Miracle-dz/UniFAR-FTUs huggingface dataset:
Health Services Research on HRQ oL and Pharmacoeconomics of Low Back Pain Patients treated with Oxycodon/Naloxon or other Step III Opioids

OBJECTIVES: Low back pain carries a high life time prevalence and a high financial and individual burden. Maintaining the patients' quality of life and sustaining their physical capacity should be the aim of an effective therapy. Strong opiods (WHO-step III) are well established as effective analgesics in the treatment of low back pain, although accompanying adverse drug effects, particularly constipation, may decrease the patient's quality of life and increase overall treatment costs. A non-interventional study was performed, comparing the efficacy of the therapy with the combination compound oxycodone/naloxone to other WHO-step III opioids (strong opioids) in patients with chronic low back pain, in order to allow for a complete and comprehensive documentation of health-related quality-of-life parameters as well as a presentation of all costs resulting from back pain therapy under everyday practice conditions. METHODS: A multi-center, parallel-group design was chosen. Opioid-naive and opioid-pretreated female and male adults with chronic low back pain who require round-the-clock-WHO-step III opioid therapy were considered for the observation for the duration of 12 months after enrolment. Quality of life was assessed using questionnaires, rating scales and patient diaries, serving the measurement of pain, bowel movements and general well-being. Pharmacoeconomic aspects of the study included the calculation of direct and indirect costs, as well as a cost-effectiveness analysis of the two alternative therapies. RESULTS: 10 months following FPFV, 757 patients were considered for the interim analysis providing data of the 6 months observation period. Patients of cohort 1 (oxycodone/naloxone) experienced a better quality of life and less back pain after 6 months compared to patients of cohort 2. Direct and indirect costs were higher for patients in cohort 2 (other strong opioids). The cost-effectiveness-analysis yielded greater effectiveness at a lower price for the therapy with oxycodone/naloxone. CONCLUSION: After a treatment phase of 6 months it is evident that therapy with oxycodone/naloxone results in significantly lower direct and indirect costs while exhibiting superior effectiveness and higher quality of life, when compared to therapy with other strong opioids.
Resistance to Amoxicillin and Clarithromycin in Helicobacter pylori isolates in a tertiary care hospital

Introduction: Amoxicillin and Clarithromycin are the two most often chosen first line drugs in eradication therapy for H. Pylori, owing to their better efficacy. Unlike other bacterial infections, therapy here is not based on individual susceptibility report for this bacterium. Rather an empirical regimen is chosen based on the prevalent resistance report in a given population. Current study is aimed at determining the resistance rate in our study population for amoxicillin (AMX) & Clarithromycin (CLA) and to determine if these two drugs can be continued as first line regimen. Materials and Methods: Gastric biopsy samples from 165 dyspeptic patients obtained through endoscopy was cultured in Brucella Chocolate agar (BCA). Among them 46 samples were showing sufficient growth of H. Pylori for antibiotic susceptibility testing. MIC for AMX and CLA was determined by E-strip method based on standard guidelines. Results: 91.3 percent of isolates were susceptible or moderately susceptible to CLA. Percentage of isolates within the proposed epidemiological cut off for AMX was 76. Conclusion: H.pylori isolates in our study population had resistance rate well below the cut off (20%) for CLA and it could be safely used as first line regimen in treating patients from this region of the country. On the contrary, more than 20% of the strains had MIC above the proposed Epidemiological cut off for AMX. Hence an increase in dosage and frequency of dosage, in combination with a potent anti-H.pylori drug could be used in treatment. Clinical trials are required to study efficacy of AMX based regimen in non susceptible patients. Serial monitoring of biological resistance is required for both these drugs, to determine the change in susceptibility pattern at the earliest. Keywords: Amoxicillin, Clarithromycin, Antibiotic susceptibility test, Minimum Inhibitory Concentration, Helicobacter pylori.
hsTropI: an early biomarker of acute coronary syndrome & MI

