Miracle-dz/UniFAR-FTUs huggingface dataset:
Algorithms for slate bandits with non-separable reward functions

In this paper, we study a slate bandit problem where the function that determines the slate-level reward is non-separable: the optimal value of the function cannot be determined by learning the optimal action for each slot. We are mainly concerned with cases where the number of slates is large relative to the time horizon, so that trying each slate as a separate arm in a traditional multi-armed bandit, would not be feasible. Our main contribution is the design of algorithms that still have sub-linear regret with respect to the time horizon, despite the large number of slates. Experimental results on simulated data and real-world data show that our proposed method outperforms popular benchmark bandit algorithms.
Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents

Distant supervision based methods for entity and relation extraction have received increasing popularity due to the fact that these methods require light human annotation efforts. In this paper, we consider the problem of shifted label distribution, which is caused by the inconsistency between the noisy-labeled training set subject to external knowledge graph and the human-annotated test set, and exacerbated by the pipelined entity-then-relation extraction manner with noise propagation. We propose a joint extraction approach to address this problem by re-labeling noisy instances with a group of cooperative multiagents. To handle noisy instances in a fine-grained manner, each agent in the cooperative group evaluates the instance by calculating a continuous confidence score from its own perspective; To leverage the correlations between these two extraction tasks, a confidence consensus module is designed to gather the wisdom of all agents and re-distribute the noisy training set with confidence-scored labels. Further, the confidences are used to adjust the training losses of extractors. Experimental results on two real-world datasets verify the benefits of re-labeling noisy instance, and show that the proposed model significantly outperforms the state-of-the-art entity and relation extraction methods.
Rice grain disease identification using dual phase convolutional neural network-based system aimed at small dataset

Abstract Although Convolutional neural networks (CNNs) are widely used for plant disease detection, they require a large number of training samples while dealing with wide variety of heterogeneous background. In this paper, a CNN based dual phase method has been proposed which can work effectively on small rice grain disease dataset with heterogeneity. At the first phase, Faster RCNN method is applied for cropping out the significant portion (rice grain) from an image. This initial phase results in a secondary dataset of rice grains devoid of heterogeneous background. Disease classification is performed on such derived and simplified samples using CNN architecture. Comparison of the dual phase approach with straight forward application of CNN on the small grain dataset shows the effectiveness of the proposed method which provides a 5 fold cross validation accuracy of 88.92%.
Toward Terabits-per-second Communications: Low-Complexity Parallel Decoding of GN-coset Codes

Recently, a parallel decoding framework of GN-coset codes was proposed. High throughput is achieved by decoding the independent component polar codes in parallel. Various algorithms can be employed to decode these component codes, enabling a flexible throughput-performance tradeoff. In this work, we adopt successive cancellation (SC) as the component decoders to achieve the highest-throughput end of the tradeoff. The benefits over soft-output component decoders are reduced complexity and simpler (binary) interconnections among component decoders. To reduce performance degradation, we integrate an error detector and a log-likelihood ratio (LLR) generator into each component decoder. The LLR generator, specifically the damping factors therein, is designed by a genetic algorithm. This low-complexity design can achieve an area efficiency of 533$Gbps/mm^{2}$ under 7nm technology.
Stabilising quasi-time-optimal nonlinear model predictive control with variable discretisation

This paper deals with novel time-optimal point-to-point model predictive control concepts for nonlinear systems. Recent approaches in the literature apply a time transformation, however, which do not maintain recursive feasibility for a piecewise constant control parameterisation. The key idea in this paper is to introduce uniform grids with variable discretisation. A shrinking-horizon grid adaptation scheme ensures convergence to a specific region around the target state and recursive feasibility. The size of the region is configurable by design parameters. This facilitates the systematic dual-mode design for quasi-time-optimal control to restore asymptotic stability and establish a smooth stabilisation. Two nonlinear programme formulations with different sparsity patterns are introduced to realise and implement the underlying optimal control problem. For a class of numerical integration schemes, even nominal asymptotic stability and true time-optimality are achieved without dual-mode. A comparative analysis as well as experimental results demonstrate the effectiveness of the proposed techniques.
Classical perspectives on the Newton–Wigner position observable

