Miracle-dz/UniFAR-FTUs huggingface dataset:
Head-to-Head Comparison of the Performance of 17 Risk Models for Predicting Presence of Advanced Neoplasms in Colorectal Cancer Screening

OBJECTIVES: Many risk scores have been proposed to predict presence of advanced colorectal neoplasms, but a comprehensive comparison conducted in the same population is sparse. The aim of this study was to evaluate and directly compare the diagnostic performance of published risk prediction models for advanced colorectal neoplasms. METHODS: Data were drawn from 2 cohorts of subjects undergoing screening colonoscopy in Germany, i.e., KolosSal (n = 16,195) and BliTz (n = 7,444). Absolute risks and relative risks were generated for the presence of at least 1 advanced neoplasm, taking the lowest risk group as the reference group. Performance of risk models was assessed by the area under the receiver operating characteristic curve (AUC) and compared by the net reclassification improvement. RESULTS: The 2 cohorts included 1,917 (11.8%) and 848 (11.4%) participants with advanced neoplasm, respectively. Absolute risks were mostly between 5% and 10% among participants in the lowest risk group and between 15% and 20% among participants in the highest risk group, and relative risks mostly ranged from 2.0 to 4.0 across the risk models in both cohorts. The AUCs ranged from 0.58 to 0.65 in KolosSal and from 0.57 to 0.61 in BliTz for all risk scores. Compared to models with lower AUC, classification was significantly improved in most models with higher AUC. DISCUSSION: Risk models for advanced colorectal neoplasms generally yielded modest discriminatory power, despite some variation in performance between models. Future studies should evaluate the performance of these risk models in racially diverse populations and investigate possible extensions, such as combination with polygenic risk scores.
Moduli spaces on the Kuznetsov component of Fano threefolds of index 2

General hyperplane sections of a Fano threefold $Y$ of index 2 and Picard rank 1 are del Pezzo surfaces, and their Picard group is related to a root system. To the corresponding roots, we associate objects in the Kuznetsov component of $Y$ and investigate their moduli spaces, using the stability condition constructed by Bayer, Lahoz, Macr\`i, and Stellari, and the Abel--Jacobi map. We identify a subvariety of the moduli space isomorphic to $Y$ itself, and as an application we prove a (refined) categorical Torelli theorem for general quartic double solids.
Private Heavy Hitters and Range Queries in the Shuffled Model

An exciting new development in differential privacy is the shuffled model, which makes it possible to circumvent the large error lower bounds that are typically incurred in the local model, while relying on much weaker trust assumptions than in the central model. In this work, we study two basic statistical problems, namely, heavy hitters and $d$-dimensional range counting queries, in the shuffled model of privacy. For both problems we devise algorithms with polylogarithmic communication per user and polylogarithmic error; a consequence is an algorithm for approximating the median with similar communication and error. These bounds significantly improve on what is possible in the local model of differential privacy, where the error must provably grow polynomially with the number of users.
Stability threshold of two-dimensional Couette flow in Sobolev spaces

We study the stability threshold of the 2D Couette flow in Sobolev spaces at high Reynolds number $Re$. We prove that if the initial vorticity $\Omega_{in}$ satisfies $\|\Omega_{in}-(-1)\|_{H^{\sigma}}\leq \epsilon Re^{-1/3}$, then the solution of the 2D Navier-Stokes equation approaches to some shear flow which is also close to Couette flow for time $t\gg Re^{1/3}$ by a mixing-enhanced dissipation effect and then converges back to Couette flow when $t\to +\infty$.
Characteristics of a Gradual Filament Eruption and Subsequent CME Propagation in Relation to a Strong Geomagnetic Storm

