Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Fermi-liquid Kondo screening under Rabi driving

We investigate a Rabi-Kondo model describing an optically driven two-channel quantum dot device featuring a non-Fermi-liquid Kondo effect. Optically induced Rabi oscillation between the valence and conduction levels of the dot gives rise to a two-stage Kondo effect: Primary screening of the local spin is followed by secondary nonequilibrium screening of the local orbital degree of freedom. Using bosonization arguments and the numerical renormalization group, we compute the dot emission spectrum and residual entropy. Remarkably, both exhibit two-stage Kondo screening with non-Fermi-liquid properties at both stages.
Learning Bayesian Networks with Low Rank Conditional Probability Tables

In this paper, we provide a method to learn the directed structure of a Bayesian network using data. The data is accessed by making conditional probability queries to a black-box model. We introduce a notion of simplicity of representation of conditional probability tables for the nodes in the Bayesian network, that we call "low rankness". We connect this notion to the Fourier transformation of real valued set functions and propose a method which learns the exact directed structure of a `low rank` Bayesian network using very few queries. We formally prove that our method correctly recovers the true directed structure, runs in polynomial time and only needs polynomial samples with respect to the number of nodes. We also provide further improvements in efficiency if we have access to some observational data.
On the Expected Dynamics of Nonlinear TD Learning

While there are convergence guarantees for temporal difference (TD) learning when using linear function approximators, the situation for nonlinear models is far less understood, and divergent examples are known. Here we take a first step towards extending theoretical convergence guarantees to TD learning with nonlinear function approximation. More precisely, we consider the expected dynamics of the TD(0) algorithm. We prove that this ODE is attracted to a compact set for smooth homogeneous functions including some ReLU networks. For over-parametrized and well-conditioned functions in sufficiently reversible environments we prove convergence to the global optimum. This result improves when using k-step or λ returns. Finally, we generalize a divergent counterexample to a family of divergent problems to motivate the assumptions needed to prove convergence.
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness

Many recent works have shown that adversarial examples that fool classifiers can be found by minimally perturbing a normal input. Recent theoretical results, starting with Gilmer et al. (2018b), show that if the inputs are drawn from a concentrated metric probability space, then adversarial examples with small perturbation are inevitable. A concentrated space has the property that any subset with $\Omega(1)$ (e.g., 1/100) measure, according to the imposed distribution, has small distance to almost all (e.g., 99/100) of the points in the space. It is not clear, however, whether these theoretical results apply to actual distributions such as images. This paper presents a method for empirically measuring and bounding the concentration of a concrete dataset which is proven to converge to the actual concentration. We use it to empirically estimate the intrinsic robustness to $\ell_\infty$ and $\ell_2$ perturbations of several image classification benchmarks. Code for our experiments is available at this https URL.
On the Containment Problem for Unambiguous Single-Register Automata with Guessing

Register automata extend classical finite automata with a finite set of registers that can store data from an infinite data domain for later equality comparisons with data from an input data word. While the registers in the original model of register automata, introduced in 1994 by Kaminski and Francez, can only store data occurring in the data word processed so far, we study here the more expressive class of register automata with guessing, where registers can nondeterministically take any value from the infinite data domain, even if this data does not occur in the input data word. It is well known that the containment problem, i.e., the problem of deciding for two given register automata with guessing A and B, whether the language L(A) accepted by A is contained in the language L(B) accepted by B, is undecidable, even if B only uses a single register. We prove that the problem is decidable if B is unambiguous and uses a single register.
MetaChem: An Algebraic Framework for Artificial Chemistries

We introduce MetaChem, a language for representing and implementing artificial chemistries. We motivate the need for modularization and standardization in representation of artificial chemistries. We describe a mathematical formalism for Static Graph MetaChem, a static-graph-based system. MetaChem supports different levels of description, and has a formal description; we illustrate these using StringCatChem, a toy artificial chemistry. We describe two existing artificial chemistries—Jordan Algebra AChem and Swarm Chemistry—in MetaChem, and demonstrate how they can be combined in several different configurations by using a MetaChem environmental link. MetaChem provides a route to standardization, reuse, and composition of artificial chemistries and their tools.
Support Vector Machines with L1 penalty for detecting gene-gene interactions

