Miracle-dz/UniFAR-FTUs huggingface dataset:
Bringing Events into Video Deblurring with Non-consecutively Blurry Frames

where ⊗ is matrix multiplication, FEncoder is convolutional Encoder, FEvent is convolutional module that can guarantee e has the same spatial dimension with z,R is the reshape operation that changes dimension from (c, h, w) to (c, h ∗ w), andR′ is the reshape operation that changes dimension from (c, h∗w) to (c, h, w). z is features of frames from latent space of encoder, and e is the feature of events Ei extracted using CNN along with downsampling. m is a reweighting map which can be used to facilitate deblurring by matrix multiplication with the features of frames. We, Wz and Wv are learnable weight matrices. Finally, y is the input of decoder to recover latent sharp frame.
Two-sample Hypothesis Testing for Random Dot Product Graphs via Adjacency Spectral Embedding

Two-sample hypothesis testing for random graphs arises naturally in neuroscience, social networks, and machine learning. In this paper, we consider a semiparametric problem of two-sample hypothesis testing for a class of latent position random graphs. We formulate a notion of consistency in this context and propose a valid test for the hypothesis that two finite-dimensional random dot product graphs on a common vertex set have the same generating latent positions or have generating latent positions that are scaled or diagonal transformations of one another. Our test statistic is a function of a spectral decomposition of the adjacency matrix for each graph and our test procedure is consistent across a broad range of alternatives. We apply our test procedure to real biological data: in a test-retest data set of neural connectome graphs, we are able to distinguish between scans from different subjects; and in the {\em C.elegans} connectome, we are able to distinguish between chemical and electrical networks. The latter example is a concrete demonstration that our test can have power even for small sample sizes. We conclude by discussing the relationship between our test procedure and generalized likelihood ratio tests.
A Probabilistic Approach to Web Portal's Data Quality Evaluation

Advances in technology and the use of the Internet have favoured the emergence of a large number of Web applications, including Web Portals. Web portals provide the means to obtain a large amount of information therefore it is crucial that the information provided is of high quality. In recent years, several research projects have investigated Web Data Quality; however none has focused on data quality within the context of Web Portals. Therefore, the contribution of this research is to provide a framework centred on the point of view of data consumers, and that uses a probabilistic approach for Web portal's data quality evaluation. This paper shows the definition of operational model, based in our previous work.
Forest‐Type‐Dependent Water Use Efficiency Trends Across the Northern Hemisphere

Changing climate and increasing atmospheric CO2 significantly regulate forest water use efficiency (WUE). However, magnitudes of the WUE trends and underlying processes driving these patterns in two major forest types, deciduous broadleaf forests (DBFs) and evergreen needleleaf forests (ENFs), across the Northern Hemisphere remain poorly understood. We investigated the WUE trends over the past two decades using eddy covariance observations from 26 forest sites from the FLUXNET2015 data set. Our analyses revealed a greater increase in WUE in DBFs than that in ENFs. The decreased stomatal conductance (Gs) mostly contributed to the increase in WUE in the DBFs, whereas the increased gross ecosystem productivity acted as the main trigger for the increase in WUE in the ENFs. The vapor pressure deficit substantially increased in the DBFs, triggering the decrease in Gs. In contrast, the slight CO2 fertilization and the limited stomatal constraint contributed to the increased gross ecosystem productivity in the ENFs.
THE ROLE OF THE PHYTOMICROBIOME IN MAINTAINING BIOFUEL CROP PRODUCTION IN A CHANGING CLIMATE

The marked increase in persistent anthropogenic changes to the biogeochemical cycles on Earth, beginning with the industrial revolution at the end of the 18th century and developing even faster with the “Great Acceleration” of the mid‐20th century, has prompted a proposal for a new geological epoch termed the Anthropocene (Waters et al., 2016; Lewis and Maslin, 2015; Ogden et al., 2015; Zalasiewicz et al., 2011). The combined effects of rapid population growth, industrialization and globalization in the Anthropocene have allowed the greatest gains in standard of living ever, while
Title Generalization of specialized onthe-fly composition

In the Weighted Finite State Transducer (WFST) framework for speech recognition, we can reduce memory usage and increase flexibility by using on-the-fly composition which generates the search network dynamically during decoding. Methods have also been proposed for optimizing WFSTs in on-the-fly composition, however, these operations place restrictions on the structure of the component WFSTs. We propose extended on-the-fly optimization operations which can operate on WFSTs of arbitrary structure by utilizing a filter composition. The evaluations illustrate the proposed method is able to generate more efficient WFSTs.
Bayesian inference for multivariate point processes observed at sparsely distributed times