An unexpected strong geomagnetic storm occurred on 2018 August 26, which was caused by a slow coronal mass ejection (CME) from a gradual eruption of a large quiet-region filament. We investigate the eruption and propagation characteristics of this CME in relation to the strong geomagnetic storm with remote sensing and in situ observations. Coronal magnetic fields around the filament are extrapolated and compared with extreme ultraviolet observations. We determine the propagation direction and tilt angle of the CME flux rope near the Sun using a graduated cylindrical shell (GCS) model and the Sun-to-Earth kinematics of the CME with wide-angle imaging observations from STEREO A. We reconstruct the flux-rope structure using a Grad–Shafranov technique based on the in situ measurements at the Earth and compare it with those from solar observations and the GCS results. Our conclusions are as follows: (1) the eruption of the filament was unusually slow and occurred in the regions with relatively low critical heights of the coronal field decay index; (2) the axis of the CME flux rope rotated in the corona as well as in interplanetary space, which tended to be aligned with the local heliospheric current sheet; (3) the CME was bracketed between slow and fast solar winds, which enhanced the magnetic field inside the CME at 1 au; (4) the geomagnetic storm was caused by the enhanced magnetic field and a southward orientation of the flux rope at 1 au from the rotation of the flux rope.
Time-varying Polarized Gamma-Rays from GRB 160821A: Evidence for Ordered Magnetic Fields

GRB 160821A is the third most energetic gamma-ray burst observed by the Fermi gamma-ray space telescope. Based on the observations made by the Cadmium Zinc Telluride Imager on board AstroSat, here we report the most conclusive evidence to date of (i) high linear polarization ( detection), and (ii) variation of polarization angle with time, occurring twice during the rise and decay phase of the burst at 3. 5σ and 3. 1σ detections, respectively. All confidence levels are reported for two parameters of interest. These observations strongly suggest synchrotron radiation produced in magnetic field lines that are highly ordered on angular scales of 1/Γ, where Γ is the Lorentz factor of the outflow.
Protective Impact of Curcumin against Paracetamol-Induced Hepatotoxicity in Rats

The present study was planned to investigate the protective impact of curcumin on hepatotoxicity of paracetamol in male albino rats. Thirty-five male albino rats were allocated into seven equal groups, each of five rats, 1st group was kept as control, 2nd group orally administered tween 80 (0.5 ml/200 g b. wt) for 15 days, 3rd group received silymarin (200 mg/kg b. wt) orally for 15 days, 4th group received curcumin (200 mg/kg b. wt) orally for 15 days, 5th group orally administered paracetamol (500 mg/kg b. wt) for the last 5 days, 6th group was given silymarin and paracetamol, 7th group was given curcumin and paracetamol. The hepatotoxicity of paracetamol in rats displayed a significant reduction in erythrocytic count (RBCs), concentration of hemoglobin (Hb), the values of packed cell volume (PCV) and mean corpuscular hemoglobin concentration (MCHC) with a critical elevation in mean corpuscular volume (MCV), platelets (Plts), white blood cells (WBCs) and neutrophils count. Silymarin or curcumin administration with paracetamol in rats produced a significant increase in RBCs, Hb, PCV and MCHC with a significant decrease in MCV, Plts, WBCs and neutrophils count. Liver enzymes (ALT and AST) and total bilirubin were significantly increased post administration of paracetamol whereas administration of silymarin or curcumin with paracetamol significantly decreased the activities of liver enzymes and total bilirubin. Paracetamol administration in rats produced a significant increase in tumor-necrosis factor-alpha (TNF-α) level and lactate-dehydrogenase enzyme (LDH) activity. Silymarin or curcumin administration with paracetamol displayed a critical decrease in TNF-α and LDH activity. Paracetamol elicited a critical decrease in catalase (CAT), superoxide dismutase (SOD) and glutathione peroxidase (GPX) activities with a critical elevation in malondialdehyde (MDA) concentration. Silymarin or curcumin administration with paracetamol evoked a critical elevation in CAT, SOD and GPX activities with a significant decrease in MDA concentration. Histopathological examination of paracetamol treated rats displayed alterations in liver histoarchitecture with degenerative and necrotic changes in hepatic cells but restored to nearly normal picture by pretreatment with silymarin and curcumin. Curcumin has a hepatoprotective effect on paracetamol induced-hepatotoxicity in rats.
Test Optimization of Core based SOCs Using Various Heuristic Algorithms

