Miracle-dz/UniFAR-FTUs huggingface dataset:
AVEC 2014: the 4th international audio/visual emotion challenge and workshop

The fourth Audio-Visual Emotion Challenge and workshop AVEC 2014 was held in conjunction ACM Multimedia'14. Like the 2013 edition of AVEC, the workshop/challenge addresses the interpretation of social signals represented in both audio and video in terms of high-level continuous dimensions from a large number of clinically depressed patients and controls, with a sub-challenge in self-reported severity of depression estimation. In this summary, we mainly describe participation and its conditions.
Optimal molecular crowding accelerates group II intron folding and maximizes catalysis

Significance Biological processes take place in living cells and have adapted to environmental conditions such as temperature, salt concentration, and high-density cellular contents. However, our molecular understanding of most biological processes relies on in vitro experiments under nonphysiological, highly diluted, and high-salt conditions. To overcome this, we mimic the cellular environment by introducing crowding agents. To understand how such conditions affect large-ribozyme folding and function, we studied a group IIB intron ribozyme under crowded, low-salt conditions. The data show how crowded environments enhance the activity of such large ribozymes, even in low-salt concentrations. Interestingly, we find that “optimal” crowding yields maximum activity, beyond which dense crowding becomes detrimental for activity. Unlike in vivo conditions, group II intron ribozymes are known to require high magnesium(II) concentrations ([Mg2+]) and high temperatures (42 °C) for folding and catalysis in vitro. A possible explanation for this difference is the highly crowded cellular environment, which can be mimicked in vitro by macromolecular crowding agents. Here, we combined bulk activity assays and single-molecule Förster Resonance Energy Transfer (smFRET) to study the influence of polyethylene glycol (PEG) on catalysis and folding of the ribozyme. Our activity studies reveal that PEG reduces the [Mg2+] required, and we found an “optimum” [PEG] that yields maximum activity. smFRET experiments show that the most compact state population, the putative active state, increases with increasing [PEG]. Dynamic transitions between folded states also increase. Therefore, this study shows that optimal molecular crowding concentrations help the ribozyme not only to reach the native fold but also to increase its in vitro activity to approach that in physiological conditions.
Programming languages, natural languages, and mathematics

Some social aspects of pro gramming are illuminated through analogies with similar aspects of mathematics and natural languages. The split between pure and applied mathematics is found similarly in programming. The development of natural languages toward flexionless, word-order based language types speaks for programming language design based on general, abstract constructs. By analogy with incidents of the history of artificial, auxiliary languages it is suggested that Fortran and Cobol will remain dominant for a long time to come. The most promising avenues for further work of wide influence are seen to be high quality program literature (i.e. programs) of general utility and studies of questions related to program style.
Acute Post-Streptococcal Glomerulonephritis in the Northern Territory of Australia: A Review of Data from 2009 to 2016 and Comparison with the Literature

Abstract. Acute post-streptococcal glomerulonephritis (APSGN) is an inflammatory kidney disease following infection with nephritogenic strains of Group A Streptococcus. In 1991, APSGN became notifiable in the Northern Territory (NT) of Australia with cases recorded on the NT Notifiable Disease Database (NTNDS). The case definition of a confirmed case requires laboratory definitive evidence or laboratory suggestive evidence in conjunction with a clinically compatible illness. Probable cases require clinical evidence only. Acute post-streptococcal glomerulonephritis notifications from 2009 to 2016 were extracted from the NTNDS. Of the 322 cases, 261 were confirmed and 61 probable. The majority, 304 (94%), were Aboriginal and the median age was 8 years (range: 0–62 years). Incidence for confirmed cases was 13.8/100,000 person-years, with inclusion of probable cases increasing incidence to 17.0/100,000 person-years. Highest incidence of confirmed cases was in Aboriginal children less than 15 years of age at 124.0 cases/100,000 person-years. The rate ratio of confirmed cases in Aboriginal to non-Aboriginal Australians was 18.9 (95% confidence interval: 11.4–33.6). Recent trends show a consistently high number of notifications annually with less frequent outbreaks. The Aboriginal population of the NT continues to have high rates of APSGN with recent trends showing higher rates than previously reported. Sustained preventative efforts and continued surveillance strategies are needed.
A generalization of algebraic surface drawing

