Miracle-dz/UniFAR-FTUs huggingface dataset:
DeepPERF: A Deep Learning-Based Approach For Improving Software Performance

Improving software performance is an important yet challenging part of the software development cycle. Today, the majority of performance inefficiencies are identified and patched by performance experts. Recent advancements in deep learning approaches and the wide-spread availability of open source data creates a great opportunity to automate the identification and patching of performance problems. In this paper, we present DeepPERF, a transformer-based approach to suggest performance improvements for C# applications. We pretrain DeepPERF on English and Source code corpora and followed by finetuning for the task of generating performance improvement patches for C# applications. Our evaluation shows that our model can generate the same performance improvement suggestion as the developer fix in ~53% of the cases, getting ~34% of them verbatim in our expert-verified dataset of performance changes made by C# developers. Additionally, we evaluate DeepPERF on 50 open source C# repositories on GitHub using both benchmark and unit tests and find that our model is able to suggest valid performance improvements that can improve both CPU usage and Memory allocations. So far we've submitted 19 pull-requests with 28 different performance optimizations and 11 of these PRs have been approved by the project owners.
Multi-Fault Diagnosis Of Industrial Rotating Machines Using Data-Driven Approach: A Review Of Two Decades Of Research

Industry 4.0 is an era of smart manufacturing. Manufacturing is impossible without the use of machinery. Majority of these machines comprise rotating components and are called rotating machines. The engineers' top priority is to maintain these critical machines to reduce the unplanned shutdown and increase the useful life of machinery. Predictive maintenance (PDM) is the current trend of smart maintenance. The challenging task in PDM is to diagnose the type of fault. With Artificial Intelligence (AI) advancement, data-driven approach for predictive maintenance is taking a new flight towards smart manufacturing. Several researchers have published work related to fault diagnosis in rotating machines, mainly exploring a single type of fault. However, a consolidated review of literature that focuses more on multi-fault diagnosis of rotating machines is lacking. There is a need to systematically cover all the aspects right from sensor selection, data acquisition, feature extraction, multi-sensor data fusion to the systematic review of AI techniques employed in multi-fault diagnosis. In this regard, this paper attempts to achieve the same by implementing a systematic literature review on a Data-driven approach for multi-fault diagnosis of Industrial Rotating Machines using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method. The PRISMA method is a collection of guidelines for the composition and structure of systematic reviews and other meta-analyses. This paper identifies the foundational work done in the field and gives a comparative study of different aspects related to multi-fault diagnosis of industrial rotating machines. The paper also identifies the major challenges, research gap. It gives solutions using recent advancements in AI in implementing multi-fault diagnosis, giving a strong base for future research in this field.
TAMOLS: Terrain-Aware Motion Optimization for Legged Systems

Terrain geometry is, in general, nonsmooth, nonlinear, nonconvex, and, if perceived through a robot-centric visual unit, appears partially occluded and noisy. This article presents the complete control pipeline capable of handling the aforementioned problems in real-time. We formulate a trajectory optimization problem that jointly optimizes over the base pose and footholds, subject to a height map. To avoid converging into undesirable local optima, we deploy a graduated optimization technique. We embed a compact, contact-force free stability criterion that is compatible with the nonflat ground formulation. Direct collocation is used as transcription method, resulting in a nonlinear optimization problem that can be solved online in less than ten milliseconds. To increase robustness in the presence of external disturbances, we close the tracking loop with a momentum observer. Our experiments demonstrate stair climbing, walking on stepping stones, and over gaps, utilizing various dynamic gaits.
FuSeBMC v4: Improving code coverage with smart seeds via BMC, fuzzing and static analysis

