Miracle-dz/UniFAR-FTUs huggingface dataset:
A Simple and Quantitative Method to Evaluate Ice Recrystallization Kinetics Using the Circle Hough Transform Algorithm

The formation of large ice crystals via recrystallization processes in foods and water-based materials often decreases the quality and structural integrity of the materials. Hence, there is a widespread academic and commercial interest in natural and synthetic ice crystal growth modifiers that inhibit the recrystallization of ice. Well-known natural ice crystal growth modifiers are antifreeze proteins (AFPs), which inhibit ice recrystallization by adsorbing on the surface of ice crystals. Reliable quantification of the ice recrystallization inhibition (IRI) efficiency is a long-sought goal. In this work, we describe a simple method to quantitatively evaluate IRI efficiency, based on automated image analysis using the circle Hough transform (CHT) algorithm. It enables robust and high throughput analysis of natural and synthetic ice recrystallization inhibitors. Here we use the method to evaluate the impact of a single-point mutation in the ice-binding site of QAE on its IRI activity. We find that the T18N .
Improving Fairness of AI Systems with Lossless De-biasing

In today's society, AI systems are increasingly used to make critical decisions such as credit scoring and patient triage. However, great convenience brought by AI systems comes with troubling prevalence of bias against underrepresented groups. Mitigating bias in AI systems to increase overall fairness has emerged as an important challenge. Existing studies on mitigating bias in AI systems focus on eliminating sensitive demographic information embedded in data. Given the temporal and contextual complexity of conceptualizing fairness, lossy treatment of demographic information may contribute to an unnecessary trade-off between accuracy and fairness, especially when demographic attributes and class labels are correlated. In this paper, we present an information-lossless de-biasing technique that targets the scarcity of data in the disadvantaged group. Unlike the existing work, we demonstrate, both theoretically and empirically, that oversampling underrepresented groups can not only mitigate algorithmic bias in AI systems that consistently predict a favorable outcome for a certain group, but improve overall accuracy by mitigating class imbalance within data that leads to a bias towards the majority class. We demonstrate the effectiveness of our technique on real datasets using a variety of fairness metrics.
Facial Emotion Recognition: State of the Art Performance on FER2013

Facial emotion recognition (FER) is significant for human-computer interaction such as clinical practice and behavioral description. Accurate and robust FER by computer models remains challenging due to the heterogeneity of human faces and variations in images such as different facial pose and lighting. Among all techniques for FER, deep learning models, especially Convolutional Neural Networks (CNNs) have shown great potential due to their powerful automatic feature extraction and computational efficiency. In this work, we achieve the highest single-network classification accuracy on the FER2013 dataset. We adopt the VGGNet architecture, rigorously fine-tune its hyperparameters, and experiment with various optimization methods. To our best knowledge, our model achieves state-of-the-art single-network accuracy of 73.28 % on FER2013 without using extra training data.
Trials and Tribulations of Developing Hybrid Quantum-Classical Microservices Systems

Quantum computing holds great promise to solve to problems where classical computers cannot reach. To the point where it already arouses the interest of both scientific and industrial communities. Thus, it is expected that hybrid systems will start to appear where quantum software interacts with classical systems. Such coexistence can be fostered by service computing. Unfortunately, the way in which quantum code can be offered as a service still misses out on many of the potential benefits of service computing. This paper takes the traveling salesman problem, and tackles the challenge of giving it an implementation in the form of a quantum microservice. Then it is used to detect which of the benefits of service computing are lost in the process. The conclusions help to measure the distance between the current state of technology and the state that would be desirable in order to have a real quantum service engineering.
Decentralized Quality of Service Based System for Energy Trading Among Electric Vehicles