Interactions among genetic variants are likely to affect risk for human complex diseases, and their identification should increase the power to detect disease-associated variants and elucidate biological pathways underlying diseases. We propose a two-stage approach: model selection with support vector machines identifies the most promising single nucleotide polymorphisms and interactions; logistic regression ensures a valid type I error by excluding non-significant candidates after Bonferroni correction. Simulation studies for case-control data suggest that our method powerfully detects gene-gene interactions. We analyze a published genome-wide case-control dataset, where our method successfully identifies an interaction term, which was missed in previous studies.
Metabarcoding reveals a high diversity of woody host-associated Phytophthora spp. in soils at public gardens and amenity woodlands in Britain

Forests and woodlands worldwide are being severely impacted by invasive Phytophthora species, with initial outbreaks in some cases occurring on host trees located in public parks and gardens. These highly disturbed sites with diverse planting practices may indeed act as harbours for invasive Phytophthora pathogens which are particularly well adapted to surviving in soil. High throughput Illumina sequencing was used to analyse Phytophthora species diversity in soil samples collected from 14 public garden/amenity woodland sites in northern Britain. Bioinformatic analyses revealed some limitations to using internal transcribed spacer as the barcode region; namely reporting of false positives and ambiguous species matches. Taking this into account, 35 distinct sequences were amplified across the sites, corresponding to 23 known Phytophthora species as well as twelve oomycete sequences with no match to any known Phytophthora species. Phytophthora pseudosyringae and P. austrocedri, both of which cause serious damage to trees and are regarded as fairly recent introductions to Britain, were the two most abundant Phytophthora species detected. There was no evidence that any of the detected Phytophthora species were more associated with any one type of host, healthy or otherwise. This study has demonstrated the ubiquity and diversity of Phytophthora species endemic in highly managed, extensively planted soil environments in Britain. Suggested improvements to the methodology and the practical implications of the findings in terms of mitigating Phytophthora spread and impact are discussed.
Bispectral index score and observer's assessment of awareness/sedation score may manifest divergence during onset of sedation: Study with midazolam and propofol

Background: Correlation between the clinical and electroencephalogram-based monitoring has been documented sporadically during the onset of sedation. Propofol and midazolam have been studied individually using the observer's assessment of awareness/sedation (OAA/S) score and Bispectral index score (BIS). The present study was designed to compare the time to onset of sedation for propofol and midazolam using both BIS and OAA/S scores, and to find out any correlation. Methods: A total of 46 patients (18-60 years, either sex, American Society of Anesthesiologists (ASA) I/II) posted for infraumbilical surgeries under spinal anaesthesia were randomly allocated to receive either injection propofol 1 mg/kg bolus followed by infusion 3 mg/kg/h (Group P, n=23) or injection midazolam 0.05 mg/kg bolus followed by infusion 0.06 mg/kg/h (Group M, n=23). Spinal anaesthesia was given with 2.5 ml to 3.0 ml of 0.5% bupivacaine heavy. When sensory block reached T6 level, sedation was initiated. The time to reach BIS score 70 and time to achieve OAA/S score 3 from the start of study drug were noted. OAA/S score at BIS score 70 was noted. Data from 43 patients were analyzed using SPSS 12 for Windows. Results: Time to reach BIS score 70 using propofol was significantly lower than using the midazolam (P<0.05). Time to achieve OAA/S score 3 using propofol was comparable with midazolam (P=0.358). Conclusion: A divergence exists between the time to reach BIS score 70 and time to achieve OAA/S score 3 using midazolam, compared with propofol, during the onset of sedation.
Ipsilateral brain cavernoma under scleroderma plaque: a case report

