Miracle-dz/UniFAR-FTUs huggingface dataset:
AttentionFlow: Visualising Influence in Networks of Time Series

The collective attention on online items such as web pages, search terms, and videos reflects trends that are of social, cultural, and economic interest. Moreover, attention trends of different items exhibit mutual influence via mechanisms such as hyperlinks or recommendations. Many visualisation tools exist for time series, network evolution, or network influence; however, few systems connect all three. In this work, we present AttentionFlow, a new system to visualise networks of time series and the dynamic influence they have on one another. Centred around an ego node, our system simultaneously presents the time series on each node using two visual encodings: a tree ring for an overview and a line chart for details. AttentionFlow supports interactions such as overlaying time series of influence, and filtering neighbours by time or flux. We demonstrate AttentionFlow using two real-world datasets, VevoMusic and WikiTraffic. We show that attention spikes in songs can be explained by external events such as major awards, or changes in the network such as the release of a new song. Separate case studies also demonstrate how an artist's influence changes over their career, and that correlated Wikipedia traffic is driven by cultural interests. More broadly, AttentionFlow can be generalised to visualise networks of time series on physical infrastructures such as road networks, or natural phenomena such as weather and geological measurements.
An Analytics-based Decision Support System for Resource Planning under Heterogeneous Service Demand of Nursing Home Residents

Nursing homes (NHs) are critical healthcare infrastructures for caring frail older adults with 24/7 formal care and personal assistance. Adequate NH resource planning is of great importance to ensure desired quality of care and resident outcomes yet challenging. The challenge lies in the heterogeneous service demand of NH residents, due to the varied individual characteristics, the diverse dwelling duration with multiple competing discharge dispositions, and the diverse service need. Existing healthcare staffing literatures often assumed a homogeneous population of NH residents and neglected the complexity of service demand heterogeneity. This work proposes an analytics-based modeling framework with a userfriendly decision support platform for NH resource planning. The proposed framework characterizes the heterogeneous service demand of NH residents via novel integration of advanced statistical modeling, computer simulation and optimization techniques. We further provide a case study using real data from our industrial collaborator to demonstrate the effectiveness and superior performance of the proposed work. The impacts of service utilization heterogeneity and service need heterogeneity on resource planning decisions are investigated as well. Note to Practitioners This work is motivated by the challenging task of nursing home resource planning under the complex and heterogeneous service demand of nursing home residents. This research work integrates predictive statistical models with computer simulation and optimization techniques to characterize heterogeneous service demand of NH residents at higher modeling fidelity and to facilitate resource planning decision making in response to service demand heterogeneity. The strategies and policies derived from the proposed decision support system are implementable and actionable in current nursing home industry practice. The proposed framework can easily adapt to varied residents census compositions across different nursing facilities or different seasonal periods with minimum adjustment. Moreover, the proposed methodological framework can be extended for resource preparedness and care delivery decisions under extreme event scenarios, such as influenza seasons or pandemic.
Short Communication: Clearing Prices under Margin Calls and the Short Squeeze

In this paper, we propose a clearing model for prices in a ﬁnancial market due to margin calls on short sold assets. In doing so, we construct an explicit formulation for the prices that would result immediately following asset purchases and a margin call. The key result of this work is the determination of a threshold short interest ratio which, if exceeded, results in the discontinuity of the clearing prices due to a feedback loop.
UPHDR-GAN: Generative Adversarial Network for High Dynamic Range Imaging With Unpaired Data

The paper proposes a method to effectively fuse multi-exposure inputs and generate high-quality high dynamic range (HDR) images with unpaired datasets. Deep learning-based HDR image generation methods rely heavily on paired datasets. The ground truth images play a leading role in generating reasonable HDR images. Datasets without ground truth are hard to be applied to train deep neural networks. Recently, Generative Adversarial Networks (GAN) have demonstrated their potentials of translating images from source domain $X$ to target domain $Y$ in the absence of paired examples. In this paper, we propose a GAN-based network for solving such problems while generating enjoyable HDR results, named UPHDR-GAN. The proposed method relaxes the constraint of the paired dataset and learns the mapping from the LDR domain to the HDR domain. Although the pair data are missing, UPHDR-GAN can properly handle the ghosting artifacts caused by moving objects or misalignments with the help of the modified GAN loss, the improved discriminator network and the useful initialization phase. The proposed method preserves the details of important regions and improves the total image perceptual quality. Qualitative and quantitative comparisons against the representative methods demonstrate the superiority of the proposed UPHDR-GAN.
Status and Direction of Atom Probe Analysis of Frozen Liquids