We consider statistical and computational aspects of simulation-based Bayesian inference for a multivariate point process which is only observed at sparsely distributed times. For specificity we consider a particular data set which has earlier been analyzed by a discrete time model involving unknown normalizing constants. We discuss the advantages and disadvantages of using continuous time processes compared to discrete time processes in the setting of the present paper as well as other spatial-temporal situations.
Data imputation using a trust network for recommendation via matrix factorization

Disclosed is a missing grade imputation device for imputing a grade on an item which a certain user does not evaluate with grades by neighbor users connected through a reliability network. The missing grade can be imputed through: a step of determining users who can reach a certain user in the forward direction of an edge and users who can reach the certain user in the forward direction of the edge as neighbor users; a step of acquiring grades by the neighbor users on an item which is not graded by the certain user from the grade matrix of the certain user; and a step of imputing the grade on the item which is not graded by the certain user by the certain user from a value calculated from the grades by the neighbor users on the item which is not graded by the certain user.
Variational Inference over Combinatorial Spaces

A Extended derivations and proofs A.1 Markov random field reformulation We prove in this section that under the Rich Sufficient Statistics condition (RSS)1, the log-partition function is the same in the original exponential family and in the bipartite MRF described in Section 2.2. Let us denote the latter log-partition function by Ã(θ). We first prove the following identity, introduced in the main paper as Equation (3): Lemma 1 ∑ s1∈{0,1} ∑ s2∈{0,1} · · · ∑ sJ∈{0,1} I ∏
Unsupervised Ontology Generation from Unstructured Text

The findings and opinions expressed in this report are those of the authors and do not necessarily reflect the positions or policies of The Bill and Melinda Gates Foundation. Abstract Ontologies are a vital component of most knowledge acquisition systems, and recently there has been a huge demand for generating ontologies automatically since manual or supervised techniques are not scalable. In this paper, we introduce OntoMiner, a rule-based, iterative method to extract and populate ontologies from unstructured or free text. OntoMiner transforms text into a graph structure called a textGraph in which nodes are candidate terms and words from the text and edges are grammatical, semantic, and categorical relations between nodes. OntoMiner iteratively uses graph pattern rules over the textGraphs to mine ontological information and at the end of each iteration, based on the newly found information, OntoMiner improves the existing ontology. Our preliminary experiments indicate that OntoMiner achieves up to 93.4% accuracy, which to our knowledge exceeds the accuracy levels of previous work.
Structural and Semantic Contrastive Learning for Self-supervised Node Representation Learning

Graph Contrastive Learning (GCL) recently has drawn much research interest for learning generalizable, transferable and robust node representations in a self-supervised fashion. In general, the contrastive learning process in GCL is performed on top of the representations learned by a graph neural network (GNN) backbone, which transforms and propagates the node contextual information based on its local neighborhoods. However, existing GCL efforts have severe limitations in terms of both encoding architecture, augmentation, and contrastive objective, making them commonly inefficient and ineffective to use in different datasets. In this work, we go beyond the existing unsupervised GCL counterparts and address their limitations by proposing a simple yet effective framework S-CL. Specifically, by virtue of the proposed structural and semantic contrastive learning, even a simple neural network is able to learn expressive node representations that preserve valuable structural and semantic patterns. Our experiments demonstrate that the node representations learned by S-CL achieve superior performance on different downstream tasks compared to the state-of-the-art GCL methods.
Communications of the Association for Information Systems

: Gathering insights on users and the contexts they use mobile applications is at the core of the user-centered design (UCD). Organizations find it strategically important to efficiently and effectively use these insights. With the proliferation of mobile applications, gaining timely and relevant insights is increasingly challenging due to the heterogeneous and dynamic context of use, the abundant availability of information on use behavior and the intense time constraints imposed by the highly competitive mobile market. This paper develops a research model that considers strategy foci as motivators that affect the efficient and effective use of insights on users and context in design practices. We examine the mediating effects of UCD resources, such as time and financial constraints, organizational practices, and UCD competence. To test the model, we conducted a survey with 100 mobile practitioners and used PLS to estimate the model. The model shows that focus on an innovation strategy both directly and indirectly affected data use on user and their context (i.e., mediated by organizational practices and UCD competence) in design practices. Strategies with a focus on cost had no direct effect on the use of user insights but led to negative impacts on UCD competence and organizational practices.
Efficient Index Generation for Compiling Two-Level Mappings in Data-Parallel Programs 1

This paper presents compilation techniques used to compress holes, which are caused by the nonunit alignment stride in a two-level data-processor mapping. Holes are the memory locations mapped by useless template cells. To fully utilize the memory space, memory holes should be removed. In a two-level data-processor mapping, there is a repetitive pattern for array elements mapped onto processors. We classify blocks into classes and use a class table to record the distribution of each class in the first repetitive data distribution pattern. Similarly, data distribution on a processor also has a repetitive pattern. We use a compression table to record the distribution of each block in the first repetitive data distribution pattern on a processor. By using a class table and a compression table, hole compression can be easily and efficiently achieved. Compressing holes can save memory usage, improve spatial locality and further improve system performance. The proposed method is efficient, stable, and easy to implement. The experimental results do confirm the advantages of our proposed method over existing methods.
Hardware–Software Coherence Protocol for the Coexistence of Caches and Local Memories

