Miracle-dz/UniFAR-FTUs huggingface dataset:
A 3D Mesh-Based Lifting-and-Projection Network for Human Pose Transfer

Human pose transfer has typically been modeled as a 2D image-to-image translation problem. This formulation ignores the human body shape prior in 3D space and inevitably causes implausible artifacts, especially when facing occlusion. To address this issue, we propose a lifting-and-projection framework to perform pose transfer in the 3D mesh space. The core of our framework is a foreground generation module, that consists of two novel networks: a lifting-and-projection network (LPNet) and an appearance detail compensating network (ADCNet). To leverage the human body shape prior, LPNet exploits the topological information of the body mesh to learn an expressive visual representation for the target person in the 3D mesh space. To preserve texture details, ADCNet is further introduced to enhance the feature produced by LPNet with the source foreground image. Such design of the foreground generation module enables the model to better handle difficult cases such as those with occlusions. Experiments on the iPER and Fashion datasets empirically demonstrate that the proposed lifting-and-projection framework is effective and outperforms the existing image-to-image-based and mesh-based methods on human pose transfer task in both self-transfer and cross-transfer settings.
An Improved Frequent Directions Algorithm for Low-Rank Approximation via Block Krylov Iteration

Frequent directions (FDs), as a deterministic matrix sketching technique, have been proposed for tackling low-rank approximation problems. This method has a high degree of accuracy and practicality but experiences a lot of computational cost for large-scale data. Several recent works on the randomized version of FDs greatly improve the computational efficiency but unfortunately sacrifice some precision. To remedy such an issue, this article aims to find a more accurate projection subspace to further improve the efficiency and effectiveness of the existing FDs' techniques. Specifically, by utilizing the power of the block Krylov iteration and random projection technique, this article presents a fast and accurate FDs algorithm named r-BKIFD. The rigorous theoretical analysis shows that the proposed r-BKIFD has a comparable error bound with original FDs, and the approximation error can be arbitrarily small when the number of iterations is chosen appropriately. Extensive experimental results on both synthetic and real data further demonstrate the superiority of r-BKIFD over several popular FDs algorithms both in terms of computational efficiency and accuracy.
Evolutionary Clustering of Streaming Trajectories

The widespread deployment of smartphones and location-enabled, networked in-vehicle devices renders it increasingly feasible to collect streaming trajectory data of moving objects. The continuous clustering of such data can enable a variety of real-time services, such as identifying representative paths or common moving trends among objects in real-time. However, little attention has so far been given to the quality of clusters -- for example, it is beneficial to smooth short-term fluctuations in clusters to achieve robustness to exceptional data. We propose the notion of evolutionary clustering of streaming trajectories, abbreviated ECO, that enhances streaming-trajectory clustering quality by means of temporal smoothing that prevents abrupt changes in clusters across successive timestamps. Employing the notions of snapshot and historical trajectory costs, we formalize ECO and then formulate ECO as an optimization problem and prove that ECO can be performed approximately in linear time, thus eliminating the iterative processes employed in previous studies. Further, we propose a minimal-group structure and a seed point shifting strategy to facilitate temporal smoothing. Finally, we present all algorithms underlying ECO along with a set of optimization techniques. Extensive experiments with two real-life datasets offer insight into ECO and show that it outperforms state-of-the-art solutions in terms of both clustering quality and efficiency.
Indoor Navigation Algorithm Based on a Smartphone Inertial Measurement Unit and Map Matching

We propose an indoor navigation algorithm based on pedestrian dead reckoning (PDR) using an inertial measurement unit in a smartphone and map matching. The proposed indoor navigation system is user-friendly and convenient because it requires no additional device except a smartphone and works with a pedestrian in a casual posture who is walking with a smartphone in their hand. Because the performance of the PDR decreases over time, we greatly reduced the position error of the trajectory estimated by PDR using a map matching method with a known indoor map. To verify the proposed indoor navigation algorithm, we conducted an experiment in a real indoor environment using a commercial Android smartphone. The performance of our algorithm was demonstrated through the results of the experiment.
A dynamic programming algorithm for informative measurements and near-optimal path-planning

