Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Media Popularity Prediction: A Multiple Feature Fusion Approach with Deep Neural Networks

Social media popularity prediction (SMPD) aims to predict the popularity of the post shared on online social media platforms. This task is crucial for content providers and consumers in a wide range of real-world applications, including multimedia advertising, recommendation system and trend analysis. In this paper, we propose to fuse features from multiple sources by deep neural networks (DNNs) for popularity prediction. Specifically, high-level image and text features are extracted by the advanced pretrained DNN, and numerical features are captured from the metadata of the posts. All of the features are concatenated and fed into a regressor with multiple dense layers. Experiments have demonstrated the effectiveness of the proposed model on the ACM Multimedia Challenge SMPD2019 dataset. We also verify the importance of each feature via univariate test and ablation study, and provide the insights of feature combination for social media popularity prediction.
SALMM'19: First International Workshop on Search as Learning with Multimedia Information

The First International Workshop on "Search as Learning with Multimedia Information" (SALMM) presents interdisciplinary contributions that address multimedia aspects insearch as learning scenarios. The research topic of search as learning (SAL) recently emerged in the field of information retrieval and investigates informal, web-based learning processes as they happen every day with the help of search engines. While highly active, the SAL community still shows a focus on textual documents. This conflicts with research on multimedia learning in educational psychology: Humans tend to grasp and internalize new knowledge easier and more efficiently when it is conveyed using multiple modalities. The workshop SALMM aims to bridge the gap between the communities of SAL and multimedia. Related Workshop Proceedings are available in the ACM DL at: \hrefhttps://dl.acm. org/citation.cfm? id=3347451 https://dl.acm. org/citation.cfm? id=3347451
Urinary Tract Immunology

Complicated urinary tract infections (cUTIs) are a major cause of hospital admissions and are associated with significant morbidity and health care costs. Knowledge of baseline risk of urinary tract infection can help clinicians make informed diagnostic and therapeutic decisions. Prevalence rates of UTI vary by age, gender, race, and other predisposing risk factors. In this regard, this book provides comprehensive information on etiology, epidemiology, immunology, pathology, pathogenic mechanisms, symptomatology, investigation and management of urinary tract infection. Chapters cover common problems in urinary tract infection and put emphasis on the importance of making a correct clinical decision and choosing the appropriate therapeutic approach. Topics are organized to address all of the major complicated conditions frequently seen in urinary tract infection. The authors have paid particular attention to urological problems like the outcome of patients with vesicoureteric reflux, the factors affecting renal scarring, obstructive uropathy, voiding dysfunction and catheter associated problems. This book will be indispensable for all professionals involved in the medical care of patients with urinary tract infection.
User's Scientific Data Retrieval Behavior Study Based on the Model of TPB

Theory of Planned Behavior (TPB) can explain the user's behavior process. Whether user's scientific data retrieval behavior can explained by TPB model become the focus of this research. Data analyzed based on questionnaires and structural equation model. The results show that TPB has strong explanatory power to user's scientific data retrieval behavior. In the model, scientific data retrieval behavioral intention has a direct influence on actual system using behavior. Scientific data user's behavioral attitude, subjective norm, perceived behavioral control of scientific data retrieval system all have indirect influence on actual behavior through intention. This study is helpful to understand the use of scientific data retrieval system from the perspective of users, and provides certain support for the construction, improvement and management of scientific data retrieval and sharing platform.
iDFusion: Globally Consistent Dense 3D Reconstruction from RGB-D and Inertial Measurements

We present a practical fast, globally consistent and robust dense 3D reconstruction system, iDFusion, by exploring the joint benefit of both the visual (RGB-D) solution and inertial measurement unit (IMU). A global optimization considering all the previous states is adopted to maintain high localization accuracy and global consistency, yet its complexity of being linear to the number of all previous camera/IMU observations seriously impedes real-time implementation. We show that the global optimization can be solved efficiently at the complexity linear to the number of keyframes, and further realize a real-time dense 3D reconstruction system given the estimated camera states. Meanwhile, for the sake of robustness, we propose a novel loop-validity detector based on the estimated bias of the IMU state. By checking the consistency of camera movements, a false loop closure constraint introduces manifest inconsistency between the camera movements and IMU measurements. Experiments reveal that iDFusion owns superior reconstruction performance running in 25 fps on CPU computing of portable devices, under challenging yet practical scenarios including texture-less, motion blur, and repetitive contents.
Status Antioxidante, Diabetes Mellitus II e Aterosclerose

