Miracle-dz/UniFAR-FTUs huggingface dataset:
A multi-objective perspective on jointly tuning hardware and hyperparameters

In addition to the best model architecture and hyperparameters, a full AutoML solution requires selecting appropriate hardware automatically. This can be framed as a multi-objective optimization problem: there is not a single best hardware configuration but a set of optimal ones achieving different trade-offs between cost and runtime. In practice, some choices may be overly costly or take days to train. To lift this burden, we adopt a multi-objective approach that selects and adapts the hardware configuration automatically alongside neural architectures and their hyperparameters. Our method builds on Hyperband and extends it in two ways. First, we replace the stopping rule used in Hyperband by a non-dominated sorting rule to preemptively stop unpromising configurations. Second, we leverage hyperparameter evaluations from related tasks via transfer learning by building a probabilistic estimate of the Pareto front that finds promising configurations more efficiently than random search. We show in extensive NAS and HPO experiments that both ingredients bring significant speed-ups and cost savings, with little to no impact on accuracy. In three benchmarks where hardware is selected in addition to hyperparameters, we obtain runtime and cost reductions of at least 5.8x and 8.8x, respectively. Furthermore, when applying our multi-objective method to the tuning of hyperparameters only, we obtain a 10\% improvement in runtime while maintaining the same accuracy on two popular NAS benchmarks.
Trends in the composition of Australian wine 1984–2014

Data for wines from the 2004–2014 vintages were collated from the database of analytical results of The Australian Wine Research Institute's Commercial Services Group and are reviewed in the context of historical trends in wine composition. Data for those 11 vintages were generated from 24 066 commercially bottled Australian table wines (8384 white and rose wines, and 15 682 red wines), which were submitted to The Australian Wine Research Institute for analysis required to comply with the export/import requirements of destination countries. The wines include multiple vintages of a broad cross-section of Australian wines from commodity to icon status, and producers of all sizes. The wines represent a broad geographical and cultivar spread, with the proportion of wines of each cultivar strongly correlating with the planted vineyard area. The data relate to the compositional variables: alcohol, glucose plus fructose, total dry extract excluding alcohol, sugar and volatile acidity (TDE), titratable acidity at pH 8.2, pH, free sulfur dioxide (SO2), total SO2, bound SO2, and the ratio of free to total SO2. Certain previously identified year-on-year trends have continued, and in some cases appear to have accelerated in the most recent vintages, particularly with increasing glucose plus fructose concentration and pH in red wines. In other cases, previously identified trends appear to have gone into reverse, notably the rise in alcohol concentration in red wines, and for a period, rising TDE in red wines, and one new trend in red wines is apparent, namely decreasing titrable acidity. There is some indication that new trends are also apparent with white and rose wines, namely decreasing alcohol, increasing glucose plus fructose, increasing pH and an apparent downward shift in TDE, although ANOVA indicates little statistical significance in those trends. Data related to the concentration of SO2 demonstrate upward trends in free SO2 for white and rose, and red wines. This has occurred with a concurrent decrease in the concentration of total SO2 in red wines for the most recent vintages, leading to a consequent rise in the ratio of free to total SO2. A rise in the ratio of free to total SO2 is also seen in white and rose wines due to increasing free SO2 concentration. Overall white and rose wines display fewer upward or downward trends compared with that of red wines, but greater year-on-year variability in the data.
Algorithm Auditing at a Large-Scale: Insights from Search Engine Audits

Algorithm audits have increased in recent years due to a growing need to independently assess the performance of automatically curated services that process, filter and rank the large and dynamic amount of information available on the internet. Among several methodologies to perform such audits, virtual agents stand out because they offer the ability to perform systematic experiments, simulating human behaviour without the associated costs of recruiting participants. Motivated by the importance of research transparency and replicability of results, this paper focuses on the challenges of such an approach. It provides methodological details, recommendations, lessons learned and limitations that researchers should take into consideration when setting up experiments with virtual agents. We demonstrate the successful performance of our research infrastructure across multiple data collections, with diverse experimental designs, and point to different changes and strategies that improve the quality of the method. We conclude that virtual agents are a promising venue for monitoring the performance of algorithms across long periods of time, and we hope that this paper can serve as a basis for further research in this area.
Score-based Generative Modeling in Latent Space

