Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Distributed Average Consensus Algorithms for Robust IoT networks

Internet of Things(IoT) is a heterogeneous network consists of various physical objects such as large number of sensors, actuators, RFID tags, smart devices, and servers connected to the internet. IoT networks have potential applications in healthcare, transportation, smart home, and automotive industries. To realize the IoT applications, all these devices need to be dynamically cooperated and utilize their resources effectively in a distributed fashion. Consensus algorithms have attracted much research attention in recent years due to their simple execution, robustness to topology changes, and distributed philosophy. These algorithms are extensively utilized for synchronization, resource allocation, and security in IoT networks. Performance of the distributed consensus algorithms can be effectively quantified by the Convergence Time, Network Coherence, Maximum Communication Time-Delay. In this work, we model the IoT network as a q-triangular r-regular ring network as q-triangular topologies exhibit both small-world and scale-free features. Scale-free and small-world topologies widely applied for modelling IoT as these topologies are effectively resilient to random attacks. In this paper, we derive explicit expressions for all eigenvalues of Laplacian matrix for q-triangular r-regular networks. We then apply the obtained eigenvalues to determine the convergence time, network coherence, and maximum communication timedelay. Our analytical results indicate that the effects of noise and communication delay on the consensus process are negligible for q-triangular r-regular networks. We argue that q-triangulation operation is responsible for the strong robustness with respect to noise and communication time-delay in the proposed network topologies.
Novel Aficionados and Doppelgängers: a referential task for semantic representations of individual entities

In human semantic cognition, proper names (names which refer to individual entities) are harder to learn and retrieve than common nouns. This seems to be the case for machine learning algorithms too, but the linguistic and distributional reasons for this behaviour have not been investigated in depth so far. To tackle this issue, we show that the semantic distinction between proper names and common nouns is reflected in their linguistic distributions by employing an original task for distributional semantics, the Doppelg\"anger test, an extensive set of models, and a new dataset, the Novel Aficionados dataset. The results indicate that the distributional representations of different individual entities are less clearly distinguishable from each other than those of common nouns, an outcome which intriguingly mirrors human cognition.
Turning Federated Learning Systems Into Covert Channels

Federated learning (FL) goes beyond traditional, centralized machine learning by distributing model training among a large collection of edge clients. These clients cooperatively train a global, e.g., cloud-hosted, model without disclosing their local, private training data. The global model is then shared among all the participants which use it for local predictions. This paper proves that FL systems can be turned into covert channels to implement a stealth communication infrastructure. The main intuition is that, during federated training, a malicious sender can poison the global model by submitting purposely crafted examples. Although the effect of the model poisoning is negligible to other participants and does not alter the overall model performance, it can be observed by a malicious receiver and used to transmit a sequence of bits. We mounted our attack on an FL system to verify its feasibility. Experimental evidence shows that this covert channel is reliable, efficient, and extremely hard to counter. These results highlight that our new attacker model threatens FL infrastructures.
Superpixels and Graph Convolutional Neural Networks for Efficient Detection of Nutrient Deficiency Stress from Aerial Imagery

Advances in remote sensing technology have led to the capture of massive amounts of data. Increased image resolution, more frequent revisit times, and additional spectral channels have created an explosion in the amount of data that is available to provide analyses and intelligence across domains, including agriculture. However, the processing of this data comes with a cost in terms of computation time and money, both of which must be considered when the goal of an algorithm is to provide real-time intelligence to improve efficiencies. Specifically, we seek to identify nutrient deficient areas from remotely sensed data to alert farmers to regions that require attention; detection of nutrient deficient areas is a key task in precision agriculture as farmers must quickly respond to struggling areas to protect their harvests. Past methods have focused on pixel-level classification (i.e. semantic segmentation) of the field to achieve these tasks, often using deep learning models with tens-of-millions of parameters. In contrast, we propose a much lighter graph-based method to perform node-based classification. We first use Simple Linear Iterative Cluster (SLIC) to produce superpixels across the field. Then, to perform segmentation across the non-Euclidean domain of superpixels, we lever-age a Graph Convolutional Neural Network (GCN). This model has 4-orders-of-magnitude fewer parameters than a CNN model and trains in a matter of minutes.
Skimming and Scanning for Untrimmed Video Action Recognition