This paper incorporates a new perspective into P2P energy trading coordination schemes for EVs by considering Quality of Service (QoS) management. QoS could be utilized as a control metric to facilitate resilient and reliable transactions according to user preferences. To that end, this paper proposes a novel decentralized QoS-based system for P2P energy trading among EV energy providers and consumers. The system utilizes smart contracts to carry out the matching between EVs and monitor the delivery of a QoS-based P2P contract without the presence of a third party. Two QoS-based mechanisms are proposed to match trading EVs in this system. The proposed mechanisms are designed to match single-consumer to multiple-providers and multiple-consumers to multiple-providers based on consumers’ and providers’ QoS requirements and offers, respectively. A fuzzy-based approach with minimum and intelligible input is introduced to determine the weight values of each QoS attribute. Further, a penalty mechanism is developed to discourage dishonest requests/offers and ensure that trading parties stick to their contractual obligations. Numerical simulations are conducted to validate the effectiveness of the proposed QoS-based mechanisms.
Si3N4 as a biomaterial and its tribo‐characterization under water lubrication

Hip implant wear is recognised as the main cause of hip implant failure therefore has been widely investigated both experimentally and clinically, demonstrating the coexistence of abrasive, adhesive, fatigue and corrosive wear. Many clinical in vivo and bulk material wear rate data from published literature have been presented for non‐oxide ceramic implants. Several studies have shown that the coefficient of friction of self‐mated silicon nitride in water decreases from an initially high value to about 0.002 after a certain run‐in period. Since the worn surfaces become extremely smooth, the low friction is attributed to the initiation of hydrodynamic lubrication by a thin water film at the interface. The possibility of mixed lubrication, i.e. hydrodynamic lubrication by water and boundary lubrication due to the presence of colloidal silica on the wearing surfaces, has also been proposed.
Benchmark Study of Quantum Algorithms for Combinatorial Optimization: Unitary versus Dissipative

Krishanu Sankar, Artur Scherer, Satoshi Kako, Sam Reifenstein, Navid Ghadermarzy, Willem B. Krayenhoff, Yoshitaka Inui, Edwin Ng, 4 Tatsuhiro Onodera, 5 Pooya Ronagh, 6, 7, ∗ and Yoshihisa Yamamoto ∗ 1QB Information Technologies (1QBit), Vancouver, BC, Canada 1QB Information Technologies (1QBit), Waterloo, ON, Canada Physics & Informatics Laboratories, NTT Research Inc, Sunnyvale, CA, USA E. L. Ginzton Laboratory, Stanford University, Stanford, CA, USA School of Applied and Engineering Physics, Cornell University, Ithaca, NY, USA Institute for Quantum Computing, University of Waterloo, Waterloo, ON, Canada Department of Physics & Astronomy, University of Waterloo, Waterloo, ON, Canada (Dated: May 11, 2021)
Millimeter-wave 45º linearly polarized corporate-fed slot array antenna with low profile and reduced complexity

—A millimeter-wave 45° linearly polarized corporate-fed slot array antenna is presented in this communication. A key feature is the much reduced number of metallic layers and therefore the complexity of the array, as compared with other competitive designs. With the reduced antenna profile, high cross polarization discrimination (XPD) and low sidelobe level (SLL) are still achieved by a combination of techniques. The back cavity of the 2×2-slot sub-array is optimized to confine the H-field along the direction of the radiation slots, enhancing its XPD. Stubs are loaded in the same cavity to compensate for the phase imbalance, suppressing both cross-polarization and grating lobes without introducing additional metallic layers. Combining these features, the proposed array antenna demonstrates high performance with only three essential metallic layers. A prototype with 8×8 radiation slots at Ka-band is implemented for demonstration. Experimental results show that the grating lobes are suppressed to below -30.5 dB, and the first SLL (FSLL) of less than -26.2 dB. XPD of more than 31.3 dB is achieved over the frequency range of 26.5 GHz - 32 GHz. The peak gain of better than 25 dBi and antenna efficiency of over 71.7% are also obtained over the same frequency range.
Production of Honeydew by Scale Insects Associated with Bracatinga (Mimosa scabrella Benth) in Serra Catarinense, Southern Brazil

