Miracle-dz/UniFAR-FTUs huggingface dataset:
Vector Field Map Representation for Near Conformal Surface Correspondence

Based on a new spectral vector field analysis on triangle meshes, we construct a compact representation for near conformal mesh surface correspondences. Generalizing the functional map representation, our representation uses the map between the low‐frequency tangent vector fields induced by the correspondence. While our representation is as efficient, it is also capable of handling a more generic class of correspondence inference. We also formulate the vector field preservation constraints and regularization terms for correspondence inference, with function preservation treated as a special case. A number of important vector field–related constraints can be implicitly enforced in our representation, including the commutativity of the mapping with the usual gradient, curl, divergence operators or angle preservation under near conformal correspondence. For function transfer between shapes, the preservation of function values on landmarks can be strictly enforced through our gradient domain representation, enabling transfer across different topologies. With the vector field map representation, a novel class of constraints can be specified for the alignment of designed or computed vector field pairs. We demonstrate the advantages of the vector field map representation in tests on conformal datasets and near‐isometric datasets.
Control Engagement During Sentence and Inhibition fMRI Tasks in Children With Reading Difficulties

Abstract Recent reading research implicates executive control regions as sites of difference in struggling readers. However, as studies often employ only reading or language tasks, the extent of deviation in control engagement in children with reading difficulties is not known. The current study investigated activation in reading and executive control brain regions during both a sentence comprehension task and a nonlexical inhibitory control task in third‐fifth grade children with and without reading difficulties. We employed both categorical (group‐based) and individual difference approaches to relate reading ability to brain activity. During sentence comprehension, struggling readers had less activation in the left posterior temporal cortex, previously implicated in language, semantic, and reading research. Greater negative activity (relative to fixation) during sentence comprehension in a left inferior parietal region from the executive control literature correlated with poorer reading ability. Greater comprehension scores were associated with less dorsal anterior cingulate activity during the sentence comprehension task. Unlike the sentence task, there were no significant differences between struggling and nonstruggling readers for the nonlexical inhibitory control task. Thus, differences in executive control engagement were largely specific to reading, rather than a general control deficit across tasks in children with reading difficulties, informing future intervention research.
Patient information organization in the intensive care setting: expert knowledge elicitation with card sorting methods

Introduction Many electronic health records fail to support information uptake because they impose low-level information organization tasks on users. Clinical concept-oriented views have shown information processing improvements, but the specifics of this organization for critical care are unclear. Objective To determine high-level cognitive processes and patient information organization schema in critical care. Methods We conducted an open card sort of 29 patient data elements and a modified Delphi card sort of 65 patient data elements. Study participants were 39 clinicians with varied critical care training and experience. We analyzed the open sort with a hierarchical cluster analysis (HCA) and factor analysis (FA). The Delphi sort was split into three initiating groups that resulted in three unique solutions. We compared results between open sort analyses (HCA and FA), between card sorting exercises (open and Delphi), and across the Delphi solutions. Results Between the HCA and FA, we observed common constructs including cardiovascular and hemodynamics, infectious disease, medications, neurology, patient overview, respiratory, and vital signs. The more comprehensive Delphi sort solutions also included gastrointestinal, renal, and imaging constructs. Conclusions We identified primarily system-based groupings (e.g., cardiovascular, respiratory). Source-based (e.g., medications, laboratory) groups became apparent when participants were asked to sort a longer list of concepts. These results suggest a hybrid approach to information organization, which may combine systems, source, or problem-based groupings, best supports clinicians' mental models. These results can contribute to the design of information displays to better support clinicians' access and interpretation of information for critical care decisions.
Channel Estimation and Hybrid Precoding for Multi-Panel Millimeter Wave MIMO

Multi-panel MIMO is a promising technology in millimeter wave communications. Due to its partially hybrid structure and non-uniform antenna array, existing channel estimation and hybrid precoding cannot be directly applied to multipanel MIMO. In this paper, we study channel estimation and hybrid precoding for multi-panel MIMO. We first transform the channel response vector into angular domain and then reconstruct channel state information (CSI) by using the estimated angular CSI. Moreover, by exploiting the sparse nature of mmWave channels, a sparsity-based channel estimation method is proposed to reduce training overhead and the computational complexity of hybrid precoding. Numerical results show that the proposed channel estimation and hybrid precoding scheme achieve satisfactory spectral efficiency performance with greatly reduced training overhead and low computational complexity.
Antitumor Activity of Extract From the Sporoderm-Breaking Spore of Ganoderma lucidum: Restoration on Exhausted Cytotoxic T Cell With Gut Microbiota Remodeling