This paper deals with the Newton–Wigner position observable for Poincaré-invariant classical systems. We prove an existence and uniqueness theorem for elementary systems that parallels the well-known Newton–Wigner theorem in the quantum context. We also discuss and justify the geometric interpretation of the Newton–Wigner position as “center of spin”, already proposed by Fleming in 1965 again in the quantum context.
Transposon Regulation: Control of Expression in Drosophila Melanogaster and Consequences of Disregulation in Human Cells

Transposons were first discovered as “jumping genes” by Barbara McClintock, who continued to study them in maize through the 1940’s and 1950’s. Since then, transposons have been shown to make up a large percentage of eukaryotic genomes, including close to half of the human genome, but have been dismissed as simply “junk DNA. ” Recently, the importance of keeping transposons tightly regulated within the cellular environment has begun to be appreciated; the mechanisms to accomplish this have been studied and the current understanding of pathways governing transposon regulation is discussed within this dissertation. However, recent work presented within the scope of this dissertation in Drosophila melanogaster revealed a previously unknown function for condensin complexes in transposable element regulation. These studies provide a link between pathways governing chromosome pairing and transposon regulation. The potential interplay between these two pathways is intriguing and until now, largely unexplored. Aside from how transposons themselves are regulated, studies into potential roles they may play in the regulation of other protein coding genes within the cell may provide clues into the functionality of these elements within our genome. As a specific example, BRCA1 has a high density of retrotransposon sequences within its primary transcript, and studies of BRCA1 regulation presented within this dissertation has led to the development of a
Network Anomaly Detection based on Tensor Decomposition

The problem of detecting anomalies in time series from network measurements has been widely studied and is a topic of fundamental importance. Many anomaly detection methods are based on packet inspection collected at the network core routers, with consequent disadvantages in terms of computational cost and privacy. We propose an alternative method in which packet header inspection is not needed. The method is based on the extraction of a normal subspace obtained by the tensor decomposition technique considering the correlation between different metrics. We propose a new approach for online tensor decomposition where changes in the normal subspace can be tracked efficiently. Another advantage of our proposal is the interpretability of the obtained models. The flexibility of the method is illustrated by applying it to two distinct examples, both using actual data collected on residential routers.
AANet: Adaptive Aggregation Network for Efficient Stereo Matching

Despite the remarkable progress made by learning based stereo matching algorithms, one key challenge remains unsolved. Current state-of-the-art stereo models are mostly based on costly 3D convolutions, the cubic computational complexity and high memory consumption make it quite expensive to deploy in real-world applications. In this paper, we aim at completely replacing the commonly used 3D convolutions to achieve fast inference speed while maintaining comparable accuracy. To this end, we first propose a sparse points based intra-scale cost aggregation method to alleviate the well-known edge-fattening issue at disparity discontinuities. Further, we approximate traditional cross-scale cost aggregation algorithm with neural network layers to handle large textureless regions. Both modules are simple, lightweight, and complementary, leading to an effective and efficient architecture for cost aggregation. With these two modules, we can not only significantly speed up existing top-performing models (e.g., 41x than GC-Net, 4x than PSMNet and 38x than GA-Net), but also improve the performance of fast stereo models (e.g., StereoNet). We also achieve competitive results on Scene Flow and KITTI datasets while running at 62ms, demonstrating the versatility and high efficiency of the proposed method. Our full framework is available at https://github.com/haofeixu/aanet.
Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models