Score-based generative models (SGMs) have recently demonstrated impressive results in terms of both sample quality and distribution coverage. However, they are usually applied directly in data space and often require thousands of network evaluations for sampling. Here, we propose the Latent Score-based Generative Model (LSGM), a novel approach that trains SGMs in a latent space, relying on the variational autoencoder framework. Moving from data to latent space allows us to train more expressive generative models, apply SGMs to non-continuous data, and learn smoother SGMs in a smaller space, resulting in fewer network evaluations and faster sampling. To enable training LSGMs end-to-end in a scalable and stable manner, we (i) introduce a new score-matching objective suitable to the LSGM setting, (ii) propose a novel parameterization of the score function that allows SGM to focus on the mismatch of the target distribution with respect to a simple Normal one, and (iii) analytically derive multiple techniques for variance reduction of the training objective. LSGM obtains a state-of-the-art FID score of 2.10 on CIFAR-10, outperforming all existing generative results on this dataset. On CelebA-HQ-256, LSGM is on a par with previous SGMs in sample quality while outperforming them in sampling time by two orders of magnitude. In modeling binary images, LSGM achieves state-of-the-art likelihood on the binarized OMNIGLOT dataset. Our project page and code can be found at https://nvlabs.github. io/LSGM .
Towards an Automated Pipeline for Detecting and Classifying Malware through Machine Learning

The constant growth in the number of malware - software or code fragment potentially harmful for computers and information networks - and the use of sophisticated evasion and obfuscation techniques have seriously hindered classic signature-based approaches. On the other hand, malware detection systems based on machine learning techniques started offering a promising alternative to standard approaches, drastically reducing analysis time and turning out to be more robust against evasion and obfuscation techniques. In this paper, we propose a malware taxonomic classification pipeline able to classify Windows Portable Executable files (PEs). Given an input PE sample, it is first classified as either malicious or benign. If malicious, the pipeline further analyzes it in order to establish its threat type, family, and behavior(s). We tested the proposed pipeline on the open source dataset EMBER, containing approximately 1 million PE samples, analyzed through static analysis. Obtained malware detection results are comparable to other academic works in the current state of art and, in addition, we provide an in-depth classification of malicious samples. Models used in the pipeline provides interpretable results which can help security analysts in better understanding decisions taken by the automated pipeline.
MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training

Symbolic music understanding, which refers to the understanding of music from the symbolic data (e.g., MIDI format, but not audio), covers many music applications such as genre classification, emotion classification, and music pieces matching. While good music representations are beneficial for these applications, the lack of training data hinders representation learning. Inspired by the success of pre-training models in natural language processing, in this paper, we develop MusicBERT, a large-scale pre-trained model for music understanding. To this end, we construct a large-scale symbolic music corpus that contains more than 1 million music songs. Since symbolic music contains more structural (e.g., bar, position) and diverse information (e.g., tempo, instrument, and pitch), simply adopting the pre-training techniques from NLP to symbolic music only brings marginal gains. Therefore, we design several mechanisms, including OctupleMIDI encoding and bar-level masking strategy, to enhance pre-training with symbolic music data. Experiments demonstrate the advantages of MusicBERT on four music understanding tasks, including melody completion, accompaniment suggestion, genre classification, and style classification. Ablation studies also verify the effectiveness of our designs of OctupleMIDI encoding and bar-level masking strategy in MusicBERT.
A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint

