nils-herrmann/clean_scirepeval huggingface dataset:
In Defense of Intelligent Design

Anyone new to the debate over intelligent design encounters many conflicting claims about whether it is science. A Washington Post front page story (Slevin 2005) asserts that intelligent design is “not science [but] politics.” In that same story, Barry Lynn, the director of Americans United for Separation of Church and State, claims that intelligent design is merely “a veneer over a certain theological message,” thus identifying intelligent design not with science but with religion. In a related vein, University of Copenhagen philosopher Jakob Wolf (2004) argues that intelligent design is not science but philosophy (albeit a philosophy useful for understanding science). And finally, proponents of intelligent design argue that it is indeed science (e.g., Dembski 2002a, ch. 6). Who is right?
Energy metabolism in tumor cells.

In early studies on energy metabolism of tumor cells, it was proposed that the enhanced glycolysis was induced by a decreased oxidative phosphorylation. Since then it has been indiscriminately applied to all types of tumor cells that the ATP supply is mainly or only provided by glycolysis, without an appropriate experimental evaluation. In this review, the different genetic and biochemical mechanisms by which tumor cells achieve an enhanced glycolytic flux are analyzed. Furthermore, the proposed mechanisms that arguably lead to a decreased oxidative phosphorylation in tumor cells are discussed. As the O(2) concentration in hypoxic regions of tumors seems not to be limiting for the functioning of oxidative phosphorylation, this pathway is re-evaluated regarding oxidizable substrate utilization and its contribution to ATP supply versus glycolysis. In the tumor cell lines where the oxidative metabolism prevails over the glycolytic metabolism for ATP supply, the flux control distribution of both pathways is described. The effect of glycolytic and mitochondrial drugs on tumor energy metabolism and cellular proliferation is described and discussed. Similarly, the energy metabolic changes associated with inherent and acquired resistance to radiotherapy and chemotherapy of tumor cells, and those determined by positron emission tomography, are revised. It is proposed that energy metabolism may be an alternative therapeutic target for both hypoxic (glycolytic) and oxidative tumors.
Parietofrontal network upregulation after motor stroke

Motor recovery after stroke shows a high inter-subject variability. The brain's potential to form new connections determines individual levels of recovery of motor function. Most of our daily activities require visuomotor integration, which engages parietal areas. Compared to the frontal motor system, less is known about the parietal motor system's reconfiguration related to stroke recovery. Here, we tested if functional connectivity among parietal and frontal motor areas undergoes plastic changes after stroke and assessed the behavioral relevance for motor function after stroke.



We investigated stroke lesion-induced changes in functional connectivity by measuring high-density electroencephalography (EEG) and assessing task-related changes in coherence during a visually guided grip task with the paretic hand in 30 chronic stroke patients with variable motor deficits and 19 healthy control subjects. Quantitative changes in task-related coherence in sensorimotor rhythms were compared to the residual motor deficit.



Parietofrontal coupling was significantly stronger in patients compared to controls. Whereas motor network coupling generally increased during the task in both groups, the task-related coherence between the parietal and primary motor cortex in the stroke lesioned hemisphere showed increased connectivity across a broad range of sensorimotor rhythms. Particularly the parietofrontal task-induced coupling pattern was significantly and positively related to residual impairment in the Nine-Hole Peg Test performance and grip force.



These results demonstrate that parietofrontal motor system integration during visually guided movements is stronger in the stroke-lesioned brain. The correlation with the residual motor deficit could either indicate an unspecific marker of motor network damage or it might indicate that upregulated parietofrontal connectivity has some impact on post-stroke motor function.
Catastrophic Forgetting in Connectionist Networks : Causes , Consequences and Solutions