As breast cancer is the leading cause of cancer-related deaths in women population worldwide, ongoing endeavor has been made for alternative regimens with improved efficacy but fewer adverse effects. Recently, active components from the spores of Ganoderma lucidum have attracted much attention for their versatile biological activities owing to the advance in sporoderm-breaking technology. Here, anticancer potential of an extract derived from the sporoderm-breaking spores of G. lucidum (ESG) was explored in a 4T1-breast cancer xenograft mice model. Results showed that ESG was able to suppress 4T1 tumor growth in vivo rather than in vitro. Flowcytometry analysis revealed that ESG could significantly increase both cytotoxic T cell (Tc) population and the ratio of Tc to helper T cell (Th) in peripheral blood of the tumor-bearing mouse; similar promotion on Tc was also found in tumor-infiltrating lymphocyte. Moreover, ESG evidently downregulated the two immune checkpoints, programmed cell death protein-1 (PD-1, in the spleen) and cytotoxic T lymphocyte antigen-4 (CTLA-4, in the tumor), suggesting that ESG could effectively restore the T cell paradigm by recovering the exhaustion status via suppressing the co-inhibitory checkpoints. By 16S rRNA gene sequence analysis on the fecal microbiota, it was found that ESG would remodeling the overall structure of the samples from tumor-bearing mice toward that of the normal counterparts, including 18 genera in 5 phyla, together with regulations on several genes that are responsible for signaling pathways involved in metabolism, cellular processes, and environmental information processing. Collectively, this study demonstrated that ESG would serve as a natural anticancer adjuvant via a restoration on the exhausted Tc, highlighting important clinical implications for the treatment of breast cancer.
Multiway Physical-Layer Network Coding via Uniquely Decodable Codes

We focus on a multiway relay channel (MWRC) network where two or more users simultaneously exchange information with each other through the help of a relay node. We propose for the first time to apply ternary uniquely decodable (UD) code sets that we have developed to allow each user to uniquely recover the information bits from the noisy channel environment. One of the key features of the proposed scheme is that it utilizes a very simple decoding algorithm, which requires only a few logical comparisons. Simulation results in terms of bit error rate (BER) demonstrate that the performance of the proposed decoder is almost as good as the maximum-likelihood (ML) decoder. In addition to that through simulations, we show that the proposed scheme can significantly improve the sum-rate capacity, which in turn can potentially improve overall throughput, as it needs only two time slots (TSs) to exchange information compared to the conventional methods.
Parameter Estimation of a Gaussian Mixture Model for Wind Power Forecast Error by Riemann L-BFGS Optimization

With the increasing penetration of wind power into the electricity grid, wind power forecast error analysis plays an important role in operations scheduling. To better describe the characteristics of power forecast error, a probability density function should be established. Compared with the Kernel density estimation method, this paper adopts the Gaussian mixture model (GMM), which is flexible enough to capture different error distribution characteristics, such as bias, heavy tail, multi-peak, and so on. In addition, for GMM parameter estimation, when dealing with a large number of multi-dimensional data sets or unbalanced overlapping mixtures, the expectation maximization (EM) algorithm shows a slower convergence speed and requires a high number of iterations. In this paper, a new L-BFGS optimization method, based on the Riemannian manifold, is used for GMM parameter estimation. Based on actual wind power forecast error data, the suitability of the model and the new optimization algorithm was verified in large, multi-dimensional data sets. The new optimization algorithm has fewer iterations than the EM algorithm, with an improved convergence speed.
Low-Rank Riemannian Optimization on Positive Semidefinite Stochastic Matrices with Applications to Graph Clustering

Ahmed Douik 1 Babak Hassibi 1 Abstract This paper develops a Riemannian optimization framework for solving optimization problems on the set of symmetric positive semidefinite stochastic matrices. The paper first reformulates the problem by factorizing the optimization variable as X = YY and deriving conditions on p, i.e., the number of columns of Y, under which the factorization yields a satisfactory solution. The reparameterization of the problem allows its formulation as an optimization over either an embedded or quotient Riemannian manifold whose geometries are investigated. In particular, the paper explicitly derives the tangent space, Riemannian gradients and retraction operator that allow the design of efficient optimization methods on the proposed manifolds. The numerical results reveal that, when the optimal solution has a known low-rank, the resulting algorithms present a clear complexity advantage when compared with stateof-the-art Euclidean and Riemannian approaches for graph clustering applications.
Gestão da Permanência: um estudo sobre o perfil socioeconômico, permanência e evasão dos discentes do Curso de Bacharelado em Administração Pública da UAB/UNEMAT

Dissertacao apresentada como requisito para obtencao de titulo de Mestre pelo Programa de Mestrado Profissional em Administracao Publica (PROFIAP), Instituicao Associada Universidade Federal de Rondonia.Orientadora: Profa. Dra. Marlene Valerio dos Santos Arenas
Genetic Algorithm-based Optimal Investment Scheduling for Public Rental Housing Projects in South Korea

