Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Ultrasound Tongue Contour Extraction Using U-Net and Shape Consistency-Based Regularizer

B-mode ultrasound tongue imaging is widely used to visualize the tongue motion, due to its appearing properties. Extracting the tongue surface contour in the B-mode ultrasound image is still a challenge, while it is a prerequisite for further quantitative analysis. Recently, deep learning-based approach has been adopted in this task. However, the standard deep models fail to address faint contour when the ultrasound wave goes parallel to the tongue surface. To address the faint or missing contours in the sequence, we explore the shape consistency-based regularizer, which can take sequential information into account. By incorporating the regularizer, the deep model not only can extract frame-specific contours, but also can enforce the similarity between the contours extracted from adjacent frames. Extensive experiments are conducted both on the synthetic and real ultrasound tongue imaging dataset and the results demonstrate the effectiveness of proposed method. To better promote the research in this field, we have released our code at1.
Inferring High-Resolutional Urban Flow With Internet Of Mobile Things

Monitoring urban flow timely and accurately is crucial for many industrial applications – from urban planning to traffic control in the smart cities. This work introduces a new method for inferring fine-grained urban flow with the internet of mobile things such as taxis and bikes. We tackle the problem from a new perspective and present a novel deep learning method UrbanODE (Urban flow inference with Neural Ordinary Differential Equations). Furthermore, UrbanODE provides a flexible balance between flow inference accuracy and computational efficiency, which is important in computation restricted scenarios such as pervasive edge computing. Extensive evaluations on real-world traffic flow data demonstrate the superiority of the proposed method.
Safe Screening for Sparse Regression with the Kullback-Leibler Divergence

Safe screening rules are powerful tools to accelerate iterative solvers in sparse regression problems. They allow early identification of inactive coordinates (i.e., those not belonging to the support of the solution) which can thus be screened out in the course of iterations. In this paper, we extend the GAP Safe screening rule to the ℓ1-regularized Kullback-Leibler divergence which does not fulfil the regularity assumptions made in previous works. The proposed approach is experimentally validated on synthetic and real count data sets.
A Parallel Algorithm for Phase Retrieval with Dictionary Learning

We propose a new formulation for the joint phase retrieval and dictionary learning problem with a reduced number of regularization parameters to be tuned. A parallel algorithm based on the block successive convex approximation framework is developed for the proposed formulation. The performance of the algorithm is evaluated when applied to sparse channel estimation in a multi-antenna random access network. Simulation results on synthetic data show the efficiency of the proposed technique compared to the state-of-the-art method.
Abstraction in Data Integration

Data integration provides a unified and abstract view over a set of existing data sources. The typical architecture of a data integration system comprises the global schema, which is the structure for the unified view, the source schema, and the mapping, which is a formal account of how data at the sources relate to the global view. Most of the research work on data integration in the last decades deals with the problem of processing a query expressed on the global schema by computing a suitable query over the sources, and then evaluating the latter in order to derive the answers to the original query. Here, we address a novel issue in data integration: starting from a query expressed over the sources, the goal is to find an abstraction of such query, i.e., a query over the global schema that captures the original query, modulo the mapping. The goal of the paper is to provide an overview of the notion of abstraction in data integration, by presenting a formal framework, illustrating the results that have appeared in the recent literature, and discussing interesting directions for future research.
Constructions of Binary Locally Repairable Codes With Multiple Recovering Sets

Locally repairable codes (LRCs) with multiple recovering sets are highly demanded in distributed storage systems. In this letter, we generalize the construction of WZL code proposed by Wang et al. and give a construction of optimal binary LRCs with multiple disjoint recovering sets which can reach the upper bound on the code rate given by Kadhe et al. Then we further generalize our idea to obtain a construction of binary LRCs with intersect recovering sets. The code rate is much higher than that of WZL code and is very closed to the construction of Kruglik et al. Moreover, two special cases of this construction can reach the upper bound on the minimum distance.
A Novel end-to-end Speech Emotion Recognition Network with Stacked Transformer Layers