All natural cognitive systems, and, in particular, our own, gradually forget previously learned information. Consequently, plausible models of human cognition should exhibit similar patterns of gradual forgetting old information as new information is acquired. Only rarely (see Box 3) does new learning in natural cognitive systems co pletely disrupt or erase previously learned information. In other words, natural cognitive systems do not, in general, forget catastrophically. Unfortunately, however, this is precisely what occurs under certain circumstances in distributed connectionist networks. It turns out that the very features that give these networks their much-touted abilities to generalize, to function in the presence of degraded input, etc., are the root cause of catastrophic forgetting. The challenge is how to keep the advantages of distributed connectionist networks while avoiding the problem of catastrophic forgetting. In this article, we examine the causes, consequences and numerous solutions to the problem of catastrophic forgetting in neural networks. We consider how the brain might have overcome this problem and explore the consequences of this solution.  By the end of the 1980’s many of the early problems with connectionist networks, such as their difficulties with sequence-learning and the profoundly stimulus-response nature of supervised learning algorithms such as error backpropagation had been largely solved. However, as these problems were being solved, another was discovered by McCloskey and Cohen and Ratcliff . They suggested that there might be a fundamental imitation to this type of distributed architecture, in the same way that Minsky and Papert 3 had shown twenty years before that there were certain fundamental limitations to what a perceptron 4,5 could do. They observed that under certain conditions, the process of learning a new set of patterns suddenly and completely erased a network’s knowledge of what it had already learned. They referred to this phenomenon as catastrophic interference (or catastrophic forgetting) and suggested that the underlying reason for this difficulty was the very thing — a single set of shared weights — that gave the networks their remarkable abilities to generalize and degrade gracefully. Catastrophic interference is a radical manifestation of a more general problem for connectionist models of memory — in fact, for any model of memory —, the so-called “stability-plasticity” problem. 6,7 The problem is how to design a system that is simultaneously sensitive to, but not radically disrupted by, new input. In this article we will focus primarily on a particular, widely used class of distributed neural network architectures — namely, those with a single set of shared (or partially shared) multiplicative weights. While this defines a very broad class of networks, this definition is certainly not exhaustive. In the remainder of this article we will discuss the numerous attempts over the last decade to solve this problem within the context of this type of network.
Natural Language Metaphors Covertly Influence Reasoning

Metaphors pervade discussions of social issues like climate change, the economy, and crime. We ask how natural language metaphors shape the way people reason about such social issues. In previous work, we showed that describing crime metaphorically as a beast or a virus, led people to generate different solutions to a city's crime problem. In the current series of studies, instead of asking people to generate a solution on their own, we provided them with a selection of possible solutions and asked them to choose the best ones. We found that metaphors influenced people's reasoning even when they had a set of options available to compare and select among. These findings suggest that metaphors can influence not just what solution comes to mind first, but also which solution people think is best, even when given the opportunity to explicitly compare alternatives. Further, we tested whether participants were aware of the metaphor. We found that very few participants thought the metaphor played an important part in their decision. Further, participants who had no explicit memory of the metaphor were just as much affected by the metaphor as participants who were able to remember the metaphorical frame. These findings suggest that metaphors can act covertly in reasoning. Finally, we examined the role of political affiliation on reasoning about crime. The results confirm our previous findings that Republicans are more likely to generate enforcement and punishment solutions for dealing with crime, and are less swayed by metaphor than are Democrats or Independents.
Coupled Learning for Facial Deblur

Blur in facial images significantly impedes the efficiency of recognition approaches. However, most existing blind deconvolution methods cannot generate satisfactory results due to their dependence on strong edges, which are sufficient in natural images but not in facial images. In this paper, we represent point spread functions (PSFs) by the linear combination of a set of pre-defined orthogonal PSFs, and similarly, an estimated intrinsic (EI) sharp face image is represented by the linear combination of a set of pre-defined orthogonal face images. In doing so, PSF and EI estimation is simplified to discovering two sets of linear combination coefficients, which are simultaneously found by our proposed coupled learning algorithm. To make our method robust to different types of blurry face images, we generate several candidate PSFs and EIs for a test image, and then, a non-blind deconvolution method is adopted to generate more EIs by those candidate PSFs. Finally, we deploy a blind image quality assessment metric to automatically select the optimal EI. Thorough experiments on the facial recognition technology database, extended Yale face database B, CMU pose, illumination, and expression (PIE) database, and face recognition grand challenge database version 2.0 demonstrate that the proposed approach effectively restores intrinsic sharp face images and, consequently, improves the performance of face recognition.
A framework for on-line trend extraction and fault diagnosis

Qualitative trend analysis (QTA) is a process-history-based data-driven technique that works by extracting important features (trends) from the measured signals and evaluating the trends. QTA has been widely used for process fault detection and diagnosis. Recently, Dash et al. (2001, 2003) presented an intervalhalving-based algorithm for off-line automatic trend extraction from a record of data, a fuzzy-logic based methodology for trend-matching and a fuzzy-rule-based framework for fault diagnosis (FD). In this article, an algorithm for on-line extraction of qualitative trends is proposed. A framework for on-line fault diagnosis using QTA also has been presented. Some of the issues addressed are (i) development of a robust and computationally efficient QTA-knowledge-base, (ii) fault detection, (iii) estimation of the fault occurrence time, (iv) on-line trend-matching and (v) updating the QTA-knowledge-base when a novel fault is diagnosed manually. Some results for FD of the Tennessee Eastman (TE) process using the developed framework are presented.
Exploiting LIDAR-based features on pedestrian detection in urban scenarios

Reliable detection and classification of vulnerable road users constitute a critical issue on safety/protection systems for intelligent vehicles driving in urban zones. In this subject, most of the perception systems have LIDAR and/or Radar as primary detection modules and vision-based systems for object classification. This work, on the other hand, presents a valuable analysis of pedestrian detection in urban scenario using exclusively LIDAR-based features. The aim is to explore how much information can be extracted from LIDAR sensors for pedestrian detection. Moreover, this study will be useful to compose multi-sensor based pedestrian detection systems using not only LIDAR but also vision sensors. Experimental results using our data set and a detailed classification performance analysis are presented, with comparisons among various classification techniques.
Multi-detector row CT systems and image-reconstruction techniques.