Randomized trials typically estimate average relative treatment effects, but decisions on the benefit of a treatment are possibly better informed by more individualized predictions of the absolute treatment effect. In case of a binary outcome, these predictions of absolute individualized treatment effect require knowledge of the individual's risk without treatment and incorporation of a possibly differential treatment effect (ie, varying with patient characteristics). In this article, we lay out the causal structure of individualized treatment effect in terms of potential outcomes and describe the required assumptions that underlie a causal interpretation of its prediction. Subsequently, we describe regression models and model estimation techniques that can be used to move from average to more individualized treatment effect predictions. We focus mainly on logistic regression‐based methods that are both well‐known and naturally provide the required probabilistic estimates. We incorporate key components from both causal inference and prediction research to arrive at individualized treatment effect predictions. While the separate components are well known, their successful amalgamation is very much an ongoing field of research. We cut the problem down to its essentials in the setting of a randomized trial, discuss the importance of a clear definition of the estimand of interest, provide insight into the required assumptions, and give guidance with respect to modeling and estimation options. Simulated data illustrate the potential of different modeling options across scenarios that vary both average treatment effect and treatment effect heterogeneity. Two applied examples illustrate individualized treatment effect prediction in randomized trial data.
Synergistic activity of quorum sensing inhibitor, pyrizine-2-carboxylic acid and antibiotics against multi-drug resistant V. cholerae

Antibiotic resistance is one of the most pivotal health menaces in the human population worldwide. Use of conventional antibiotics at high concentration for prolonged periods facilitates a selective pressure on the bacterial strain to develop resistance to these antibiotics. The ascent of multi-drug resistance in Vibrio cholerae strains is a key issue for alternative drug development against cholera. A drug that could curb the virulence of V. cholerae is an elective choice for treatment. A few research studies have been carried out to discover such alternative medication that has an anti-virulent property rather than antibacterial activity. Combinatorial antibiotic treatments along with anti-biofilm compounds have diverse effects on bacterial growth i.e. it can be additive, synergistic or antagonistic. In the present investigation, combinatorial action of 2,3-pyrazine dicarboxylic acid derivatives (QSIVc targeted against the quorum regulator, LuxO) along with antibiotics was evaluated. The results indicated that QSIVc could inhibit biofilm at a very low concentration (IC50 varying between 1 μM and 70 μM). The synergism of the compounds with the reported antibiotics (doxycycline, tetracycline, chloramphenicol and erythromycin) was evaluated by the checkerboard method. The fractional inhibitory concentration (FIC) values varied from 0.0 to 0.5 for each combination. The ΔE value (750–1900), according to the bliss independence model showed that the QSIVc synergistically enhanced the inhibitory effect of the reported antibiotics against the multidrug resistant clinical isolates of V. cholerae.
Designing a Clinical Trial Protocol about the Impact of Family-Based Multimedia Education Based on Telephone Tracking (Tele Nursing) to Improve the Quality of Life and Self-Efficacy in Patients with Myocardial Infarction

Background: Provision of education to a person with myocardial infarction and an active family member, who takes care of the patient can prevent or delay the onset of the disease. Telephone tracking is a very useful and inexpensive way to assess the patients’ needs and help them with their care problems. This clinical trial (interventional) protocol was conducted over the impact of family-based multimedia education based on the telephone tracking (tele-nursing) to improve the quality of life and self-efficacy in patients with myocardial infarction. Methods: Two phases are identified to design this study; the first phase includes designing a curriculum by investigating various studies and the panel of experts’ opinions. This phase will be conducted in the form of multimedia training and telephone contact. Multimedia training (including audio, video, image, and animation) over the patients’ lifestyle, nutrition, and care will be conducted through a one-day workshop in 2-3 hours for patients and one of their active family members. The active family member is defined as the primary caregiver, who spends more time with the patient. The educational course will be conducted at a coordinated date and time in the ward where the patients are hospitalized. The researcher will make telephone calls as the educational intervention and continue the follow-ups once a week for one month. The second phase of the intervention will contain a pre/post-test design along with application of Minnesota quality of life and Scherer general self-efficacy standard questionnaires in the intervention (with training) and control (without training) groups. The target participants will include all patients (and their active family members) admitted to CCUs of hospitals affiliated to Shahrekord University of Medical Sciences. Discussion: The present study provides useful data for designing a family-based multimedia educational intervention using the telephone-tracking method (tele-nursing) to improve the quality of life and self-efficacy in patients with myocardial infarction. It can also reduce their medical and treatment costs. The strategies of this program could be important and cost effective, and therefore we hope that the success of such a program is a step forward in improving cardiovascular patient’s health status. Highlights Family-based multimedia educational (tele-nursing) to improve the quality of life and self-efficacy in patients with myocardial infarction. Family-based multimedia educational (tele-nursing) can also reduce their medical and treatment costs. The program is a step forward in improving cardiovascular patient’s health status.
Effect of melanoma stem cells on melanoma metastasis