Abstract Imaging of liquids and cryogenic biological materials by electron microscopy has been recently enabled by innovative approaches for specimen preparation and the fast development of optimized instruments for cryo-enabled electron microscopy (cryo-EM). Yet, cryo-EM typically lacks advanced analytical capabilities, in particular for light elements. With the development of protocols for frozen wet specimen preparation, atom probe tomography (APT) could advantageously complement insights gained by cryo-EM. Here, we report on different approaches that have been recently proposed to enable the analysis of relatively large volumes of frozen liquids from either a flat substrate or the fractured surface of a wire. Both allowed for analyzing water ice layers which are several micrometers thick consisting of pure water, pure heavy water, and aqueous solutions. We discuss the merits of both approaches and prospects for further developments in this area. Preliminary results raise numerous questions, in part concerning the physics underpinning field evaporation. We discuss these aspects and lay out some of the challenges regarding the APT analysis of frozen liquids.
A generalised feature for low level vision

This papers presents a novel quantised transform (the SinclairTown or ST transform for short) that subsumes the rolls of both edgedetector, MSER style region detector and corner detector. The transform is similar to the unsharp transform but the difference from the local mean is quantised to 3 values (dark-neutral-light). The transform naturally leads to the definition of an appropriate local scale. A range of methods for extracting shape features form the transformed image are presented. The generalised feature provides a robust basis for establishing correspondence between images. The transform readily admits more complicated kernel behaviour including multi-scale and asymmetric elements to prefer shorter scale or oriented local features.
Quadratic Signaling Games with Channel Combining Ratio

In this study, Nash and Stackelberg equilibria of single-stage and multi-stage quadratic signaling games between an encoder and a decoder are investigated. In the considered setup, the objective functions of the encoder and the decoder are misaligned, there is a noisy channel between the encoder and the decoder, the encoder has a soft power constraint, and the decoder has also noisy observation of the source to be estimated. We show that there exist only linear encoding and decoding strategies at the Stackelberg equilibrium, and derive the equilibrium strategies and costs. Regarding the Nash equilibrium, we explicitly characterize affine equilibria for the single-stage setup and show that the optimal encoder (resp. decoder) is affine for an affine decoder (resp. encoder) for the multi-stage setup. On the decoder side, between the information coming from the encoder and noisy observation of the source, our results describe what should be the combining ratio of these two channels. Regarding the encoder, we derive the conditions under which it is meaningful to transmit a message.
LSSED: A Large-Scale Dataset and Benchmark for Speech Emotion Recognition

Speech emotion recognition is a vital contributor to the next generation of human-computer interaction (HCI). However, current existing small-scale databases have limited the development of related research. In this paper, we present LSSED, a challenging large-scale english speech emotion dataset, which has data collected from 820 subjects to simulate real- world distribution. In addition, we release some pre-trained models based on LSSED, which can not only promote the development of speech emotion recognition, but can also be transferred to related downstream tasks such as mental health analysis where data is extremely difficult to collect. Finally, our experiments show the necessity of large-scale datasets and the effectiveness of pre-trained models. The dateset will be released on https://github.com/tobefans/LSSED.
A model of Clocked Cubical Type Theory

—Guarded recursion is a powerful modal approach to recursion that can be seen as an abstract form of step-indexing. It is currently used extensively in separation logic to model programming languages with advanced features by solving domain equations also with negative occurrences. In its multi-clocked version, guarded recursion can also be used to program with and reason about coinductive types, encoding the productivity condition required for recursive deﬁnitions in types. This paper presents the ﬁrst denotational model of a type theory combining multi-clocked guarded recursion with the features of Cubical Type Theory. Using the combination of Higher Inductive Types (HITs) and guarded recursion allows for simple programming and reasoning about coinductive types that are traditionally hard to represent in type theory, such as the type of ﬁnitely branching labelled transition systems. For example, our results imply that bisimilarity for these imply path equality, and so proofs can be transported along bisimilarity proofs.
Session-based Recommendation with Self-Attention Networks

Session-based recommendation aims to predict user's next behavior from current session and previous anonymous sessions. Capturing long-range dependencies between items is a vital challenge in session-based recommendation. A novel approach is proposed for session-based recommendation with self-attention networks (SR-SAN) as a remedy. The self-attention networks (SAN) allow SR-SAN capture the global dependencies among all items of a session regardless of their distance. In SR-SAN, a single item latent vector is used to capture both current interest and global interest instead of session embedding which is composed of current interest embedding and global interest embedding. Some experiments have been performed on some open benchmark datasets. Experimental results show that the proposed method outperforms some state-of-the-arts by comparisons.
A Global-Local Attention Framework for Weakly Labelled Audio Tagging