Bounded model checking (BMC) and fuzzing techniques are among the most effective methods for detecting errors and security vulnerabilities in software. However, there are still shortcomings in detecting these errors due to the inability of existent methods to cover large areas in target code. We propose FuSeBMC v4, a test generator that synthesizes seeds with useful properties, that we refer to as smart seeds, to improve the performance of its hybrid fuzzer thereby achieving high C program coverage. FuSeBMC works by first analyzing and incrementally injecting goal labels into the given C program to guide BMC and Evolutionary Fuzzing engines. After that, the engines are employed for an initial period to produce the so-called smart seeds. Finally, the engines are run again, with these smart seeds as starting seeds, in an attempt to achieve maximum code coverage / find bugs. During both seed generation and normal running, coordination between the engines is aided by the Tracer subsystem. This subsystem carries out additional coverage analysis and updates a shared memory with information on goals covered so far. Furthermore, the Tracer evaluates test cases dynamically to convert cases into seeds for subsequent test fuzzing. Thus, the BMC engine can provide the seed that allows the fuzzing engine to bypass complex mathematical guards (e.g., input validation). As a result, we received three awards for participation in the fourth international competition in software testing (Test-Comp 2022), outperforming all state-of-the-art tools in every category, including the coverage category.
Disentangling positive and negative partisanship in social media interactions using a coevolving latent space network with attractors model

We develop a broadly applicable class of coevolving latent space network with attractors (CLSNA) models, where nodes represent individual social actors assumed to lie in an unknown latent space, edges represent the presence of a specified interaction between actors, and attractors are added in the latent level to capture the notion of attractive and repulsive forces. We apply the CLSNA models to understand the dynamics of partisan polarization on social media, where we expect Republicans and Democrats to increasingly interact with their own party and disengage with the opposing party. Using longitudinal social networks from the social media platforms Twitter and Reddit, we investigate the relative contributions of positive (attractive) and negative (repulsive) forces among political elites and the public, respectively. Our goals are to disentangle the positive and negative forces within and between parties and explore if and how they change over time. Our analysis confirms the existence of partisan polarization in social media interactions among both political elites and the public. Moreover, while positive partisanship is the driving force of interactions across the full periods of study for both the public and Democratic elites, negative partisanship has come to dominate Republican elites' interactions since the run-up to the 2016 presidential election.
a repository copy of Cold sintered LiMgPO4 based composites for low temperature co fired ceramic (LTCC) applications

Cold sintered, Li 2 MoO 4 -based ceramics have recently been touted as candidates for electronic packaging and low temperature co-fired ceramic (LTCC) technology but MoO 3 is an expensive and endangered raw material, not suited for large scale commercialization. Here, we present cold sintered temperature-stable composites based on LiMgPO 4 (LMP) in which the Mo (and Li) concentration has been reduced, thereby significantly decreasing raw material costs. Optimum compositions, 0.5LMP-0.1CaTiO 3 -0.4K 2 MoO 4 (LMP-CTO-KMO), achieved 97% density at <300°C and 600 MPa for 60 minutes. Raman spectroscopy, X-ray diffraction, scanning electron microscopy, and energy dispersive X-ray mapping confirmed the coexistence of end-members, LMP, CTO, and KMO, with no interdiffusion and par-asitic phases. Composites exhibited temperature coefficient of resonant frequency ~ –6 ppm/°C, relative permittivity ~9.1, and Q × f values ~8500 GHz, properties suitable for LTCC technology and competitive with commercial incumbents.
loss older men

The association between dietary patterns (DP) and prevalence of hearing loss in men enrolled in the Caerphilly Prospective Study was investigated. During 1979 – 1983, the study recruited 2512 men aged 45 – 59 years. At baseline, dietary data were collected using a semi-quantitative FFQ, and a 7-d weighed food intake (WI) in a 30% subsample. Five years later, pure-tone unaided audiometric threshold was assessed at 0 · 5, 1, 2 and 4kHz. Principal component analysis (PCA) identi ﬁ ed three DP and multiple logistic and ordinal logistic regression models examined the association with hearing loss (de ﬁ ned as pure-tone average of frequencies 0 · 5, 1, 2 and 4kHz > 25 dB). Traditional, healthy and high-sugar/low-alcohol DP were found with both FFQ and WI data. With the FFQ data, fully adjusted models demonstrated signi ﬁ cant inverse association between the healthy DP and hearing loss both as a dichotomous variable (OR = 0 · 83; 95% CI 0 · 77, 0 · 90; P < 0 · 001) and as an ordinal variable (OR = 0 · 87; 95% CI 0 · 81, 0 · 94; P < 0 · 001). With the WI data, fully adjusted models showed a signi ﬁ cant and inverse association between the healthy DP and hearing loss (OR = 0 · 85; 95% CI 0 · 73, 0 · 99; P < 0 · 03), and a signi ﬁ cant association between the traditional DP (per ﬁ fth increase) and hearing loss both as a dichotomous variable (OR = 1 · 18; 95% CI 1 · 02, 1 · 35; P = 0 · 02) and as an ordinal variable (OR = 1 · 17; 95% CI 1 · 03, 1 · 33; P = 0 · 02). A healthy DP was signi ﬁ cantly and inversely associated with hearing loss in older men. The role of diet in age-related hearing loss warrants further investigation.
MEASUREMENT OF BRIX LEVEL OF MALAYSIAN LOCAL ORANGES AT RESONANCE FREQUENCY