Scleroderma is a rare disease of unknown etiology, which is characterized by thickening and hardening of skin due to an increased collagen production. A 44-year-old female patient with a scleroderma on the scalp known by our department, also presented an ipsilateral brain lesion since 2015, which was showing growth without any clinical symptomatology and the patient wanted the lesion to be removed. This atypical lesion underneath the scleroderma shows that diagnosis can be missed without brain imaging and biopsy.
Activity Profiles by Position in Youth Elite Soccer Players in Official Matches

Abstract In order to investigate activity profiles and external load patterns in elite youth soccer players, we studied high-intensity activity patterns, maximum running speed, and temporary and end-of-match decline in external load in 54 U17 players (96 match observations) over a full season of official match play. Wide midfielders covered most high-intensity running (HIR) distance (1044.2 m), most sprinting distance (224.4 m), and the highest number of accelerations (185.2); center defenders had the lowest values for these activities (508.3 m, 85.1 m, and 119.0), respectively. Wide midfielders had the highest and center defenders had the lowest maximum speed (30.3 km · h − 1 and 28.6 km · h − 1), respectively. During the matches, players in all playing positions displayed a significant drop in HIR distance, sprinting distance, and number of accelerations. This was especially pronounced in the 5 min following the 5-min peak period and in the last 5-min period for sprinting distance. There are substantial differences in activity profiles by positions, but all players show temporary and end-of-match drop in external load. The variation in activity profiles by playing position in this study may aid in the design of training programs. The considerable end-of-match drop in external load observed raises the question of the favorability of 90 min match times for U17 players.
Proposing a standardized, step-by-step model for creating post-traumatic stress disorder (PTSD) related mobile mental health apps in a framework based on technical and medical norms

ABSTRACT Background: Prevalence of post-traumatic stress disorder (PTSD) is a problem all over the world. There are high barriers for entry into formal psychotherapy, which results in a lack of mental health care for a significant part of the population. Mobile mental Health (mMHealth) applications (apps) seem to be a promising new development for countering this lack of care, building on the success of mHealth (Mobile Health) apps in general. Unfortunately, the overall quality of such apps stands in high contrast to their number. The aim of this manuscript is to propose a standard for creating PTSD-related mMHealth apps incorporating a process for evaluation and assessment of their usability and impact. Methods: This is done by first defining each step of the process and its relation to the other steps. The steps themselves, divided into those concerned with development, evaluation and implementation, are bound to the established medical and technical norms pertaining to them. Existing protocols from recent literature have been integrated into these steps. Results: As a result, a comprehensive model covering the process of creating, assessing and implementing an mMHealth app from start to finish was developed. The model may be adapted to other disorders or specialized for certain symptoms of PTSD. Conclusion: Adopting such a model could result in a ‘blueprint’ for creating mMHealth apps in a standardized way, thereby facilitating the testing and comparing of such apps. HIGHLIGHTS • Coping with post-traumatic stress disorder (PTSD) can be flanked by mental health apps. Unfortunately, the quality of design and evaluation of these apps is somewhat lacking. • A step-by-step model for the creation of such apps is proposed. • Emphasis is put on conforming to medical and technical norms, replicable methods of evaluation and standardizing the design process. • This would make apps more testable and comparable, enabling caregivers to make certified recommendations for their use.
Oral Microbiome Metabolism