The mathematical description of three dimensional surfaces usually falls in one of two classifications: parametric and algebraic. The form is defined as all points which satisfy some equation: F(x,y,z)=0. This form is ideally suited for image space shaded picture drawing, the pixel coordinates are substituted for x and y and the equation is solved for z. Algorithms for drawing such objects have been developed primarily for first and second order polynomial functions. This paper presents a new algorithm applicable to other functional forms, in particular to the summation of several gaussian density distributions. The algorithm was created to model electron density maps of molecular structures but can be used for other artistically interesting shapes.
Improved Patient-Independent System for Detection of Electrical Onset of Seizures

Purpose: To design a non–patient-specific system to detect the electrical onset of seizures in patients with temporal lobe epilepsy. Methods: We used EEG data from 29 seizures of 18 temporal lobe epilepsy patients who underwent multiday video-scalp EEG monitoring as part of their presurgical evaluations. We segmented each data set into preictal and ictal phases, and identified spectral entropy, spectral energy, and signal energy as useful features for discriminating normal and seizure conditions. The performance of five different classifiers was analyzed using these features to design an automated detection system. Results: Among the five classifiers, decision tree, k-nearest neighbor, and support vector machine performed with sensitivity (specificity) of 79% (81%), 75% (85%), and 80% (86%), respectively. The other two, linear discriminant algorithm and Naive Bayes classifiers, performed with sensitivity (specificity) of 54% (94%), 47% (96%), respectively. Conclusions: The support vector machine–based seizure detection system showed better detection capability in terms of sensitivity and specificity measures as compared to linear discriminant algorithm, Naive Bayes, decision tree, and k-nearest neighbor classifiers. Conclusions: Our study shows that a generalized system to detect the electrical onset of seizures in temporal lobe epilepsy using scalp-recorded EEG is possible. If confirmed on a larger data set, our findings may have significant implications for the management of seizures, especially in patients with drug-resistant epilepsy.
CarePre

Clinical decision support systems are widely used to assist with medical decision making. However, clinical decision support systems typically require manually curated rules and other data that are difficult to maintain and keep up to date. Recent systems leverage advanced deep learning techniques and electronic health records to provide a more timely and precise result. Many of these techniques have been developed with a common focus on predicting upcoming medical events. However, although the prediction results from these approaches are promising, their value is limited by their lack of interpretability. To address this challenge, we introduce CarePre, an intelligent clinical decision assistance system. The system extends a state-of-the-art deep learning model to predict upcoming diagnosis events for a focal patient based on his or her historical medical records. The system includes an interactive framework together with intuitive visualizations designed to support diagnosis, treatment outcome analysis, and the interpretation of the analysis results. We demonstrate the effectiveness and usefulness of the CarePre system by reporting results from a quantities evaluation of the prediction algorithm, two case studies, and interviews with senior physicians and pulmonologists.
Approximate distance oracles

