Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Covariate and Concept Shift for Detection and Calibration of Out-of-Distribution Data

Moving beyond testing on in-distribution data works on Out-of-Distribution (OOD) detection have recently increased in popularity. A recent attempt to categorize OOD data introduces the concept of near and far OOD detection. Specifically, prior works define characteristics of OOD data in terms of detection difficulty. We propose to characterize the spectrum of OOD data using two types of distribution shifts: covariate shift and concept shift, where covariate shift corresponds to change in style, e.g., noise, and concept shift indicates a change in semantics. This characterization reveals that sensitivity to each type of shift is important to the detection and confidence calibration of OOD data. Consequently, we investigate score functions that capture sensitivity to each type of dataset shift and methods that improve them. To this end, we theoretically derive two score functions for OOD detection, the covariate shift score and concept shift score, based on the decomposition of KL-divergence for both scores, and propose a geometrically-inspired method (Geometric ODIN) to improve OOD detection under both shifts with only in-distribution data. Additionally, the proposed method naturally leads to an expressive post-hoc calibration function which yields state-of-the-art calibration performance on both in-distribution and out-of-distribution data. We are the first to propose a method that works well across both OOD detection and calibration and under different types of shifts. View project page at https://sites.google. com/view/geometric-decomposition.
Nearest neighbor empirical processes

In the regression framework, the empirical measure based on the responses resulting from the nearest neighbors, among the covariates, to a given point $x$ is introduced and studied as a central statistical quantity. First, the associated empirical process is shown to satisfy a uniform central limit theorem under a local bracketing entropy condition on the underlying class of functions reflecting the localizing nature of the nearest neighbor algorithm. Second a uniform non-asymptotic bound is established under a well-known condition, often referred to as Vapnik-Chervonenkis, on the uniform entropy numbers. The covariance of the Gaussian limit obtained in the uniform central limit theorem is simply equal to the conditional covariance operator given the covariate value. This suggests the possibility of using standard formulas to estimate the variance by using only the nearest neighbors instead of the full data. This is illustrated on two problems: the estimation of the conditional cumulative distribution function and local linear regression.
S IM CNN: Exploiting Computational Similarity to Accelerate CNN Training in Hardware

—Convolution neural networks (CNN) are computation intensive to train. It consists of a substantial number of multidimensional dot products between many kernels and inputs. We observe that there are notable similarities among the vectors extracted from inputs (i.e., input vectors). If one input vector is similar to another one, its computations with the kernels are also similar to those of the other and therefore, can be skipped by reusing the already-computed results. Based on this insight, we propose a novel scheme based on locality sensitive hashing (LSH) to exploit the similarity of computations during CNN training in a hardware accelerator. The proposed scheme, called S IM CNN, uses a cache (S IM C ACHE ) to store LSH signatures of recent input vectors along with the computed results. If the LSH signature of a new input vector matches with that of an already existing vector in the S IM C ACHE , the already-computed result is reused for the new vector. S IM CNN is the first work that exploits computational similarity for accelerating CNN training in hardware. The paper presents a detailed design, workflow, and implementation of S IM CNN. Our experimental evaluation with four different deep learning models shows that S IM CNN saves a significant number of computations and therefore, improves training time up to 43%.
Constraining non-minimally coupled β-exponential inflation with CMB data

The β-exponential inflation is driven by a class of primordial potentials, derived in the framework of braneworld scenarios, that generalizes the well-known power law inflation. In this paper we update previous constraints on the minimal coupled β-exponential model [1] and extend the results also deriving the equations for the non-minimal coupled scenario. The predictions of both models are tested in light of the latest temperature and polarization maps of the Cosmic Microwave Background and clustering data. We also compare the predictions of these models with the standard ΛCDM cosmology using the Deviance Information Criterion (DIC), and find that the observational data show a moderate preference for the non-minimally coupled β-exponential inflationary model.
Algorithmic encoding of protected characteristics and its implications on disparities across subgroups

It has been rightfully emphasized that the use of AI for clinical decision making could amplify health disparities. A machine learning model may pick up undesirable correlations, for example, between a patient’s racial identity and clinical outcome. Such correlations are often present in (historical) data used for model development. There has been an increase in studies reporting biases in disease detection models across patient subgroups. Besides the scarcity of data from underserved populations, very little is known about how these biases are encoded and how one may reduce or even remove disparate performance. There is some speculation whether algorithms may recognize patient characteristics such as biological sex or racial identity, and then directly or indirectly use this information when making predictions. But it remains unclear how we can establish whether such information is actually used. This article aims to shed some light on these issues by exploring new methodology allowing intuitive inspections of the inner working of machine learning models for image-based detection of disease. We also evaluate an effective yet debatable technique for addressing disparities leveraging the automatic prediction of patient characteristics, resulting in models with comparable true and false positive rates across subgroups. Our ﬁndings may stimulate the discussion about safe and ethical use of AI.
Towards a Numerical Solution of the Bosonic Master-Field Equation of the IIB Matrix Model