The advancement in design methodologies and semiconductor process technologies has led to the development of systems with excessive functionality implemented on a single die, called system-on-chip. A set of pre-designed and pre-verified design modules in the form of soft, hard or firm cores brought from vendors are integrated into a system using user-defined logic (UDL) and interconnects. Complex systems can be implemented having digital, analog and mixed signal components. The urgent time to market requirement poses many challenges for the design and test engineers. Testing cost has made IC testing more difficult. ITRS semiconductor roadmap represents that there will be a need of hundred of processors for the future generation of SOC designs which will further increase the test cost. Testing of SOC is costly due to large data volume introduced due to increase in the integration and interconnection intricacies, huge power dissipation during test, expensive test generation procedures, heterogeneous mix of cores and their long test application times. Many techniques have been proposed to reduce the cost by test scheduling, reducing test data volume and optimizing test design mechanisms. Test generation can either be done off-chip by employing ATPG (Automatic test pattern generation) algorithms running on expensive automatic test equipments or on-chip using a built-in hardware called BIST (Built In Self Test). BIST offers the benefits in case if on-chip the availability of TAM is less. However BIST ready cores are not always available, also the multi site testing of SOC for test time reduction makes the ATE more promising. TAM optimization and test scheduling have been the integral part of the research and test optimization for past three decades. Test scheduling is proved to be an NP-hard problem. This paper proposes a greedy algorithm based approach and enhanced ant colony optimization algorithm based approach for test scheduling to reduce the test time subject to test power, bandwidth and hierarchical constraint. We can reduce the problem into a rectangle packing problem. Experimental results for ITC’02 benchmark circuits show the optimal results achieved. The proposed schemes are applied on three benchmark circuits from Duke University and Philips and they are d695, p22810, p93791.
Multimodal neuroimaging data integration and pathway analysis

With advancements in technology, the collection of multiple types of measurements on a common set of subjects is becoming routine in science. Some notable examples include multimodal neuroimaging studies for the simultaneous investigation of brain structure and function and multi‐omics studies for combining genetic and genomic information. Integrative analysis of multimodal data allows scientists to interrogate new mechanistic questions. However, the data collection and generation of integrative hypotheses is outpacing available methodology for joint analysis of multimodal measurements. In this article, we study high‐dimensional multimodal data integration in the context of mediation analysis. We aim to understand the roles that different data modalities play as possible mediators in the pathway between an exposure variable and an outcome. We propose a mediation model framework with two data types serving as separate sets of mediators and develop a penalized optimization approach for parameter estimation. We study both the theoretical properties of the estimator through an asymptotic analysis and its finite‐sample performance through simulations. We illustrate our method with a multimodal brain pathway analysis having both structural and functional connectivity as mediators in the association between sex and language processing.
Corporate Social Responsibility and the Cost of Debt

In this paper, we analyze the relationship between corporate social responsibility (CSR) and the cost of debt financing. Using a large sample of U.S. firms across all industries from 2006 to 2013, we find that firms with strong CSR have a lower cost of debt. This is especially evident in the manufacturing and financial industries. Further, we analyze the impact of managerial ownership on the CSR/cost of debt relationship. Practically speaking, to reduce the cost of debt financing our results suggest that it would be beneficial for firms to strengthen involvement and engagement in CSR activities.
Demystifying the MLPerf Benchmark Suite

MLPerf, an emerging machine learning benchmark suite strives to cover a broad range of applications of machine learning. We present a study on its characteristics and how the MLPerf benchmarks differ from some of the previous deep learning benchmarks like DAWNBench and DeepBench. We find that application benchmarks such as MLPerf (although rich in kernels) exhibit different features compared to kernel benchmarks such as DeepBench. MLPerf benchmark suite contains a diverse set of models which allows unveiling various bottlenecks in the system. Based on our findings, dedicated low latency interconnect between GPUs in multi-GPU systems is required for optimal distributed deep learning training. We also observe variation in scaling efficiency across the MLPerf models. The variation exhibited by the different models highlight the importance of smart scheduling strategies for multi-GPU training. Another observation is that CPU utilization increases with increase in number of GPUs used for training. Corroborating prior work we also observe and quantify improvements possible by compiler optimizations, mixed-precision training and use of Tensor Cores.
Collaborative Computation Offloading in Wireless Powered Mobile-Edge Computing Systems