Video action recognition (VAR) is a primary task of video understanding, and untrimmed videos are more common in real-life scenes. Untrimmed videos have redundant and diverse clips containing contextual information, so sampling dense clips is essential. Recently, some works attempt to train a generic model to select the N most representative clips. However, it is difficult to model the complex relations from intra-class clips and inter-class videos within a single model and fixed selected number, and the entanglement of multiple relations is also hard to explain. Thus, instead of"only look once", we argue"divide and conquer"strategy will be more suitable in untrimmed VAR. Inspired by the speed reading mechanism, we propose a simple yet effective clip-level solution based on skim-scan techniques. Specifically, the proposed Skim-Scan framework first skims the entire video and drops those uninformative and misleading clips. For the remaining clips, it scans clips with diverse features gradually to drop redundant clips but cover essential content. The above strategies can adaptively select the necessary clips according to the difficulty of the different videos. To trade off the computational complexity and performance, we observe the similar statistical expression between lightweight and heavy networks, thus it supports us to explore the combination of them. Comprehensive experiments are performed on ActivityNet and mini-FCVID datasets, and results demonstrate that our solution surpasses the state-of-the-art performance in terms of both accuracy and efficiency.
Voice2Mesh: Cross-Modal 3D Face Model Generation from Voices

This work focuses on the analysis that whether 3D face models can be learned from only the speech inputs of speakers. Previous works for cross-modal face synthesis study image generation from voices. However, image synthesis includes variations such as hairstyles, backgrounds, and facial textures, that are arguably irrelevant to voice or without direct studies to show correlations. We instead investigate the ability to reconstruct 3D faces to concentrate on only geometry, which is more physiologically grounded. We propose both the supervised learning and unsupervised learning frameworks. Especially we demonstrate how unsupervised learning is possible in the absence of a direct voice-to-3D-face dataset under limited availability of 3D face scans when the model is equipped with knowledge distillation. To evaluate the performance, we also propose several metrics to measure the geometric fitness of two 3D faces based on points, lines, and regions. We find that 3D face shapes can be reconstructed from voices. Experimental results suggest that 3D faces can be reconstructed from voices, and our method can improve the performance over the baseline. The best performance gains (15% - 20%) on ear-to-ear distance ratio metric (ER) coincides with the intuition that one can roughly envision whether a speaker's face is overall wider or thinner only from a person's voice. See our project page for codes and data.
Modeling Classroom Occupancy Using Data of WiFi Infrastructure in a University Campus

Universities worldwide are experiencing a surge in enrollments, therefore campus estate managers are seeking continuous data on attendance patterns to optimize the usage of classroom space. As a result, there is an increasing trend to measure classroom attendance by employing various sensing technologies, among which pervasive WiFi infrastructure is seen as a low-cost method. In a dense campus environment, the number of connected WiFi users does not well estimate room occupancy since connection counts are polluted by adjoining rooms, outdoor walkways, and network load balancing. This paper develops machine learning-based models, including unsupervised clustering and a combination of classification and regression algorithms, to infer classroom occupancy from WiFi sensing infrastructure. Our contributions are three-fold: (1) We analyze metadata from a dense and dynamic wireless network comprising of thousands of access points (APs) to draw insights into coverage of APs, the behavior of WiFi-connected users, and challenges of estimating room occupancy; (2) We propose a method to automatically map APs to classrooms and evaluate K-means, Expectation-Maximization (EM-GMM) and Hierarchical Clustering (HC) algorithms; and (3) We model classroom occupancy and evaluate varying algorithms, namely Logistic Regression, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Linear Regression (LR) and Support Vector Regression (SVR). We achieve 84.6% accuracy in mapping APs to classrooms, while our estimation for room occupancy (with symmetric Mean Absolute Percentage Error (sMAPE) of 13.10%) is comparable to beam counter sensors.
Sattiy at SemEval-2021 Task 9: An Ensemble Solution for Statement Verification and Evidence Finding with Tables

