Miracle-dz/UniFAR-FTUs huggingface dataset:
A Combined Fit of the Diffuse Neutrino Spectrum using IceCube Muon Tracks and Cascades

The IceCube Neutrino Observatory first observed a diffuse flux of high energy astrophysical neutrinos in 2013. Since then, this observation has been confirmed in multiple detection channels such as high energy starting events, cascades, and through-going muon tracks. Combining these event selections into a high statistics global fit of 10 years of IceCube's neutrino data could strongly improve the understanding of the diffuse astrophysical neutrino flux: challenging or confirming the simple unbroken power-law flux model as well as the astrophysical neutrino flux composition. One key component of such a combined analysis is the consistent modelling of systematic uncertainties of different event selections. This can be achieved using the novel SnowStorm Monte Carlo method which allows constraints to be placed on multiple systematic parameters from a single simulation set. We will report on the status of a new combined analysis of through-going muon tracks and cascades. It is based on a consistent all flavor neutrino signal and background simulation using, for the first time, the SnowStorm method to analyze IceCube's high-energy neutrino data. Estimated sensitivities for the energy spectrum of the diffuse astrophysical neutrino flux will be shown.
Federated Learning using Smart Contracts on Blockchains, based on Reward Driven Approach

Over the recent years, Federated machine learning continues to gain interest and momentum where there is a need to draw insights from data while preserving the data provider's privacy. However, one among other existing challenges in the adoption of federated learning has been the lack of fair, transparent and universally agreed incentivization schemes for rewarding the federated learning contributors. Smart contracts on a blockchain network provide transparent, immutable and independently verifiable proofs by all participants of the network. We leverage this open and transparent nature of smart contracts on a blockchain to define incentivization rules for the contributors, which is based on a novel scalar quantity - federated contribution. Such a smart contract based reward-driven model has the potential to revolutionize the federated learning adoption in enterprises. Our contribution is two-fold: first is to show how smart contract based blockchain can be a very natural communication channel for federated learning. Second, leveraging this infrastructure, we can show how an intuitive measure of each agents' contribution can be built and integrated with the life cycle of the training and reward process.
Leaking Secrets Through Modern Branch Predictors in the Speculative World

Transient execution attacks that exploit speculation have raised significant concerns in computer systems. Typically, branch predictors are leveraged to trigger mis-speculation in transient execution attacks. In this work, we demonstrate a new class of speculation-based attacks that targets the branch prediction unit (BPU). We find that speculative resolution of conditional branches (i.e., in nested speculation) alter the states of pattern history table (PHT) in modern processors, which are not restored after the corresponding branches are later squashed. Such characteristic allows attackers to exploit the BPU as the secret transmitting medium in transient execution attacks. To evaluate the discovered vulnerability, we build a novel attack framework, BranchSpectre, that enables exfiltration of unintended secrets through observing speculative PHT updates (in the form of covert and side channels). We further investigate the PHT collision mechanism in the history-based predictor and the branch prediction mode transitions in Intel processors. Built upon such knowledge, we implement an ultra-high speed covert channel (BranchSpectre-cc) as well as two side channels (i.e., BranchSpectre-v1 and BranchSpectre-v2) that merely rely on BPU for mis-speculation trigger and secret inference in the speculative domain. Notably, BranchSpectre side channels can take advantage of much simpler code patterns than those used in Spectre attacks. We present an extensive BranchSpectre code gadget analysis on a set of popular real-world application code bases followed by a demonstration of side channel attack on OpenSSL. The evaluation results show substantially wider existence and higher exploitability of BranchSpectre code patterns in real-world software. Finally, we discuss several secure branch prediction mechanisms that can mitigate transient execution attacks exploiting modern branch predictors.
Conditional Sound Generation Using Neural Discrete Time-Frequency Representation Learning

Deep generative models have recently achieved impressive performance in speech and music synthesis. However, compared to the generation of those domain-specific sounds, generating general sounds (such as siren, gunshots) has received less attention, despite their wide applications. In previous work, the SampleRNN method was considered for sound generation in the time domain. However, SampleRNN is potentially limited in capturing long-range dependencies within sounds as it only back-propagates through a limited number of samples. In this work, we propose a method for generating sounds via neural discrete time-frequency representation learning, conditioned on sound classes. This offers an advantage in efficiently modelling long-range dependencies and retaining local fine-grained structures within sound clips. We evaluate our approach on the UrbanSound8K dataset, compared to SampleRNN, with the performance metrics measuring the quality and diversity of generated sounds. Experimental results show that our method offers comparable performance in quality and significantly better performance in diversity.
Catalytic Hydrolysis of Cellulose to Glucose Using Weak-Acid Surface Sites on Postsynthetically Modified Carbon