An informative measurement is the most efﬁcient way to gain information about an unknown state. We give a ﬁrst-principles derivation of a general-purpose dynamic programming algorithm that returns a sequence of informative measurements by sequentially maximizing the entropy of possible measurement outcomes. This algorithm can be used by an autonomous agent or robot to decide where best to measure next, planning a path corresponding to an optimal sequence of informative measurements. This algorithm is applicable to states and controls that are continuous or discrete, and agent dynamics that is either stochastic or deterministic; including Markov decision processes. Recent results from approximate dynamic programming and reinforcement learning, including on-line approximations such as rollout and Monte Carlo tree search, allow an agent or robot to solve the measurement task in real-time. The resulting near-optimal solutions include non-myopic paths and measurement sequences that can generally outperform, sometimes substantially, commonly-used greedy heuristics such as maximizing the entropy of each measurement outcome. This is demonstrated for a global search problem, where on-line planning with an extended local search is found to reduce the number of measurements in the search by half.
Char2Subword: Extending the Subword Embedding Space Using Robust Character Compositionality

Byte-pair encoding (BPE) is a ubiquitous algorithm in the subword tokenization process of language models as it provides multiple benefits. However, this process is solely based on pre-training data statistics, making it hard for the tokenizer to handle infrequent spellings. On the other hand, though robust to misspellings, pure character-level models often lead to unreasonably long sequences and make it harder for the model to learn meaningful words. To alleviate these challenges, we propose a character-based subword module (char2subword) that learns the subword embedding table in pre-trained models like BERT. Our char2subword module builds representations from characters out of the subword vocabulary, and it can be used as a drop-in replacement of the subword embedding table. The module is robust to character-level alterations such as misspellings, word inflection, casing, and punctuation. We integrate it further with BERT through pre-training while keeping BERT transformer parameters fixed--and thus, providing a practical method. Finally, we show that incorporating our module to mBERT significantly improves the performance on the social media linguistic code-switching evaluation (LinCE) benchmark.
Evaluation Toolkit For Robustness Testing Of Automatic Essay Scoring Systems

Automatic scoring engines have been used for scoring approximately fifteen million test-takers in just the last three years. This number is increasing further due to COVID-19 and the associated automation of education and testing. Yet, the AI-based testing literature of these ‘intelligent‘ models is highly lacking as most of the papers propose new models that rely only on quadratic weighted kappa (QWK) based agreement with human raters for showing model efficacy. However, this effectively ignores the highly multi-feature nature of essay scoring. Essay scoring depends on features like coherence, grammar, relevance, etc. and to date, there has been no study testing Automated Essay Scoring (AES) systems holistically on all these features. With this motivation, we propose a model agnostic adversarial evaluation scheme and associated metrics for AES systems to test their natural language understanding capabilities and overall robustness. We evaluate the current state-of-the-art AES models using the proposed scheme and report the results on five recent models. These models range from feature-engineering-based approaches to the latest deep learning algorithms. We find that AES models are highly overstable such that even heavy modifications (as much as 25%) with content unrelated to the topic of the questions do not decrease the score produced by the models. On the other hand, unrelated content, on average, increases the scores, thus showing that the models’ evaluation strategy and rubrics should be reconsidered. We conduct a human survey with 200 human raters and observe that they can easily detect differences between the original and perturbed responses and have a general disagreement with the scores assigned by auto scorers.
Does priva za on make Brazilian airports more efficient ?