Question answering from semi-structured tables can be seen as a semantic parsing task and is significant and practical for pushing the boundary of natural language understanding. Existing research mainly focuses on understanding contents from unstructured evidence, e.g., news, natural language sentences and documents. The task of verification from structured evidence, such as tables, charts, and databases, is still less-explored. This paper describes sattiy team’s system in SemEval-2021 task 9: Statement Verification and Evidence Finding with Tables (SEM-TAB-FACT)(CITATION). This competition aims to verify statements and to find evidence from tables for scientific articles and to promote proper interpretation of the surrounding article. In this paper we exploited ensemble models of pre-trained language models over tables, TaPas and TaBERT, for Task A and adjust the result based on some rules extracted for Task B. Finally, in the leadboard, we attain the F1 scores of 0.8496 and 0.7732 in Task A for the 2-way and 3-way evaluation, respectively, and the F1 score of 0.4856 in Task B.
Voxel Structure-Based Mesh Reconstruction From a 3D Point Cloud

Mesh reconstruction from a 3D point cloud is an important topic in the fields of computer graphic, computer vision, and multimedia analysis. In this paper, we propose a voxel structure-based mesh reconstruction framework. It provides the intrinsic metric to improve the accuracy of local region detection. Based on the detected local regions, an initial reconstructed mesh can be obtained. With the mesh optimization in our framework, the initial reconstructed mesh is optimized into an isotropic one with the important geometric features such as external and internal edges. The experimental results indicate that our framework shows great advantages over peer ones in terms of mesh quality, geometric feature keeping, and processing speed. The source code of the proposed method is publicly available1.
Efficient Sparse Coding Using Hierarchical Riemannian Pursuit

Sparse coding is a class of unsupervised methods for learning a sparse representation of the input data in the form of a linear combination of a dictionary and a sparse code. This learning framework has led to state-of-the-art results in various signal processing tasks. However, classical methods learn the dictionary and the sparse code based on alternating optimizations, usually without theoretical guarantees for either optimality or convergence due to the non-convexity of the problem. Recent works on sparse coding with a complete dictionary provide strong theoretical guarantees thanks to the development of non-convex optimization. However, initial non-convex approaches learned the dictionary in the sparse coding problem sequentially in an atom-by-atom manner, which led to a long execution time. More recent works have sought to directly learn the entire dictionary at once, which substantially reduces the execution time. However, the associated recovery performance is degraded with a finite number of data samples. In this paper, we propose an efficient sparse coding scheme with a two-stage optimization. The proposed scheme leverages the global and local Riemannian geometry of the two-stage optimization problem and facilitates fast implementation for superb dictionary recovery performance by a finite number of samples. We further prove that, with high probability, the proposed scheme can exactly recover any atom in the target dictionary with a finite number of samples. Experiments on both synthetic and real-world data verify the efficiency and robustness of the proposed scheme.1
Improving Biomedical Pretrained Language Models with Knowledge

Pretrained language models have shown success in many natural language processing tasks. Many works explore to incorporate the knowledge into the language models. In the biomedical domain, experts have taken decades of effort on building large-scale knowledge bases. For example, UMLS contains millions of entities with their synonyms and defines hundreds of relations among entities. Leveraging this knowledge can benefit a variety of downstream tasks such as named entity recognition and relation extraction. To this end, we propose KeBioLM, a biomedical pretrained language model that explicitly leverages knowledge from the UMLS knowledge bases. Specifically, we extract entities from PubMed abstracts and link them to UMLS. We then train a knowledge-aware language model that firstly applies a text-only encoding layer to learn entity representation and then applies a text-entity fusion encoding to aggregate entity representation. In addition, we add two training objectives as entity detection and entity linking. Experiments on the named entity recognition and relation extraction tasks from the BLURB benchmark demonstrate the effectiveness of our approach. Further analysis on a collected probing dataset shows that our model has better ability to model medical knowledge.
Optical and ultraviolet monitoring of the black hole X-ray binary MAXI J1820+070/ASASSN-18ey for 18 months