T Scale insects Stigmacoccus paranaensis (Foldi, 2006) associated with bracatinga trees (Mimosa scabrella) in Santa Catarina, Brazil, can play an important economic role and guarantee the production of “bracatinga honeydew honey”. This scale insect has the ability to excrete large amounts of honeydew, of economic importance for beekeepers, especially due to the natural occurrence of host plants at high density, known as “bracatingais”. In this sense, the objective of this study was to evaluate the honeydew produced by scale insects associated with M. scabrella in the Bom Retiro, Santa Catarina, Brazil. We determined the density of the host plant in the studied area, the intensity of infestation by scale insects, and the volume and sugar concentration of excreted honeydew. The average density of bracatinga was 437.5 individuals ha -1 , and the average density of scale insects per plant was 8,287. During 10 minutes, the average rate of honeydew excretion by scale insects was 0.45 µl and 0.34 µl, respectively, in 2002 and 2004. The estimated volume of honeydew production was 0.54 L in 2002 and 0.41 L in 2004 tree -1 day -1 . The highest sugar concentration was observed in 2004 (28.13%). The excretion rate was affected by climatological factors and the size of the insects. This is the first study presenting a systematic evaluation of honeydew production by scale insects in Southern Brazil. The results of the present study support production of honeydew honey in the Serra Catarinense region as an important sustainable economic and traditional
Spiral-shaped scattered field from incident evanescent acoustic waves on a Mie Particle

We consider theoretically the scattering of an incident evanescent plane wave by a spherical particle. The scattering problem is treated in a classic way by applying the T-matrix formalism and the resulting field is expressed on the basis of the different vibration modes of the particle. Compared to the case of a homogeneous plane incident wave, additional azimuthal scattered modes are excited and their contribution provokes a symmetry breaking of the field. Importantly, if a mode is preferentially excited by choosing the corresponding reduced frequency, the scattered radiation exhibits a spiral structure. The scattered field has a rotating phase around the scatterer which comes from the formation of spiral scattered waves and this effect is accentuated by increasing the evanescence degree of the incident wave. These results could have important implications for the contactless manipulation of objects with acoustic radiation forces and torques.
A bioinformatics analysis to evaluate the prognostic value of stemness-related genes in gastric cancer

Background This study aimed to identify potential stemness-related targets in gastric cancer (GC) in order to support the development of new treatment strategies and improve patient survival. Methods Using the edgeR package, we identified stemness-related differentially expressed genes (DEGs) using GSE112631 and the stemness-related signaling pathways in the Gene Set Enrichment Analysis (GSEA) database. Lasso-penalized Cox regression analysis and multivariate Cox regression analysis tested by Akaike Information Criterion (AIC) were used to screen out survival genes in order to construct a prognostic model. We verified the accuracy of our prognostic model using a nomogram and receiver operating characteristic (ROC) curve analysis. Patients were divided into two groups based on the median risk score, and functional enrichment analysis was used to explore the differences between the two groups. Results Eight genes were selected to establish a prognostic model of The Cancer Genome Atlas (TCGA) and a validation model of the GSE84437 dataset from the Genome Expression Omnibus (GEO). In both models, we found that the low risk score group had better overall survival (OS) than the high-risk score group. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways between the two risk groups were totally different. Conclusions We used eight stemness-related genes to build a prognostic model. The high-risk score group had a worse prognosis compared to the low-risk score group.
Development and Continuous Control of an Intelligent Upper-Limb Neuroprosthesis for Reach and Grasp Motions Using Biological Signals

The upper-limb prosthesis has been extensively studied using electromyography (EMG) signals to overcome the physical and functional deficiencies of amputees in recent years. However, most studies focus on the discrete classification of gestures and ignore the interconnection between the classification results and the neuroprosthesis control interface, which plays a vital role in system development. In this article, a new continuous control scheme is proposed to achieve an effective control of the developed upper-limb prosthesis. It utilizes eight channels of EMG signals of the human upper limb to model and control the developed prosthesis. A continuous control scheme is proposed that combines the state of the system and the decoding results to dynamically produce the expected angular velocity of the joint based on the results of the classification. Finally, experiments are performed to demonstrate the effectiveness of the proposed algorithm using an upper-limb neuroprosthesis, achieving the reach and grasp tasks. The results showed that it improves performance with a regular angular velocity of the joint, which underlines the importance of an adequate control scheme for the EMG-guided prosthesis.
NLP-IIS@UT at SemEval-2021 Task 4: Machine Reading Comprehension using the Long Document Transformer