Recebido: 14 de fevereiro de 2020 Aceito para publicação: 5 de fevereiro de 2021 Publicado: 25 de agosto de 2021 Editor de área: Alexandre Gomes de Barros ABSTRACT Airport concessions to private management began in Brazil in 2011 to address infrastructure bo9lenecks and the need for investments. Produc3vity improvements a;er priva3za3on need to be measured and efficiency gains can help to iden3fy strategic points where to intervene both in current and upcoming concessions. However, previous studies have applied neither the Slacks-Based Measure Data Analysis Envelopment models nor the second stage Tobit regression to evaluate airport efficiency, especially in the context of Brazilian airport priva3za3on. The objec3ve of this study is to verify if priva3za3on contributes to improving Brazilian airports' produc3ve efficiency compared with public airports, using variable slacks and iden3fying new socio-economic variables that may influence the produc3ve efficiency index. A two-stage SBM-DEA approach found that only 40% of the sample among 28 airports are efficient, evidencing poor management overall, and the slacks show the sectors that need most a9en3on and investments. Based on the produc3ve process and the sample evaluated, the study concludes that priva3za3on of Brazilian airports did not improve their produc3on efficiency. A revision of the contracts in the first round of concessions is suggested, as contractual obliga3on may result in lower produc3on efficiency.
Training dataset generation for bridge game registration

This paper presents a method for automatic generation of a training dataset for a deep convolutional neural network used for playing card detection. The solution allows to skip the time-consuming processes of manual image collecting and labelling recognised objects. The YOLOv4 network trained on the generated dataset achieved an efficiency of 99.8% in the cards detection task. The proposed method is a part of a project that aims to automate the process of broadcasting duplicate bridge competitions using a vision system and neural networks.
Evaluating of the Effects of Sub-MIC Concentrations of Gentamicin on Biofilm Formation in Clinical Isolates of Pseudomonas aeruginosa

Background & Objective: The ability of Pseudomonas aeruginosa to form biofilm has an important role in establishment of chronic phase of infections. Biofilm formation can be affected by antibiotics sub-MIC concentrations. The principal aim of the present study was to evaluate the effect of gentamicin at sub-MIC concentrations on biofilm formation in 100 Pseudomonas aeruginosa clinical isolates. Methods: Determination of minimal inhibitory concentration of gentamicin for clinical isolates was done using micro broth dilution method. The amount of biofilm formation in the treated and untreated isolates with gentamicin sub-MIC (1/2&1/4MIC) concentrations was evaluated using microtitre plate assay. pelA and pslA genes were detected in clinical isolates by PCR method. Results: 99% of clinical isolates were biofilm producer. Different changes in amount of biofilm formation were observed in the treated clinical isolates with sub-MIC concentrations of gentamicin. Two dominant changes were observed in 80% of clinical isolates. These concentrations had inhibitory effect on biofilm formation in 46.4% of isolates and caused a significant decrease in its amount. While in 31.3% of the isolates, the biofilm formation was significantly increased. The frequency of pelA and pslA genes among clinical isolates was 100%. Conclusion: gentamicin sub-MIC concentrations cause different changes on biofilm formation of Pseudomonas aeruginosa clinical isolates. Therefore, further studies are needed for discovering new treatment strategies and using sub-MIC concentrations of the antibiotic in prevention and treatment of Pseudomonas aeruginosa infections.
An Inflammation-Related Nine-Gene Signature to Improve Prognosis Prediction of Lung Adenocarcinoma

Background A novel predictive model was rarely reported based on inflammation-related genes to explore clinical outcomes of lung adenocarcinoma (LUAD) patients. Methods Using TCGA database, we screened nine inflammation-related genes with a prognostic value, and LASSO regression was applied for model construction. The predictive value of the prognostic signature developed from inflammation-related genes was assessed by survival assays and multivariate assays. PCA and t-SNE analysis were performed to demonstrate clustering abilities of risk scores. Results Thirteen inflammation-related genes (BTG2, CCL20, CD69, DCBLD2, GPC3, IL7R, LAMP3, MMP14, NMUR1, PCDH7, PIK3R5, RNF144B, and TPBG) with prognostic values were finally identified. LASSO regression further screened nine candidates (BTG2, CCL20, CD69, IL7R, MMP14, NMUR1, PCDH7, RNF144B, and TPBG). Then, a prognostic prediction model using the above nine genes was constructed. A reliable clustering ability of risk score was demonstrated by PCA and t-SNE assays in 500 LUAD patients. The survival assays revealed that the overall survivals of the high-risk group were distinctly poorer than those of the low-risk group with 1-, 3-, and 5-year AUC values of 0.695, 0.666, and 0.694, respectively. Finally, multivariate assays demonstrated the scoring system as an independent prognostic factor for overall survival. Conclusions Our study shows that the signature of nine inflammation-related genes can be used as a prognostic marker for LUAD.
Determination of Acidic Dissociation Constants of Chlorthalidone and Terbutaline in Water Using DFT and Ab Initio Methods