Speech emotion recognition (SER) aims to automatically recognize emotional category for a given speech utterance. The performance of a SER system heavily relies on the effectiveness of global representation expressed at utterance level. To effectively extract such a global feature, the mainstream of recent SER architectures adopts a pipeline with two key modules, feature extraction and aggregation. Although variant module designs have brought impressive progresses, SER is still a challenging task. In contrast with those previous works, herein we propose a novel strategy for global SER feature extraction by applying an additional enhancement module on top of the current SER pipeline. To verify its effect, an end-to-end SER architecture is proposed where stacked multiple transformer layers are explored to enhance the aggregated global feature. Such an architecture is evaluated on IEMO-CAP and results strongly substantiate the effectiveness of our proposal. In terms of weighted accuracy on four emotion categories, our proposed SER system outperforms the prior arts by a large margin of relatively 20% improvement. Our codes and the pre-trained SER models are made publicly available.
Optimizing Task Allocation for Edge Micro-Clusters in Smart Cities

Current urban technology trends like Internet-of-Things and 5G require ultra low latency compute resource to be distributed liberally at the network edge. We characterize and advocate the need for heterogeneous edge micro-clusters; these are pragmatic, low-power, low-cost, minimal footprint units that can provide sufficient resource for typical edge compute applications in smart cities. However, to make best use of heterogeneous edge micro-clusters, we require resource management techniques that are both efficient and effective. In this paper, we report on an empirical study to demonstrate that mathematical optimization (in particular, mixed integer programming) for resource management is appropriate in terms of overhead, also highly effective for executing batch-arrival workloads in smart city use cases.
More: A Metric Learning Based Framework for Open-Domain Relation Extraction

Open relation extraction (OpenRE) is the task of extracting relation schemes from open-domain corpora. Most existing OpenRE methods either do not fully benefit from high-quality labeled corpora or can not learn semantic representation directly, affecting downstream clustering efficiency. To address these problems, in this work, we propose a novel learning framework named MORE (Metric learning-based Open Relation Extraction). The framework utilizes deep metric learning to obtain rich supervision signals from labeled data and drive the neural model to learn semantic relational representation directly. Experiments result in two real-world datasets show that our method outperforms other state-of-the-art baselines. Our source code is available on Github 1.
Beam Focusing for Multi-User MIMO Communications with Dynamic Metasurface Antennas

Recently, dynamic metasurface antennas (DMAs) have emerged as a promising technology for realizing massive multiple-input multiple-output (MIMO) wireless systems. The usage of large arrays, jointly with higher transmitted frequencies, often results in the communicating devices operating in the near-field (Fresnel) region, thus requiring different considerations compared to traditional systems, assumed to operate in the far-field regime. In this paper, we study the potential of beam focusing, feasible in near-field operation, for multi-user MIMO systems, where the base station is equipped with a DMA. We introduce a mathematical model for DMA-based near-field MIMO communications. Then, we characterize the sum-rate maximization problem of the considered system, and propose an efficient solution to jointly design the DMA weights and digital precoding vector. Simulation results show that our design generates focused beams such that users residing at the same angular direction can communicate reliably without interfering, which is not achievable using conventional far-field beam steering.
Complexity Lower Bounds from Algorithm Design

Since the beginning of the theory of computation, researchers have been fascinated by the prospect of proving impossibility results on computing. When and how can we argue that a task cannot be efficiently solved, no matter what algorithm we try to use? In this short article, I will briefly introduce some of the ideas behind a research program in computational complexity that I and others have studied, for the last decade. (The accompanying talk will contain more details.) The program begins with the observations that:(a) Computer scientists know a great deal about how to design efficient algorithms. (b) However, we do not know how to prove many weak-looking complexity lower bounds.It turns out that certain knowledge we have from (a) can be leveraged to prove complexity lower bounds in a systematic way, making progress on (b). For example, progress on faster circuit satisfiability algorithms (even those that barely improve upon exhaustive search) automatically imply circuit complexity lower bounds for interesting functions.1
Numerical Simulation of Anisotropic Tissue Growth Using a Total Lagrangian Formulation