This paper presents a technical report of our submission to the 4th task of SemEval-2021, titled: Reading Comprehension of Abstract Meaning. In this task, we want to predict the correct answer based on a question given a context. Usually, contexts are very lengthy and require a large receptive field from the model. Thus, common contextualized language models like BERT miss fine representation and performance due to the limited capacity of the input tokens. To tackle this problem, we used the longformer model to better process the sequences. Furthermore, we utilized the method proposed in the longformer benchmark on wikihop dataset which improved the accuracy on our task data from (23.01% and 22.95%) achieved by the baselines for subtask 1 and 2, respectively, to (70.30% and 64.38%).
Macronutrient requirement of groundnut : Effects on growth and yield components

A series of sand culture pot experiments were conducted to find out the optimum concentration of N, P, K, Ca, and S, in the nutrient solution, for growing groundnut with maximum growth and yield by taking each macronutrient in the range of 2-200 ppm. The observations on plant height, dry matter production, and podding at 20, 40, 60 days after emergence (DAE) and at harvest, chlorophyll content and transpiration at 40 and 70 DAE, flowering from 25-70 DAE and finally pod and haulm yields, at harvest showed that increasing the macronutrients levels, in the nutrients solution, increased these parameters significantly up to a certain level only which was optimum dose for that parameter. The number of flowers produced during the first two weeks (25-40 DAE) of its flowering increased significantly up to 10 ppm of N, P, K, and S and 20 ppm of Ca, but the number of pods at harvest increased up to 50 ppm of N and 20 ppm of P, K, Ca and S. The pod yield and dry matter, chlorophyll, flowering, podding and transpiration at either of the stages, however, increased up to 20 ppm of P and S and 50 ppm of N, K, and Ca and further increase in the levels of these macro-nutrients did not increase these parameters any more significantly. The macronutrient levels of 50 ppm of N, K, and Ca, and 20 ppm of P and S, in nutrient solution, are being recommended for growing groundnut in nutrient culture.
Non-Gaussianity in D3-brane inflation

We update predictions for observables in the `delicate' D3/D3 inflationary model on the conifold. We use a full CMB likelihood calculation to assess goodness-of-fit, which is necessary because in this model the ζ power spectrum often cannot be approximated as a power-law over observable scales. For the first time we are able to provide accurate forecasts for the amplitude of three-point correlations. In a significant portion of its parameter space the model follows Maldacena's single-field prediction f_NL≈ -(5/12)(ns-1) if |nt| ≪ 1. Therefore |fNL| is usually small when the power spectrum satisfies observational constraints. In a small number of cases the bispectrum is instead dominated by effects from rapid switching between angular minima. The resulting amplitudes are larger, but mostly with unacceptable spectral behaviour. In the most extreme case we obtain |fNL eq| ∼ 75 at kt/3 = 0.002 Mpc-1. It has been suggested that the quasi-single field inflation (`QSFI') mechanism could produce significant 3-point correlations in this model. We do observe rare shifts in amplitude between equilateral and squeezed configurations that could possibly be associated with QSFI effects, but more investigation is needed to establish the full bispectrum shape. There is evidence of `shape' running between equilateral and squeezed configurations that may be inherited from the scale dependence of the spectrum. We explore the dependence of observables on discrete choices such as the truncation point of the potential. Our analysis illustrates the advantages of a standard format for information exchange within the inflationary model-building and testing community.
HiTyper: A Hybrid Static Type Inference Framework with Neural Prediction

