nils-herrmann/clean_scirepeval huggingface dataset:
Human-Object Interactions Are More than the Sum of Their Parts.

Understanding human-object interactions is critical for extracting meaning from everyday visual scenes and requires integrating complex relationships between human pose and object identity into a new percept. To understand how the brain builds these representations, we conducted 2 fMRI experiments in which subjects viewed humans interacting with objects, noninteracting human-object pairs, and isolated humans and objects. A number of visual regions process features of human-object interactions, including object identity information in the lateral occipital complex (LOC) and parahippocampal place area (PPA), and human pose information in the extrastriate body area (EBA) and posterior superior temporal sulcus (pSTS). Representations of human-object interactions in some regions, such as the posterior PPA (retinotopic maps PHC1 and PHC2) are well predicted by a simple linear combination of the response to object and pose information. Other regions, however, especially pSTS, exhibit representations for human-object interaction categories that are not predicted by their individual components, indicating that they encode human-object interactions as more than the sum of their parts. These results reveal the distributed networks underlying the emergent representation of human-object interactions necessary for social perception.
An efficient K -means clustering algorithm for massive data

The analysis of continously larger datasets is a task of major importance in a wide variety of scientific fields. In this sense, cluster analysis algorithms are a key element of exploratory data analysis, due to their easiness in the implementation and relatively low computational cost. Among these algorithms, the K-means algorithm stands out as the most popular approach, besides its high dependency on the initial conditions, as well as to the fact that it might not scale well on massive datasets. In this article, we propose a recursive and parallel approximation to the K-means algorithm that scales well on both the number of instances and dimensionality of the problem, without affecting the quality of the approximation. In order to achieve this, instead of analyzing the entire dataset, we work on small weighted sets of points that mostly intend to extract information from those regions where it is harder to determine the correct cluster assignment of the original instances. In addition to different theoretical properties, which deduce the reasoning behind the algorithm, experimental results indicate that our method outperforms the state-of-the-art in terms of the trade-off between number of distance computations and the quality of the solution obtained.
ChronoStream: Elastic stateful stream computation in the cloud

We introduce ChronoStream, a distributed system specifically designed for elastic stateful stream computation in the cloud. ChronoStream treats internal state as a first-class citizen and aims at providing flexible elastic support in both vertical and horizontal dimensions to cope with workload fluctuation and dynamic resource reclamation. With a clear separation between application-level computation parallelism and OS-level execution concurrency, ChronoStream enables transparent dynamic scaling and failure recovery by eliminating any network I/O and state-synchronization overhead. Our evaluation on dozens of computing nodes shows that ChronoStream can scale linearly and achieve transparent elasticity and high availability without sacrificing system performance or affecting collocated tenants.
Multi-photon absorption enhancement by dual-wavelength double-pulse laser irradiation for efficient dicing of sapphire wafers

The evidence of multi-photon absorption enhancement by the dual-wavelength double-pulse laser irradiation in transparent sapphire was demonstrated experimentally and explained theoretically for the first time. Two collinearly combined laser beams with the wavelengths of 1064 nm and 355 nm, inter-pulse delay of 0.1 ns, and pulse duration of 10 ps were used to induce intra-volume modifications in sapphire. The theoretical prediction of using a particular orientation angle of 15 degrees of the half-wave plate for the most efficient absorption of laser irradiation is in good agreement with the experimental data. The new innovative effect of multi-photon absorption enhancement by dual-wavelength double-pulse irradiation allowed utilisation of the laser energy up to four times more efficiently for initiation of internal modifications in sapphire. The new absorption enhancement effect has been used for efficient intra-volume dicing and singulation of transparent sapphire wafers. The dicing speed of 150 mm/s was achieved for the 430 μm thick sapphire wafer by using the laser power of 6.8 W at the repetition rate of 100 kHz. This method opens new opportunities for the manufacturers of the GaN-based light-emitting diodes by fast and precise separation of sapphire substrates.
One tool to rule them all: a modular troubleshooting framework for SDN (and other) networks

Due to the heterogeneous and distributed nature of computer networks, the detection of misconfigurations and software/hardware failures is frequently reported to be notoriously non-trivial. The advent of SDN complicates the situation even more, since besides troubleshooting, the problem of finding software bugs in controller/switch/VNF implementations also has to be solved. Today a wealth of general and SDN-specific troubleshooting tools are available which are usually tailored to identify network-related errors and bugs of a particular nature. In this paper we define a troubleshooting framework which can assemble many of these tools in a single platform and makes possible to flexibly combine them. As we see network operators and SDN developers execute similar tasks anyway, e.g. combine ping, traceroute and tcpdump (or more complex tools) manually to see what is going on in the network. Our framework can ease their work by consolidating the available troubleshooting tools in a flexible and automated manner.
Distributed Ledgers & Linked Data