MAXI J1820+070 is a low-mass black hole X-ray binary system with high luminosity in both optical and X-ray bands during the outburst periods. We present extensive photometry in X-ray, ultraviolet, and optical bands, as well as densely-cadenced optical spectra, covering the phase from the beginning of optical outburst to ∼550 days. During the rebrightening process, the optical emission preceded the X-ray by 20.80 ± 2.85 days. The spectra are characterized by blue continua and emission features of Balmer series, He i, He ii lines and broad Bowen blend. The pseudo equivalent width (pEW) of emission lines are found to show anticorrelations with the X-ray flux measured at comparable phases, which is due to the increased suppression by the optical continuum. At around the X-ray peak, the full width at half maximums (FWHMs) of Hβ and He ii λ4686 tend to stabilize at 19.4 Angstrom and 21.8 Angstrom, which corresponds to the line forming region at a radius of 1.7 and 1.3 R within the disk. We further analyzed the absolute fluxes of the lines and found that the fluxes of Hβ and He ii λ4686 show positive correlations with the X-ray flux, favoring that the irradiation model is responsible for the optical emission. However, the fact that X-ray emission experiences a dramatic flux drop at t∼200 days after the outburst, while the optical flux only shows little variations suggests that additional energy such as viscous energy may contribute to the optical radiation in addition to the X-ray irradiation.
Crohn’s disease with infliximab treatment complicated by rapidly progressing colorectal cancer: A case report

BACKGROUND Crohn's disease (CD) causes a range of digestive symptoms including recurrent diarrhea, abdominalgia, and flatulence, and severely impacts the quality of life of patients. Infliximab, a monoclonal antibody against tumor necrosis factor alpha, has recently been promoted as a therapeutic treatment for CD, but its safety margins remain uncertain. We report a case of rapidly progressive colorectal cancer that was diagnosed in a patient with CD who had previously been treated with infliximab. CASE SUMMARY This case report refers to a 40-year-old male with a 6-year history of CD. The patient underwent transverse colostomy because of inflammatory ileus in 2017. He subsequently received infliximab treatment in 2018. Ten months later, worsening contracture of the transverse colostomy was observed. Imaging tests indicated that the patient may have developed colon cancer with extensive peritoneal implantation. At the same time, colonoscopy revealed a rectal mass and pathological examination indicated well-differentiated adenocarcinoma. Palliative ileostomy was performed to improve defecation in 2019. During the operation, a small nodular mass in the mesentery of the small intestine was identified and pathological examination of the mass revealed advanced adenocarcinoma. The patient was diagnosed with advanced colorectal cancer and administered palliative chemotherapy. He died in June 2020. CONCLUSION We stress the importance of recognizing the possible occurrence of malignance in patients with CD receiving infliximab.
A Synthetic Curcuminoid Analogue, 2,6-Bis-4-(Hydroxyl-3-Methoxybenzylidine)-Cyclohexanone (BHMC) Ameliorates Acute Airway Inflammation of Allergic Asthma in Ovalbumin-Sensitized Mice