Weakly labelled audio tagging aims to predict the classes of sound events within an audio clip, where the onset and offset times of the sound events are not provided. Previous works have used the multiple instance learning (MIL) framework, and exploited the information of the whole audio clip by MIL pooling functions. However, the detailed information of sound events such as their durations may not be considered under this framework. To address this issue, we propose a novel two-stream framework for audio tagging by exploiting the global and local information of sound events. The global stream aims to analyze the whole audio clip in order to capture the local clips that need to be attended using a class-wise selection module. These clips are then fed to the local stream to exploit the detailed information for a better decision. Experimental results on the AudioSet show that our proposed method can significantly improve the performance of audio tagging under different baseline network architectures.
Estimating the 3D Time Variable Water Vapor Contents of the Troposphere from a Single GNSS Receiver

We describe here a new algorithm to model the water contents of the atmosphere from GNSS slant wet delays relative to a single receiver. We first make the assumption that the water vapor contents are mainly governed by a scale height (exponential law), and secondly that the departures from this decaying exponential can be mapped as a set of low degree 3D Zernike functions (w.r.t. space) and Tchebyshev polynomials (w.r.t. time.) We give an example of inversion with data acquired over a one day time span at the Geodesy Observatory of Tahiti.
The Use of Principal Components in the Analysis of Near-Infrared Spectra

The statistical technique of principal components is used to analyze two sets of near-infrared spectra, wheat flour samples for which % moisture and % protein values are included, and milled barley samples for which hot water extract values are included. The methodology and interpretation of this technique are described within the context of NIR data, and its advantages both in providing insight into the variation of the spectra, and as a method of avoiding the problems caused by highly correlated reflectance energy values in the derivation of calibration equations, are highlighted. In each set of samples the first principal component accounts for the vast majority of the variation. These components also have an almost identical shape, which is interpreted as reflecting particle size. The second wheat component and the third barley component are also almost identical, with a shape very similar to that of the spectrum of water. Both fourth components share peaks at points in the spectrum which are used by fixed-filter instruments to measure protein in cereals.
The Effect of Thoracolumbar Injury Classification in the Clinical Outcome of Operative and Non-Operative Treatments

This review assesses the validity of a biomechanical approach using finite element analysis in the Thoracolumbar Injury Classification and Severity Score System (TLICS) by addressing the “gray zone” decision discrepancy of thoracolumbar spinal injuries. A systematic review was performed using the keywords “Thoracolumbar Injury Classification” AND “finite element analysis of the spinal column” to evaluate the validity of the TLICS and finite element analysis of the thoracolumbar spinal column. Results were classified according to the main conclusions and level of evidence. Thirteen articles are included. Four of the articles evaluated the TLICS in comparison to other classification systems of thoracolumbar spinal injuries. A notable finding is that the TLICS had inconsistencies with other classification systems in the treatment of burst fractures without neurological deficits. One article evaluated the TLICS with the inclusion of magnetic resonance imaging (MRI) in the evaluation, which decreased the agreement between the suggested and actual treatment. Among the three finite element analysis studies, limited data have been published on the posterior ligamentous complex (PLC) status when an injury is suspected or indeterminate. The TLICS has been a reliable classification system in the management of single-column fractures and three-column injuries treated with surgical stabilization. Special attention to enhancing the TLICS classification system by eliminating the “gray zone” of a TLICS score of 4 is essential. Biomedical computational modeling evaluating the PLC status of indeterminate or injury suspected is needed to enhance the current TLICS system and to clarify the decision discrepancy in the “gray zone. ”
Synthesis and properties of phosphorus-containing epoxy resins by novel method

Novel phosphorus-containing epoxy resins (1–3% phosphorus content) were synthesized by the reaction of 9,10-dihydro-9-oxa-10-phosphaphenanthrene 10-oxide (DOPO) and the diglycidyl ether of bisphenol A and then cured with 4,4′-diaminodiphenyl sulfone or phenol novolac. Differential scanning calorimetry, high performance liquid chromatography, and epoxide equivalent weight titration were used to trace the reaction between the DOPO and the epoxy. The thermal stability and flame retardancy were checked by thermal gravimetric analysis, the limiting oxygen index, and the UL-94 vertical test. The glass transitions were measured by dynamic mechanical analysis. The relation between these properties (thermal stability, flame retardancy, and glass transition) and the DOPO contents (phosphorus content) were discussed. © 1999 John Wiley & Sons, Inc. J Polym Sci A: Polym Chem 37: 3903–3909, 1999
Systematic identification of non-coding somatic single nucleotide variants associated with altered transcription and DNA methylation in adult and pediatric cancers