The introduction in 1998 of multi-detector row computed tomography (CT) by the major CT vendors was a milestone with regard to increased scan speed, improved z-axis spatial resolution, and better utilization of the available x-ray power. In this review, the general technical principles of multi-detector row CT are reviewed as they apply to the established four- and eight-section systems, the most recent 16-section scanners, and future generations of multi-detector row CT systems. Clinical examples are used to demonstrate both the potential and the limitations of the different scanner types. When necessary, standard single-section CT is referred to as a common basis and starting point for further developments. Another focus is the increasingly important topic of patient radiation exposure, successful dose management, and strategies for dose reduction. Finally, the evolutionary steps from traditional single-section spiral image-reconstruction algorithms to the most recent approaches toward multisection spiral reconstruction are traced.
Case report: surviving a tiger attack

Attacks on humans by large predators are rare, especially in Northern Europe. In cases of involvement of the craniocervical compartment, most of the attacks are not survived. We report on a case where the patient survived a tiger attack despite severe head trauma and discuss the circumstances leading to the patient’s survival and excellent outcome. The patient we report on is a 28-year-old tamer, who was attacked by three tigers during an evening show. A bite to the head resulted in multiple injuries including left-sided skull penetration wounds with dislocated fractures, dural perforations, and brain parenchyma lesions. The patient recovered without neurological deficits after initial ICU treatment. No infection occurred. In order to understand the mechanism of the tiger’s bite to the patient’s cranium, a simulation of the attack was performed using a human and a tiger skull put together at identical positions to the bite marks in a CT scan. It seems that during the bite, the animal was not able to clamp down on the patient’s skull between its canine teeth and therefore reduced bite forces were applied. Survival of an attack by a large predator that targeted the cervical–cranial compartment with an excellent outcome is not described in the literature. We were surprised to find only minor lesions of the brain parenchyma despite the obvious penetration of the skull by the tiger’s canines. This seems to be related to the specific dynamics of the cranial assault and the reduced forces applied to the patient’s head demonstrated in a 3D bite simulation.
BOPS, Not FLOPS! A New Metric and Roofline Performance Model For Datacenter Computing

The past decades witness FLOPS (Floating-point Operations per Second) as an important computation-centric performance metric. However, for datacenter (in short, DC) computing workloads, such as Internet services or big data analytics, previous work reports that they have extremely low floating point operation intensity, and the average FLOPS efficiency is only 0.1%, while the average IPC is 1.3 (the theoretic IPC is 4 on the Intel Xeon E5600 platform). Furthermore, we reveal that the traditional FLOPS based Roofline performance model is not suitable for modern DC workloads, and gives misleading information for system optimization. These observations imply that FLOPS is inappropriate for evaluating DC computer systems. To address the above issue, we propose a new computation-centric metric BOPs (Basic OPerations) that measures the efficient work defined by the source code, includes floatingpoint operations and the arithmetic, logical, comparing, and array addressing parts of integer operations. We define BOPS as the average number of BOPs per second, and propose replacing FLOPS with BOPS to measure DC computer systems. On the basis of BOPS, we propose a new Roofline performance model for DC computing, which we call DC-Roofline model, with which we optimize DC workloads with the improvement varying from 119% to 325%.
OrthoMCL: identification of ortholog groups for eukaryotic genomes.

The identification of orthologous groups is useful for genome annotation, studies on gene/protein evolution, comparative genomics, and the identification of taxonomically restricted sequences. Methods successfully exploited for prokaryotic genome analysis have proved difficult to apply to eukaryotes, however, as larger genomes may contain multiple paralogous genes, and sequence information is often incomplete. OrthoMCL provides a scalable method for constructing orthologous groups across multiple eukaryotic taxa, using a Markov Cluster algorithm to group (putative) orthologs and paralogs. This method performs similarly to the INPARANOID algorithm when applied to two genomes, but can be extended to cluster orthologs from multiple species. OrthoMCL clusters are coherent with groups identified by EGO, but improved recognition of "recent" paralogs permits overlapping EGO groups representing the same gene to be merged. Comparison with previously assigned EC annotations suggests a high degree of reliability, implying utility for automated eukaryotic genome annotation. OrthoMCL has been applied to the proteome data set from seven publicly available genomes (human, fly, worm, yeast, Arabidopsis, the malaria parasite Plasmodium falciparum, and Escherichia coli). A Web interface allows queries based on individual genes or user-defined phylogenetic patterns . Analysis of clusters incorporating P. falciparum genes identifies numerous enzymes that were incompletely annotated in first-pass annotation of the parasite genome.
Grounded coplanar waveguide-based 0.5–50 GHz sensor for dielectric spectroscopy