2,6-Bis-(4-hydroxyl-3-methoxybenzylidine) cyclohexanone (BHMC), a synthetic curcuminoid analogue, has been shown to exhibit anti-inflammatory properties in cellular models of inflammation and improve the survival of mice from lethal sepsis. We further evaluated the therapeutic effect of BHMC on acute airway inflammation in a mouse model of allergic asthma. Mice were sensitized and challenged with ovalbumin (OVA), followed by intraperitoneal administration of 0.1, 1, and 10 mg/kg of BHMC. Bronchoalveolar lavage fluid, blood, and lung samples were collected, and the respiratory function was measured. OVA sensitization and challenge increased airway hyperresponsiveness (AHR) and pulmonary inflammation. All three doses of BHMC (0.1-10 mg/kg) significantly reduced the number of eosinophils, lymphocytes, macrophages, and neutrophils, as well as the levels of Th2 cytokines (IL-4, IL-5 and IL-13) in bronchoalveolar lavage fluid (BALF) as compared to OVAchallenged mice. However, serum level of IgE was not affected. All three doses of BHMC (0.1-10 mg/kg) were effective in suppressing the infiltration of inflammatory cells at the peribronchial and perivascular regions, with the greatest effect observed at 1 mg/kg which was comparable to dexamethasone. Goblet cell hyperplasia was inhibited by 1 and 10 mg/kg of BHMC, while the lowest dose (0.1 mg/kg) had no significant inhibitory effect. These findings demonstrate that BHMC, a synthetic nonsteroidal small molecule, ameliorates acute airway inflammation associated with allergic asthma, primarily by suppressing the release of inflammatory mediators and goblet cell hyperplasia to a lesser extent in acute airway inflammation of allergic asthma.
Nanotechnology and pancreatic cancer management: State of the art and further perspectives

Pancreatic ductal adenocarcinoma (PDAC) represents a leading cause of cancer death and is often diagnosticated too late to allow adequate treatments. Lots of biomarkers have been discovered in lasts years but, to date, there is a lack of low-cost and non-invasive tools for PDAC early detection. Nonetheless, drugs commonly used in PDAC treatment do not allow achieving long-term satisfying results. Nanotechnology is gaining importance in both PDAC early detection and treatment. The main implications of nanotechnology in cancer diagnosis lay in the ability that nanoparticles have on concentrate the alteration in human proteome caused by cancer. Nanoparticle-enabled blood tests have been demonstrated to reach high rate of sensitivity (up to 85%) and specificity (up to 100%). In the field of cancer therapy nanoparticles can be used as nanocarriers able to reach specific tumour’s cells and selectively release the drug they contain into them. A literature review was carried out with the aim to assess the state of the art and highlight the future perspectives of nanotechnology in PDAC early detection and therapy.
Model for End‐Stage Liver Disease Lactate Score and Prediction of Inpatient Mortality in Critically Ill Patients With Cirrhosis

The burden of decompensated liver disease is high with a significant proportion of cirrhosis patients requiring inpatient hospitalization. Patients with cirrhosis have high inpatient mortality in the intensive care unit (ICU) setting, especially as compared to other chronic conditions. Objective models that predict short-term mortality at the time of ICU admission are needed for assessing response to therapy, transplant candidacy as well as futility of care.
HSGAN: Hierarchical Graph Learning for Point Cloud Generation

Point clouds are the most general data representations of real and abstract objects, and have a wide variety of applications in many science and engineering fields. Point clouds also provide the most scalable multi-resolution composition for geometric structures. Although point cloud learning has shown remarkable results in shape estimation and semantic segmentation, the unsupervised generation of 3D object parts still pose significant challenges in the 3D shape understanding problem. We address this problem by proposing a novel Generative Adversarial Network (GAN), named HSGAN, or Hierarchical Self-Attention GAN, with remarkable properties for 3D shape generation. Our generative model takes a random code and hierarchically transforms it into a representation graph by incorporating both Graph Convolution Network (GCN) and self-attention. With embedding the global graph topology in shape generation, the proposed model takes advantage of the latent topological information to fully construct the geometry of 3D object shapes. Different from the existing generative pipelines, our deep learning architecture articulates three significant properties HSGAN effectively deploys the compact latent topology information as a graph representation in the generative learning process and generates realistic point clouds, HSGAN avoids multiple discriminator updates per generator update, and HSGAN preserves the most dominant geometric structures of 3D shapes in the same hierarchical sampling process. We demonstrate the performance of our new approach with both quantitative and qualitative evaluations. We further present a new adversarial loss to maintain the training stability and overcome the potential mode collapse of traditional GANs. Finally, we explore the use of HSGAN as a plug-and-play decoder in the auto-encoding architecture.