I n the present study, pKa values of both drug called Chlorthalidone and Terbutaline were determined in aqueous solution. For this purpose, the B3LYP calculation with the 6-31+G(d) basis set was utilized. The reactions and equilibria that possess a high hydrogen-band-donor capability and constitute the indispensable theoretical basis to calculate the acidity constants of Chlorthalidone and Terbutaline, are shown. To analyze the formation of intermolecular hydrogen bonds between the existent species and water molecules, Tomasi’s method was used. In this way, it was determined that in alkaline aqueous solutions the cation, anion, and neutral species of Chlorthalidone and Terbutaline are solvated with one, two, three, and four molecules of water, respectively. To proceed, the calculated pKa were compared with the experimental values, which there is comparable agreement between them. The resulting data illustrated that the method was likely to be useful for the prediction of pKa values in aqueous solution.
Multiscale Entropy Analysis of Heart Rate Variability in Neonatal Patients with and without Seizures

The complex physiological dynamics of neonatal seizures make their detection challenging. A timely diagnosis and treatment, especially in intensive care units, are essential for a better prognosis and the mitigation of possible adverse effects on the newborn’s neurodevelopment. In the literature, several electroencephalographic (EEG) studies have been proposed for a parametric characterization of seizures or their detection by artificial intelligence techniques. At the same time, other sources than EEG, such as electrocardiography, have been investigated to evaluate the possible impact of neonatal seizures on the cardio-regulatory system. Heart rate variability (HRV) analysis is attracting great interest as a valuable tool in newborns applications, especially where EEG technologies are not easily available. This study investigated whether multiscale HRV entropy indexes could detect abnormal heart rate dynamics in newborns with seizures, especially during ictal events. Furthermore, entropy measures were analyzed to discriminate between newborns with seizures and seizure-free ones. A cohort of 52 patients (33 with seizures) from the Helsinki University Hospital public dataset has been evaluated. Multiscale sample and fuzzy entropy showed significant differences between the two groups (p-value < 0.05, Bonferroni multiple-comparison post hoc correction). Moreover, interictal activity showed significant differences between seizure and seizure-free patients (Mann-Whitney Test: p-value < 0.05). Therefore, our findings suggest that HRV multiscale entropy analysis could be a valuable pre-screening tool for the timely detection of seizure events in newborns.
A Portable Micro-Gas Chromatography with Integrated Photonic Crystal Slab Sensors on Chip

The miniaturization of gas chromatography (GC) systems has made it possible to utilize the analytical technique in various on-site applications to rapidly analyze complex gas samples. Various types of miniaturized sensors have been developed for micro-gas chromatography (µGC). However, the integration of an appropriate detector in µGC systems still faces a significant challenge. We present a solution to the problem through integration of µGC with photonic crystal slab (PCS) sensors using transfer printing technology. This integration offers an opportunity to utilize the advantages of optical sensors, such as high sensitivity and rapid response time, and at the same time, compensate for the lack of detection specificity from which label-free optical sensors suffer. We transfer printed a 2D defect free PCS on a borofloat glass, bonded it to a silicon microfluidic gas cell or directly to a microfabricated GC column, and then coated it with a gas responsive polymer. Realtime spectral shift in Fano resonance of the PCS sensor was used to quantitatively detect analytes over a mass range of three orders. The integrated µGC–PCS system was used to demonstrate separation and detection of a complex mixture of 10 chemicals. Fast separation and detection (4 min) and a low detection limit (ng) was demonstrated.
The “Federica” Hand