Type 2 diabetes mellitus is a polygenic disease which is aggravated by environmental factors. Besides that, it is associated to the acceleration of the atherosclerotic process. The role of oxidative stress and antioxidant status in both diabetes mellitus and atherosclerotic disease has been discussed lately. Various studies have suggested that both type 2 diabetes and hyperglycemia increase the oxidative stress and, as a consequence, might initiate the atherosclerotic process. On the other hand, literature has shown that exogenous antioxidants and antioxidant enzymes such as SOD2 can reduce the impact of oxidative stress on cells or even prevent pathologic status. In this context, the present article intends to deepen the discussion and lay new ideas about a possible relation between antioxidant status, type 2 diabetes and atherogenesis.
Discovering Research Teams from Scientific Papers and Patents

Most existing team discovery methods are based on collaboration networks using papers or patents data. They usually have low efficiency because they have to create the whole network containing all researchers. In addition, these methods can't immediately output research topics for each discovered team. A novel team discovery method is presented to solve these problems. The method extracts institutional names from papers and patents to build the institution base, and extracts authors and inventors to build the researcher base after name disambiguation. Then, the method exploits Author Topic model to mine distributions of topics and researchers in papers and patents and builds research topic base. The component analysis technique is used to discover teams under each research topic by analyzing its collaboration network. Experiments show the proposed method can identify teams without establishing a whole network by integrating papers and patent data. Meanwhile, the method can provide research topics for found teams.
Geographical Entity Community Mining Based on Spatial and Semantic Association

The relevance of geographic entities has always been the focus of research on geographic information retrieval, geographic knowledge graph and recommendation systems. Traditional research methods, which use spatial or semantic similarity to calculate the correlation between regions, have certain one-sidedness and limitations. The network topology can clearly represent the relationship between entities. However, semantic relationships are difficult to define, so there are few cases where network-related algorithms are used to solve the relevance of geographic entities. With the development of the Internet, web pages provide people with a huge amount of information, and geographical names as a key element are often ignored by researchers, and the rich semantic information contained in it needs further research. This study attempts to explore the geographic entity relevance of integrated semantics and spatial factors based on textual data from a network perspective. Based on the community mining algorithm, the experiment studies the aggregation characteristics of geographic entities and can find areas that are close to each other and tightly related, which is more satisfied with people's common sense.
Computational Models of Retrieval Processes in Sentence Processing

Sentence comprehension requires that the comprehender work out who did what to whom. This process has been characterized as retrieval from memory. This review summarizes the quantitative predictions and empirical coverage of the two existing computational models of retrieval and shows how the predictive performance of these two competing models can be tested against a benchmark data-set. We also show how computational modeling can help us better understand sources of variability in both unimpaired and impaired sentence comprehension.
Controlling the Output Length of Neural Machine Translation

The recent advances introduced by neural machine translation (NMT) are rapidly expanding the application fields of machine translation, as well as reshaping the quality level to be targeted. In particular, if translations have to fit some given layout, quality should not only be measured in terms of adequacy and fluency, but also length. Exemplary cases are the translation of document files, subtitles, and scripts for dubbing, where the output length should ideally be as close as possible to the length of the input text. This pa-per addresses for the first time, to the best of our knowledge, the problem of controlling the output length in NMT. We investigate two methods for biasing the output length with a transformer architecture: i) conditioning the output to a given target-source length-ratio class and ii) enriching the transformer positional embedding with length information. Our experiments show that both methods can induce the network to generate shorter translations, as well as acquiring inter- pretable linguistic skills.
WHAMR!: Noisy and Reverberant Single-Channel Speech Separation

While significant advances have been made with respect to the separation of overlapping speech signals, studies have been largely constrained to mixtures of clean, near anechoic speech, not representative of many real-world scenarios. Although the WHAM! dataset introduced noise to the ubiquitous wsj0-2mix dataset, it did not include reverberation, which is generally present in indoor recordings outside of recording studios. The spectral smearing caused by reverberation can result in significant performance degradation for standard deep learning-based speech separation systems, which rely on spectral structure and the sparsity of speech signals to tease apart sources. To address this, we introduce WHAMR! , an augmented version of WHAM! with synthetic reverberated sources, and provide a thorough baseline analysis of current techniques as well as novel cascaded architectures on the newly introduced conditions.
An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervision

The notion of individual fairness requires that similar people receive similar treatment. However, this is hard to achieve in practice since it is difficult to specify the appropriate similarity metric. In this work, we attempt to learn such similarity metric from human annotated data. We gather a new dataset of human judgments on a criminal recidivism prediction (COMPAS) task. By assuming the human supervision obeys the principle of individual fairness, we leverage prior work on metric learning, evaluate the performance of several metric learning methods on our dataset, and show that the learned metrics outperform the Euclidean and Precision metric under various criteria. We do not provide a way to directly learn a similarity metric satisfying the individual fairness, but to provide an empirical study on how to derive the similarity metric from human supervisors, then future work can use this as a tool to understand human supervision.
Noncommutative Geometry, the Spectral Standpoint