A direct algebraic solution has been obtained from the full bosonic master-field equation of the IIB matrix model for low dimensionality D = 3 and small matrix size N = 3. A different method is needed for larger values of D and N . Here, we explore an indirect numerical approach and obtain an approximate numerical solution for the nontrivial case (D, N) = (10, 4) with a complex Pfaffian. We also present a suggestion for numerical calculations at larger values of N . Acta Phys. Pol. B arXiv:2110.15309 (v4)
Differential expression of transposable elements in the medaka melanoma model

Malignant melanoma incidence is rising worldwide. Its treatment in an advanced state is difficult, and the prognosis of this severe disease is still very poor. One major source of these difficulties is the high rate of metastasis and increased genomic instability leading to a high mutation rate and the development of resistance against therapeutic approaches. Here we investigate as one source of genomic instability the contribution of activation of transposable elements (TEs) within the tumor. We used the well-established medaka melanoma model and RNA-sequencing to investigate the differential expression of TEs in wildtype and transgenic fish carrying melanoma. We constructed a medaka-specific TE sequence library and identified TE sequences that were specifically upregulated in tumors. Validation by qRT- PCR confirmed a specific upregulation of a LINE and an LTR element in malignant melanomas of transgenic fish.
A Multi-Marker Test for Analyzing Paired Genetic Data in Transplantation

Emerging evidence suggests that donor/recipient matching in non-HLA (human leukocyte antigen) regions of the genome may impact transplant outcomes and recognizing these matching effects may increase the power of transplant genetics studies. Most available matching scores account for either single-nucleotide polymorphism (SNP) matching only or sum these SNP matching scores across multiple gene-coding regions, which makes it challenging to interpret the association findings. We propose a multi-marker Joint Score Test (JST) to jointly test for association between recipient genotype SNP effects and a gene-based matching score with transplant outcomes. This method utilizes Eigen decomposition as a dimension reduction technique to potentially increase statistical power by decreasing the degrees of freedom for the test. In addition, JST allows for the matching effect and the recipient genotype effect to follow different biological mechanisms, which is not the case for other multi-marker methods. Extensive simulation studies show that JST is competitive when compared with existing methods, such as the sequence kernel association test (SKAT), especially under scenarios where associated SNPs are in low linkage disequilibrium with non-associated SNPs or in gene regions containing a large number of SNPs. Applying the method to paired donor/recipient genetic data from kidney transplant studies yields various gene regions that are potentially associated with incidence of acute rejection after transplant.
Barriers of Doctors and Patients in Starting Insulin for Type 2 Diabetes Mellitus

Background Management of patients with type 2 diabetes mellitus (T2DM) may involve insulin therapy. However, this treatment may be avoided or delayed by physicians or patients due to the presence of certain barriers. This study aimed to evaluate the barriers to initiating insulin therapy for both physicians and patients with T2DM. Method This was a cross-sectional, questionnaire-based study. Data related to the physicians’ personal and professional experience were collected, and 15 barriers to initiating insulin therapy were scored by each physician on a four-point Likert scale. Also, the patients’ general data were collected, including previous insulin experience, discontinuation reason, and willingness to start insulin therapy if indicated. Twenty-one other barriers were examined with yes/no questions as well. Results For physicians, the patient's treatment compliance, motive, dependence on others for insulin therapy, hypoglycemia, socioeconomic status, occupation, and lack of follow-up were the most highly ranked barriers to initiating insulin therapy. A history of insulin use was reported in 42 (20.7%) patients, 31 of whom had decided to discontinue insulin therapy themselves (73.8%). The three most common reasons for discontinuing insulin therapy among patients were deterioration of T2DM and causing complications, hypoglycemia, and needle injections. Based on the findings, 99 (48.8%) patients were willing to start insulin therapy, if indicated. The family history of insulin therapy was positively correlated with the patient’s willingness to start insulin. On the other hand, it was negatively correlated with a low educational level and some barriers to insulin therapy, such as fear of death, dependence on others, the difficulty of carrying insulin while traveling, follow-up challenges, the difficulty of dosing accuracy, the difficulty of keeping insulin, inconveniences in daily life, considering insulin as the last resort, the deterioration of T2DM with insulin, and social stigma. Conclusion The physicians believed that the barriers to initiating insulin therapy were mainly related to the patient's attitudes and thoughts about this therapy. While hypoglycemia and weight gain are well-known side effects of insulin therapy, the most important patient-related barriers to insulin therapy were related to its impact on the patient’s social life and misperceptions about the side effects of insulin.
Protective effect of a new generation of activated and purified bentonite in combination with yeast and phytogenic substances on mycotoxin challenge in pigs

