nils-herrmann/clean_scirepeval huggingface dataset:
Pressure tuning of the spin-orbit coupled ground state in Sr2IrO4.

X-ray absorption spectroscopy studies of the magnetic-insulating ground state of Sr2IrO4 at ambient pressure show a clear deviation from a strong spin-orbit (SO) limit J(eff)=1/2 state, a result of local exchange interactions and a nonzero tetragonal crystal field mixing SO split J(eff)=1/2, 3/2 states. X-ray magnetic circular dichroism measurements in a diamond anvil cell show a magnetic transition at a pressure of ∼17  GPa, where the "weak" ferromagnetic moment is quenched despite transport measurements showing insulating behavior to at least 40 GPa. The magnetic transition has implications for the origin of the insulating gap and the nature of exchange interactions in this SO coupled system. The expectation value of the angular part of the SO interaction, , extrapolates to zero at ∼80-90  GPa where an increased bandwidth strongly mixes J(eff)=1/2, 3/2 states and SO interactions no longer dominate the electronic ground state of Sr2IrO4.
Let's get physical: a contemporary review of the anxiolytic effects of exercise for anxiety and its disorders.

Research over the past few decades has focused on the therapeutic effects of physical exercise among those affected by mood disorders. Only recently has attention turned to maladaptive and persistent expressions of anxiety, with a growing body of evidence indicating promise for exercise as an effective treatment for some of the anxiety disorders. The current review provides a comprehensive account of contemporary research examining the anxiolytic effects of exercise for anxiety disorders. We synthesize pertinent research regarding the effects of various types of exercise within the different anxiety disorders, consider impact of various types of exercise regimens on anxiety, and examine potential anxiolytic mechanisms responsible for positive mental health gains. We conclude with important considerations for implementing exercise as a treatment for clinically significant anxiety as well as future research directions.
Unilateral Hydronephrosis and Renal Damage after Acute Leukemia

A 14-year-old boy presented with asymptomatic right hydronephrosis detected on routine yearly ultrasound examination. Previously, he had at least two normal renal ultrasonograms, 4 years after remission of acute myeloblastic leukemia, treated by AML-BFM-93 protocol. A function of the right kidney and no damage on the left was confirmed by a DMSA scan. Right retroperitoneoscopic nephrectomy revealed 3 renal arteries with the lower pole artery lying on the pelviureteric junction. Histologically chronic tubulointerstitial nephritis was detected. In the pathogenesis of this severe unilateral renal damage, we suspect the exacerbation of deleterious effects of cytostatic therapy on kidneys with intermittent hydronephrosis.
A Comparative Study for Email Classification

Email has become one of the fastest and most economical forms of communication. However, the increase of email users have resulted in the dramatic increase of spam emails during the past few years. In this paper, email data was classified using four different classifiers (Neural Network, SVM classifier, Naïve Bayesian Classifier, and J48 classifier). The experiment was performed based on different data size and different feature size. The final classification result should be ‘1’ if it is finally spam, otherwise, it should be ‘0’. This paper shows that simple J48 classifier which make a binary tree, could be efficient for the dataset which could be classified as binary tree.
Immune and Stromal Classification of Colorectal Cancer Is Associated with Molecular Subtypes and Relevant for Precision Immunotherapy.

The tumor microenvironment is formed by many distinct and interacting cell populations, and its composition may predict patients' prognosis and response to therapies. Colorectal cancer is a heterogeneous disease in which immune classifications and four consensus molecular subgroups (CMS) have been described. Our aim was to integrate the composition of the tumor microenvironment with the consensus molecular classification of colorectal cancer.



We retrospectively analyzed the composition and the functional orientation of the immune, fibroblastic, and angiogenic microenvironment of 1,388 colorectal cancer tumors from three independent cohorts using transcriptomics. We validated our findings using immunohistochemistry.



We report that colorectal cancer molecular subgroups and microenvironmental signatures are highly correlated. Out of the four molecular subgroups, two highly express immune-specific genes. The good-prognosis microsatellite instable-enriched subgroup (CMS1) is characterized by overexpression of genes specific to cytotoxic lymphocytes. In contrast, the poor-prognosis mesenchymal subgroup (CMS4) expresses markers of lymphocytes and of cells of monocytic origin. The mesenchymal subgroup also displays an angiogenic, inflammatory, and immunosuppressive signature, a coordinated pattern that we also found in breast (n = 254), ovarian (n = 97), lung (n = 80), and kidney (n = 143) cancers. Pathologic examination revealed that the mesenchymal subtype is characterized by a high density of fibroblasts that likely produce the chemokines and cytokines that favor tumor-associated inflammation and support angiogenesis, resulting in a poor prognosis. In contrast, the canonical (CMS2) and metabolic (CMS3) subtypes with intermediate prognosis exhibit low immune and inflammatory signatures.