This paper studies a novel user cooperation model in a wireless powered mobile edge computing system where two wireless users harvest wireless power transferred by one energy node and can offload part of their computation tasks to an edge server (ES) for remote execution. In particular, we consider that the direct communication link between one user to the ES is blocked, such that the other user acts as a relay to forward its offloading data to the server. Meanwhile, instead of forwarding all the received task data, we also allow the helping user to compute part of the received task locally to reduce the potentially high energy and time cost on task offloading to the ES. Our aim is to maximize the amount of data that can be processed within a given time frame of the two users by jointly optimizing the amount of task data computed at each device (users and ES), the system time allocation, the transmit power and CPU frequency of the users. We propose an efficient method to find the optimal solution and show that the proposed user cooperation can effectively enhance the computation performance of the system compared to other representative benchmark methods under different scenarios.
Equation for self-diffusivity applied to hard sphere and Lennard-Jones systems

An equation for the self-diffusion coefficient is derived. The main theoretical tool is a description in terms of transition probabilities combined with the Chapman-Enskog transport theory for small concentration and long-time tail corrections. The equation satisfactorily reproduces the behavior of self-diffusivity in the whole density range for fluids composed by interacting particles with hard sphere or Lennard-Jones potentials, with deviations close to the critical point in the last case.
Single-Stage Multi-Person Pose Machines

Multi-person pose estimation is a challenging problem. Existing methods are mostly two-stage based-one stage for proposal generation and the other for allocating poses to corresponding persons. However, such two-stage methods generally suffer low efficiency. In this work, we present the first \emph{single-stage} model, Single-stage multi-person Pose Machine (SPM), to simplify the pipeline and lift the efficiency for multi-person pose estimation. To achieve this, we propose a novel Structured Pose Representation (SPR) that unifies person instance and body joint position representations. Based on SPR, we develop the SPM model that can directly predict structured poses for multiple persons in a single stage, and thus offer a more compact pipeline and attractive efficiency advantage over two-stage methods. In particular, SPR introduces the root joints to indicate different person instances and human body joint positions are encoded into their displacements w.r. t. ~the roots. To better predict long-range displacements for some joints, SPR is further extended to hierarchical representations. Based on SPR, SPM can efficiently perform multi-person poses estimation by simultaneously predicting root joints (location of instances) and body joint displacements via CNNs. Moreover, to demonstrate the generality of SPM, we also apply it to multi-person 3D pose estimation. Comprehensive experiments on benchmarks MPII, extended PASCAL-Person-Part, MSCOCO and CMU Panoptic clearly demonstrate the state-of-the-art efficiency of SPM for multi-person 2D/3D pose estimation, together with outstanding accuracy.
A New Proposal of Applications of Statistical Depth Functions in Causal Analysis of Socio-Economic Phenomena Based on Official Statistics -- A Study of EU Agricultural Subsidies and Digital Developement in Poland

Results of a convincing causal statistical inference related to socio-economic phenomena are treated as especially desired background for conducting various socio-economic programs or government interventions. Unfortunately, quite often real socio-economic issues do not fulfill restrictive assumptions of procedures of causal analysis proposed in the literature. This paper indicates certain empirical challenges and conceptual opportunities related to applications of procedures of data depth concept into a process of causal inference as to socio-economic phenomena. We show, how to apply a statistical functional depths in order to indicate factual and counterfactual distributions commonly used within procedures of causal inference. The presented framework is especially useful in a context of conducting causal inference basing on official statistics, i.e., basing on already existing databases. Methodological considerations related to extremal depth, modified band depth, Fraiman-Muniz depth, and multivariate Wilcoxon sum rank statistic are illustrated by means of example related to a study of an impact of EU direct agricultural subsidies on a digital development in Poland in a period of 2012-2019.
Hundert Jahre kentum-satem Theorie