In the Emergency Department cardiac troponins are the preferred biomarkers for the diagnosis of acute myocardial infarction (MI) and are useful for risk stratification of patients with acute coronary syndrome (ACS) and stable ischemic heart disease. The introduction of ‘high sensitivity’ Troponins (hscTn) in many tertiary care centers has transformed the diagnostic scenario, wherein, the time from onset of chest pain to its diagnosis has significantly been reduced. With the advent of new hsTnI assays, which have a low limit of detection, low imprecision and low reference limits, the possibility of more patients with unstable angina being classified as having non-ST-elevation myocardial infarction (NSTEMI) will now increase. Its use will identify more high-risk patients with undifferentiated chest pain, that will help ‘rule-in’ or ‘rule-out’ acute myocardial infarction. In conclusion, it may be said that hsTropI is a sensitive, albeit less specific marker of MI. In patients of mildly elevated hsTropI and without evidence of ST elevation, a serial assessment of the biomarker is suggested. This will likely translate into more improved outcomes in this difficult patient population when ECG findings and clinical presentation do not suggest a clear diagnosis.a
Smoking status in patients with a new cancer diagnosis and effectiveness of smoking cessation efforts.

e17699 Background: Smoking is associated with increased incidence of morbidity and mortality, but its effects after a diagnosis of cancer are less well studied. We hypothesized that patients who presented with a new cancer diagnosis who were active smokers would be less likely to undergo chemotherapy, have higher mortality and be only modestly successful at smoking cessation efforts. Methods: The database at our community cancer center was queried to look at rates of smoking, smoking cessation, chemotherapy treatments and mortality. Results: Between November 1, 2012 and January 31, 2014, 2448 patients with an oncology diagnosis were assessed for smoking status. Of those, 412 (16.8%) were current smokers. Current smokers were offered smoking cessation tools including in office counseling and enrollment into a focused smoking cessation program called Quitworks. Subsequently, 30 (7.3%) patients quit smoking. Of the 412 current smokers, 159 (38.6%) patients were given literature on smoking cessation. Of those.
Qi Gong and Heart Rate Variability: A Systematic Review

Qi Gōng is a type of exercise that involves meditation and is used for health. With a background from the martial arts, it is known also as an auto healing exercise with three main elements according to Traditional Chinese Medicine (TCM): body focus (posture and movement), breathing and mind focus (meditative elements). Heart rate variability is a tool that allows us to measure the autonomic system velocity to detect and respond to unexpected stimuli. So our goal was to build a systematic review of Qi Gong and Heart rate variability. PUBMED, EBSCO and Science Direct data bases were searched. Key terms (MeSH) were Heart Rate Variability and Ch’i Kung OR Qi Gong with a 10-year filter. Four independent reviewers analyzed all articles and excluded duplicated articles, articles in Chinese and articles with no adherence to the subject. The end result was 5 studies selected. Our results showed no significant improvements in HRV with the practice of Qi Gōng, probably because of data heterogeneity. We found 5 types of Qi Gōng, 3 different instruments and different time intervals to measure HRV and 2 different age groups in the studies. We can conclude that there is evidence that Qi Gōng can improve HRV but studies that would use the same parameters are needed to further confirm that.
Interpolated Adjoint Method for Neural ODEs

In this paper, we propose a method, which allows us to alleviate or completely avoid the notorious problem of numerical instability and stiffness of the adjoint method for training neural ODE. On the backward pass, we propose to use the machinery of smooth function interpolation to restore the trajectory obtained during the forward integration. We show the viability of our approach, both in theory and practice.
How the Brain might use Division