We study a class of weakly identifiable location-scale mixture models for which the maximum likelihood estimates based on $n$ i.i. d. samples are known to have lower accuracy than the classical $n^{- \frac{1}{2}}$ error. We investigate whether the Expectation-Maximization (EM) algorithm also converges slowly for these models. We provide a rigorous characterization of EM for fitting a weakly identifiable Gaussian mixture in a univariate setting where we prove that the EM algorithm converges in order $n^{\frac{3}{4}}$ steps and returns estimates that are at a Euclidean distance of order ${ n^{- \frac{1}{8}}}$ and ${ n^{-\frac{1} {4}}}$ from the true location and scale parameter respectively. Establishing the slow rates in the univariate setting requires a novel localization argument with two stages, with each stage involving an epoch-based argument applied to a different surrogate EM operator at the population level. We demonstrate several multivariate ($d \geq 2$) examples that exhibit the same slow rates as the univariate case. We also prove slow statistical rates in higher dimensions in a special case, when the fitted covariance is constrained to be a multiple of the identity.
Consensus for Crosschain Communications

Crosschain communications allows information to be communicated between blockchains. Consensus, in the context of crosschain communications, relates to how participants are able to agree on the state of one blockchain and communicate that information to another blockchain such that the information is trusted. This chapter reviews the usage scenarios of crosschain communications: value transfer and atomic swaps, reading, writing, and state pinning. It analyses what attributes must be maintained for permissionless and permissioned blockchains using a crosschain consensus mechanism such that the properties of each blockchain are not compromised. Finally, this chapter reviews the main categories of crosschain communications techniques: Hash Time Locked Contracts, block header relaying (BTC Relay, Pegged Sidechains and Ion), relay chains (Polkadot and Cosmos), and coordination chains with threshold schemes (Atomic Crosschain Transaction).
Flow-based Algorithms for Improving Clusters: A Unifying Framework, Software, and Performance

Clustering points in a vector space or nodes in a graph is a ubiquitous primitive in statistical data analysis, and it is commonly used for exploratory data analysis. In practice, it is often of interest to "refine" or "improve" a given cluster that has been obtained by some other method. In this survey, we focus on principled algorithms for this cluster improvement problem. Many such cluster improvement algorithms are flow-based methods, by which we mean that operationally they require the solution of a sequence of maximum flow problems on a (typically implicitly) modified data graph. These cluster improvement algorithms are powerful, both in theory and in practice, but they have not been widely adopted for problems such as community detection, local graph clustering, semi-supervised learning, etc. Possible reasons for this are: the steep learning curve for these algorithms; the lack of efficient and easy to use software; and the lack of detailed numerical experiments on real-world data that demonstrate their usefulness. Our objective here is to address these issues. To do so, we guide the reader through the whole process of understanding how to implement and apply these powerful algorithms. We present a unifying fractional programming optimization framework that permits us to distill out in a simple way the crucial components of all these algorithms. It also makes apparent similarities and differences between related methods. Viewing these cluster improvement algorithms via a fractional programming framework suggests directions for future algorithm development. Finally, we develop efficient implementations of these algorithms in our LocalGraphClustering python package, and we perform extensive numerical experiments to demonstrate the performance of these methods on social networks and image-based data graphs.
Classification of Lung Nodules Based on Deep Residual Networks and Migration Learning

The classification process of lung nodule detection in a traditional computer-aided detection (CAD) system is complex, and the classification result is heavily dependent on the performance of each step in lung nodule detection, causing low classification accuracy and high false positive rate. In order to alleviate these issues, a lung nodule classification method based on a deep residual network is proposed. Abandoning traditional image processing methods and taking the 50-layer ResNet network structure as the initial model, the deep residual network is constructed by combining residual learning and migration learning. The proposed approach is verified by conducting experiments on the lung computed tomography (CT) images from the publicly available LIDC-IDRI database. An average accuracy of 98.23% and a false positive rate of 1.65% are obtained based on the ten-fold cross-validation method. Compared with the conventional support vector machine (SVM)-based CAD system, the accuracy of our method improved by 9.96% and the false positive rate decreased by 6.95%, while the accuracy improved by 1.75% and 2.42%, respectively, and the false positive rate decreased by 2.07% and 2.22%, respectively, in contrast to the VGG19 model and InceptionV3 convolutional neural networks. The experimental results demonstrate the effectiveness of our proposed method in lung nodule classification for CT images.
Effects of evolocumab therapy and low LDL‐C levels on vitamin E and steroid hormones in Chinese and global patients with type 2 diabetes