Let <italic>G=(V,E)</italic> be an undirected <italic>weighted</italic> graph with <italic>|V|=n</italic> and <italic>|E|=m</italic>. Let <italic>k\ge 1</italic> be an integer. We show that <italic>G=(V,E)</italic> can be preprocessed in <italic>O(kmn^{1/k})</italic> expected time, constructing a data structure of size <italic>O(kn^{1+1/k})</italic>, such that any subsequent <italic> distance query</italic> can be answered, approximately, in <italic>O(k)</italic> time. The approximate distance returned is of stretch at most <italic>2k-1</italic>, i.e., the quotient obtained by dividing the estimated distance by the actual distance lies between 1 and <italic>2k-1</italic>. We show that a 1963 girth conjecture of Erd{\H{o}}s, implies that <italic>&ohgr(n^{1+1/k})</italic> space is needed in the worst case for any real stretch strictly smaller than <italic>2k+1</italic>. The space requirement of our algorithm is, therefore, essentially optimal. The most impressive feature of our data structure is its <italic> constant</italic> query time, hence the name ``oracle''. Previously, data structures that used only <italic>O(n^{1+1/k})<italic> space had a query time of <italic>&ohgr(n^{1/k})</italic> and a slightly larger, non-optimal, stretch. Our algorithms are extremely simple and easy to implement efficiently. They also provide faster constructions of sparse <italic>spanners</italic> of weighted graphs, and improved <italic>tree covers</italic> and <italic>distance labelings</italic> of weighted or unweighted graphs. }
An Analytical Framework for Multi-Tier NOMA Networks With Underlay D2D Communications

This paper develops a new spatial model for non-orthogonal multiple access (NOMA)-based two tier-cellular network underlying inband device-to-device (D2D) communication with randomly deployed base stations and users. In particular, we model the locations of users by considering two independent Poisson hole process and Poisson cluster process. Using the developed model, we first derive the interference distributions at typical user (cellular/D2D receiver) by considering contribution from dominant interferers. In addition, the performance of NOMA with successive interference cancelation (SIC) is impacted by intra-user interference. Hence, for cellular users, we propose an SIC with intra-user interference estimation (SIE) receiver which attempts to locally estimate and remove intra-user interference for better decoding. Based on the interference distributions results, we analyze the performance of a typical user in terms of outage probability. In particular, the performance of typical cellular user is evaluated under conventional SIC and proposed SIE techniques with perfect and imperfect intra-user estimation. Using these outage probability results, we also characterize the average link throughput experienced by a typical cellular user or D2D receiver (D2D-Rx). Moreover, simulation results are also presented to validate the accuracy of the derived results.
Who Believes in Nonlimited Willpower? In Search of Correlates of Implicit Theories of Self-Control

Recently, the most prominent model of self-control, the strength model, was criticized, and other explanations of self-control have been proposed. One of them is a concept of lay, implicit, willpower theories, that is, believing either that willpower is limited (as in the strength model) or nonlimited. Research shows that holding a nonlimited-resource belief prevents individuals from suffering ego depletion and is related to successful self-regulation. The current study explored how personality, time perspective, and intelligence predict willpower theories. Additionally, two aspects of willpower theories, strenuous mental activity and resistance to temptations, were measured separately. The results indicated that the two aspects of willpower theories were not correlated with each other. This supports hypothesis that willpower theories may be domain specific and also suggests that these two aspects should not be aggregated into one, homogenous scale as was done in some previous research. Both aspects of holding a nonlimited-resource theory were related positively to emotional stability and negatively to past negative time perspective. Strenuous mental activity was positively associated with intellect and negatively with present fatalism, whereas resistance to temptations was correlated positively with conscientiousness and future time perspective. There were no relations between willpower theories and intelligence, which posits that similar life outcomes related with these two attributes are based on distinct mechanisms. Regression analyses revealed that only personality traits (emotional stability and conscientiousness) remained significant predictors of willpower beliefs.
CUNI Transformer Neural MT System for WMT18

We describe our NMT system submitted to the WMT2018 shared task in news translation. Our system is based on the Transformer model (Vaswani et al., 2017). We use an improved technique of backtranslation, where we iterate the process of translating monolingual data in one direction and training an NMT model for the opposite direction using synthetic parallel data. We apply a simple but effective filtering of the synthetic data. We pre-process the input sentences using coreference resolution in order to disambiguate the gender of pro-dropped personal pronouns. Finally, we apply two simple post-processing substitutions on the translated output. Our system is significantly (p < 0.05) better than all other English-Czech and Czech-English systems in WMT2018.
An evaluation of depression, anxiety and fatigue in patients with Behçet’s disease