We report on the following highlights from among the many discoveries made in Noncommutative Geometry since year 2000: 1) The interplay of the geometry with the modular theory for noncommutative tori, 2) Advances on the Baum-Connes conjecture, on coarse geometry and on higher index theory, 3) The geometrization of the pseudo-differential calculi using smooth groupoids, 4) The development of Hopf cyclic cohomology, 5) The increasing role of topological cyclic homology in number theory, and of the lambda operations in archimedean cohomology, 6) The understanding of the renormalization group as a motivic Galois group, 7) The development of quantum field theory on noncommutative spaces, 8) The discovery of a simple equation whose irreducible representations correspond to 4-dimensional spin geometries with quantized volume and give an explanation of the Lagrangian of the standard model coupled to gravity, 9) The discovery that very natural toposes such as the scaling site provide the missing algebro-geometric structure on the noncommutative adele class space underlying the spectral realization of zeros of L-functions.
Perceptual-Assisted Adversarial Adaptation for Choroid Segmentation in Optical Coherence Tomography

Accurate choroid segmentation in optical coherence tomography (OCT) image is vital because the choroid thickness is a major quantitative biomarker of many ocular diseases. Deep learning has shown its superiority in the segmentation of the choroid region but subjects to the performance degeneration caused by the domain discrepancies (e.g., noise level and distribution) among datasets obtained from the OCT devices of different manufacturers. In this paper, we present an unsupervised perceptual-assisted adversarial adaptation (PAAA) framework for efficiently segmenting the choroid area by narrowing the domain discrepancies between different domains. The adversarial adaptation module in the proposed framework encourages the prediction structure information of the target domain to be similar to that of the source domain. Besides, a perceptual loss is employed for matching their shape information (the curvatures of Bruch's membrane and choroid-sclera interface) which can result in a fine boundary prediction. The results of quantitative experiments show that the proposed PAAA segmentation framework outperforms other state-of-the-art methods.
Quantum speed-up based on classical-field and moving-velocity

In this work, we provide a method of solving the model of a dissipatve moving qubit under the classical field. We obtain the analytic solution of the density operator of the qubit and investigate the quantum speed limit time(QSLT) and the non-Markovianity based on the classical field and the moving velocity of the qubit. The results show that, the transition from Markovian to non-Markovian dynamics is the intrinsic physical reason of the quantum speed-up process, and both of the driving field and the strong-coupling can enhance the non-Markovianity in the dynamics process and speed up the evolution of the qubit, but the moving velocity of the qubit can decrease the non-Markovianity in the dynamics process and delay the evolution of the qubit.
RTOP: A Conceptual and Computational Framework for General Intelligence

A novel general intelligence model is proposed with three types of learning. A unified sequence of the foreground percept trace and the command trace translates into direct and time-hop observation paths to form the basis of Raw learning. Raw learning includes the formation of image-image associations, which lead to the perception of temporal and spatial relationships among objects and object parts; and the formation of image-audio associations, which serve as the building blocks of language. Offline identification of similar segments in the observation paths and their subsequent reduction into a common segment through merging of memory nodes leads to Generalized learning. Generalization includes the formation of interpolated sensory nodes for robust and generic matching, the formation of sensory properties nodes for specific matching and superimposition, and the formation of group nodes for simpler logic pathways. Online superimposition of memory nodes across multiple predictions, primarily the superimposition of images on the internal projection canvas, gives rise to Innovative learning and thought. The learning of actions happens the same way as raw learning while the action determination happens through the utility model built into the raw learnings, the utility function being the pleasure and pain of the physical senses.
The Bitlet Model: Defining a Litmus Test for the Bitwise Processing-in-Memory Paradigm

This paper describes an analytical modeling tool called Bitlet that can be used, in a parameterized fashion, to understand the affinity of workloads to processing-in-memory (PIM) as opposed to traditional computing. The tool uncovers interesting trade-offs between operation complexity (cycles required to perform an operation through PIM) and other key parameters, such as system memory bandwidth, data transfer size, the extent of data alignment, and effective memory capacity involved in PIM computations. Despite its simplicity, the model has already proven useful. In the future, we intend to extend and refine Bitlet to further increase its utility.
Evaluation of Public Health approaches of obesity prevention and management in England: lessons learned for Brunei