Distributed are increasing being thought of as a platform for decentralised applications — DApps — and the the focus for many is shifting from Bitcoin to Smart Contracts. It’s thought that encoding contracts and putting them “on the blockchain” will result in a new generation of organisations that are leaner and more efficient than their forebears (“Capps”?”), disrupting these forebears in the process. However, the most interesting aspect of Bitcoin and blockchain is that it involved no new technology,  Their emergence was due to changes in the environment: the priceperformance and penetration of broadband networks reached a point that it was economically viable for a decentralised solution, such as Bitcoin to compete with traditional payment (international remittance) networks. This is combining with another trend — the shift from monolithic firms to multi-sided markets such as AirBnb et al and the rise of “platform businesses” — to enable a new class of solution to emerge. These new solutions enable firms to interact directly, without the need for a facilitator such as a market, exchange, or even a blockchain. In the past these facilitators were firms. More recently they have been “platform businesses.” In the future they may not exist at all. The shift to a distributed environment enables us to reconsider many of the ideas from distributed AI and linked data. Where are the opportunities? How can we avoid the mistakes of the past?
Biological functions of hCG and hCG-related molecules

hCG is a term referring to 4 independent molecules, each produced by separate cells and each having completely separate functions. These are hCG produced by villous syncytiotrophoblast cells, hyperglycosylated hCG produced by cytotrophoblast cells, free beta-subunit made by multiple primary non-trophoblastic malignancies, and pituitary hCG made by the gonadotrope cells of the anterior pituitary.



hCG has numerous functions. hCG promotes progesterone production by corpus luteal cells; promotes angiogenesis in uterine vasculature; promoted the fusion of cytotrophoblast cell and differentiation to make syncytiotrophoblast cells; causes the blockage of any immune or macrophage action by mother on foreign invading placental cells; causes uterine growth parallel to fetal growth; suppresses any myometrial contractions during the course of pregnancy; causes growth and differentiation of the umbilical cord; signals the endometrium about forthcoming implantation; acts on receptor in mother's brain causing hyperemesis gravidarum, and seemingly promotes growth of fetal organs during pregnancy. Hyperglycosylated hCG functions to promote growth of cytotrophoblast cells and invasion by these cells, as occurs in implantation of pregnancy, and growth and invasion by choriocarcinoma cells. hCG free beta-subunit is produced by numerous non-trophoblastic malignancies of different primaries. The detection of free beta-subunit in these malignancies is generally considered a sign of poor prognosis. The free beta-subunit blocks apoptosis in cancer cells and promotes the growth and malignancy of the cancer. Pituitary hCG is a sulfated variant of hCG produced at low levels during the menstrual cycle. Pituitary hCG seems to mimic luteinizing hormone actions during the menstrual cycle.
Urinary levels of catecholamines among individuals with and without sleep bruxism

Sleep bruxism (SB) is characterized by repetitive and coordinated mandible movements and non-functional teeth contacts during sleep time. Although the etiology of SB is controversial, the literature converges on its multifactorial origin. Occlusal factors, smoking, alcoholism, drug usage, stress, and anxiety have been described as SB trigger factors. Recent studies on this topic discussed the role of neurotransmitters on the development of SB. Thus, the purpose of this study was to detect and quantify the urinary levels of catecholamines, specifically of adrenaline, noradrenaline and dopamine, in subjects with SB and in control individuals. Urine from individuals with SB (n = 20) and without SB (n = 20) was subjected to liquid chromatography. The catecholamine data were compared by Mann–Whitney’s test (p ≤ 0.05). Our analysis showed higher levels of catecholamines in subjects with SB (adrenaline = 111.4 µg/24 h; noradrenaline = 261,5 µg/24 h; dopamine = 479.5 µg/24 h) than in control subjects (adrenaline = 35,0 µg/24 h; noradrenaline = 148,7 µg/24 h; dopamine = 201,7 µg/24 h). Statistical differences were found for the three catecholamines tested. It was concluded that individuals with SB have higher levels of urinary catecholamines.
Primary retroperitoneal masses: what is the differential diagnosis?