We demonstrate depolymerization of adsorbed (1 → 4)- β-d-glucans (β-glu) derived from crystalline cellulose (Avicel), using weak-acid sites of postsynthetically surface-functionalized mesoporous carbon nanoparticle (MCN) catalysts HT5-HSO3-MCN and COOH-MCN and investigate the role of acid-site density and β-glu molecular weight on this depolymerization. Both HT5-HSO3-MCN and COOH-MCN hydrolyze adsorbed β-glu strands and afford glucose yields of 73% and 90%, respectively, at a buffered pH of 2.0 after 3 h treatment at 180 °C. These yields are significantly higher than the 16% yield of an unfunctionalized MCN-control catalyst under otherwise identical conditions, demonstrating the importance of postsynthetic surface functionalization for achieving weak-acid catalytic hydrolysis. Highlighting the important role of confinement in this catalysis, all yields are generally depressed when using a lower rather than higher molecular weight of adsorbed β-glu strands on the same catalyst. The catalytic hydrolysis rat.
Architecture-Specific Performance Optimization of Compute-Intensive FaaS Functions

FaaS allows an application to be decomposed into functions that are executed on a FaaS platform. The FaaS platform is responsible for the resource provisioning of the functions. Recently, there is a growing trend towards the execution of compute-intensive FaaS functions that run for several seconds. However, due to the billing policies followed by commercial FaaS offerings, the execution of these functions can incur significantly higher costs. Moreover, due to the abstraction of underlying processor architectures on which the functions are executed, the performance optimization of these functions is challenging. As a result, most FaaS functions use pre-compiled libraries generic to x86-64 leading to performance degradation. In this paper, we examine the underlying processor architectures for Google Cloud Functions (GCF) and determine their prevalence across the 19 available GCF regions. We modify, adapt, and optimize three compute-intensive FaaS workloads written in Python using Numba, a JIT compiler based on LLVM, and present results wrt performance, memory consumption, and costs on GCF. Results from our experiments show that the optimization of FaaS functions can improve performance by 12.8x (geometric mean) and save costs by 73.4% on average for the three functions. Our results show that optimization of the FaaS functions for the specific architecture is very important. We achieved a maximum speedup of 1.79x by tuning the function especially for the instruction set of the underlying processor architecture.
DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity

Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to “learn” intrinsic patterns in a complex plane of data is the strength of the approach. Here, we have incorporated convolutional neural networks to find spatial relationships among data to help us predict affinity of binding of proteins in whole superfamilies toward a diverse set of ligands without the need of a docked pose or complex as user input. The models were trained and validated using a stringent methodology for feature extraction. Our model performs better in comparison to some existing methods used widely and is suitable for predictions on high-resolution protein crystal (⩽2.5 Å) and nonpeptide ligand as individual inputs. Our approach to network construction and training on protein-ligand data set prepared in-house has yielded significant insights. We have also tested DEELIG on few COVID-19 main protease-inhibitor complexes relevant to the current public health scenario. DEELIG-based predictions can be incorporated in existing databases including RSCB PDB, PDBMoad, and PDBbind in filling missing binding affinity data for protein-ligand complexes.
Adherence to an eHealth Self-Management Intervention for Patients with Both COPD and Heart Failure: Results of a Pilot Study

Background Chronic obstructive pulmonary disease (COPD) and chronic heart failure (CHF) often coexist and share periods of symptom deterioration. Electronic health (eHealth) might play an important role in adherence to interventions for the self-management of COPD and CHF symptoms by facilitating and supporting home-based care. Methods In this pilot study, an eHealth self-management intervention was developed based on paper versions of multi-morbid exacerbation action plans and evaluated in patients with both COPD and CHF. Self-reporting of increased symptoms in diaries was linked to an automated decision support system that generated self-management actions, which was communicated via an eHealth application on a tablet. After participating in self-management training sessions, patients used the intervention for a maximum of four months. Adherence to daily symptom diary completion and follow-up of actions were analyzed. An add-on sensorized (Respiro®) inhaler was used to analyze inhaled medication adherence and inhalation technique. Results In total, 1148 (91%) of the daily diaries were completed on the same day by 11 participating patients (mean age 66.8 ± 2.9 years; moderate (55%) to severe (45%) COPD; 46% midrange left ventricular function (LVF) and 27% reduced LVF). Seven patients received a total of 24 advised actions because of increased symptoms of which 11 (46%) were followed-up. Of the 13 (54%) unperformed advised actions, six were “call the case manager”. Adherence to inhaled medication was 98.4%, but 51.9% of inhalations were performed incorrectly, with “inhaling too shortly” (<1.25 s) being the most frequent error (79.6%). Discussion Whereas adherence to completing daily diaries was high, advised actions were inadequately followed-up, particularly the action “call the case manager”. Inhaled medication adherence was high, but inhalations were poorly performed. Future research is needed to identify adherence barriers, further tailor the intervention to the individual patient and analyse the intervention effects on health outcomes.
New mechanistic insights into hepatoprotective activity of milk thistle and chicory quantified extract: The role of hepatic Farnesoid-X activated receptors