Cancer stem cells (CSCs) are involved in the metastatic process, the resistance of many types of cancer to therapeutic treatments and consequently the onset of recurrences. The CSC concept therefore significantly extends our understanding of melanoma biology. More recently, melanoma stem cells (MSCs) have been described in melanoma as expressing specific biomarkers. These primitive melanoma cells are not only capable of self-renewal and differentiation plasticity, but may also confer virulence via immune evasion and multidrug resistance, and potentially, via vasculogenic mimicry and transition to migratory and metastasizing derivatives. This review will present the specific biomarkers of MSCs, including CD133, ATP binding cassette subfamily B member 5, CD271, CD20 and aldehyde dehydrogenase, which can regulate the transduction of tumor-related signals. These signal molecules can reversely act on tumor cells and regulate tumor angiogenesis, leading to the occurrence of melanoma metastasis. Targeting these specific biomarkers could inhibit the progression of melanoma and may help the development of novel therapeutic strategies for melanoma.
Association Between Blood Pressure Indicators and Stroke in Aged Population: A Community-Based Nested Case-Control Study

Background and Aim Any single discrete blood pressure (BP) measurement is not enough to estimate adverse cardiovascular events. We aim to comprehensively investigate the association between BP indicators and stroke. Methods An observational cohort study was conducted among 2888 Shanghai community-aged residents from 2014 to 2018, and a nested case-control study was designed to identify the association between BP indicators and stroke. In total 415 cases of stroke detected during the study period were selected as the case group and 415 non-stroke subjects, matched with factors of age and gender, were randomly selected from the cohort as control group. Results Multivariate logistic regression analysis revealed that systolic blood pressure (SBP) (adjusted odds ratio [AOR] 1.02, 95% confidence interval [CI] 1.02–1.03), pulse pressure (PP) (AOR 1.03, 95% CI 1.02–1.04), mean arterial pressure (MAP) (AOR1.02, 95% CI 1.01–1.04) and pulse pressure index (PPI) (AOR 25.68, 95% CI 3.19–206.90) increased the risk of stroke, respectively. After fitting both BP indicators and covariates, isolated abnormal SBP (AOR 2.55, 95% CI 1.74–3.72) or PP ≥50 mmHg (AOR 1.66, 95% CI 1.08–2.56) independently increased risk of stroke. Conclusion Besides SBP, PP and multiple factors, assessment should be taken into account comprehensively for stroke prevention and management.
Application of a Simple Microfluidic Chip Analysis Technology to Evaluate the Inhibitory Role of Protocatechuic Acid on Shear-Induced Platelet Aggregation