We assessed the change from baseline in vitamin E, steroid hormones, adrenocorticotropic hormone (ACTH), and gonadotropins, overall and by lowest achieved low‐density lipoprotein‐cholesterol (LDL‐C) level, in patients with type 2 diabetes and dyslipidaemia after 12 weeks of treatment with evolocumab.
Factors Associated with Postpartum Depressive Symptoms in Community of Central Nepal

Background Pregnancy and postpartum are considered as high risk periods for the emergence of psychiatric disorder. Although postpartum depressive symptoms have been associated with tragic outcome, such as maternal suicide and infanticide, it is a neglected area of mental health care in developing countries. This study was conducted to find the prevalence and factors associated with postpartum depressive symptoms. Method A community-based cross-sectional research design was carried out after selecting the three wards of Bharatpur submetropolitan by nonprobability purposive sampling method. A total of 160 mothers in their 1 month to 12 months of postpartum period were interviewed through semistructured interview schedule and Edinburgh Postnatal Depression Scale (EPDS). Collected data were entered in Epi, data 3.1, and was exported into IBM SPSS 20 version. Results The prevalence of depressive symptoms among postpartum mothers was 27.5%. The multivariate analysis identified two factors significantly associated with postpartum depression including respondents who had education level of ≤10 class (odds ratio [AOR] = 3.25, P = 0.03, confidence interval [CI] = 1.10 − 9.58), chronic disease in their family (odds ratio [AOR] = 3.25, P = 0.01, confidence interval [CI] = 1.19 − 8.16). Conclusion More than one out of four mothers is suffering from depressive symptoms. The major factors associated with postpartum depressive symptoms are education of respondents and chronic disease in the family. Screening and timely management of depressive symptoms should be incorporated in routine maternal care so as to enhance maternal and child health. Likewise, concerned authority should plan and organize awareness-raising programs and provide attractive package to attract the female population for higher education.
Studies on clinical and subclinical mastitis in Menoufia Governate with application of PCR for diagnosis

Bovine mastitis is a serious problem in the dairy herds all over the world. In this study 105 mastitic milk samples were collected from small holder’s cows for bacteriological and molecular diagnosis. The prevalence of clinical and subclinical mastitis were 20.5% and 32% respectively which were detected by clinical examination and California Mastitis Test respectively. Bacteriological results revealed that Staphylococcus aureus was the most common isolated bacteria from both clinical and subclinical mastitis. Streptococcus species, Pseudomonas species, E. coli and Enterobacter species were also isolated. Application of multiplex PCR was effective in identification of bacteria causing mastitis directly from milk samples and from extracted DNA of bacteria
Provider Perspective in the Health Care Economics of Lower-Limb Prosthetic Rehabilitation

Health care economic science holds great potential for improving the quality of lower-limb prosthetic care. To realize this potential, health care providers need to adopt an outlook that economic analysis is not strictly limited to the scrutiny of cost, but includes a better understanding of the value generated in the provision of lower-limb prostheses. Health care providers in the United States currently have a negative perception of cost containment efforts implemented by regulators to rein in health care spending. The United States currently spends more on health care than any other developed country (17.9% of GDP; National Health Expenditure Projections 2017–2026. Centers for Medicare and Medicaid Services; 2017), whereas other countries have reduced cost and increased quality of care through health care economic science. The clinical outcome of restoring mobility and independence to a person with lower-limb amputation has an appreciable impact on quality of life. By measuring the quality improvement and comparing costs associated with available lower-limb prosthetic alternatives, health care economic science can improve decision making regarding the best use of resources and improve the overall value of care. Although limited economic evidence exists concerning lower-limb prostheses, a better understanding of the purpose of health care economic science can lead health care providers to contribute to the economic research that is needed. (J Prosthet Orthot. 2018;31:P43–P48)
Zum Vorkommen des Mykotoxins Ochratoxin A in Muttermilchproben aus Deutschland.