One of the most fundamental questions in Biology or Artificial Intelligence is how the human brainperforms mathematical functions. How does a neural architecture that may organise itself mostly throughstatistics, know what to do? One possibility is to extract the problem to something more abstract. This becomesclear when thinking about how the brain handles large numbers, for example to the power of something, whensimply summing to an answer is not feasible. In this paper, the author suggests that the maths question can beanswered more easily if the problem is changed into one of symbol manipulation and not just number counting.If symbols can be compared and manipulated, maybe without understanding completely what they are, then themathematical operations become relative and some of them might even be rote learned. The proposed systemmay also be suggested as an alternative to the traditional computer binary system. Any of the actual maths stillbreaks down into binary operations, while a more symbolic level above that can manipulate the numbers andreduce the problem size, thus making the binary operations simpler. An interesting result of looking at this is thepossibility of a new fractal equation resulting from division, that can be used as a measure of good fit and wouldhelp the brain decide how to solve something through self-replacement and a comparison with this good fit.
An Introduction to Pursuit-evasion Differential Games

Pursuit and evasion conflicts represent challenging problems with important applications in aerospace and robotics. In pursuit-evasion problems, synthesis of intelligent actions must consider the adversary’s potential strategies. Differential game theory provides an adequate framework to analyze possible outcomes of the conflict without assuming particular behaviors by the opponent. This article presents an organized introduction of pursuit-evasion differential games with an overview of recent advances in the area. First, a summary of the seminal work is outlined, highlighting important contributions. Next, more recent results are described by employing a classification based on the number of players: one-pursuer-one-evader, N-pursuers-one-evader, one-pursuer-M-evaders, and N-pursuer-M-evader games. In each scenario, a brief summary of the literature is presented. Finally, two representative pursuit-evasion differential games are studied in detail: the two-cutters and fugitive ship differential game and the active target defense differential game. These problems provide two important applications and, more importantly, they give great insight into the realization of cooperation between friendly agents in order to form a team and defeat the adversary.
Tensorized Random Projections

We introduce a novel random projection technique for efficiently reducing the dimension of very high-dimensional tensors. Building upon classical results on Gaussian random projections and Johnson-Lindenstrauss transforms~(JLT), we propose two tensorized random projection maps relying on the tensor train~(TT) and CP decomposition format, respectively. The two maps offer very low memory requirements and can be applied efficiently when the inputs are low rank tensors given in the CP or TT format. Our theoretical analysis shows that the dense Gaussian matrix in JLT can be replaced by a low-rank tensor implicitly represented in compressed form with random factors, while still approximately preserving the Euclidean distance of the projected inputs. In addition, our results reveal that the TT format is substantially superior to CP in terms of the size of the random projection needed to achieve the same distortion ratio. Experiments on synthetic data validate our theoretical analysis and demonstrate the superiority of the TT decomposition.
Multi-task Learning with Multi-head Attention for Multi-choice Reading Comprehension

Multiple-choice Machine Reading Comprehension (MRC) is an important and challenging Natural Language Understanding (NLU) task, in which a machine must choose the answer to a question from a set of choices, with the question placed in context of text passages or dialog. In the last a couple of years the NLU field has been revolutionized with the advent of models based on the Transformer architecture, which are pretrained on massive amounts of unsupervised data and then fine-tuned for various supervised learning NLU tasks. Transformer models have come to dominate a wide variety of leader-boards in the NLU field; in the area of MRC, the current state-of-the-art model on the DREAM dataset (see[Sunet al., 2019]) fine tunes Albert, a large pretrained Transformer-based model, and addition-ally combines it with an extra layer of multi-head attention between context and question-answer[Zhuet al., 2020]. The purpose of this note is to document a new state-of-the-art result in the DREAM task, which is accomplished by, additionally, performing multi-task learning on two MRC multi-choice reading comprehension tasks (RACE and DREAM).
Disrupting resilient criminal networks through data analysis: The case of Sicilian Mafia