This study aimed to develop a simple microfluidic chip analysis technology to study the inhibitory effect of protocatechuic acid on shear-induced platelet aggregation. The microfluidic chip designed in this study simulates 80% fixed narrow microchannels. This microchannel narrow model uses the finite element analysis module of the three-dimensional modeling software solidwork to analyze fluid dynamic behavior. Blood treated with protocatechuic acid at 1, 2, 4, 8, or 16 µg/mL was passed through the microchannel stenosis model at a shear rate of 10,000 s−1. The platelet adhesion and aggregation behaviors were then measured using fluorescence microscopy and observed in real time. Simultaneously, the antiplatelet aggregation effect of protocatechuic acid was analyzed using thromboelastography and photoelectric turbidimetry. The designed stenosis model of the microfluidic chip can produce a gradient of fluid shear rate, and the gradient of fluid shear rate can induce platelet aggregation. Under this model, the degree of platelet adhesion and aggregation increased as the shear rate increased. In the experimental concentration range of 0–8 µmol/mL, protocatechuic acid exerted a concentration-dependent inhibition of platelet aggregation. In contrast, thromboelastography and photoelectric turbidimetry failed to demonstrate an inhibitory effect. The microfluidic chip analysis technology developed in this study can be used to study the effect of protocatechin in inhibiting platelet aggregation induced by shear rate in vitro. This technology is simple to operate and can be used as a new type of antiplatelet aggregation analysis technology for screening studies of novel potential antiplatelet aggregation drugs.
Complete Revascularization of Stable STEMI Patients Offers a Significant Benefit if Done During the Index PCI, but Not if It’s Done as a Staged Procedure

Background Complete revascularization (CR) of hemodynamically stable STEMI improves outcomes when compared to culprit-only PCI. However, the optimal timing for CR (CR during index PCI [iCR] versus staged PCI [sCR]) is unknown. sCR is defined as revascularization of non-culprit lesions not done during the index procedure (mean 31.5±24.6 days after STEMI). Our goal was to determine whether iCR was the superior strategy when compared to sCR. Methods A systematic review of Medline, Cochrane, and Embase was performed for RCTs reporting outcomes of stable STEMI patients who had undergone CR. Only RCTs with a clearly defined timing of CR, for the classification into iCR and sCR, and a follow-up of at least 12 months were included. Seven RCTs comprising 6647 patients (mean age:62.9±1.4 years, male sex:79.4%) met these criteria and were included. Results After a mean follow-up of 25.1±9.4 months, iCR was associated with a significant reduction in cardiovascular mortality (risk ratio [RR] 0.48, 95% confidence interval [CI] 0.26–0.90, p=0.02, relative risk reduction [RRR] 52%) and non-fatal reinfarctions (RR 0.42, 95% CI 0.25–0.70, p=0.001, RRR: 58%). sCR showed a significant reduction in non-fatal reinfarctions only (RR 0.68, 95% CI 0.54–0.85, p=0.0008, RRR: 32%). There was no difference in the safety outcome of contrast-induced nephropathy between groups. Conclusion iCR of stable STEMI patients is associated with a significant reduction in cardiovascular death and a trend towards reduction in all-cause mortality. These benefits are not seen in sCR. Both strategies are associated with a reduction in non-fatal reinfarctions.
Whole genome sequence analysis of multi drug resistant community associated methicillin resistant Staphylococcus aureus from food fish: detection of clonal lineage ST 28 and its antimicrobial resistance and virulence genes

Methicillin-resistant staphylococcus aureus (MRSA) sequence type 28 (ST 28) and spa type t021 is a CC30, prototype of ST-30, Community Associated-MRSA (CA-MRSA) (lukS-lukF +). It is a multi-drug resistant strain harbouring staphylococcal endotoxins, haemolysins, ureolysin, serine protease, and antimicrobial resistance genes. In this study, we report the draft genome sequence of this MRSA isolated from the most commonly used food fish, ribbon fish (Trichiurus lepturus). The total number of assembled paired-end high-quality reads was 7,731,542 with a total length of 2.8Mb of 2797 predicted genes. The unique ST28/ t021 CA- MRSA in fish is the first report from India, and in addition to antibiotic resistance is known to co-harbour virulence genes, haemolysins, aureolysins and endotoxins. Comprehensive comparative genomic analysis of CA-MRSA strain7 can help further understand their diversity, genetic structure, diversity and a high degree of virulence to aid in fisheries management.