The production of citrus in worldwide has increased over the years. This is due to demand from consumer as well as positive progress in agriculture-related industry. Malaysia also produces a lot of citrus but at the same time statistics shows that the number of citrus being imported from other countries is also high. In order to maintain the quality of citrus production, a non-destructive method by using monopole antenna is used in this study. The reflection coefficient is measured at range operating frequency of 8MHz to 5 GHz. The relationship between dielectric constant and S 11 is determined. The other variables are compared during S 11 at resonance. S 11 of oranges is the highest at 2.6 GHz with a value of -42.84 dB.
Exploring the First Momentary Unboxing Experience with Aesthetic Interaction

Abstract Increasingly, the unboxing experience is regarded as a critical moment in product appraisal. As such, designers and companies should pay more attention to product packaging due to this increased interest in the momentary unboxing experience. In this study, we examine aesthetic interaction’s influence upon emotional and semantic appraisals of the packaged product at the moment of unboxing. Three factors of aesthetic interaction were adopted and used in the design of packaging stimuli: freedom of interaction, interaction pattern, and richness of motor actions. The findings indicated that differences in aesthetic interaction between the three packaging designs evoked particular positive emotions and delivered different semantic appraisals of the packaged products. Thus, the study provides design researchers and practitioners with an increased understanding of how aesthetic interaction can be leveraged in packaging design to enhance the unboxing experience.
May 09, 2020 Glioblastoma adaptation traced through decline of an IDH1 clonal driver and macro-evolution of

Background: Glioblastoma (GBM) is the most common malignant brain cancer occurring in adults, and is associated with dismal outcome and few therapeutic options. GBM has been shown to predominantly disrupt three core pathways through somatic aberrations, rendering it ideal for precision medicine approaches. Methods: We describe a 35-year-old female patient with recurrent GBM following surgical removal of the primary tumour, adjuvant treatment with temozolomide and a 3-year disease-free period. Rapid whole-genome sequencing (WGS) of three separate tumour regions at recurrence was carried out and interpreted relative to WGS of two regions of the primary tumour. Results: We found extensive mutational and copy-number heterogeneity within the primary tumour. We identi ﬁ ed a TP53 mutation and two focal ampli ﬁ cations involving PDGFRA , KIT and CDK4 , on chromosomes 4 and 12. A clonal IDH1 R132H mutation in the primary, a known GBM driver event, was detectable at only very low frequency in the recurrent tumour. After sub-clonal diversi ﬁ cation, evidence was found for a whole-genome doubling event and a translocation between the ampli ﬁ ed regions of PDGFRA , KIT and CDK4 , encoded within a double-minute chromosome also incorpor-ating miR26a-2. The WGS analysis uncovered progressive evolution of the double-minute chromosome converging on the KIT/PDGFRA/PI3K/mTOR axis, superseding the IDH1 mutation in dominance in a mutually exclusive manner at recurrence, consequently the patient was treated with imatinib. Despite rapid sequencing and cancer genome-guided therapy against ampli ﬁ ed oncogenes, the disease progressed, and the patient died shortly after. Conclusion: This case sheds light on the dynamic evolution of a GBM tumour, de ﬁ ning the origins of the lethal sub-clone, the macro-evolutionary genomic events dominating the disease at recurrence and the loss of a clonal driver. Even in the era of rapid WGS analysis, cases such as this illustrate the signi ﬁ cant hurdles for precision medicine success.
HIV-1 Infection in Macrophages of Exosomes and Microvesicles as Facilitators Biochemical and Biologic Characterization of