Einfuhrung: Muttermilch ist die optimale Nahrung fur die kindliche Entwicklung. Wegen der unvermeidbaren Exposition gegenuber Fremdstoffen enthalt sie aber auch in der Umwelt ubiquitar vorkommende Schadstoffe, die vom Menschen hauptsachlich uber Lebensmittel aufgenommen werden. Das Mykotoxin Ochratoxin A (OTA) ist ein potentes Nierentoxin und eines der wirksamsten renalen Kanzerogene. Aktuelle Belastungsdaten zu OTA in Muttermilch liegen aus Deutschland nicht vor. Methode: OTA wurde als Biomarker der Belastung in Muttermilchproben von Muttern aus Nordrhein-Westfalen (Dortmund) und Niedersachsen eingesetzt. Die Bestimmung von OTA in Muttermilch wurde mithilfe eines empfindlichen HPLC-Tandem-Massenspektrometrie-Verfahrens vorgenommen. Die Nachweis- und Bestimmungsgrenzen des Verfahrens betragen 10 bzw. 30 ng/L. Untersucht wurden 90 Proben. Die orientierende toxikologische Bewertung erfolgte auf Basis von Literaturangaben zur Tolerierbaren Taglichen Aufnahme (TDI). Ergebnisse: OTA wurde in mehr als der Halfte der Gesamtheit aller 90 Proben aus Dortmund (Bereich: Schlussfolgerung: Im Vergleich zu anderen Landern ist die OTA-Exposition von gestillten Sauglingen in Deutschland gering. Es sollten vor dem Hintergrund, dass in einigen Fallen TDI-Uberschreitung in der Folge des Stillens vorliegen, weitergehende Anstrengungen zur Regulation der OTA-Gehalte in Lebensmitteln im Sinne der Reduktion der Kontamination getroffen werden, um die Exposition stillender Mutter gegenuber Ochratoxin A zu minimieren. Addendum Methode: Die niedersachsischen Proben stammen aus 2006–2010, jedoch weituberwiegend aus 2009 und 2010. Die Proben aus Nordrhein-Westfalen (Dortmund) wurden von September bis Dezember 2009 genommen.
Measuring Chemotherapy Response in Breast Cancer Using Optical and Ultrasound Spectroscopy

V CANDIDATE’S STATEMENT . VII ACKNOWLEDGEMENTS . VIII SYMBOLS, UNITS AND ABBREVIATIONS . IX LIST OF FIGURES . XII LIST OF TABLES . XV CHAPTER 1 . 1 Background and Review of the Literature . 1 1.0 Chapter Overview. 1 1.1 Cancer . 2 1.1.1 Tumourigenesis and Cancer Progression . 2 1.2 Breast Cancer . 5 1.2. 1 Natural History and Presentation . 5 1.2. 2 Epidemiology: Incidence and Mortality . 8 1.2. 3 Risk Factors for Developing Breast Cancer . 9 1.2. 4 Screening and Diagnosis . 13 1.3 Locally Advanced Breast Cancer and Treatment . 16 1.3. 1 Multimodality Treatments: Historical Perspective . 16 1.4 Study Motivations and Aims 1 . 19 Identifying the Target Study Population . 19 1.5 Breast Cancer Chemotherapy . 20 1.6 Chemotherapy Side Effects . 23 1.6. 1 Cardiac Complications . 23 1.6. 2 Haematopoietic Toxicity . 24 1.6. 3 Gastrointestinal Toxicity . 24 1.6. 4 Alopecia . 25