Abstract Whole-genome sequencing combined with transcriptomics can reveal impactful non-coding single nucleotide variants (SNVs) in cancer. Here, we developed an integrative analytical approach that, as a first step, identifies genes altered in expression or DNA methylation in association with nearby somatic SNVs, in contrast to alternative approaches that first identify mutational hotspots. Using genomic datasets from the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium and the Children's Brain Tumor Tissue Consortium (CBTTC), we identified hundreds of genes and associated CpG islands for which the nearby presence of a non-coding somatic SNV recurrently associated with altered expression or DNA methylation, respectively. Genomic regions upstream or downstream of genes, gene introns and gene untranslated regions were all involved. The PCAWG adult cancer cohort yielded different significant SNV-expression associations from the CBTTC pediatric brain tumor cohort. The SNV-expression associations involved a wide range of cancer types and histologies, as well as potential gain or loss of transcription factor binding sites. Notable genes with SNV-associated increased expression include TERT, COPS3, POLE2 and HDAC2—involving multiple cancer types—MYC, BCL2, PIM1 and IGLL5—involving lymphomas—and CYHR1—involving pediatric low-grade gliomas. Non-coding somatic SNVs show a major role in shaping the cancer transcriptome, not limited to mutational hotspots.
Protective Effects of Oroxylin A against Doxorubicin-Induced Cardiotoxicity via the Activation of Sirt1 in Mice

Doxorubicin- (DOX-) related cardiac injury impairs the life quality of patients with cancer. This largely limited the clinical use of DOX. It is of great significance to find a novel strategy to reduce DOX-related cardiac injury. Oroxylin A (OA) has been identified to exert beneficial effects against inflammatory diseases and cancers. Here, we investigated whether OA could attenuate DOX-induced acute cardiotoxicity in mice. A single dose of DOX was used to induce acute cardiac injury in mice. To explore the protective effects, OA was administered to mice for ten days beginning from five days before DOX injection. The data in our study indicated that OA inhibited DOX-induced heart weight loss, reduction in cardiac function, and the elevation in myocardial injury markers. DOX injection resulted in increased oxidative damage, inflammation accumulation, and myocardial apoptosis in vivo and in vitro, and these pathological alterations were alleviated by treatment of OA. OA activated the sirtuin 1 (Sirt1) signaling pathway via the cAMP/protein kinase A, and its protective effects were blocked by Sirt1 deficiency. OA treatment did not affect the tumor-killing action of DOX in tumor-bearing mice. In conclusion, OA protected against DOX-related acute cardiac injury via the regulation of Sirt1.
Distinction of High- and Low-Frequency Repetitive Transcranial Magnetic Stimulation on the Functional Reorganization of the Motor Network in Stroke Patients

Objective To investigate the functional reorganization of the motor network after repetitive transcranial magnetic stimulation (rTMS) in stroke patients with motor dysfunction and the distinction between high-frequency rTMS (HF-rTMS) and low-frequency rTMS (LF-rTMS). Methods Thirty-three subcortical stroke patients were enrolled and assigned to the HF-rTMS group, LF-rTMS group, and sham group. Each patient of rTMS groups received either 10.0 Hz rTMS over the ipsilesional primary motor cortex (M1) or 1.0 Hz rTMS over the contralesional M1 for 10 consecutive days. A resting-state functional magnetic resonance imaging (fMRI) scan and neurological examinations were performed at baseline and after rTMS. The motor network and functional connectivities intramotor network with the core brain regions including the bilateral M1, premotor area (PMA), and supplementary motor area (SMA) were calculated. Comparisons of functional connectivities and Pearson correlation analysis between functional connectivity changes and behavioral improvement were calculated. Results Significant motor improvement was found after rTMS in all groups which was larger in two rTMS groups than in the sham group. The functional connectivities of the motor network were significantly increased in bilateral M1, SMA, and contralesional PMA after real rTMS. These changes were only detected in the regions of the ipsilesional hemisphere in the HF-rTMS group and in the regions of the contralesional hemisphere in the LF-rTMS group. Significantly changed functional connectivities of the intramotor network were found between the ipsilesional M1 and SMA and contralesional PMA, between contralesional M1 and contralesional SMA, between contralesional SMA and ipsilesional SMA and contralesional PMA in the HF-rTMS group in which the changed connectivity between ipsilesional M1 and contralesional PMA was obviously correlated with the motor improvement. In addition, the functional connectivity of the intramotor network between ipsilesional M1 and contralesional PMA was significantly higher in the HF-rTMS group than in the LF-rTMS group. Conclusion Both HF-rTMS and LF-rTMS have a positive effect on motor recovery in patients with subcortical stroke and could promote the reorganization of the motor network. HF-rTMS may contribute more to the functional connectivity reorganization of the ipsilesional motor network and realize greater benefit to the motor recovery.