Exosomes and microvesicles (MV) are cell membranous sacs originating from multivesicular bodies and plasma membranes that facilitate long-distance intercellular communications. Their functional biology, however, remains incompletely understood. Macrophage exosomes and MV isolated by immunoafﬁnity and sucrose cushion centrifugation were characterized by morphologic, biochemical, and molecular assays. Lipidomic, proteomic, and cell biologic approaches uncovered novel processes by which exosomes and MV facilitate HIV-1 infection and dissemination. HIV-1 was “entrapped” in exosome aggregates. Robust HIV-1 replication followed infection with exosome-enhanced fractions isolated from infected cell supernatants. MV- and exosome-facilitated viral infections are affected by a range of cell surface receptors and adhesion proteins. HIV-1 containing exosomes readily completed its life cycle in human monocyte-derived macrophages but not in CD4 2 cells. The data support a signiﬁcant role for exosomes as facilitators of viral infection. The Journal of Immunology , 2012, 189: 000–000.
Encryption of Images using a User-controlled Automatically-generated Key

— Traditional symmetrical cryptographic algorithms generally provide an adequate degree of immunity to attacks aimed at revealing secret keys. A number of approaches exist for the automated generation of secret keys, but, for high security applications, some end users remain wary of approaches that are controlled by third parties. Consequently, there remains interest in certain high-security applications in being able to retain control over the method used for the generation of keys. In this paper, keys for both image encryption and decryption are obtained using the evolutionary computing tool Eureqa, in its modelling of pseudo-random input data. The secret keys generated by this approach and when applied to the encryption and decryption of gray-scale images are validated in a range of statistical tests, namely histogram, chi-square, correlation of adjacent pixel pairs, correlation between original and encrypted images, entropy and key sensitivity. Experimental results obtained from methods show that the proposed image encryption and decryption algorithms are secure and reliable, with the potential to be adapted to high-security image communication applications.
Evaluation of Radiomics Models Based on Computed Tomography for Distinguishing Between Benign and Malignant Thyroid Nodules

Aim The aim of the study was to investigate the diagnostic value of radiomics models based on computed tomography (CT) in distinguishing between benign and malignant thyroid nodules. Materials and Methods We conducted a retrospective analysis of the clinical and imaging data of 172 patients with pathology-confirmed thyroid nodules (83 benign nodules and 89 malignant nodules). All patients underwent a plain CT scan + arterial and venous contrast enhancement before the operation. Using the stratified random sampling method, patients were divided into a training group (121 cases) and a test group (51 cases) at a ratio of 7:3. A.K. software was used to extract radiomics features from the preoperative CT images, and minimum redundancy maximum relevance and least absolute shrinkage and selection operator regression analyses were then used for feature screening and model construction. Receiver operating characteristic (ROC) curves were constructed for the training and test groups to verify model performance and evaluate the efficacy of the radiomics features in identifying benign and malignant thyroid nodules. We then used the most efficient models to construct a nomogram. For the training group, 1-way analysis of variance and multivariate logistic regression analysis were used to screen statistically significant clinical features, and the radiomics scores were combined to construct a radiomics nomogram. We used ROC curve analysis to evaluate the predictive performance of the model. Results Screening yielded 21 radiomics features that were used to construct a model for differentiating between benign and malignant thyroid nodules. For the training group, the area under the ROC curve of the prediction models for the noncontrast, arterial phase, and venous phase scans were 0.86 (95% confidence interval [CI], 0.79–0.92), 0.89 (95% CI, 0.83–0.95), and 0.88 (95% CI, 0.82–0.94), respectively, and the corresponding diagnostic accuracy was 0.78, 0.84, and 0.83. For the test group, the corresponding 3-phase under the ROC curves for the test group were 0.76 (95% CI, 0.63–0.90), 0.78 (95% CI, 0.65–0.91), and 0.76 (95% CI, 0.62–0.90), and the corresponding accuracy was 0.63, 0.77, and 0.75. Thus, the arterial phase model exhibited the best diagnostic performance. The multivariate logistic regression results showed that morphology regularity and the cystic degeneration ratio were independent clinical risk factors for predicting benign and malignant thyroid nodules. The arterial phase radiomics score and clinically independent factors were then used to construct a nomogram. The nomogram had good discriminability for the training group (0.93; 95% CI, 0.88–0.98) and the test group (0.84; 95% CI, 0.73–0.95), achieving significantly higher accuracies than the radiomics score and clinical characteristics alone. Conclusions The radiomics nomogram constructed by combining radiomics characteristics and clinical risk factors was efficacious for distinguishing benign and malignant thyroid nodules.
Abscisic acid influences ammonium transport via regulation of kinase CIPK23 and ammonium transporters