Recent advances in molecular biology have facilitated analyses of the oral microbiome (“Who are they? ”); however, its functions (e.g., metabolic activities) are poorly understood (“What are they doing? ”). This review aims to summarize our current understanding of the metabolism of the oral microbiome. Saccharolytic bacteria—including Streptococcus, Actinomyces, and Lactobacillus species—degrade carbohydrates into organic acids via the Embden-Meyerhof-Parnas pathway and several of its branch pathways, resulting in dental caries, while alkalization and acid neutralization via the arginine deiminase system, urease, and so on, counteract acidification. Proteolytic/amino acid–degrading bacteria, including Prevotella and Porphyromonas species, break down proteins and peptides into amino acids and degrade them further via specific pathways to produce short-chain fatty acids, ammonia, sulfur compounds, and indole/skatole, which act as virulent and modifying factors in periodontitis and oral malodor. Furthermore, it is suggested that ethanol-derived acetaldehyde can cause oral cancer, while nitrate-derived nitrite can aid caries prevention and systemic health. Microbial metabolic activity is influenced by the oral environment; however, it can also modify the oral environment, enhance the pathogenicity of bacteria, and induce microbial selection to create more pathogenic microbiome. Taking a metabolomic approach to analyzing the oral microbiome is crucial to improving our understanding of the functions of the oral microbiome.
Multimodal analgesia as an essential part of enhanced recovery protocols in the ambulatory settings

Enhanced recovery after surgery protocols are multimodal perioperative care pathways designed to achieve early recovery in patients after surgical procedures by defining and maintaining preoperative organ function and minimizing the profound stress response following surgery. Enhanced recovery protocols have primarily been studied for major abdominal surgeries, however, the knowledge acquired from studying these protocols has facilitated treating patients in ambulatory settings. The key components of enhanced recovery protocols include preoperative counseling, preoperative nutrition, altering the standard perioperative fasting guidelines, and the value of carbohydrate loading up to 2 hours preoperatively, standardized analgesic, and anesthetic regimens (epidural and nonopioid analgesia) and early mobilization. A PubMed search was performed with the following key words: multimodal analgesia, enhanced recovery, ambulatory care, pain management, and opioids. We discuss the use of enhanced recovery protocols and multimodal pain care plans in the ambulatory setting.
Predictive Factors for Seizures Accompanying Intracranial Meningiomas

Objective: Seizures represent a common manifestation of intracranial meningiomas. Their predictive factors before and after excision merit studying. Materials and Methods: Patients having intracranial meningioma were prospectively studied. There were two groups; Group “A” with seizures and Group “B” with no preoperative epilepsy. Results: This study included 40 patients. Their ages ranged from 40 to 60 years old, and female-to-male ratio was 2.3:1 in both groups. In Group A, partial seizures were the most common pattern (60%). Manifestations other than fits included headache in most patients (97.5%), symptoms of increased intracranial pressure were found in 50% in Group A and 20% in Group B patients, peritumoral edema was present in 14 (70%) patients of Group A, compared to 6 (25%) patients of Group “B. ” There was a statistically significant relation between peritumoral edema and presentation with fits (P < 0.1). Complication after surgery included nonsurgical hematoma in three patients and contusion in 7 patients. Following surgery for Group “A”, 8 (40%) patients had good seizure control. While, in Group “B” 3 (15%), patients developed new-onset seizures. Good seizure control in 7 (53%) patients with frontal, frontotemporal tumors than in other locations. In addition, better control was obtained in left sided, small tumors, and no peritumoral edema. Postoperative complication was significantly associated with new-onset epilepsy and poor seizure control (P < 0.05). Neither tumor size nor location had a significant relation to either pre or postoperative epilepsy. Conclusion: Predictive factors for epilepsy accompanying intracranial meningioma included males, elderly patients and patients with small lesions, frontal and left-sided locations but were statistically insignificant predictors. Peritumoral edema and postoperative complications are the most significant predictors.
Suppressor of Cytokine Signaling 1–Derived Peptide Inhibits Janus Kinase/Signal Transducers and Activators of Transcription Pathway and Improves Inflammation and Atherosclerosis in Diabetic Mice