Cache coherence protocols limit the scalability of multicore and manycore architectures and are responsible for an important amount of the power consumed in the chip. A good way to alleviate these problems is to introduce a local memory alongside the cache hierarchy, forming a hybrid memory system. Local memories are more power-efficient than caches and do not generate coherence traffic, but they suffer from poor programmability. When non-predictable memory access patterns are found, compilers do not succeed in generating code because of the incoherence between the two storages. This paper proposes a coherence protocol for hybrid memory systems that allows the compiler to generate code even in the presence of memory aliasing problems. Coherence is ensured by a software/hardware co-design where the compiler identifies potentially incoherent memory accesses and the hardware diverts them to the correct copy of the data. The coherence protocol introduces overheads of 0.26% in execution time and of 2.03% in energy consumption to enable the usage of the hybrid memory system, which outperforms cache-based systems by an speedup of 38% and an energy reduction of 27%.
Proteomic mass spectra classification using decision tree based ensemble methods

MOTIVATION Modern mass spectrometry allows the determination of proteomic fingerprints of body fluids like serum, saliva or urine. These measurements can be used in many medical applications in order to diagnose the current state or predict the evolution of a disease. Recent developments in machine learning allow one to exploit such datasets, characterized by small numbers of very high-dimensional samples. RESULTS We propose a systematic approach based on decision tree ensemble methods, which is used to automatically determine proteomic biomarkers and predictive models. The approach is validated on two datasets of surface-enhanced laser desorption/ionization time of flight measurements, for the diagnosis of rheumatoid arthritis and inflammatory bowel diseases. The results suggest that the methodology can handle a broad class of similar problems.
A GenLOT-based progressive image coder for low resolution images

The popular EZW (embedded zerotree wavelet) and its improved version SPIHT (set partitioning in hierarchical trees) are high-performance progressive transmission image coders based on the wavelet transform which gives excellent compression results for images with significant low-frequency content. For images with high texture contents, the GenLOT-based coder outperforms SPIHT in PSNR measure by a wide margin. On the other hand, low bit rate video finds applications in videophone and surveillance systems, where smaller size image in QCIF format is often transmitted. We show that using the conventional zero-tree algorithm for the QCIF image is suboptimal and we propose several progressive algorithms with modified zero-tree structures. The extensive coding results using both the DCT and GenLOT transform confirms that our proposed modified zero-tree algorithm outperforms the conventional zerotree algorithm for QCIF-sized images.
A Flexible Predictive Density Combination Model for Large Financial Data Sets in Regular and Crisis Periods

A flexible predictive density combination model is introduced for large financial data sets which allows for dynamic weight learning and model set incompleteness. Dimension reduction procedures allocate the large sets of predictive densities and combination weights to relatively small sets. Given the representation of the probability model in extended nonlinear state-space form, e cient simulation-based Bayesian inference is proposed using parallel sequential clustering as well as nonlinear filtering, implemented on graphics processing units. The approach is applied to combine predictive densities based on a large number of individual stock returns of daily observations over a period that includes the Covid-19 crisis period. Evidence on the quantification of predictive accuracy, uncertainty and risk, in particular, in the tails, may provide useful information for investment fund management. Information on dynamic cluster composition, weight patterns and model set incompleteness give also valuable signals for improved modelling and policy specification. JEL codes: C11, C15, C53, E37.
Digital Collections @ Dordt

A number of rare variant statistical methods have been proposed for analysis of the impending wave of next-generation sequencing data. To date, there are few direct comparisons of these methods on real sequence data. Furthermore, there is a strong need for practical advice on the proper analytic strategies for rare variant analysis. We compare four recently proposed rare variant methods (combined multivariate and collapsing, weighted sum, proportion regression, and cumulative minor allele test) on simulated phenotype and next-generation sequencing data as part of Genetic Analysis Workshop 17. Overall, we find that all analyzed methods have serious practical limitations on identifying causal genes. Specifically, no method has more than a 5% true discovery rate (percentage of truly causal genes among all those identified as significantly associated with the phenotype). Further exploration shows that all methods suffer from inflated false-positive error rates (chance that a noncausal gene will be identified as associated with the phenotype) because of population stratification and gametic phase disequilibrium between noncausal SNPs and causal SNPs. Furthermore, observed true-positive rates (chance that a truly causal gene will be identified as significantly associated with the phenotype) for each of the four methods was very low (<19%). The combination of larger than anticipated false-positive rates, low true-positive rates, and only about 1% of all genes being causal yields poor discriminatory ability for all four methods. Gametic phase disequilibrium and population stratification are important areas for further research in the analysis of rare variant data.
Unsupervised Learning of Na Ve Morphology with Genetic Algorithms