Objective: Farnesoid-X-activated receptors (FXR) are key modulators of liver regeneration. Milk thistle and Chicory are known as potent protective remedies in several liver disorders. The objective of this work was to examine the role of FXR in the hepato-healing properties of milk thistle (MTE) and chicory extracts (CE) in a rat model of acetaminophen-induced hepatotoxicity. Materials and Methods: Male Wistar rats were randomly divided into seven groups including control, vehicle, acetaminophen (500 mg/kg/day, oral), acetaminophen plus oral MTE 200 and 400 mg/kg/day, and acetaminophen plus oral CE 500 and 1000 /kg/day for 28 days. Liver function and histology as well as the pattern of hepatic FXR expression were assessed after 4 weeks. Results: Administration of acetaminophen was associated with a significant elevation of liver transaminase along with the architectural injuries. In contrast, chronic concomitant administration of both MTE and CE significantly restored the liver function and structural abnormality. The main molecular findings of the study revealed that the lower doses of both MTE and CE led to a marked upregulation of hepatic FXR expression. Conclusion: Discovery of the involvement of the nuclear modulating pathways in hepatoprotective activity of the extracts, providesa new mechanistic insight which needs further investigations.
Endogenous Spatial Attention Modulates the Magnitude of the Colavita Visual Dominance Effect

The Colavita effect refers to the phenomenon wherein people tend to not respond to an auditory stimulus when a visual stimulus is simultaneously presented. Although previous studies have shown that endogenous modality attention influences the Colavita effect, whether the Colavita effect is influenced by endogenous spatial attention remains unknown. In the present study, we established endogenous spatial cues to investigate whether the size of the Colavita effect changes under visual or auditory cues. We measured three indexes to investigate the effect of endogenous spatial attention on the size of the Colavita effect. These three indexes were developed based on the following observations in bimodal trials: (a) The proportion of the “only vision” response was significantly higher than that of the “only audition” response; (b) the proportion of the “vision precedes audition” response was significantly higher than that of the “audition precedes vision” response; and (c) the reaction time difference of the “vision precedes audition” response was significantly higher than that of the “audition precedes vision” response. Our results showed that the Colavita effect was always influenced by endogenous spatial attention and that its size was larger at the cued location than at the uncued location; the cue modality (visual vs. auditory) had no effect on the size of the Colavita effect. Taken together, the present results shed light on how endogenous spatial attention affects the Colavita effect.
Dartanan: Prototype evaluations of a serious game to engage children in the calibration of their hearing aid functionalities

Introduction It is notoriously difficult to obtain a perfect fitting of hearing aids (HAs) for children as they often struggle to understand their hearing loss well enough to discuss the fitting adequately with their audiologist. Dartanan is an ‘edutainment’ game developed to help children understand the functions of their HA in different sound contexts. Dartanan also has elements of a leisure game for all children, in order to create an inclusive activity. Methods Game prototypes were evaluated during two formative evaluations and a summative evaluation. In total 106 children with and without hearing loss in Italy, Spain and the UK played Dartanan. A built-in virtual HA enabled children with hearing loss to use headphones to play. Results and conclusions: During the formative stages, feedback was discussed during focus groups on factors such as the audiological aspects, the extent to which children learned about HA functions, accessibility and usability, and this feedback was presented to the developers. After redevelopment, a summative evaluation was performed using an online survey. It was concluded that the game had met the goals of helping children understand their HA functionalities and providing an inclusive activity. User-evaluations were crucial in the development of the app into a useful and useable service.
A Corneal Perforation Related to Beauveria Bassiana and Post-Penetrating Keratoplasty Management Discussion

Fungal keratitis is an infection that is insidious and frequently misdiagnosed. Those with chronic eye surface conditions, contact lenses, systemic immunosuppression, and diabetes have been the most frequently affected with fungal keratitis. An 84-year-old male patient with a history of bilateral penetrating keratoplasty (PK) for keratoconus presented with pain and decreased visual acuity on his left eye. A corneal perforation was found, which was treated immediately with a full-thickness corneal transplant. The specimen was sent for bacterial and fungal cultures. Topical corticosteroids were prescribed postoperatively. Beauveria bassiana was isolated from the corneal scrapings. The postoperative treatment was modified by reducing the dose of corticosteroid and adding topical natamycin together with systemic posaconazole. No recurrence occurred in the transplant four months postoperatively under topical dexamethasone 0.1% b.i. d. This is the first case of keratitis and perforation in a previously transplanted cornea. Due to the rarity of the infection, there are no clear guidelines for postoperative prophylaxis in B. bassiana infection. Either the continuation of corticosteroids or the switch to another immunosuppressive therapy and selecting the appropriate antifungal regimen posed a significant therapeutic dilemma.
Complete mitochondrial genome of Ostrinia kasmirica (Lepidoptera: Crambidae)