Compared to other types of social networks, criminal networks present particularly hard challenges, due to their strong resilience to disruption, which poses severe hurdles to Law-Enforcement Agencies (LEAs). Herein, we borrow methods and tools from Social Network Analysis (SNA) to (i) unveil the structure and organization of Sicilian Mafia gangs, based on two real-world datasets, and (ii) gain insights as to how to efficiently reduce the Largest Connected Component (LCC) of two networks derived from them. Mafia networks have peculiar features in terms of the links distribution and strength, which makes them very different from other social networks, and extremely robust to exogenous perturbations. Analysts also face difficulties in collecting reliable datasets that accurately describe the gangs’ internal structure and their relationships with the external world, which is why earlier studies are largely qualitative, elusive and incomplete. An added value of our work is the generation of two real-world datasets, based on raw data extracted from juridical acts, relating to a Mafia organization that operated in Sicily during the first decade of 2000s. We created two different networks, capturing phone calls and physical meetings, respectively. Our analysis simulated different intervention procedures: (i) arresting one criminal at a time (sequential node removal); and (ii) police raids (node block removal). In both the sequential, and the node block removal intervention procedures, the Betweenness centrality was the most effective strategy in prioritizing the nodes to be removed. For instance, when targeting the top 5% nodes with the largest Betweenness centrality, our simulations suggest a reduction of up to 70% in the size of the LCC. We also identified that, due the peculiar type of interactions in criminal networks (namely, the distribution of the interactions’ frequency), no significant differences exist between weighted and unweighted network analysis. Our work has significant practical applications for perturbing the operations of criminal and terrorist networks.
Timing of the accreting millisecond pulsar IGR J17591–2342: evidence of spin-down during accretion

We report on the phase-coherent timing analysis of the accreting millisecond X-ray pulsar IGR J17591–2342, using Neutron Star Interior Composition Explorer (NICER) data taken during the outburst of the source between 2018 August 15 and 2018 October 17. We obtain an updated orbital solution of the binary system. We investigate the evolution of the neutron star spin frequency during the outburst, reporting a refined estimate of the spin frequency and the first estimate of the spin frequency derivative ($\dot{\nu }\sim -7\times 10^{-14}$ Hz s−1), confirmed independently from the modelling of the fundamental frequency and its first harmonic. We further investigate the evolution of the X-ray pulse phases adopting a physical model that accounts for the accretion material torque as well as the magnetic threading of the accretion disc in regions where the Keplerian velocity is slower than the magnetosphere velocity. From this analysis we estimate the neutron star magnetic field Beq = 2.8(3) × 108 G. Finally, we investigate the pulse profile dependence on energy finding that the observed behaviour of the pulse fractional amplitude and lags as a function of energy is compatible with the down-scattering of hard X-ray photons in the disc or the neutron star surface.
Decoherence as a detector of the Unruh effect

We propose a new type of the Unruh-DeWitt detector which measures the decoherence of the reduced density matrix of the detector interacting with the massless quantum scalar field. We find that the decoherence decay rates are different in the inertial and accelerated reference frames. We show that the exponential phase decay can be observed for relatively low accelerations, that can significantly improve the conditions for measuring the Unruh effect.
Self-Supervised Point Set Local Descriptors for Point Cloud Registration

Descriptors play an important role in point cloud registration. The current state-of-the-art resorts to the high regression capability of deep learning. However, recent deep learning-based descriptors require different levels of annotation and selection of patches, which make the model hard to migrate to new scenarios. In this work, we learn local registration descriptors for point clouds in a self-supervised manner. In each iteration of the training, the input of the network is merely one unlabeled point cloud. Thus, the whole training requires no manual annotation and manual selection of patches. In addition, we propose to involve keypoint sampling into the pipeline, which further improves the performance of our model. Our experiments demonstrate the capability of our self-supervised local descriptor to achieve even better performance than the supervised model, while being easier to train and requiring no data labeling.
Designing constraint-based False Data Injection attacks for unbalanced distribution smart grids