The distinct immune orientations of the colorectal cancer molecular subtypes pave the way for tailored immunotherapies.  22(16); 4057-66.
MARLA: MapReduce for Heterogeneous Clusters

MapReduce has gradually become the framework of choice for "big data". The MapReduce model allows for efficient and swift processing of large scale data with a cluster of compute nodes. However, the efficiency here comes at a price. The performance of widely used MapReduce implementations such as Hadoop suffers in heterogeneous and load-imbalanced clusters. We show the disparity in performance between homogeneous and heterogeneous clusters in this paper to be high. Subsequently, we present MARLA, a MapReduce framework capable of performing well not only in homogeneous settings, but also when the cluster exhibits heterogeneous properties. We address the problems associated with existing MapReduce implementations affecting cluster heterogeneity, and subsequently present through MARLA the components and trade-offs necessary for better MapReduce performance in heterogeneous cluster and cloud environments. We quantify the performance gains exhibited by our approach against Apache Hadoop and MARIANE in data intensive and compute intensive applications.
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting

Quantization can improve the execution latency and energy efficiency of neural networks on both commodity GPUs and specialized accelerators. The majority of existing literature focuses on training quantized DNNs, while this work examines the less-studied topic of quantizing a floatingpoint model without (re)training. DNN weights and activations follow a bell-shaped distribution post-training, while practical hardware uses a linear quantization grid. This leads to challenges in dealing with outliers in the distribution. Prior work has addressed this by clipping the outliers or using specialized hardware. In this work, we propose outlier channel splitting (OCS), which duplicates channels containing outliers, then halves the channel values. The network remains functionally identical, but affected outliers are moved toward the center of the distribution. OCS requires no additional training and works on commodity hardware. Experimental evaluation on ImageNet classification and language modeling shows that OCS can outperform state-of-the-art clipping techniques with only minor overhead.
Realtime Dynamic 3D Facial Reconstruction for Monocular Video In-the-Wild

With the increasing amount of videos recorded using 2D mobile cameras, the technique for recovering the 3D dynamic facial models from these monocular videos has become a necessity for many image and video editing applications. While methods based parametric 3D facial models can reconstruct the 3D shape in dynamic environment, large structural changes are ignored. Structure-from-motion methods can reconstruct these changes but assume the object to be static. To address this problem we present a novel method for realtime dynamic 3D facial tracking and reconstruction from videos captured in uncontrolled environments. Our method can track the deforming facial geometry and reconstruct external objects that protrude from the face such as glasses and hair. It also allows users to move around, perform facial expressions freely without degrading the reconstruction quality.
Herding Behavior In Online P2P Lending: An Empirical Investigation

We study lender behavior in the peer-to-peer (P2P) lending market, where individuals bid on unsecured microloans requested by other individual borrowers. Online P2P exchanges are growing, but lenders in this market are not professional investors. In addition, lenders have to take big risks because loans in P2P lending are granted without collateral. While the P2P lending market shares some characteristics of online markets with respect to herding behavior, it also has characteristics that may discourage it. This study empirically investigates herding behavior in the P2P lending market where seemingly conflicting conditions and features of herding are present. Using a large sample of daily data from one of the largest P2P lending platforms in Korea, we find strong evidence of herding and its diminishing marginal effect as bidding advances. We employ a multinomial logit market-share model in which relevant variables from prior studies on P2P lending are assessed.
Social media affects the timing, location, and severity of school shootings

Javier Garcia-Bernardo, ∗ Hong Qi, James M. Shultz, Alyssa M. Cohen, Neil F. Johnson, † and Peter Sheridan Dodds ‡ Department of Computer Science, University of Vermont, Burlington VT 05405, USA§ Department of Physics, University of Miami, Coral Gables, FL 33124, USA Center for Disaster & Extreme Event Preparedness (DEEP Center), University of Miami, Miller School of Medicine, FL 33124, USA 10858 Limeberry Drive, Cooper City, FL 33026, USA Department of Mathematics & Statistics, Vermont Complex Systems Center, Computational Story Lab, & the Vermont Advanced Computing Core, The University of Vermont, Burlington, VT 05401, USA§ ( June 23, 2015)
Shadow And Highlight Invariant Colour Segmentation Algorithm For Traffic Signs

Shadows and highlights represent a challenge to the computer vision researchers due to a variance in the brightness on the surfaces of the objects under consideration. This paper presents a new colour detection and segmentation algorithm for road signs in which the effect of shadows and highlights are neglected to get better colour segmentation results. Images are taken by a digital camera mounted in a car. The RGB images are converted into HSV colour space and the shadow-highlight invariant method is applied to extract the colours of the road signs under shadow and highlight conditions. The method is tested on hundreds of outdoor images under such light conditions, and it shows high robustness; more than 95% of correct segmentation is achieved
A Markov chain sequence generator for power macromodeling