This paper describes a new method for simulating tissue growth which can handle anisotropic changes in volume. The method takes advantage of the total Lagrangian formulation which allows the computation of nodal forces for each element in a finite element mesh based on a theoretical stress-free configuration, obtained by considering the unconstrained anisotropic growth of the considered element. The method allows the modelling of shrinking (atrophy), swelling, or tissue growth and the computation of the resulting mechanical stresses in the surrounding tissue. The steady-state solution is obtained using an explicit integration method and dynamic relaxation. The proposed method allows the coupling of continuum mechanical simulations with underlying growth mechanisms, offering a tool for the multiscale study of tissue growth.
A comparative study for adsorption of carbolic acid by synthetic resins

Carbolic Acid which is called phenol is one of the important starting and/or intermediate materials in various industrial processes. However, its excessive release into environment poses a threat to living organisms, as it is a highly carcinogens and hazardous pollutant even at the very low concentration. Thus removal of phenol from polluted environments is very crucial for sustainable remediation process. We developed a low cost adsorption method for separating phenol from a model aqueous solution. The phenol adsorption was studied using two adsorbents i.e., Amber lite XAD-16 and Amber lite XAD-7 HP with a constant amount of resin 0.1 g at varying aqueous phenol concentrations () at room temperature. We compared the efficacy of two phenol adsorbents for removing higher phenol concentrations from the media. We investigated equilibrium and kinetics studies of phenol adsorption employing Freundlich, Temkin and Langmuir isotherms. Amberlite XAD-16 performed better than Amberlite XAD-7 HP in terms of phenol removal efficiency that amounted to 95.52%. Pseudo second order model was highly fitted for both of the adsorption systems. The coefficient of determination () with Langmuir isotherm was found to be 0.98 for Amberlite XAD-7 HP. However, Freundlich isotherm showed value of 0.95 for Amberlite XAD-16, indicating that both isotherms could be described for the isotherms on XAD-7 HP and Amberlite XAD-16, respectively.
Phase Relations of Earth’s Core-Forming Materials

Recent updates on phase relations of Earth’s core-forming materials, Fe alloys, as a function of pressure (P), temperature (T), and composition (X) are reviewed for the Fe, Fe-Ni, Fe-O, Fe-Si, Fe-S, Fe-C, Fe-H, Fe-Ni-Si, and Fe-Si-O systems. Thermodynamic models for these systems are highlighted where available, starting with 1 bar to high-P-T conditions. For the Fe and binary systems, the longitudinal wave velocity and density of liquid alloys are discussed and compared with the seismological observations on Earth’s outer core. This review may serve as a guide for future research on the planetary cores.
Running Head: ANOTHER PERSPECTIVE ON ANOSOGNOSIA

1 Institute of Psychiatry, King’s College London, UK. 2 Department of Psychology, University of Cape Town, South Africa. 3 Department of Rehabilitation, Sacro Cuore Hospital, Negrar, Verona, Italy Department of Philosophy, Pedagogy and Psychology University of Verona, Italy 5 Research Department of Clinical, Educational and Heath Psychology, University College London, UK. Correspondence concerning this article should be addressed to:
DLSTM-Based Successive Cancellation Flipping Decoder for Short Polar Codes

Polar code has been adopted as the control channel coding scheme for the fifth generation (5G), and the performance of short polar codes is receiving intensive attention. The successive cancellation flipping (SC flipping) algorithm suffers a significant performance loss in short block lengths. To address this issue, we propose a double long short-term memory (DLSTM) neural network to locate the first error bit. To enhance the prediction accuracy of the DLSTM network, all frozen bits are clipped in the output layer. Then, Gaussian approximation is applied to measure the channel reliability and rank the flipping set to choose the least reliable position for multi-bit flipping. To be robust under different codewords, padding and masking strategies aid the network architecture to be compatible with multiple block lengths. Numerical results indicate that the error-correction performance of the proposed algorithm is competitive with that of the CA-SCL algorithm. It has better performance than the machine learning-based multi-bit flipping SC (ML-MSCF) decoder and the dynamic SC flipping (DSCF) decoder for short polar codes.
Exploration and Explanation: An Interactive Course Recommendation System for University Environments