Smart grid is equipped with various kinds of smart devices such as meters, sensors, and actuators, to name a few, and it can be characterized as the Internet of Things. The cyber domain of this cyber-physical system also has to deal with many cyber-threats, including the stealthy False Data Injection (FDI) attack. This man-in-the-middle data-driven attack is extremely dangerous due to its ability to disrupt the operation without being detected. The unbalanced distribution network is a highly probable target for an FDI attack due to its easy physical access. This paper will investigate an attack design scheme based on a nonlinear physical-constraint model that is able to produce an FDI attack with the same theoretical stealthy characteristic. To demonstrate the effectiveness of the proposed design scheme, simulations with the IEEE 13-node Test Feeder are conducted. The experimental results indicate that the false positive rate of the bad data detection mechanism is 100\%. This figurative number opens a serious challenge for operators in maintaining the integrity of measurement data.
Prediction of Bayesian Intervals for Tropical Storms

Building on recent research for prediction of hurricane trajectories using recurrent neural networks (RNNs), we have developed improved methods and generalized the approach to predict Bayesian intervals in addition to simple point estimates. Tropical storms are capable of causing severe damage, so accurately predicting their trajectories can bring significant benefits to cities and lives, especially as they grow more intense due to climate change effects. By implementing the Bayesian interval using dropout in an RNN, we improve the actionability of the predictions, for example by estimating the areas to evacuate in the landfall region. We used an RNN to predict the trajectory of the storms at 6-hour intervals. We used latitude, longitude, windspeed, and pressure features from a Statistical Hurricane Intensity Prediction Scheme (SHIPS) dataset of about 500 tropical storms in the Atlantic Ocean. Our results show how neural network dropout values affect predictions and intervals.
Illuminating the dark ages: cosmic backgrounds from accretion onto primordial black hole dark matter

The recent interpretation of cold dark matter as the sum of contributions of different mass Primordial Black Hole (PBH) families [1] could explain a number of so far unsolved astrophysical mysteries. Here I assume a realistic 10−8–1010 M⊙ PBH mass distribution providing the bulk of the dark matter, consistent with all observational constraints. I estimate the contribution of baryon accretion onto this PBH population to various cosmic background radiations, concentrating first on the cross-correlation signal between the Cosmic X-ray and the Cosmic infrared background fluctuations discovered in deep Chandra and Spitzer surveys. I assume Bondi capture and advection dominated disk accretion with reasonable parameters like baryon density and effective relative velocity between baryons and PBH, as well as appropriate accretion and radiation efficiencies, and integrate these over the PBH mass spectrum and cosmic time. The prediction of the PBH contribution to the X-ray background is indeed consistent with the residual X-ray background signal and the X-ray/infrared fluctuations. The predicted flux peaks at redshifts z≈17–30, consistent with other constraints requiring the signal to come from such high redshifts. The PBH contribution to the 2–5 μm cosmic infrared background fluctuations is only about 1%, so that these likely come from star formation processes in regions associated with the PBH. I discuss a number of other phenomena, which could be significantly affected by the PBH accretion. Magnetic fields are an essential ingredient in the Bondi capture process, and I argue that the PBH can play an important role in amplifying magnetic seed fields in the early universe and maintaining them until the galactic dynamo processes set in. Next I study the contribution of the assumed PBH population to the re-ionization history of the universe and find that they do not conflict with the stringent ionization limits set by the most recent Planck measurements. X-ray heating from the PBH population can provide a contribution to the entropy floor observed in groups of galaxies. The tantalizing redshifted 21-cm absorption line feature observed by EDGES could well be connected to the radio emission contributed by PBH to the cosmic background radiation. Finally, the number of intermediate-mass black holes and the diffuse X-ray emission in the Galactic Center region are not violated by the assumed PBH dark matter, on the contrary, some of the discrete sources resolved in the deepest {\em Chandra} observations of the Galactic Ridge could indeed be accreting PBH.
Integrating Physics-Based Modeling with Machine Learning: A Survey

In this manuscript, we provide a structured and comprehensive overview of techniques to integrate machine learning with physics-based modeling. First, we provide a summary of application areas for which these approaches have been applied. Then, we describe classes of methodologies used to construct physics-guided machine learning models and hybrid physics-machine learning frameworks from a machine learning standpoint. With this foundation, we then provide a systematic organization of these existing techniques and discuss ideas for future research.