In this paper, we present a novel sequence generator based on a Markov chain model. Specifically, we formulate the problem of generating a sequence of vectors with given average input probability p, average transition density d, and spatial correlation s as a transition matrix computation problem, in which the matrix elements are subject to constraints derived from the specified statistics. We also give a practical heuristic that computes such a matrix and generates a sequence of l n-bit vectors in O(nl + n2) time. Derived from a strongly mixing Markov chain, our generator yields binary vector sequences with accurate statistics, high uniformity, and high randomness. Experimental results show that our sequence generator can cover more than 99% of the parameter space. Sequences of 2,000 48-bit vectors are generated in less than 0.05 seconds, with average deviations of the signal statistics p, d, and s equal to 1.6%, 1.8%, and 2.8%, respectively.Our generator enables the detailed study of power macromodeling. Using our tool and the ISCAS-85 benchmark circuits, we have assessed the sensitivity of power dissipation to the three input statistics p, d, and s. Our investigation reveals that power is most sensitive to transition density, while only occasionally exhibiting high sensitivity to signal probability and spatial correlation. Our experiments also show that input signal imbalance can cause estimation errors as high as 100% in extreme cases, although errors are usually within 25%.
Excerpt from FRACTAL SPACE-TIME AND MICROPHYSICS Towards a Theory of Scale Relativity

Let us now come to our first attempts to define fractals in an intrinsic way and to deal with infinities and with their non-differentiability. We first consider the case of fractal curves drawn in a plane. The von Koch construction may be generalized in the complex plane by first giving ourselves a base (or “generator”) F1 made of p segments of length 1/q. The coordinates of the p points Pj of F1 are given, either in Cartesian or in polar coordinates (see Figs. 3.5 and 3.6)
Using nicotine gum and varenicline ( Chantix )

You are smart to question how different medications interact when used concurrently. Champix, called Chantix in the United States and globally by its generic name varenicline , is a prescription medication that can help individuals quit smoking by partially stimulating nicotine receptors in cells throughout the body. Nicorette gum, a type of nicotine replacement therapy (NRT), is also a tool to help smokers quit by providing individuals with the nicotine they crave by delivering the substance in controlled amounts through the lining of the mouth. NRT is available in many other forms including lozenges, patches, inhalers, and nasal sprays. The short answer is that there is disagreement among researchers about whether or not there are negative consequences to chewing nicotine gum while taking varenicline. While some studies suggest no harmful side effects to using them together, others have found that adverse effects from using both at the same time. So, what does the current evidence say?
Efficient Production of Transgenic Cassava Using Negative and Positive Selection

In order to improve the efficiency of cassava (Manihot esculenta Crantz) transformation, two different selection systems were assessed, a positive one based on the use of mannose as the selective agent, and a negative one based on hygromycin resistance encoded by an intron-containing hph gene. Transgenic plants selected on mannose or hygromycin were regenerated for the first time from embryogenic suspensions cocultivated with Agrobacterium. After the initial selection using mannose and hygromycin, 82.6% and 100% of the respective developing embryogenic callus lines were transgenic. A system allowing plant regeneration from only transgenic lines was designed by combining chemical selection with histochemical GUS assays. In total, 12 morphologically normal transgenic plant lines were produced, five using mannose and seven using hygromycin. The stable integration of the transgenes into the nuclear genome was verified using PCR and Southern analysis. RT-PCR and northern analyses confirmed the transgene expression in the regenerated plants. A rooting test on mannose containing medium was developed as an alternative to GUS assays in order to eliminate escapes from the positive selection system. Our results show that transgenic cassava plants can be obtained by using either antibiotic resistance genes that are not expressed in the micro-organisms or an antibiotic-free positive selection system.
Rear-Stitched View Panorama: A Low-Power Embedded Implementation for Smart Rear-View Mirrors on Vehicles

Automobiles are currently equipped with a three-mirror system for rear-view visualization. The two side-view mirrors show close the periphery on the left and right sides of the vehicle, and the center rear-view mirror is typically adjusted to allow the driver to see through the vehicle's rear windshield. This three-mirror system, however, imposes safety concerns in requiring drivers to shift their attention and gaze to look in each mirror to obtain a full visualization of the rear-view surroundings, which takes attention off the scene in front of the vehicle. We present an alternative to the three-mirror rear-view system, which we call Rear-Stitched View Panorama (RSVP). The proposed system uses four rear-facing cameras, strategically placed to overcome the traditional blind spot problem, and stitches the feeds from each camera together to generate a single panoramic view, which can display the entire rear surroundings. We project individually captured frames onto a single virtual view using precomputed system calibration parameters. Then we determine optimal seam lines, along which the images are fused together to form the single RSVP view presented to the driver. Furthermore, we highlight techniques that enable efficient embedded implementation of the system and showcase a real-time system utilizing under 2W of power, making it suitable for in-cabin deployment in vehicles.
Where do helpers look?: gaze targets during collaborative physical tasks