The study aimed to investigate the efficacy of new mycotoxin adsorbents based on purified and activated bentonites combined with yeast and phytogenic compounds in fattening pigs. The experiment involved 96 pigs (31.2±2.4 kg). Control (C) group was fed a diet naturally contaminated with mycotoxins (5 mg/kg deoxynivalenol, DON) without an adsorbent. Treated groups received the feed with mycotoxin adsorbents: purified and activated bentonite (T1), purified and activated bentonite, yeast derivatives, phytogenic substances (T2), and purified, activated, and sulphurated bentonite with phytogenic substances (T3). Evaluated parameters involved growth performance, organ weight, small intestine and liver histopathology, complete blood count, serum biochemistry, antioxidant status of the organism and total and free DON content in urine. In all treated groups, an significant increase in intestinal GSH and GSH/GSSG ratio was observed when compared to C. No significant effects on liver and kidney weight, complete blood count, serum or intestinal malondialdehyde concentration, or total/free DON content in urine were observed. All adsorbents improved histopathological findings in the liver when compared to C. Moreover, T1, and T2 groups showed no presence of inflammatory reaction or necrotic changes in the livers. Although, mycotoxin adsorbents investigated in this study had no significant impact on pig growth performance, they reduced the oxidative stress, and on the tissue level they protected the jejunal tissue and liver parenchyma under deoxynivalenol challenge.
The impact of the traditional male role norms on the posttraumatic stress disorder among Polish male firefighters

Background The aim of this study was to explore potential associations between compliance with the traditional male role norms and posttraumatic stress disorder (PTSD) symptoms. Methods The study was conducted on 135 male Polish firefighters. The study used the Impact Event Scale-Revised, the Relations/Social Support Scale and the Male Role Norms Scale. Results The study revealed that the firefighters that suffered from probable PTSD (over 34% of all the respondents) seemed to comply with the male role norms more strictly than those without PTSD. The reverse was found to be true for the perceived social support. Social status norms and toughness norms understood as expectations that men should achieve high status by means of successful career, etc., and be tough and resilient at all costs to be perceived as “manly” were found to be positively associated with the development of PTSD, while in the case of perceived social support, a negative association was confirmed. The results might yield important clinical implications—traditional male role norms pertaining to toughness and social status might be associated with the increase in chances of developing PTSD after the exposure to potentially traumatic events. Conclusions Firefighting as a stereotypically masculine occupation may be associated with the reinforcement of stereotypically masculine behaviors, which in turn is associated with a decreased ability to cope with potentially traumatic stimuli and favoring maladaptive behaviors. The results might suggest that addressing the beliefs about masculinity during psychological intervention in the case of PTSD might be beneficial especially among such masculinized groups as firefighters. The main limitations of the study are: participation of those more eager to participate and reveal personal information; the recall and report bias; the relatively small sample size; sociodemographic data omissions; the study group almost exclusively consisting of firefighters from large urban centers.
Unraveling the Metabolic Derangements Occurring in Non-infarcted Areas of Pig Hearts With Chronic Heart Failure

Objective: After myocardial infarction (MI), the non-infarcted left ventricle (LV) ensures appropriate contractile function of the heart. Metabolic disturbance in this region greatly exacerbates post-MI heart failure (HF) pathology. This study aimed to provide a comprehensive understanding of the metabolic derangements occurring in the non-infarcted LV that could trigger cardiovascular deterioration. Methods and Results: We used a pig model that progressed into chronic HF over 3 months following MI induction. Integrated gene and metabolite signatures revealed region-specific perturbations in amino acid- and lipid metabolism, insulin signaling and, oxidative stress response. Remote LV, in particular, showed impaired glutamine and arginine metabolism, altered synthesis of lipids, glucose metabolism disorder, and increased insulin resistance. LPIN1, PPP1R3C, PTPN1, CREM, and NR0B2 were identified as the main effectors in metabolism dysregulation in the remote zone and were found differentially expressed also in the myocardium of patients with ischemic and/or dilated cardiomyopathy. In addition, a simultaneous significant decrease in arginine levels and altered PRCP, PTPN1, and ARF6 expression suggest alterations in vascular function in remote area. Conclusions: This study unravels an array of dysregulated genes and metabolites putatively involved in maladaptive metabolic and vascular remodeling in the non-infarcted myocardium and may contribute to the development of more precise therapies to mitigate progression of chronic HF post-MI. Graphical Abstract Summary of the metabolic and vascular derangements found in border and remote zones of pig hearts three months after the MI.
Cytotoxicity, Antimicrobial, and In Silico Studies of Secondary Metabolites From Aspergillus sp. Isolated From Tecoma stans (L.) Juss. Ex Kunth Leaves

