Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Understanding and Reducing Graph Structural Noise for GNNs

Graph neural networks (GNNs) have emerged as a powerful paradigm to learn from relational data mostly through applying the message passing mechanism. However, this approach may exhibit suboptimal performance when applied to graphs possessing various structural issues. In this work, we focus on understanding and alleviating the effect of graph structural noise on GNN performance. To evaluate the graph structural noise in real data, we propose edge signal-to-noise ratio (ESNR), a novel metric evaluating overall edge noise level with respect to data features or labels based on random matrix theory. We have found striking concordance between the proposed ESNR metric and the GNN performance in various simulated and real data. To reduce the effect of the noise, we propose GPS (Graph Propen-sity Score) graph rewiring, which estimates the edge likelihood for rewiring data graphs based on self-supervised link prediction. We provide a theoretical guarantee for GPS graph rewiring and demonstrate its efficacy by comprehensive benchmarks.
Consistency of Multiple Kernel Clustering

Consistency plays an important role in learning theory. However, in multiple kernel clustering (MKC), the consistency of kernel weights has not been sufficiently investigated. In this work, we fill this gap with a non-asymptotic analysis on the consistency of kernel weights of a novel method termed SimpleMKKM. Under the assumptions of the eigenvalue gap, we give an infinity norm bound as (cid:101) O ( k/ √ n ) , where k is the number of clusters and n is the number of samples. On this basis, we establish an upper bound for the excess clustering risk. Moreover, we study the difference of the kernel weights learned from n samples and r points sampled without replacement, and derive its upper bound as (cid:101) O ( k · (cid:112) 1 /r − 1 /n ) . Based on the above results, we propose a novel strategy with Nystr ¨ om method to enable Sim-pleMKKM to handle large-scale datasets with a theoretical learning guarantee. Finally, extensive experiments are conducted to verify the theoretical results and the effectiveness of the proposed large-scale strategy.
Linkages among Science, Technology, and Industry

Compared to the linkages between science and technology, the linkages among science, technology, and industry are largely under-studied. Therefore, this paper proposes a main path analysis based framework to discover the linkages among science, technology, and industry, in which scientific publications, patents and products are viewed as respective proxies of scientific research, technological advance and industrial development. To validate the feasibility and effectiveness of our framework, the DrugBank database in pharmaceutical industry is taken as our dataset. From empirical analysis on this dataset, the following conclusions can be drawn: (1) The discovered developmental trajectories indeed encode the interactions among science, technology, and industry; (2) The developments in pharmaceutical industry are mainly pushed by only science, only technology, and science and technology simultaneously; (3) The drugs can help enhance knowledge exchanges between science and technology.
Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills

Mutual information-based reinforcement learning (RL) has been proposed as a promising framework for retrieving complex skills autonomously without a task-oriented reward function through mutual information (MI) maximization or variational empowerment. However, learning complex skills is still challenging, due to the fact that the order of training skills can largely affect sample efficiency. Inspired by this, we recast variational empowerment as curriculum learning in goal-conditioned RL with an intrinsic reward function, which we name Variational Curriculum RL (VCRL). From this perspective, we propose a novel approach to unsupervised skill discovery based on information theory, called Value Uncertainty Variational Curriculum (VUVC). We prove that, under regularity conditions, VUVC accelerates the increase of entropy in the visited states compared to the uniform curriculum. We validate the effectiveness of our approach on complex navigation and robotic manipulation tasks in terms of sample efficiency and state coverage speed. We also demonstrate that the skills discovered by our method successfully complete a real-world robot navigation task in a zero-shot setup and that incorporating these skills with a global planner further increases the performance.
Bidirectional Adaptation for Robust Semi-Supervised Learning with Inconsistent Data Distributions