Abstract The complete mitochondrial genome of Ostrinia kasmirica (Moore, 1888) was sequenced in this study. The circular mitogenome is 15,214 bp in length, containing 37 typical encoded genes and a non-coding control region. The gene organization and nucleotide composition are similar to those of most other sequenced Ostrinia species. All protein-coding genes (PCGs) initiate with ATN and terminate with TAN, except cox1 starts with CGA and cox1, cox2, nad5 terminate with an incomplete codon T. The control region of 308 bp contains three conserved features including the motif ‘TTAGA’ preceded a poly-T stretch, a microsatellite-like (TA)n element, and a poly-A stretch upstream of trnM. Phylogenetic analysis based on mitogenome sequences revealed that the O. kasmirica (the second species group) was more closely related to the third species group of the genus and the first species group was not at the basal position of this genus as that Mutuura and Munroe indicated.
Does a High Pre-Treatment Nicotine Dependence Increase the Post-Cessation Diabetes Risk?

Aim:This study aimed to observe the pre-treatment nicotine dependence level (NDL) and the change in diabetes risk screening results during the post-cessation period.Material and Methods: In the current study, 527 current smokers who applied to a tertiary hospital for smoking cessation treatment between February 2019 and July 2019 were included. Firstly, a questionnaire (containing demographic data, the Fagerstrom Nicotine Dependence Test; FTND, and the American Diabetes Association; ADA Diabetes Risk Screening Tool) was applied. In the second stage, smoking cessation status and diabetes risk of 279 patients who could be reached by phone after 6 months were re-evaluated. Results:Based on initial results, 33.6% of nicotine addicts were heavy smokers and 20.5% had type 2 diabetes mellitus (T2DM) risk. Although FNBT score and diabetes risk score (p = 0.002, r = 0.133) were related, NDL was not effective in the presence of T2DM risk before quitting (p = 0.08). Increased post-cessation T2DM risk was detected in quitters (25.3%) versus current smokers (13.5%). Six-month follow-up indicated that the rate of diabetes risk among current smokers was 46% of quitters (p=0.01, OR=0.46;0.250.86). The diabetes risk presence related to pre-treatment heavy dependence among quitters (54.1%) was higher than mild (13.5%) or moderate (32.4%) nicotine dependence (p=0.004). Although final BMI was not different between quitters and smokers (p=0,58), there was a significant increase in BMI between baseline and final visits (p<0.001; Z=−10.39). Both current smokers and quitters had similar age and gender demographics (p=0.64, p=0.37, respectively). Conclusion:Particularly heavy smokers might be aware that smoking cessation would be more rational with a lifestyle change to prevent diabetes risk of the post-cessation period. Our study would contribute to knowledge about smoking, post-cessation, and T2DM risk.
LEVER SIGN TEST FOR CHRONIC ACL INJURY: A COMPARISON WITH LACHMAN AND ANTERIOR DRAWER TESTS

ABSTRACT Objective: This study aims to evaluate the sensitivity and specificity of the lever sign test in patients with and without chronic Anterior Cruciate Ligament (ACL) injuries in an outpatient setting and the inter-examiner agreement of surgeons with different levels of experience. Methods: 72 consecutive patients with a history of previous knee sprains were included. The Lachman, anterior drawer, and Lever Sign tests were performed for all subjects in a randomized order by three blinded raters with different levels of experience. Sensitivity, specificity, positive predictive value, negative predictive value, and inter-rater agreement were estimated for all tests. Results: Among the 72 patients, the prevalence of ACL injuries was 54%. The lever test showed sensitivity of 64.1% (95% CI 0.47-0.78) and specificity of 100% (95% CI 0.87-1.00) for the senior examiner. For the less experienced examiner the sensitivity was 51.8% and the specificity was 93.7%. Positive predictive values (PPV) and negative predictive values (NPV) were 100% and 70.2%, respectively. Conclusion: Lever Sign test shows to be a maneuver of easy execution, with 100% specificity and 100% PPV. Moderate agreement between experienced examiners and low agreement among experienced and inexperienced examiners was found. This test may play a role as an auxiliary maneuver. Level of Evidence I, Diagnostic Studies - Investigating a Diagnostic Test.