneration. A Collective Review

Objective: The purpose of this review article is to illustrate the current state of development of bone graft substitutes that could be used for bone defect regeneration, as well as to analyze their efficacy for clinical use. Methods: An electronic search of the PubMed, was performed for articles written in English. The focused question was “ideal graft substitute material to choose in clinical practice”? The searches were limited to articles including: bone graft, bone defect scaffold, bone substitutes, and bone regeneration. An attempt was made to identify clinical studies. During the data collection, the data were extracted from the studies including scaffold material, properties and advantages of bone substitutes, as well as clinical results if the study has been provided clinically. Results: In spite of several acceptable scaffold options available for bone regeneration, these options still need to bridge the gap between research and clinical practice. There is little information available about the cellular basis for bone regeneration in humans. Several problems limit the broad usage of such options, including lack of randomized controlled human studies, and dubious long term results. Conclusion: The studies should be nurtured and monitored by a combination of clinical experience. Future trends may focus on the effective combinations of osteoinductive materials, osteoinductive growth factors and cell-based tissue regeneration tactics using composite carriers. There is no single ideal graft material to choose in clinical practice, therefore researches are ongoing in all relevant fields, to establish modern bone regeneration protocols that may lead to the innovation of ideal graft substitutes.
An End-to-End Language-Tracking Speech Recognizer for Mixed-Language Speech

End-to-end automatic speech recognition (ASR) can significantly reduce the burden of developing ASR systems for new languages, by eliminating the need for linguistic information such as pronunciation dictionaries. This also creates an opportunity to build a monolithic multilingual ASR system with a language-independent neural network architecture. In our previous work, we proposed a monolithic neural network architecture that can recognize multiple languages, and showed its effectiveness compared with conventional language-dependent models. However, the model is not guaranteed to properly handle switches in language within an utterance, thus lacking the flexibility to recognize mixed-language speech such as code-switching. In this paper, we extend our model to enable dynamic tracking of the language within an utterance, and propose a training procedure that takes advantage of a newly created mixed-language speech corpus. Experimental results show that the extended model outperforms both language-dependent models and our previous model without suffering from performance degradation that could be associated with language switching.
Asymmetric Impurity Functions, Class Weighting, and Optimal Splits for Binary Classification Trees

We investigate how asymmetrizing an impurity function affects the choice of optimal node splits when growing a decision tree for binary classification. In particular, we relax the usual axioms of an impurity function and show how skewing an impurity function biases the optimal splits to isolate points of a particular class when splitting a node. We give a rigorous definition of this notion, then give a necessary and sufficient condition for such a bias to hold. We also show that the technique of class weighting is equivalent to applying a specific transformation to the impurity function, and tie all these notions together for a class of impurity functions that includes the entropy and Gini impurity. We also briefly discuss cost-insensitive impurity functions and give a characterization of such functions.
JPEG Optimisation for Fingerprint Recognition: Generalisation Potential of an Evolutionary Approach

For fingerprint-based biometric systems, JPEG has traditionally been one of the primary storage formats. In this paper, we investigate methods of optimising JPEG compression for increased matching scores at constant compression rates. To achieve this, an evolutionary approach is employed to optimise the quantisation matrix used by JPEG. While finding matrices that are better suited for different sets of fingerprints, different fingerprint sensors, and different recognition algorithms as they have been optimised for remains elusive, we find improvements in matching performance for dataset fitting scenarios.