The morphological lexicon is an important part of NLP systems which is typically handwritten with the help of linguist experts. Even a partial automation of this process could decrease the cost of the lexicon, being of theoretical importance for languages and dialects which have not been well analysed yet. In this work we describe an attempt to use the minimal description length (MDL) as the one bias for deriving lexicons of morphemes from a raw list of words. MDL is used as a tness function of a simple genetic algorithm. Results are reported for a rich-morphology language corpus (French) and future work is discussed.
Field trial of Tiramisu: crowd-sourcing bus arrival times to spur co-design

Crowd-sourcing social computing systems represent a new material for HCI designers. However, these systems are difficult to work with and to prototype, because they require a critical mass of participants to investigate social behavior. Service design is an emerging research area that focuses on how customers co-produce the services that they use, and thus it appears to be a great domain to apply this new material. To investigate this relationship, we developed Tiramisu, a transit information system where commuters share GPS traces and submit problem reports. Tiramisu processes incoming traces and generates real-time arrival time predictions for buses. We conducted a field trial with 28 participants. In this paper we report on the results and reflect on the use of field trials to evaluate crowd-sourcing prototypes and on how crowd sourcing can generate co-production between citizens and public services.
Sim-to-Real Strategy for Spatially Aware Robot Navigation in Uneven Outdoor Environments

Deep Reinforcement Learning (DRL) is hugely successful due to the availability of realistic simulated environments. However, performance degradation during simulation to real-world transfer still remains a challenging problem for the policies trained in simulated environments. To close this sim-to-real gap, we present a novel hybrid architecture that utilizes an intermediate output from a fully trained attention DRL policy as a navigation cost map for outdoor navigation. Our attention DRL network incorporates a robot-centric elevation map, IMU data, the robot's pose, previous actions, and goal information as inputs to compute a navigation cost-map that highlights non-traversable regions. We compute least-cost waypoints on the cost map and utilize the Dynamic Window Approach (DWA) with velocity constraints on high cost regions to follow the waypoints in highly uneven outdoor environments. Our formulation generates dynamically feasible velocities along stable, traversable regions to reach the robot's goals. We observe an increase of 5% in terms of success rate, 13.09% of the decrease in average robot vibration, and a 19.33% reduction in average velocity compared to end-to-end DRL method and state-of-the-art methods in complex outdoor environments. We evaluate the benefits of our method using a Clearpath Husky robot in both simulated and real-world uneven environments.
A Linked Dataset of Medical Educational Resources

With sharing and reusing, educational resources become increasingly important for enhancing learning and teaching experiences, particularly in medical educational domain since these resources are expensive to re-produce. In respect to this, many efforts have been applied to federate the resources to achieve the sharing and reusing goals, which led to a fragmented landscape of competing metadata schemas, such as IEEE LOM or OAI-DC, and interface mechanisms, such as OAI-PMH or SQI. However, the major issue of educational resource federating is the heterogeneity challenge of metadata and data. In this paper, we illustrate a medical educational dataset (mEducator Linked Educational Resources dataset) that is published as part of the Linked Open Data cloud following Linked Data principles. The dataset contains educational resource metadata federated from ten different (medical) educational institutes together with enriched links to related information by using Linked Data techniques and datasets. We introduce a Semantic Web Service based data extracting mechanism that is exploited for services and data integration to address heterogeneous metadata problems. The paper also discusses the dataset accessing APIs, statistics and existing applications of using the mEducator dataset.
Making place: Old Japan at the Japanese department store Shirokiya in Honolulu

ABSTRACT In comparison to common department stores all over the world, the Japanese department store Shirokiya in Honolulu, Hawai’i, seems to be more than just a department store. For sure, goods are displayed and sold effectively, accompanied by thoughtful marketing strategies. But aside from this, elderly people stroll around with their arms crossed behind their backs, obviously not intending to buy anything at all. At a closer look, it appears that Shirokiya as a specific place in Honolulu plays an essential role for the elderly Japanese Americans of the city through honouring the heritage of their ancestors. With this in mind, Shirokiya shows that analysing places like this department store leads to unique findings for diaspora research including spatial theory approaches. Therefore, in this paper, I will show how applying spatial theory, with a focus on place(s) instead of space as merely a geographical unit, extends the scope of findings in diaspora research. Drawing from the terminology of Marc Augé, I show how his concepts of places and non-places can help shed light on the important meaning of the department store for these Japanese Americans.