Semi-supervised learning (SSL) suffers from severe performance degradation when labeled and unlabeled data come from inconsistent data distributions. However, there is still a lack of sufficient theoretical guidance on how to alleviate this problem. In this paper, we propose a general theoretical framework that demonstrates how distribution discrepancies caused by pseudo-label predictions and target predictions can lead to severe generalization errors. Through theoretical analysis, we identify three main reasons why previous SSL algorithms cannot perform well with inconsistent distributions: coupling between the pseudo-label predictor and the target predictor, biased pseudo labels, and restricted sample weights. To address these challenges, we introduce a practical framework called Bidirectional Adaptation that can adapt to the distribution of unlabeled data for debiased pseudo-label prediction and to the target distribution for debiased target prediction, thereby mitigating these shortcomings. Extensive experimental results demonstrate the effectiveness of our proposed framework.
H-Likelihood Approach to Deep Neural Networks with Temporal-Spatial Random Effects for High-Cardinality Categorical Features

Deep Neural Networks (DNNs) are one of the most powerful tools for prediction, but many of them implicitly assume that the data are statistically independent. However, in the real world, it is common for large-scale data to be clustered with temporal-spatial correlation structures. Variational approaches and integrated likelihood approaches have been proposed to obtain approximate maximum likelihood estimators (MLEs) for correlated data. However, due to the large size of data, they cannot provide exact MLEs. In this study, we propose a new hierarchical likelihood approach to DNNs with correlated random effects for clustered data. By jointly optimizing the the negative h-likelihood loss, we can provide exact MLEs for both mean and dispersion parameters, as well as the best linear unbiased predictors for the random effects. Moreover, the hierarchical likelihood allows a computable procedure for restricted maximum likelihood estimators of dispersion parameters. The proposed two-step algorithm enables online learning for the neural networks, whereas the integrated likelihood cannot decompose like a widely-used loss function in DNNs. The proposed h-likelihood approach offers several advantages, which we demonstrate through numerical studies and real data analyses.
A Survey of Adversarial Defenses and Robustness in NLP

In the past few years, it has become increasingly evident that deep neural networks are not resilient enough to withstand adversarial perturbations in input data, leaving them vulnerable to attack. Various authors have proposed strong adversarial attacks for computer vision and Natural Language Processing (NLP) tasks. As a response, many defense mechanisms have also been proposed to prevent these networks from failing. The significance of defending neural networks against adversarial attacks lies in ensuring that the model’s predictions remain unchanged even if the input data is perturbed. Several methods for adversarial defense in NLP have been proposed, catering to different NLP tasks such as text classification, named entity recognition, and natural language inference. Some of these methods not only defend neural networks against adversarial attacks but also act as a regularization mechanism during training, saving the model from overfitting. This survey aims to review the various methods proposed for adversarial defenses in NLP over the past few years by introducing a novel taxonomy. The survey also highlights the fragility of advanced deep neural networks in NLP and the challenges involved in defending them.
Is Learning Summary Statistics Necessary for Likelihood-free Inference?

Likelihood-free inference (LFI) is a set of techniques for inference in implicit statistical models. A longstanding question in LFI has been how to design or learn good summary statistics of data, but this might now seem unnecessary due to the advent of recent end-to-end (i.e. neural network-based) LFI methods. In this work, we rethink this question with a new method for learning summary statistics. We show that learning sufficient statistics may be easier than direct posterior inference, as the former problem can be reduced to a set of low-dimensional, easy-to-solve learning problems. This suggests us to explicitly decouple sum-mary statistics learning from posterior inference in LFI. Experiments on diverse inference tasks with different data types validate our hypothesis.
Subsurface Thermal and Hydrological Changes Between Forest and Clear-cut Sites in the Oregon Cascades

Michael G. Davis1*, Ronald S. Waschmann2, Robert N. Harris3 and David S. Chapman1 1Department of Geology and Geophysics, University of Utah, Salt Lake City, UT 84112, USA 2U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Laboratory, Western Ecology Division, Corvallis, Oregon 97333, USA 3College of Oceanic and Atmospheric Sciences, Oregon State University, Corvallis, Oregon 97331, USA *E-mail: michael.g. davis@utah.edu; Phone: (801) 581-3588; http://thermal.gg. utah.edu/~mgdavis/
The UCSC Genome Browser database: 2016 update