Hand prostheses partially restore hand appearance and functionalities. In particular, 3D printers have provided great opportunities by simplifying the manufacturing process and reducing costs. The “Federica” hand is 3D-printed and equipped with a single servomotor, which synergically actuates its five fingers by inextensible tendons; no springs are used for hand opening. A differential mechanical system simultaneously distributes the motor force on each finger in predefined portions. The proportional control of hand closure/opening is achieved by monitoring muscle contraction by means of a thin force sensor, as an alternative to EMG. The electrical current of the servomotor is monitored to provide sensory feedback of the grip force, through a small vibration motor. A simple Arduino board was adopted as the processing unit. A closed-chain, differential mechanism guarantees efficient transfer of mechanical energy and a secure grasp of any object, regardless of its shape and deformability. The force sensor offers some advantages over the EMG: it does not require any electrical contact or signal processing to monitor muscle contraction intensity. The activation speed (about half a second) is high enough to allow the user to grab objects on the fly. The cost of the device is less then 100 USD. The “Federica” hand has proved to be a lightweight, low-cost and extremely efficient prosthesis. It is now available as an open-source project (CAD files and software can be downloaded from a public repository), thus allowing everyone to use the “Federica” hand and customize or improve it.
Recent Advances in Electrochemical Biosensors: Applications, Challenges, and Future Scope

The electrochemical biosensors are a class of biosensors which convert biological information such as analyte concentration that is a biological recognition element (biochemical receptor) into current or voltage. Electrochemical biosensors depict propitious diagnostic technology which can detect biomarkers in body fluids such as sweat, blood, feces, or urine. Combinations of suitable immobilization techniques with effective transducers give rise to an efficient biosensor. They have been employed in the food industry, medical sciences, defense, studying plant biology, etc. While sensing complex structures and entities, a large data is obtained, and it becomes difficult to manually interpret all the data. Machine learning helps in interpreting large sensing data. In the case of biosensors, the presence of impurity affects the performance of the sensor and machine learning helps in removing signals obtained from the contaminants to obtain a high sensitivity. In this review, we discuss different types of biosensors along with their applications and the benefits of machine learning. This is followed by a discussion on the challenges, missing gaps in the knowledge, and solutions in the field of electrochemical biosensors. This review aims to serve as a valuable resource for scientists and engineers entering the interdisciplinary field of electrochemical biosensors. Furthermore, this review provides insight into the type of electrochemical biosensors, their applications, the importance of machine learning (ML) in biosensing, and challenges and future outlook.
Chemically Crosslinked Methylcellulose Substrates for Cell Sheet Engineering

Methylcellulose (MC) hydrogels have been successfully proposed in the field of cell sheet engineering (CSE), allowing cell detachment from their surface by lowering the temperature below their transition temperature (Tt). Among the main limitations of pristine MC hydrogels, low physical stability and mechanical performances limit the breadth of their potential applications. In this study, a crosslinking strategy based on citric acid (CA) was used to prepare thermoresponsive MC hydrogels, with different degrees of crosslinking, to exploit their possible use as substrates in CSE. The investigated amounts of CA did not cause any cytotoxic effect while improving the mechanical performance of the hydrogels (+11-fold increase in E, compared to control MC). The possibility to obtain cell sheets (CSs) was then demonstrated using murine fibroblast cell line (L929 cells). Cells adhered on crosslinked MC hydrogels’ surface in standard culture conditions and then were harvested at selected time points as single CSs. CS detachment was achieved simply by lowering the external temperature below the Tt of MC. The detached CSs displayed adhesive and proliferative activity when transferred to new plastic culture surfaces, indicating a high potential for regenerative purposes.