Behçet's disease (BD) is a type of systemic vasculitis and inflammatory disease of unknown etiology, which is associated with fatigue and lower quality of life (QoL). The aim of this study was to assess the relationship between Behçet's Disease Current Activity Form (BDCAF) and Behçet's Disease Quality of Life (BDQoL), depression, anxiety and fatigue in Behçet's disease.
Management of visual hallucinations in dementia and Parkinson’s disease

ABSTRACT Objectives: Visual hallucinations are a common symptom in dementia and Parkinson’s disease and have been associated with greater cognitive and functional decline, but optimal management strategies are unclear. We review the frequency and pathogenesis of visual hallucinations in dementia and Parkinson’s disease and examine the evidence base for their management. Design: We undertook a systematic review of the visual hallucinations in dementia, searching studies published between January 1980 and July 2017 using PubMed with the search terms visual hallucinations AND review AND (dementia OR parkinson*). Results: We found 645 articles and screened them for relevance, finally including 89 papers (11 meta-analyses, 34 randomized controlled trials, six other trials and a number of relevant review articles). Only six of the trials reported visual hallucination outcomes separately from other neuropsychiatric symptoms. Conclusions: Atypical antipsychotics were frequently studied, but with the exception of clozapine in Parkinson’s disease dementia, results were equivocal. There was some evidence that acetylcholinesterase inhibitors may help visual hallucinations. Overall, effect sizes for most treatments were small and there were few studies with long term follow up. Treatments need to be carefully weighed up with the risks and reviewed often, and many patients improved without treatment. There is a lack of data regarding visual hallucinations due to the grouping of psychotic symptoms together in commonly used rating scales. The lack of a specific rating scales, or analyzable items within other scales, for visual hallucinations, limited efficacy of current and small evidence base with short follow up are important areas for future studies to address.
Dynamic Control of CPU Usage in a Lambda Platform

Lambda platform is a new concept based on an event-driven server-less computation that empowers application developers to build scalable enterprise software in a virtualized environment without provisioning or managing any physical servers (a server-less solution). In reality, however, devising an effective consolidation method to host multiple Lambda functions into a single machine is challenging. The existing simple resource allocation algorithms, such as the round-robin policy used in many commercial server-less systems, suffer from lack of responsiveness to a sudden surge in the incoming workload. This will result in an unsatisfactory performance degradation that is directly experienced by the end-user of a Lambda application. In this paper, we address the problem of CPU cap management in a Lambda platform for ensuring different QoS enforcement levels in a platform with shared resources, in case of fluctuations and sudden surges in the incoming workload requests. To this end, we present a closed-loop (feedback-based) CPU cap controller, which fulfills the QoS levels enforced by the application owners. The controller adjusts the number of working threads per QoS class and dispatches the outstanding Lambda functions along with the associated events to the most appropriate working thread. The proposed solution reduces the QoS violations by an average of 6.36 times compared to the round-robin policy. It can also maintain the end-to-end response time of applications belonging to the highest priority QoS class close to the target set-point while decreasing the overall response time by up to 52%.
Pharmacokinetics/Pharmacodynamics of Vaborbactam, a Novel Beta-Lactamase Inhibitor, in Combination with Meropenem