Abstract Ammonium uptake at plant roots is regulated at the transcriptional, posttranscriptional, and posttranslational levels. Phosphorylation by the protein kinase calcineurin B-like protein (CBL)-interacting protein kinase 23 (CIPK23) transiently inactivates ammonium transporters (AMT1s), but the phosphatases activating AMT1s remain unknown. Here, we identified the PP2C phosphatase abscisic acid (ABA) insensitive 1 (ABI1) as an activator of AMT1s in Arabidopsis (Arabidopsis thaliana). We showed that high external ammonium concentrations elevate the level of the stress phytohormone ABA, possibly by de-glycosylation. Active ABA was sensed by ABI1-PYR1-like () complexes followed by the inactivation of ABI1, in turn activating CIPK23. Under favorable growth conditions, ABI1 reduced AMT1;1 and AMT1;2 phosphorylation, both by binding and inactivating CIPK23. ABI1 further directly interacted with AMT1;1 and AMT1;2, which would be a prerequisite for dephosphorylation of the transporter by ABI1. Thus, ABI1 is a positive regulator of ammonium uptake, coupling nutrient acquisition to abiotic stress signaling. Elevated ABA reduces ammonium uptake during stress situations, such as ammonium toxicity, whereas ABI1 reactivates AMT1s under favorable growth conditions.
Effects of enzymolysis and fermentation on the antioxidant activity and functional components of a coarse cereal compound powder based on principal component analysis and microstructure study

Abstract In this study, a coarse cereal compound powder (CCCP) was prepared through enzymolysis, fermentation, and joint treatment with 10 coarse cereal types as raw materials. Using 10 evaluation indices, namely the scavenging capacity of 1,1‐diphenyl‐2‐picrylhydrazyl (DPPH•), 2,2′‐azino‐bis‐(3‐ethylbenzthiazoline‐6‐sulfonic acid) (ABTS+), hydroxyl (OH•) and superoxide anion (O2 –), the Fe2+ chelating capacity, the content of anthocyanin, flavone, soluble dietary fiber, reducing sugar and protein, antioxidant activity, and functional components of CCCP prepared by different methods were compared. Principal component analysis (PCA) was performed to establish a quality evaluation model of CCCP. Then, the effects of different treatments on the microstructure of CCCP were investigated. Two principal components (PCs) were extracted from PCA, with a cumulative contribution rate of 97.014%. In addition, the analysis of thermodynamic properties indicated that the initial gelatinization temperature of CCCP decreased after enzymolysis and fermentation and that it was easier to gelatinize. Particle size analyses revealed that different treatments could reduce the sample particles to different degrees. The average particle size in the three study groups decreased. Scanning electron microscopy (SEM) revealed that after different treatments, the samples were destroyed to different extents, which facilitated easy dissolution of active substances. Fourier‐transformed‐infrared spectroscopy (FTIR) revealed that the changes of CCCP functional groups after fermentation and joint treatment were more significant than those after enzymolysis. Practical Application In this study, enzymolysis and fermentation techniques were used to improve the antioxidant activity and functional components of CCCP, and the effects of different treatments on the microstructure of CCCP were investigated. The bioavailability and nutrient composition of CCCP could be significantly improved by pretreatment, provide useful reference for the development of beneficial ingredients in cereal meal products and the application of different pretreatment methods.