Objective— Activation of Janus kinase/signal transducers and activators of transcription (STAT) pathway by hyperglycemia and dislypidemia contributes to the progression of diabetic complications, including atherosclerosis. Suppressor of cytokine signaling (SOCS) proteins negatively regulate Janus kinase/STAT and have emerged as promising target for anti-inflammatory therapies. We investigated whether a cell-permeable lipopeptide corresponding to the kinase inhibitory region of SOCS1 could reduce atherosclerosis in diabetic mice and identified the mechanisms involved. Approach and Results— Streptozotocin-induced diabetic apolipoprotein E–deficient mice (aged 8 and 22 weeks) were given intraperitoneal injections of vehicle, SOCS1-derived peptide, or control mutant peptide for 6 to 10 weeks. SOCS1 therapy suppressed STAT1/STAT3 activation in atherosclerotic plaques of diabetic mice and significantly reduced lesion size at both early and advanced stages of lesion development compared with vehicle group. Plaque characterization demonstrated that SOCS1 peptide decreased the accumulation of lipids, macrophages, and T lymphocytes, whereas increasing collagen and smooth muscle cell content. This atheroprotective effect was accompanied by systemic (reduced proinflammatory Ly6Chigh monocytes and splenic cytokine expression) and local (reduced aortic expression of chemokines and cytokines) mechanisms, without impact on metabolic parameters. In vitro, SOCS1 peptide dose dependently inhibited STAT1/STAT3 activation and target gene expression in vascular smooth muscle cells and macrophages and also suppressed cytokine-induced cell migration and adhesion processes. Conclusions— SOCS1-based targeting Janus kinase/STAT restrains key mechanisms of atherogenesis in diabetic mice, thereby preventing plaque formation and increasing plaque stability. Approaches to mimic native SOCS1 functions may have a therapeutic potential to retard the progression of diabetic complications.
Why we need theory to help us better understand the nature of interprofessional education, practice and care

Theories are a set of propositions that link concepts together through a rational argument. These statements describe, explain, illuminate, prescribe or organize a particular phenomenon (Jary & Jary, 1995; Walker & Avant, 2005). Theories also generate complex and comprehensive understandings of phenomenon that are not easily explained, such as how certain cultures operate, how organizations function and why individuals interact in certain ways. They help to illuminate different aspects of a phenomenon and provide a framework for understanding them. They also generate knowledge that can be generalized across settings. Theories can be generated explicitly from data or developed implicitly from personal constructions about particular phenomena. Data-driven theories are easier to employ as their developments are usually recorded and can therefore be followed. Implicit theories – sometimes called “armchair” theories – are more difficult to implement as their construction is usually less well articulated. Merton (1968) outlined three types of theory. The first is grand theory, which is non-specific and constructed from relatively abstract concepts that are difficult to operationalize and empirically test. The second is mid-range theory, more limited in scope, as it addresses specific phenomena and has a limited number of concepts relating to a restricted range of contexts. The third is microtheory, which has the narrowest range of interest, as it is focused on specific phenomena and contexts. In this editorial, we outline the need for using (grand, mid-range and micro) theories to enhance our understanding of interprofessional education, practice and care. We argue why we need to engage in theoretical development in the interprofessional field, and why theoretical development should not be seen as a luxury, or low priority, but a central feature of the work we do developing and studying interprofessional education, practice and care.
N-REIT Performance in the Face of Index Computation and Risk Adjusted Return

REIT performance can be measured through Index construction comparison and benchmark among many methods. This paper appraise the performance of Nigeria REIT (N-REIT) using the Nigerian Stock Market All Share Index (ASI) as benchmark. A sectorial Index was constructed and plotted against the set benchmark (ASI) and the risk adjustment returns were assessed and compared. The study adopted a quantitative analysis of financial data relating to stock market transaction and trading on REIT shares in the trading floor of the Nigerian Stock Exchange over the period July 2010 to June 2014 for the construction of weekly index. N-REIT Index construction follows the All Share Index calculation of the Nigerian stock market using weighted methodology. The study found that Nigeria REIT Index exhibited a near constant value lower than the All Share Index reflecting an underperformance in the risk adjusted return. N-REIT is also found to be a low capitalised REIT and experienced decline in terms of capitalisation. N-REIT contributes an insignificant value of between 0.11% - 0.25% to the market capitalisation within the period and have no effect on the All Share Index. While the All Share Index exhibited good components of time series data, N-REIT index present a no trading situation (constant value) over the period of study.