The abundance of courses available in university and the highly personalized curriculum is often overwhelming for students who must select courses relevant to their academic interests. A large body of research in course recommendation systems focuses on optimizing prediction and improving accuracy. However, those systems usually afford little or no user interaction, and little is known about the influence of user-perceived aspects for course recommendations, such as transparency, controllability, and user satisfaction. In this paper, we argue that involving students in the course recommendation process is important, and we present an interactive course recommendation system that provides explanations and allows students to explore courses in a personalized way. A within-subject user study was conducted to evaluate our system and the results show a significant improvement in many user-centric metrics.
3D Multimodal Visualization for Neurology Screenings

This thesis is devoted to design and implementation of medical imaging viewer for virtual reality. The application is implemented in Unity3D framework with Google Cardboard API and its external part is implemented as a scripted extension for 3D Slicer. Key features are volume rendering, multi-volume rendering (e.g. MRI, CT), iEEG data visualization, multi-planar reconstruction of volumetric modalities, basic 3D model manipulations, data retrieval from 3D Slicer and basic GUI for virtual reality. The application was tested on Android phone and inside of Unity3D Editor with Google Cardboard simulation mode.
Cell Immunity Mechanism of Action of C5aR Signaling in T Gene Knockin Mouse: Implications for the C5aR Expression in a Novel GFP Reporter

C5aR is a G protein-coupled receptor for the anaphylatoxin C5a and mediates many proinﬂammatory reactions. C5aR signaling also has been shown to regulate T cell immunity, but its sites and mechanism of action in this process remain uncertain. In this study, we created a GFP knockin mouse and used GFP as a surrogate marker to examine C5aR expression. GFP was knocked into the 3 9 untranslated region of C5ar1 by gene targeting. We show that GFP is expressed highly on Gr-1 + CD11b + cells in the blood, spleen, and bone marrow and moderately on CD11b + F4/80 + circulating leukocytes and elicited peritoneal macrophages. No GFP is detected on resting or activated T lymphocytes or on splenic myeloid or plasmacytoid dendritic cells. In contrast, 5–25% cultured bone marrow-derived dendritic cells expressed GFP. Interestingly, GFP knockin prevented cell surface but not intracellular C5aR expression. We conclude that C5aR is unlikely to play an intrinsic role on murine T cells and primary dendritic cells. Instead, its effect on T cell immunity in vivo may involve CD11b + F4/80 + or other C5aR-expressing leukocytes. Further, our data reveal a surprising role for the 3 9 -untranslated region of C5aR mRNA in regulating C5aR protein targeting to the plasma membrane. The Journal of Immunology , 2012, 188: 4032–4042. BM-derived macrophages were generated as described previously (40). Brieﬂy, BM cells isolated as described above and plated in RPMI 1640 containing 10 mM HEPES, 10% FBS, and 15% (v/v) of L-cell conditioned media, which was produced according to previous protocols (39). Cells were collected at day 7 and stained for ﬂow cytometry. BMDCs were generated as described previously (40). BM cells were cultured in RPMI 1640 (10% FCS, 2 mM L -glutamine, 50 m M 2-ME, 100 U penicillin/streptomycin, 20 ng/ml GM-CSF, and 10 ng/ml IL-4). Medium was changed every other day. On day 6, DCs were stimulated with LPS at a concentration of 1 m g/ml, and cells were collected 24 h later for ﬂow cytometry analysis.
Hypoxia and Glucose Metabolism in Malignant Tumors: Evaluation by [F]Fluoromisonidazole and [F]Fluorodeoxyglucose Positron Emission Tomography Imaging