Declining birthrate is a serious problem that threatens the sustainability of Korean society. The main cause of this phenomenon is high living cost where housing cost accounts for the majority in household expenditure. South Korea has a very low supply rate in public rental housing when compared to other OECD countries. Because young people cannot afford to buy or lease a house for their new houses, some of them postpone or even give up marriage. As a countermeasure, Gyeonggi Province (surrounding area of Seoul) recently announced the supplying plan of 10,000 public rental houses by 2020. We expect this measure to alleviate the low birthrate problem and increase the demographic sustainability of the province. This study optimizes multi-annual investment scheduling for rental housing projects using genetic algorithm while satisfying the constraints such as budget, human resources, regional balance, etc. Through the optimal investment scheduling, we hope that public corporation will supply public rental houses more efﬁciently and more sustainably for the community.
Contrast Enhancement Based on Viewing Distance

In this paper, we propose an image-space contrast enhancement method for color-encoded visualization. The contrast of an image is enhanced through a perceptually-guided approach that interfaces with the user with a single and intuitive parameter of the virtual viewing distance. To this end, we analyze a multiscale contrast model of the input image and test the visibility of bandpass images of all scales at a virtual viewing distance. By adapting weights of bandpass images with a threshold model of spatial vision, this image-based method enhances contrast to compensate for contrast loss caused by viewing the image at a certain distance. Relevant features in the color image can be further emphasized by the user using overcompensation. The method is efficient and can be integrated into any visualization tool as it is a generic image-based post-processing technique. Using highly diverse datasets, we show the usefulness of perception compensation across a wide range of typical visualizations.
What makes us laugh? Investigations into Automatic Humor Classification

Most scholarly works in the field of computational detection of humour derive their inspiration from the incongruity theory. Incongruity is an indispensable facet in drawing a line between humorous and non-humorous occurrences but is immensely inadequate in shedding light on what actually made the particular occurrence a funny one. Classical theories like Script-based Semantic Theory of Humour and General Verbal Theory of Humour try and achieve this feat to an adequate extent. In this paper we adhere to a more holistic approach towards classification of humour based on these classical theories with a few improvements and revisions. Through experiments based on our linear approach and performed on large data-sets of jokes, we are able to demonstrate the adaptability and show componentizability of our model, and that a host of classification techniques can be used to overcome the challenging problem of distinguishing between various categories and sub-categories of jokes.
Smashing the strict hierarchy: three cases of clinical decision support malfunctions involving carvedilol

Abstract Clinical vocabularies allow for standard representation of clinical concepts, and can also contain knowledge structures, such as hierarchy, that facilitate the creation of maintainable and accurate clinical decision support (CDS). A key architectural feature of clinical hierarchies is how they handle parent-child relationships — specifically whether hierarchies are strict hierarchies (allowing a single parent per concept) or polyhierarchies (allowing multiple parents per concept). These structures handle subsumption relationships (ie, ancestor and descendant relationships) differently. In this paper, we describe three real-world malfunctions of clinical decision support related to incorrect assumptions about subsumption checking for β-blocker, specifically carvedilol, a non-selective β-blocker that also has α-blocker activity. We recommend that 1) CDS implementers should learn about the limitations of terminologies, hierarchies, and classification, 2) CDS implementers should thoroughly test CDS, with a focus on special or unusual cases, 3) CDS implementers should monitor feedback from users, and 4) electronic health record (EHR) and clinical content developers should offer and support polyhierarchical clinical terminologies, especially for medications.
Retrieval of the Best Counterargument without Prior Topic Knowledge

Given any argument on any controversial topic, how to counter it? This question implies the challenging retrieval task of finding the best counterargument. Since prior knowledge of a topic cannot be expected in general, we hypothesize the best counterargument to invoke the same aspects as the argument while having the opposite stance. To operationalize our hypothesis, we simultaneously model the similarity and dissimilarity of pairs of arguments, based on the words and embeddings of the arguments’ premises and conclusions. A salient property of our model is its independence from the topic at hand, i.e., it applies to arbitrary arguments. We evaluate different model variations on millions of argument pairs derived from the web portal idebate.org. Systematic ranking experiments suggest that our hypothesis is true for many arguments: For 7.6 candidates with opposing stance on average, we rank the best counterargument highest with 60% accuracy. Even among all 2801 test set pairs as candidates, we still find the best one about every third time.
Chart Constellations: Effective Chart Summarization for Collaborative and Multi‐User Analyses