For the past 15 years, the UCSC Genome Browser (http://genome.ucsc.edu/) has served the international research community by offering an integrated platform for viewing and analyzing information from a large database of genome assemblies and their associated annotations. The UCSC Genome Browser has been under continuous development since its inception with new data sets and software features added frequently. Some release highlights of this year include new and updated genome browsers for various assemblies, including bonobo and zebrafish; new gene annotation sets; improvements to track and assembly hub support; and a new interactive tool, the “Data Integrator”, for intersecting data from multiple tracks. We have greatly expanded the data sets available on the most recent human assembly, hg38/GRCh38, to include updated gene prediction sets from GENCODE, more phenotype- and disease-associated variants from ClinVar and ClinGen, more genomic regulatory data, and a new multiple genome alignment.
EROC: A Toolkit for Building NEATO Query Optimizers

EROC (Extensible, Reusable Optimization Components) is a toolkit for building query optimizers. EROC’s components are C++ classes based on abstractions we have identified as central to query optimization, not only in relational DBMSs, but in extended relational and object-oriented DBMSs as well. I EROC’s use of C++ classes clarifies the mapping from application domain (optimization) abstractions to solution domain (EROC) abstractions, and these classes provide: (1) complex predicate definition and manipulation; (2) representations for common operators, such as join and groupby, and associated property derivation functions, including key derivation; (3) management of catalog and type information; (4) implementations of common algebraic equivalence rules, and (5) System Rand Volcano-style search strategies. The classes are designed to provide optimizer implementors reusability and extensibility through layering and inheritance. EROC provides much more functionality than previous optimization tools because at1 of Pemnission to copy without fee all 01 part of this material is granted provided thaf the copies are not made OT distributed fOT’ direct commercial advantage, the VLDB copyright notice and the title of the publication and ifs date appear, and notice is given that copying is by permission of the Ve’ery Large Data Base Endowment. To copy otherwise, OT to republish, sequires a fee . andbor special permission from fhe Endowment. Proceedings of the 22nd VLDB Conference Mumbai(Bombay), India, 1996 Melissa Truong NCR Corporation melissa.truong@sandiegoca.ncr. com EROC’s optimization classes are extensible and reusable, not just the search components. In addition to describing EROC’s architecture and software engineering, we also show how EROC’s classes were extended to build NEAT0 (New EROC-based Advanced Teradata Optimizer), a join optimizer for a massively parallel environment. Based on the extensions required we give an indication of the savings EROC, provided us. To show NEATO’s efficiency and effectiveness, we present results of optimizing complex TPC/D benchmark queries and show that NEAT0 easily searches the entire space of query execution plans. We outline plans for extensions to NEAT0 and overview how the flexibility of EROC will enable these extensions.
Host response against biomaterials: the role in the pathogenesis of biomaterial-associated infections

Intracellular survival of Staphylococcus epidermidis inside macrophages, facilitated by the presence of a biomaterial, has been recognized as a pivotal process in the pathogenesis of biomaterial-associated infection (BAI). Interferon (IFN)-y is a potent activator of macrophages. Determined was whether subcutaneous injections of IFN-y can reverse macrophage-deactivation induced by implanted biomaterials. To induce a experimental BAI, mice received subcutaneous implants combined with an injection of 10 6 S. epidermidis. Subsequently, three groups of mice received subcutaneous injections of, 25,000 IU IFN-y 3 times weekly, or 10,000 IU IFN-y 3 times in 2 weeks, or saline 3 times weekly (saline-control), respectively. A fourth group received no injections (control). Segments and tissues of the IFNy-treated mice were significantly less (PO.05) culture-positive than those of the control groups. Histologically, high numbers of intracellularly persisting gram-positive cocci observed in control mice were absent in the IFNy-treated mice. These data indicates that IFN-y protects against experimental BAI.
NRL Mine Burial Experiments