This study used eye-tracking technology to assess where helpers look as they are providing assistance to a worker during collaborative physical tasks. Gaze direction was coded into one of six categories: partner's head, partner's hands, task parts and tools, the completed task, and instruction manual. Results indicated that helpers rarely gazed at their partners' faces, but distributed gaze fairly evenly across the other targets. The results have implications for the design of video systems to support collaborative physical tasks.
Additive Sequential Evolutionary Design of Experiments

Process models play important role in computer aided process engineering. Although the structure of these models are a priori known, model parameters should be estimated based on experiments. The accuracy of the estimated parameters largely depends on the information content of the experimental data presented to the parameter identification algorithm. Optimal experiment design (OED) can maximize the confidence on the model parameters. The paper proposes a new additive sequential evolutionary experiment design approach to maximize the amount of information content of experiments. The main idea is to use the identified models to design new experiments to gradually improve the model accuracy while keeping the collected information from previous experiments. This scheme requires an effective optimization algorithm, hence the main contribution of the paper is the incorporation of Evolutionary Strategy (ES) into a new iterative scheme of optimal experiment design (AS-OED). This paper illustrates the applicability of AS-OED for the design of feeding profile for a fed-batch biochemical reactor.
Advanced Wi-Fi attacks using commodity hardware

We show that low-layer attacks against Wi-Fi can be implemented using user-modifiable firmware. Hence cheap off-the-shelf Wi-Fi dongles can be used carry out advanced attacks. We demonstrate this by implementing five low-layer attacks using open source Atheros firmware. The first attack consists of unfair channel usage, giving the user a higher throughput while reducing that of others. The second attack defeats countermeasures designed to prevent unfair channel usage. The third attack performs continuous jamming, making the channel unusable for other devices. For the fourth attack we implemented a selective jammer, allowing one to jam specific frames already in the air. The fifth is a novel channel-based Man-in-the-Middle (MitM) attack, enabling reliable manipulation of encrypted traffic.
 These low-layer attacks facilitate novel attacks against higher-layer protocols. To demonstrate this we show how our MitM attack facilitates attacks against the Temporal Key Integrity Protocol (TKIP) when used as a group cipher. Since a substantial number of networks still use TKIP as their group cipher, this shows that weaknesses in TKIP have a higher impact than previously thought.
In situ melanoma of the nail unit presenting as a rapid growing longitudinal melanonychia in a 9-year-old white boy.

Longitudinal melanonychia (LM) is a pigmented longitudinal band of the nail unit, which results from pigment deposition, generally melanin, in the nail plate. Such lesion is frequently observed in specific ethnic groups, such as Asians and African Americans, typically affecting multiple nails. When LM involves a single nail plate, it may be the sign of a benign lesion within the matrix, such as a melanocytic nevus, simple lentigo, or nail matrix melanocyte activation. However, the possibility of melanoma must be considered. Nail melanoma in children is exceptionally rare and only 2 cases have been reported in fairskinned Caucasian individuals.
Mondrian: an agile information visualization framework

Data visualization is the process of representing data as pictures to support reasoning about the underlying data. For the interpretation to be as easy as possible, we need to be as close as possible to the original data. As most visualization tools have an internal meta-model, which is different from the one for the presented data, they usually need to duplicate the original data to conform to their meta-model. This leads to an increase in the resources needed, increase which is not always justified. In this work we argue for the need of having an engine that is as close as possible to the data and we present our solution of moving the visualization tool to the data, instead of moving the data to the visualization tool. Our solution also emphasizes the necessity of reusing basic blocks to express complex visualizations and allowing the programmer to script the visualization using his preferred tools, rather than a third party format. As a validation of the expressiveness of our framework, we show how we express several already published visualizations and describe the pros and cons of the approach.
AATOS - A Configurable Tool for Automatic Annotation

This paper presents an automatic annotation tool AATOS for providing documents with semantic annotations. The tool links entities found from the texts to ontologies defined by the user. The application is highly configurable and can be used with different natural language Finnish texts. The application was developed as a part of the WarSampo and Semantic Finlex projects and tested using Kansa Taisteli magazine articles and consolidated Finnish legislation of Semantic Finlex. The quality of the automatic annotation was evaluated by measuring precision and recall against existing manual annotations. The results showed that the quality of the input text, as well as the selection and configuration of the ontologies impacted the results.