Many data problems in the real world are complex and require multiple analysts working together to uncover embedded insights by creating chart‐driven data stories. How, as a subsequent analysis step, do we interpret and learn from these collections of charts? We present Chart Constellations, a system to interactively support a single analyst in the review and analysis of data stories created by other collaborative analysts. Instead of iterating through the individual charts for each data story, the analyst can project, cluster, filter, and connect results from all users in a meta‐visualization approach. Constellations supports deriving summary insights about prior investigations and supports the exploration of new, unexplored regions in the dataset. To evaluate our system, we conduct a user study comparing it against data science notebooks. Results suggest that Constellations promotes the discovery of both broad and high‐level insights, including theme and trend analysis, subjective evaluation, and hypothesis generation.
Enabling Deep Learning of Emotion With First-Person Seed Expressions

The computational treatment of emotion in natural language text remains relatively limited, and Arabic is no exception. This is partly due to lack of labeled data. In this work, we describe and manually validate a method for the automatic acquisition of emotion labeled data and introduce a newly developed data set for Modern Standard and Dialectal Arabic emotion detection focused at Robert Plutchik’s 8 basic emotion types. Using a hybrid supervision method that exploits first person emotion seeds, we show how we can acquire promising results with a deep gated recurrent neural network. Our best model reaches 70% F-score, significantly (i.e., 11%, p < 0.05) outperforming a competitive baseline. Applying our method and data on an external dataset of 4 emotions released around the same time we finalized our work, we acquire 7% absolute gain in F-score over a linear SVM classifier trained on gold data, thus validating our approach.
Orthogonal Tree Decompositions of Graphs

This paper studies graphs that have two tree decompositions with the property that every bag from the first decomposition has a bounded-size intersection with every bag from the second decomposition. We show that every graph in each of the following classes has a tree decomposition and a linear-sized path decomposition with bounded intersections: (1) every proper minor-closed class, (2) string graphs with a linear number of crossings in a fixed surface, (3) graphs with linear crossing number in a fixed surface. Here “linear size” means that the total size of the bags in the path decomposition is $O(n)$ for $n$-vertex graphs. We then show that every $n$-vertex graph that has a tree decomposition and a linear-sized path decomposition with bounded intersections has $O(\sqrt{n})$ treewidth. As a corollary, we conclude a new lower bound on the crossing number of a graph in terms of its treewidth. Finally, we consider graph classes that have two path decompositions with bounded intersections. Trees and outerpla.
Rethinking the Validation Process for Medical Devices: A Cardiac Pacemaker Case Study

Existing techniques for validation of implantable medical devices such as pacemakers are heavily dependent on expensive and timeconsuming clinical trials where the sample size is small and may not represent the variance in a larger population. To address this problem, bio-engineering researchers have proposed various high fidelity models that are used for non real-time simulation. More recently, computer science (CS) researchers have developed more abstract models that are amenable for real-time (hardware-in-theloop validation), but fail to exhibit appropriate dynamic responses. In general, the challenge remains on how to develop the organ models such that they capture the appropriate behaviour while maintaining the real-time response. In this paper, we present a generic step-by-step methodology that can aid researchers who are modelling organs for the validation of medical devices. The goal of this paper is to help: (1) introduce CS researchers to the steps involved in extracting executable organ models from bio-engineering models (2) introduce bio-engineers to emulation models and available computer hardware platforms for synthesising the organ models.
How to make decisions with algorithms: ethical decision-making using algorithms within predictive analytics

The use of automated decision-making support, such as algorithms within predictive analytics, will inevitably be more and more relevant and will be affecting society. Sometimes it is good, and sometimes there seems to be negative effect, such as with discrimination. The solution focused on in this paper is how humans and algorithms, or ICT, could interact within ethical decision-making. What predictive analytics can produce is, arguably, mostly implicit knowledge, so what a human decision-maker could, possibly, help with is the explicit thought processes. This could be one way to conceptualize an interactive effect between humans and algorithms that could be fruitful. Presently there does not seem to be very much research regarding predictive analytics and ethical decisions, concerning this human-algorithm interaction. Rather it is often a focus on pure technological solutions, or with laws and regulation.
Efficient Scene Labeling via Sparse Annotations

Scene labeling (SL) in images is a signiﬁcant part of Visual Internet of Things (VIoT). Most of existing approaches for SL employ fully supervised methods requiring massive pixel-wise annotations which are costly and time-consuming to obtain. To further deduce the required amount of manually labeled data, we propose a semi-supervised SL paradigm based on the joint optimization of deep representation and scene clustering. In order to learn the deep representation with semantic-friendly distribution, we design a novel constrained clustering which is composed of two steps: (1) over-clustering deep features into raw clusters with high self-consistence; (2) introducing sparse annotations as semantic constrains to merge raw clusters into scene clusters. Experimental results show that the proposed approach has achieved satisfying performance on SIFT Flow and Stanford Background benchmarks by leveraging very few annotations (0.1% or less).