Endophytes are prolific producers of privileged secondary metabolites with diverse therapeutic potential, although their anticancer and antimicrobial potential still have a room for further investigation. Herein, seven known secondary metabolites namely, arugosin C (1), ergosterol (2), iso-emericellin (3), sterigmatocystin (4), dihydrosterigmatocystin (5), versicolorin B (6), and diorcinol (7) were isolated from the rice culture of Aspergillus sp. retrieved from Tecoma stans (L.) Juss. ex Kunth leaves. Their anticancer and antimicrobial activities were evaluated in MTT and agar well diffusion assays, respectively. The cytotoxicity results showed that metabolite 3 displayed the best viability inhibition on the MCF-7 breast cancer cells with IC50 = 225.21 µM, while 5 on the HepG2 hepatocellular carcinoma cells with IC50 = 161.81 µM. 5 demonstrated a 60% apoptotic mode of cell death which is virtually correlated to its high docking affinity to Hsp90 ATP binding cleft (binding score −8.4 Kcal/mol). On the other side, metabolites 4 and 5 displayed promising antimicrobial activity especially on Pseudomonas aeruginosa with MIC = 125 μg/ml. The observed effect may be likely related to their excellent in silico inhibition of the bacterial DNA-gyrase kinase domain (binding score −10.28 Kcal/mol). To the best of our knowledge, this study is the first to report the promising cytotoxic and antibacterial activities of metabolites 3, 4, and 5 which needs further investigation and renovation to therapeutic leads.
Neutrophil elastase in the development of nephrogenic systemic fibrosis (NSF)-like skin lesion in renal failure mouse model

Although neutrophil elastase (NE) may play a role in lung fibrosis and liver fibrosis, NE involvement in the development of nephrogenic systemic fibrosis has been unclear. We investigated the involvement of NE in the development of nephrogenic systemic fibrosis-like skin lesions post-injections of linear gadolinium-based contrast agents in renal failure mouse models. Renal failure mouse models were randomly divided into three groups: control group (saline), gadodiamide group, and gadopentetate group. Each solution was intravenously administered three times per week for three weeks. The mice were observed daily for skin lesions. Quantification of skin lesions, infiltrating inflammatory cells, and profibrotic cytokines in the affected skin was performed by immunostaining and reverse-transcription polymerase chain reaction (RT-PCR). Blood samples were collected from the facial vein to quantify NE enzymatic activity. The 158Gd concentrations in each sample were quantified using inductively coupled plasma mass spectrometry (ICP-MS). In the gadodiamide group, the mRNA expression of fibrotic markers was increased in the skin lesions compared to the control group. In the gadopentetate group, only collagen 1α and TGF-β mRNA expression were higher than in the control group. The expression of CD3+, CD68+, NE cells and the NE activity in the blood serum were significantly higher in the gadodiamide and gadopentetate groups compared to the control group. Gadolinium concentration in the skin of the gadodiamide group was significantly higher than the gadopentetate group, while almost no traces of gadolinium were found in the control group. Although gadopentetate and gadodiamide affected the fibrotic markers in the skin differently, NE may be involved in the development of fibrosis linked to the GBCAs injections in renal failure mouse models.
Direct antiviral agents for chronic hepatitis C virus infection improve health-related quality of life significantly in the long term

Introduction Direct antiviral agents (DAAs) are new drugs for the treatment of chronic hepatitis C virus (HCV) infection. These drugs are very effective and well tolerated. HCV can cause liver disease as well as extrahepatic manifestations, including a profound negative impact on health-related quality of life (HRQL). Aim of the study To evaluate HRQL in the long term (> 6 months after finishing treatment) after successful treatment with DAAs. To the best of our knowledge, this is the first study that evaluates quality of life in the long term after DAA treatment. Material and methods This is an observational study which included 100 patients treated with DAAs for chronic HCV infection between January 2015 and August 2018. Patients were assigned randomly. The average time after finishing treatment was 29.96 months. The Liver Disease Symptom Index (LDSI) 2.0 Questionnaire was used to evaluate quality of life before and after treatment. Results Seven of 9 parameters of the LDSI 2.0 Questionnaire showed significant improvement in the long term after successful treatment with DAAs. Two parameters (arthralgia and jaundice) did not improve significantly. Quality of life improved in both males and females similarly. Improvement did not correlate with the severity of liver fibrosis. Conclusions Treatment with DAAs improves HRQL significantly in the long term.