Abstract : Seabed-structure interactions are responsible for the burial of heavy objects, such as mines, pipelines, concrete breakwaters, platforms, debris, and other objects on the seafloor. In low shear strength muds, these objects are known to bury at impact or to sink into the sediment if the buoyant weight of the object exceeds the bearing capacity of the seafloor. In higher energy sandy sediments, burial by scour and fill, momentary or cyclic wave-induced liquefaction, and seabed morphological changes (e.g., transverse bedform migration, changes in shore-rise and bar-berm conditions, sediment deposition) is common. Each of the possible burial processes will be discussed and an integrated, time-dependent object burial model is proposed. Results from six recent burial experiments using instrumented mines are used to document burial at impact and subsequent burial by biological processes, scour and fill, changes in near-shore bar morphology, liquefaction, and subaqueous dune migration.
The relationship between the perception of distributed leadership in secondary schools and teachers' and teacher leaders' job satisfaction and organizational commitment

This study investigates the relation between distributed leadership, the cohesion of the leadership team, participative decision-making, context variables, and the organizational commitment and job satisfaction of teachers and teacher leaders. A questionnaire was administered to teachers and teacher leaders (n = 1770) from 46 large secondary schools. Multiple regression analyses and path analyses revealed that the study variables explained significant variance in organizational commitment. The degree of explained variance for job satisfaction was considerably lower compared to organizational commitment. Most striking was that the cohesion of the leadership team and the amount of leadership support was strongly related to organizational commitment, and indirectly to job satisfaction. Decentralization of leadership functions was weakly related to organizational commitment and job satisfaction.
CSE 262 Readings : April 11 . 2006 GPUTeraSort : High Performance Graphics Coprocessor Sorting for Large Database Management

Sorting large datasets is often limited by I/O bandwidth in terms of memory and disk. The traditional von Neumann architecture results in high cache misses within L1-L3 cache levels. The authors realized that the highly parallelized processors and the fast memory interconnects inside commodity GPUs can help to work around the limitations that arise when sorting is done solely on the CPU. Specifically, the limitation is that most sorting performance is limited by cache misses. Although main memory sizes have gotten larger, so that disk I/O is reduced, memory access itself is a bottleneck. Utilizing the GPU is advantageous in that it may have 10 times the bandwidth for memory that a CPU has.
Genome-wide regional heritability mapping identifies a locus within the TOX2 gene associated with Major Depressive Disorder Short title: Regional Heritability Mapping for MDD

Yanni Zeng , Pau Navarro , Masoud Shirali , David M. Howard , Mark J. Adams , Lynsey S. Hall, Toni-Kim Clarke , Pippa A. Thomson , Blair H. Smith , Alison Murray , Sandosh Padmanabhan , Caroline Hayward , Thibaud Boutin , Donald J. MacIntyre , Cathryn M. Lewis , Naomi R Wray , Divya Mehta , Brenda Wjh Penninx, Yuri Milaneschi, Bernhard T. Baune, Tracy Air, Jouke-Jan Hottenga, Hamdi Mbarek, Enrique Castelao, Giorgio Pistis, Thomas G Schulze , Fabian Streit, Andreas J. Forstner, Enda M Byrne , Nicholas G Martin, Gerome Breen , Bertram Müller-Myhsok , Susanne Lucae , Stefan Kloiber , Enrico Domenici, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium , Ian J Deary , David J Porteous , Chris S. Haley 2,24 and Andrew M. McIntosh 1,3,23
Eecient and Constructive Algorithms for the Pathwidth and Treewidth of Graphs

In this paper we give, for all constants k, l, explicit algorithms, that given a graph G = (V; E) with a tree-decomposition of G with treewidth at most l, decide whether the treewidth (or pathwidth) of G is at most k, and if so, nd a tree-decomposition or (path-decomposition) of G of width at most k, and that use O(jV j) time. In contrast with previous solutions, our algorithms do not rely on non-constructive reasoning, and are single exponential in k and l. This result can be combined with a result of Reed 37], yielding explicit O(n log n) algorithms for the problem, given a graph G, to determine whether the treewidth (or pathwidth) of G is at most k, and if so, to nd a tree-(or path-)decomposition of width at most k (k constant). Also, Bodlaender 13] has used the result of this paper to obtain linear time algorithms for these problems. We also show that for all constants k, there exists a polynomial time algorithm, that, when given a graph G = (V; E) with treewidth k, computes the pathwidth of G and a path-decomposition of G of minimum width.
OSSIFICATION OF INTERCLINOID LIGAMENT AND ITS CLINICAL SIGNIFICANCE