I Vor 25 Jahren hat G. R. Solta in Band 70 dieser Zeitschrift einen vielbeachteten Aufsatz veröffentlicht, in dem er zeigt, daß die generelle Unterscheidung von Kentumund Satemsprachen innerhalb der Indogermanistik stark überschätzt wird. Als Einteilungskriterium viel brauchbarer sei vielmehr die Beobachtung, daß die einen Sprachen einen Hang zur Labialisierung, die anderen zur Palatalisierung zeigen: Eine ost-west gerichtete
Crystallization for Coulomb and Riesz interactions as a consequence of the Cohn-Kumar conjecture

The Cohn-Kumar conjecture states that the triangular lattice in dimension 2, the E 8 E_8 lattice in dimension 8, and the Leech lattice in dimension 24 are universally minimizing in the sense that they minimize the total pair interaction energy of infinite point configurations for all completely monotone functions of the squared distance. This conjecture was recently proved by Cohn-Kumar-Miller-Radchenko-Viazovska in dimensions 8 and 24. We explain in this note how the conjecture implies the minimality of the same lattices for the Coulomb and Riesz renormalized energies as well as jellium and periodic jellium energies, hence settling the question of their minimization in dimensions 8 and 24.
Wetting of Nanofluids on CVD-synthesized Graphene

Comparative wettability studies of graphene are conducted for two different nanofluids with opposite surface potentials of +53 mV (45 nm alumina nanoparticles) and −45 mV (28 nm silica nanoparticles), respectively. Aged graphene surface, which has adsorbed abundant hydrocarbon contaminants, shows weak hydrophobicity of about 90 wetting angles for both nanofluids for the tested volume concentration range from 0 to 10%. For pristine graphene surfaces, however, the contact angle of alumina nanofluids continually increases from 50 to 70 for the same volume concentration increase, but the contact angle of silica nanofluids shows first increase of up to about 1% concentration and then remains nearly unchanged
Multilingual and Multi-Aspect Hate Speech Analysis

Current research on hate speech analysis is typically oriented towards monolingual and single classification tasks. In this paper, we present a new multilingual multi-aspect hate speech analysis dataset and use it to test the current state-of-the-art multilingual multitask learning approaches. We evaluate our dataset in various classification settings, then we discuss how to leverage our annotations in order to improve hate speech detection and classification in general.
Probabilistic modeling of rumour stance and popularity in social media

Social media tends to be rife with rumours when new reports are released piecemeal during breaking news events. One can mine multiple reactions expressed by social media users in those situations, exploring users’ stance towards rumours, ultimately enabling the flagging of highly disputed rumours as being potentially false. Moreover, rumours in social media exhibit complex temporal patterns. Some rumours are discussed with an increasing number of tweets per unit of time whereas other rumours fail to gain ground. This thesis develops probabilistic models of rumours in social media driven by two applications: rumour stance classification and modeling temporal dynamics of rumours. Rumour stance classification is the task of classifying the stance expressed in an individual tweet towards a rumour. Modeling temporal dynamics of rumours is an application where rumour prevalence is modeled over time. Both applications provide insights into how a rumour attracts attention from the social media community. These can assist journalists with their work on rumour tracking and debunking, and can be used in downstream applications such as systems for rumour veracity classification. In this thesis, we develop models based on probabilistic approaches. We motivate Gaussian processes and point processes as appropriate tools and show how features not considered in previous work can be included. We show that for both applications, transfer learning approaches are successful, supporting the hypothesis that there is a common underlying signal across different rumours. We furthermore introduce novel machine learning techniques which have the potential to be used in other applications: convolution kernels for streams of text over continuous time and a sequence classification algorithm based on point processes.