Tackling obesity is a major public health challenge. Obesity strategy and policy in England has been heavily criticised for being ineffective in tackling the problem of obesity. Although many studies have evaluated interventions for obesity, there is no clear evidence about which interventions are effective in the prevention and management of obesity in primary care and community settings. The main purpose of the study was to evaluate the effectiveness of current public health approaches for obesity prevention and management, in particular focusing on behaviour change models as intervention and also policy actions implemented at local level (primary care trusts) in England. It also intended to identify obesity strategies relevant to Brunei as lessons learned from England. The methods used were systematic review, policy analysis and validation. The main finding of this study had shown that the ‘Transtheoretical model stages of change’ used in combination with diet and physical activity has limited impact on weight loss (about 2 kg or less) and there was no conclusive evidence for sustainable weight loss beyond 12 months. In addition, there were significant variations found in obesity strategies implemented by primary care trusts in north-west London based on analysis using the Imperial College Obesity Strategy Assessment Framework. The key lesson learned for Brunei from England’s experiences was the development and application of Brunei-IC-OSAF that contributed to the formulation of ‘comprehensive obesity policy’ for Brunei. The tool had identified strengths and weaknesses of the existing obesity strategies implemented in the country. The outcomes of the study contributed to the existing evidence about what works in tackling obesity in public health settings, informing better pathway development and commissioning decisions.
A Fast and Compact Invertible Sketch for Network-Wide Heavy Flow Detection

Fast detection of heavy flows (e.g., heavy hitters and heavy changers) in massive network traffic is challenging due to the stringent requirements of fast packet processing and limited resource availability. Invertible sketches are summary data structures that can recover heavy flows with small memory footprints and bounded errors, yet existing invertible sketches incur high memory access overhead that leads to performance degradation. We present MV-Sketch, a fast and compact invertible sketch that supports heavy flow detection with small and static memory allocation. MV-Sketch tracks candidate heavy flows inside the sketch data structure via the idea of majority voting, such that it incurs small memory access overhead in both update and query operations, while achieving high detection accuracy. We present theoretical analysis on the memory usage, performance, and accuracy of MV-Sketch in both local and network-wide scenarios. We further show how MV-Sketch can be implemented and deployed on P4-based programmable switches subject to hardware deployment constraints. We conduct evaluation in both software and hardware environments. Trace-driven evaluation in software shows that MV-Sketch achieves higher accuracy than existing invertible sketches, with up to $3.38\times $ throughput gain. We also show how to boost the performance of MV-Sketch with SIMD instructions. Furthermore, we evaluate MV-Sketch on a Barefoot Tofino switch and show how MV-Sketch achieves line-rate measurement with limited hardware resource overhead.
Zitterbewegung field and the fifth force

In this paper, we investigate the {\it zitterbewegung region} based on a previous paper where we had derived equations for particles and antiparticles in the aforesaid region. Here, we shall introduce a field theoretic approach resorting to the seminal paper of 1954 by Yang-Mills.
IPOD: Corpus of 190, 000 Industrial Occupations

Job titles are the most fundamental building blocks for occupational data mining tasks, such as Career Modelling and Job Recommendation. However, there are no publicly available dataset to support such efforts. In this work, we present the Industrial and Professional Occupations Dataset (IPOD), which is a comprehensive corpus that consists of over 190,000 job titles crawled from over 56,000 profiles from Linkedin. To the best of our knowledge, IPOD is the first dataset released for industrial occupations mining. We use a knowledge-based approach for sequence tagging, creating a gazzetteer with domain-specific named entities tagged by 3 experts. All title NE tags are populated by the gazetteer using BIOES scheme. Finally, We develop 4 baseline models for the dataset on NER task with several models, including Linear Regression, CRF, LSTM and the state-of-the-art bi-directional LSTM-CRF. Both CRF and LSTM-CRF outperform human in both exact-match accuracy and f1 scores.
Gauging defects in quantum spin systems: A case study

The goal of this work is to build a dynamical theory of defects for quantum spin systems. A kinematic theory for an indefinite number of defects is first introduced exploiting distinguishable Fock space. Dynamics are then incorporated by allowing the defects to become mobile via a microscopic Hamiltonian. This construction is extended to topologically ordered systems by restricting to the ground state eigenspace of Hamiltonians generalizing the golden chain. We illustrate the construction with the example of a spin chain with $\mathbf{Vec}(\mathbb{Z}/2\mathbb{Z})$ fusion rules, employing generalized tube algebra techniques to model the defects in the chain. The resulting dynamical defect model is equivalent to the critical transverse Ising model.