Vaborbactam is a novel beta-lactamase inhibitor with activity against important beta-lactamases, in particular, serine carbapenemases, and is currently approved in combination with meropenem as Vabomere for the treatment of complicated urinary tract infections, including pyelonephritis. This combination is highly active against Gram-negative pathogens, especially Klebsiella pneumoniae carbapenemase (KPC)-producing carbapenem-resistant Enterobacteriaceae. ABSTRACT Vaborbactam is a novel beta-lactamase inhibitor with activity against important beta-lactamases, in particular, serine carbapenemases, and is currently approved in combination with meropenem as Vabomere for the treatment of complicated urinary tract infections, including pyelonephritis. This combination is highly active against Gram-negative pathogens, especially Klebsiella pneumoniae carbapenemase (KPC)-producing carbapenem-resistant Enterobacteriaceae. The objective of these studies was to evaluate vaborbactam pharmacokinetics (PK) and pharmacodynamics (PD) relationships for efficacy in a neutropenic mouse thigh infection model, as well as in an in vitro hollow-fiber infection model, in combination with a fixed exposure of meropenem using KPC-containing strains of Enterobacteriaceae. For both models, the meropenem dosage regimen was designed to simulate a 2-g dose administered every eight hours (q8h) by 3-h infusion. Vaborbactam dosage regimens were designed to produce a wide range of 24-h areas under the concentration-time curves (AUCs) in the thigh infection model. However, for the hollow-fiber model, the AUCs were limited to values of 192, 320, or 550 mg · h/liter. In both the animal and in vitro models, the PK-PD parameter that best described the antibacterial activity of vaborbactam, when administered in combination with meropenem at exposures equivalent to 2 g dosed q8h by 3-h infusion in humans, was the 24-h free vaborbactam AUC/meropenem-vaborbactam (with vaborbactam at 8 mg/liter) MIC ratio. The magnitude of this ratio for bacteriostasis was 9 to 12 and the magnitude to observe a 1-log kill was 18 to 38. In addition, a magnitude greater than 24 suppressed the development of resistance in the in vitro hollow-fiber model.
Bisphosphonate-related atypical femoral fracture: Managing a rare but serious complication

Atypical femoral fracture is a rare but serious complication of long-term bisphosphonate therapy. Although the benefit of preventing osteoporotic fractures greatly outweighs the risk of atypical fracture in bisphosphonate users, concern about atypical fracture risk has led to a decrease in bisphosphonate use. What are the risks, and how do we treat atypical femoral fracture? Concern about this rare complication has led to a decrease in bisphosphonate use.
Rapid Detection of ERG11-Associated Azole Resistance and FKS-Associated Echinocandin Resistance in Candida auris

Candida auris is an emerging multidrug-resistant yeast that can cause serious invasive infections. The accurate and rapid assessment of antifungal resistance is important for effective patient management. ABSTRACT Candida auris is an emerging multidrug-resistant yeast that can cause serious invasive infections. The accurate and rapid assessment of antifungal resistance is important for effective patient management. A novel and highly accurate diagnostic platform was established for the rapid identification of ERG11 mutations conferring azole resistance and FKS1 mutations associated with echinocandin resistance in C. auris. Using allele-specific molecular beacons and DNA melting curve analysis following asymmetric PCR, a duplex ERG11 assay and a simplex FKS1 HS1 assay were developed to identify the most prominent resistance-associated mutations (Y132F and K143R in ERG11; S639F in FKS1 HS1) within 2 h. Assays were validated by testing a panel of 94 C. auris clinical isolates in a blind manner. The molecular diagnostic results from the assays were 100% concordant with DNA sequencing results. This platform has the potential to overcome the deficiencies of existing in vitro susceptibility-based assays to identify azole- and/or echinocandin-resistant C. auris, and thus, it holds promise as a surrogate diagnostic method to direct antifungal therapy more effectively.
Finding Mixed Nash Equilibria of Generative Adversarial Networks

We reconsider the training objective of Generative Adversarial Networks (GANs) from the mixed Nash Equilibria (NE) perspective. Inspired by the classical prox methods, we develop a novel algorithmic framework for GANs via an infinite-dimensional two-player game and prove rigorous convergence rates to the mixed NE, resolving the longstanding problem that no provably convergent algorithm exists for general GANs. We then propose a principled procedure to reduce our novel prox methods to simple sampling routines, leading to practically efficient algorithms. Finally, we provide experimental evidence that our approach outperforms methods that seek pure strategy equilibria, such as SGD, Adam, and RMSProp, both in speed and quality.