Type inference for dynamic programming languages is an important yet challenging task. By leveraging the natural language information of existing human annotations, deep neural networks outperform other traditional techniques and become the state-of-the-art (SOTA) in this task. However, they are facing some new challenges, such as fixed type set, type drift, type correctness, and composite type prediction. To mitigate the challenges, in this paper, we propose a hybrid type inference framework named HiTyper, which integrates static inference into deep learning (DL) models for more accurate type prediction. Specifically, HiTyper creates a new syntax graph for each program, called type graph , illustrating the type flow among all variables in the program. Based on the type graph, HiTyper statically infers the types of the variables with appropriate static constraints. HiTyper then adopts a SOTA DL model to predict the types of other variables that cannot be inferred statically, during which process a type correction algorithm is employed to validate and correct the types recommended by the DL model. Extensive experiments show that HiTyper outperforms the SOTA DL approach by 12.7% in terms of top-1 F1-score. Moreover, HiTyper filters out 50.6% of incorrect candidate types recommended by the SOTA DL model, indicating that HiTyper could improve the correctness of predicted types. Case studies also demonstrate the capability of HiTyper in alleviating the fixed type set issue
A smoothing sequential convex programming method

We consider a class of nonlinear problems with both the objective function and the constraint functions possibly nonsmooth and nonconvex, Sequential Convex Programming (SCP) methods are usually employed for solving topology optimization problems. Among the approximation methods, the SCP Method is known to work well in the context of structural optimization. One could wrongly believe that standard algorithms for smooth minimization might work to solve this kind of problems. In fact, since many real problems have their minimum points where the function is not diﬀerentiable, these algorithms have to face non-diﬀerentiability, and in many cases fail to solve the optimization problem. Therefore, the study of speciﬁc methods is needed. In this paper, We use smoothing functions with the gradient consistency property to approximate the nonsmooth functions and introduce a robust Sequential Convex Programming (SCP) algorithm, These are achieved by introducing a general deﬁnition for smoothing functions, which include almost all the existing smoothing functions as special cases. The results are presented for a special variant called Smoothing Sequential Convex Programming (SSCP).
Self-Supervised Learning with Swin Transformers

We are witnessing a modeling shift from CNN to Transformers in computer vision. In this work, we present a self-supervised learning approach called MoBY, with Vision Transformers as its backbone architecture. The approach basically has no new inventions, which is combined from MoCo v2 and BYOL and tuned to achieve reasonably high accuracy on ImageNet-1K linear evaluation: 72.8% and 75.0% top-1 accuracy using DeiT-S and Swin-T, respectively, by 300-epoch training. The performance is slightly better than recent works of MoCo v3 and DINO which adopt DeiT as the backbone, but with much lighter tricks. More importantly, the general-purpose Swin Transformer backbone enables us to also evaluate the learnt representations on downstream tasks such as object detection and semantic segmentation, in contrast to a few recent approaches built on ViT/DeiT which only report linear evaluation results on ImageNet-1K due to ViT/DeiT not tamed for these dense prediction tasks. We hope our results can facilitate more comprehensive evaluation of self-supervised learning methods designed for Transformer architectures. Our code and models are available at https://github.com/SwinTransformer/Transformer-SSL, which will be continually enriched.
Insolubilization of hexavalent chromium in highly alkaline cement sludge by anaerobic bioremediation

ABSTRACT Chemical treatment using reducing agents such as ferrous sulfate is the most popular method for remediation of hexavalent chromium–contaminated sludge. However, the use of such chemical agents poses the risk of secondary pollution through the elution of other heavy metals. Therefore, a bioremediation method was developed to remediate high-alkali cement sludge containing hexavalent chromium. When a biomediator mainly composed of lignocellulose and lignoarabinoxylan was added to hexavalent chromium–contaminated sludge under anaerobic conditions, the amount of hexavalent chromium eluted from the treated sludge decreased significantly to below the level of the environmental standard value in Japan and its pH was reduced to 8. Moreover, the oxidation-reduction potential of the treated sludge decreased and its microbiota changed. These results indicate that anaerobic microbes can facilitate the change of hexavalent chromium to an immobilized form of trivalent chromium. Nucleotide sequence analysis suggested that anaerobic microbes activated in the sludge were Exiguobacterium aurantiacum, which are known to tolerate high pH environments and produce organic acids, even in the cement sludge. Finally, treated sludge did not elute hexavalent chromium during shaking in acid or alkaline solution.