Abstract: To know a detailed anatomical knowledge of interclinoid ligament which can increase the success of diagnostic evaluation and surgical approaches. Interclinoid ligament is seen in middle cranial fossa which connects the anterior and posterior clinoid processes,when it ossifies.The ligamentous or bony interclinoid connections have important neuronal and vascular relations.The occasional presence of this ossified ligament leads to the compression of internal carotid artery interrupting the blood flow and also compress the cranial nerves. This study will be performed on 50 dry human skulls of unknown age and sex belonging to department of antomy in saveetha dental college.In all the skulls, the anterior,middle and posterior clinoid process and the incidence of the interclinoid process will be examined.The ossified interclinoid ligament is an underestimated structure which is clinically and surgically important.The main reason of this study is to present the ossified interclinod ligament morphologically and to consider its possible impact on surrounding tissues.
Oregon State University: the case of the United States wheat sector

-li CUOARE r DEPARTIIENT OF AGRICULTIJRAL AND RESOURCE ECONOrnC~ -fnVISION OF AGRIQJLTIJRE AND NATUML RESOURCES ~IVERSI1Y OF CALIFORNIA) ~orking Paper No. 494 TIIE CASE OF TIlE FLEXIBLE PUBLIC POLICY: UNITED STATES Wi-fEAT SEcroR by H. Alan Love and Gordon C. Rausser GIANNINI , OUNDATlON 0. AGRICULTURAL ECONOMICS UBRARY OCT 251988 Ca l i fornia :\~ricul tura l Ecq1cril11cnt Station Cian.nini FOlmdation ot' . \gricultural I:conomics September 1988
British Society of Gastroenterology guidelines for the management of the irritable bowel syndrome

### 1.1 PURPOSE OF GUIDELINES These guidelines were compiled by a multidisciplinary group at the request of the chairman of the British Society of Gastroenterology's Clinical Services Committee. The prime targets for these guidelines are consultant gastroenterologists, specialist registrars in training, and general practitioners. The purpose is to identify and inform the key decisions to be made in the management of patients thought to have functional diseases of the gut. As these comprise the commonest conditions seen by gastroenterologists, the working party represented a wide spectrum of practitioners in gastroenterology, including gastroenterologists from both district general hospitals and tertiary referral centres, as well as primary care practitioners, psychiatrists, psychologists, and dietitians. ### 1.2 SPECIFIC DIFFICULTIES Compared with producing guidelines for the management of well defined diseases such as peptic ulcer where there is a clear disease entity, an obvious end point, and highly effective treatments, drawing up guidelines for functional gastroenterological disorders has had many difficulties. Clinical trials have been difficult to design as the conditions being treated are highly variable with many possible end points, and most therapies only marginally more effective than placebo. Early trials were difficult to evaluate because of inadequate patient definition so that many questions have yet to be addressed with good quality randomised controlled clinical trials. Most of our recommendations are therefore supported by clinical experience rather than randomised controlled clinical trials. Finally, because functional diseases, although potentially debilitating, are non-fatal there are few uniformly available audit measures such as mortality or survival times by which to judge or compare different treatment regimens in different areas of clinical practice. ### 1.3 PROCESS OF GUIDELINE CREATION The co-chairmen were approached by the chairman of the British Society of Gastroenterology's Clinical Services Committee and invited to form a working party. Members were chosen to be broadly representative of clinicians and academics with a long term interest and publication record in the …
Strong uniform consistency of a kernel conditional quantile estimator for censored and associated data

In survival or reliability studies, it is common to deal with data which are not only incomplete but weakly dependent too. Random right-censoring and random left-truncation are two common forms of such data when they are neither independent nor strongly mixing but rather associated. In this paper, we focus on kernel estimation of the conditional quantile function of a strictly stationary associated random variable $T$ given a $d$-dimensional vector of covariates $X$, under random right-censoring. As main results, we establish a strong uniform consistency rate for the estimator. Then the finite sample performance of the estimator is illustrated on a simulation study.