Purpose: The aim of this study is to compare glucose metabolism and hypoxia in four different tumor types using positron emission tomography (PET). F-labeled fluorodeoxyglucose (FDG) evaluates energy metabolism, whereas the uptake of F-labeled fluoromisonidazole (FMISO) is proportional to tissue hypoxia. Although acute hypoxia results in accelerated glycolysis, cellular metabolism is slowed in chronic hypoxia, prompting us to look for discordance between FMISO and FDG uptake. Experimental Design: Forty-nine patients (26 with head and neck cancer, 11 with soft tissue sarcoma, 7 with breast cancer, and 5 with glioblastoma multiforme) who had both FMISO and FDG PET scans as part of research protocols through February 2003 were included in this study. The maximum standardized uptake value was used to depict FDG uptake, and hypoxic volume and maximum tissue: blood ratio were used to quantify hypoxia. Pixel-by-pixel correlation of radiotracer uptake was performed on coregistered images for each corresponding tumor plane. Results: Hypoxia was detected in all four patient groups. The mean correlation coefficients between FMISO and FDG uptake were 0.62 for head and neck cancer, 0.47 for breast cancer, 0.38 for glioblastoma multiforme, and 0.32 for soft tissue sarcoma. The correlation between the overall tumor maximum standardized uptake value for FDG and hypoxic volume was small (Spearman r 0.24), with highly significant differences among the different tumor types (P < 0.005). Conclusions: Hypoxia is a general factor affecting glucose metabolism; however, some hypoxic tumors can have modest glucose metabolism, whereas some highly metabolic tumors are not hypoxic, showing discordance in tracer uptake that can be tumor type specific.
A Real Time Microscopy Study of the Growth of Giant Au Microplates

A simple air-thermolysis of a precursor, (AuCl4)− phase transferred from aqueous medium and stabilized in toluene by tetraoctylammonium bromide (ToABr), is found to yield unusually large (∼12 000 μm2) hexagonal and triangular Au microplates. The optimal conditions for the growth of microplates with edge lengths over 100 μm are (AuCl4)−/ToABr ratio of 1:4.4 for phase transfer and thermolysis on a flat substrate such as Si at 130 °C for 24 h. Scanning electron microscopy performed using a heated stage showed nucleation of nanoparticles into dendrimeric structures and rudimentary shapes, which took several minutes to anneal into well-formed single crystalline triangles and hexagons. Amazingly quite often the plates were held vertical to the substrate surface during the growth even at large sizes, as evidenced by in situ optical microscopy. Three growth modes, namely, nucleation of tiny nanoparticles into dendrimeric structures which assemble into specific shapes, atom-by-atom addition from the precursor feed.
Use of Explainable AI to Refine Artificial Immune System Algorithms

Artificial Immune System (AIS) algorithms offer distributed Artificial Intelligence (AI) that can be effectively used to solve real-world data segregation problems. Current unmet needs include (i) refining the AIS algorithm using representative patterns based on deterministic features of the data and (ii) interpretability of the AIS based classifiers. Model agnostic eXplainable AI (XAI) approaches may cater to both of these unmet needs. Here, we propose building an AIS based classifier pipeline using XAI algorithms to identify deterministic feature patterns from data that can be used to improve the performance and interpretability of AIS classifiers.
Model Performance Approximation in Hyper-parameter Optimization

Automatic hyper-parameter optimization aims to tune hyper-parameters of machine learning algorithms without human effort. Due to necessity to learn a model to evaluate a configuration, optimization methods that avoid excessive amount of evaluations are desired for the task. A useful technique is to employ a surrogate model which approximates performance of the trained model with given configuration. This thesis reviews some of the approaches that are being used for the hyper-parameter optimization. The described methods include two traditional methods: grid search and random search as a baseline, and two state-of-the-art techniques: sequential model-based optimization (Bayesian optimization) and Hyperband. Several surrogate models that can be used to improve the optimization are described. The performance of the methods and the surrogate models is compared using two datasets of different complexity and a feed-forward artificial neural network as the machine learning algorithm. On both tasks, Hyperband outperforms the other methods. The analysis also confirms that the surrogate models positively bias the search to promising regions and, thus, speed up the optimization.