There is a need for accurate knowledge of complex permittivity of biosystems for development of electromagnetic diagnostic and therapeutic methods. However, standard open-ended coaxial probe method for measurement of complex permittivity requires rather large sample volumes which is prohibitive for analysis of many types precious biomolecules available only in small volumes. To help to alleviate this issue, we have designed, fabricated and tested a novel grounded coplanar waveguide-based sensor for dielectric spectroscopy of small volumes for the 0.5–50 GHz band. Our method (i) enables 20-fold lower sample volumes than the standard open-end coaxial probe method, (ii) does not require a reference liquid to obtain absolute values of complex permittivity, (iii) is microfluidics-free and the whole sensor can be fabricated by standard printed circuit board techniques. We believe that our sensor and method contributes to low-cost broad band acquisition of complex permittivity data of biomaterials and biomolecules which can be used for development of new therapeutic and diagnostic methods.
A wideband 16-element antenna array using leaf-shaped bowtie antenna and series-parallel feed networks

This paper presents a design of a quasi-millimeter wave wideband antenna array consisting of a leaf-shaped bowtie antenna (LSBA) and series-parallel feed networks in which parallel strip and microstrip lines are employed. A 16-element LSBA array is designed such that the antenna array operates over the frequency band of 22-30GHz. In order to demonstrate the effective performance of the presented configuration, characteristics of the designed LSBA array are evaluated by the finite-difference time domain (FDTD) analysis and measurements. Over the frequency range from 22GHz to 30GHz, the simulated reflection coefficient is observed to be less than -8dB, and the actual gain of 12.3-19.4dBi is obtained.
EURECA: Enhanced Understanding of Real Environments via Crowd Assistance

Indoor robots hold the promise of automatically handling mundane daily tasks, helping to improve access for people with disabilities, and providing on-demand access to remote physical environments. Unfortunately, the ability to understand never-before-seen objects in scenes where new items may be added (e.g., purchased) or altered (e.g., damaged) on a regular basis remains an open challenge for robotics. In this paper, we introduce EURECA, a mixed-initiative system that leverages online crowds of human contributors to help robots robustly identify 3D point cloud segments corresponding to user-referenced objects in near real-time. EURECA allows robots to understand multi-object 3D scenes on-the-fly (in ∼40 seconds) by providing groups of non-expert crowd workers with intelligent tools that can segment objects more quickly (∼70% faster) and more accurately than individuals. More broadly, EURECA introduces the first real-time crowdsourcing tool that addresses the challenge of learning about new objects in real-world settings, creating a new source of data for training robots online, as well as a platform for studying mixed-initiative crowdsourcing workflows for understanding 3D scenes.
ACR Appropriateness Criteria® Suspected upper extremity deep vein thrombosis.

Upper-extremity venous thrombosis often presents as unilateral arm swelling. The differential diagnosis includes lesions compressing the veins and causing a functional venous obstruction, venous stenosis, an infection causing edema, obstruction of previously functioning lymphatics, or the absence of sufficient lymphatic channels to ensure effective drainage. The following recommendations are made with the understanding that venous disease, specifically venous thrombosis, is the primary diagnosis to be excluded or confirmed in a patient presenting with unilateral upper-extremity swelling. Contrast venography remains the best reference-standard diagnostic test for suspected upper-extremity acute venous thrombosis and may be needed whenever other noninvasive strategies fail to adequately image the upper-extremity veins. Duplex, color flow, and compression ultrasound have also established a clear role in evaluation of the more peripheral veins that are accessible to sonography. Gadolinium contrast-enhanced MRI is routinely used to evaluate the status of the central veins. Delayed CT venography can often be used to confirm or exclude more central vein venous thrombi, although substantial contrast loads are required. The ACR Appropriateness Criteria(®) are evidence-based guidelines for specific clinical conditions that are reviewed every 2 years by a multidisciplinary expert panel. The guideline development and review include an extensive analysis of current medical literature from peer-reviewed journals and the application of a well-established consensus methodology (modified Delphi) to rate the appropriateness of imaging and treatment procedures by the panel. In those instances in which evidence is lacking or not definitive, expert opinion may be used to recommend imaging or treatment.