Primary retroperitoneal masses include a diverse, and often rare, group of neoplastic and non-neoplastic entities that arise within the retroperitoneum but do not originate from any retroperitoneal organ. Their overlapping appearances on cross-sectional imaging may pose a diagnostic challenge to the radiologist; familiarity with characteristic imaging features, together with relevant clinical information, helps to narrow the differential diagnosis. In this article, a systematic approach to identifying and classifying primary retroperitoneal masses is described. The normal anatomy of the retroperitoneum is reviewed with an emphasis on fascial planes, retroperitoneal compartments, and their contents using cross-sectional imaging. Specific radiologic signs to accurately identify an intra-abdominal mass as primary retroperitoneal are presented, first by confirming the location as retroperitoneal and secondly by excluding an organ of origin. A differential diagnosis based on a predominantly solid or cystic appearance, including neoplastic and non-neoplastic entities, is elaborated. Finally, key diagnostic clues based on characteristic imaging findings are described, which help to narrow the differential diagnosis. This article provides a comprehensive overview of the cross-sectional imaging features of primary retroperitoneal masses, including normal retroperitoneal anatomy, radiologic signs of retroperitoneal masses and the differential diagnosis of solid and cystic, neoplastic and non-neoplastic retroperitoneal masses, with a view to assist the radiologist in narrowing the differential diagnosis.
Laboratory Diagnostics and Quality of Blood Collection

Diagnostic blood samples collected by phlebotomy are the most common type of biological specimens drawn and sent to laboratory medicine facilities for being analyzed, thus supporting caring physicians in patient diagnosis, follow-up and/or therapeutic monitoring. Phlebotomy, a relatively invasive medical procedure, is indeed critical for the downstream procedures accomplished either in the analytical phase made in the laboratory or in the interpretive process done by the physicians. Diagnosis, management, treatment of patients and ultimately patient safety itself can be compromised by poor phlebotomy quality. We have read with interest a recent article where the authors addressed important aspects of venous blood collection for laboratory medicine analysis. The authors conducted a phlebotomy survey based on the Clinical and Laboratory Standard Institute (CLSI) H03-A6 document (presently replaced by the GP41-A6 document) in three government hospitals in Ethiopia to evaluate 120 professionals (101 non-laboratory professionals vs. 19 laboratory professionals) as regards the venous blood collection practice. The aim of this mini (non-systematic) review is to both take a cue from the above article and from current practices we had already observed in other laboratory settings, and discuss four questionable activities performed by health care professionals during venous blood collection. We refer to: i) diet restriction assessment; ii) puncture site cleansing; iii) timing of tourniquet removal and; iv) mixing specimen with additives.
A maximal figure-of-merit learning approach to maximizing mean average precision with deep neural network based classifiers

We propose a maximal figure-of-merit (MFoM) learning framework to directly maximize mean average precision (MAP) which is a key performance metric in many multi-class classification tasks. Conventional classifiers based on support vector machines cannot be easily adopted to optimize the MAP metric. On the other hand, classifiers based on deep neural networks (DNNs) have recently been shown to deliver a great discrimination capability in automatic speech recognition and image classification as well. However, DNNs are usually optimized with the minimum cross entropy criterion. In contrast to most conventional classification methods, our proposed approach can be formulated to embed DNNs and MAP into the objective function to be optimized during training. The combination of the proposed maximum MAP (MMAP) technique and DNNs introduces nonlinearity to the linear discriminant function (LDF) in order to increase the flexibility and discriminant power of the original MFoM-trained LDF based classifiers. Tested on both automatic image annotation and audio event classification, the experimental results show consistent improvements of MAP on both datasets when compared with other state-of-the-art classifiers without using MMAP.
6D Object Detection and Next-Best-View Prediction in the Crowd

6D object detection and pose estimation in the crowd (scenes with multiple object instances, severe foreground occlusions and background distractors), has become an important problem in many rapidly evolving technological areas such as robotics and augmented reality. Single shotbased 6D pose estimators with manually designed features are still unable to tackle the above challenges, motivating the research towards unsupervised feature learning and next-best-view estimation. In this work, we present a complete framework for both single shot-based 6D object pose estimation and next-best-view prediction based on Hough Forests, the state of the art object pose estimator that performs classification and regression jointly. Rather than us ing manually designed features we a) propose an unsupervised feature learnt from depth-invariant patches using a Sparse Autoencoder and b) offer an extensive evaluation of various state of the art features. Furthermore, taking advantage of the clustering performed in the leaf nodes of Hough Forests, we learn to estimate the reduction of uncertainty in other views, formulating the problem of select ing the next-best-view. To further improve 6D object pose estimation, we propose an improved joint registration and hypotheses verification module as a final refinement step to reject false detections. We provide two additional challen ging datasets inspired from realistic scenarios to extensiv ely evaluate the state of the art and our framework. One is related to domestic environments and the other depicts a bin-picking scenario mostly found in industrial settings. We show that our framework significantly outperforms state of the art both on public and on our datasets.
Assessing molecular and morpho-agronomical diversity and identification of ISSR markers associated with fruit traits in quince (Cydonia oblonga).

Quince is a deciduous tree known to the countries around the Mediterranean since antiquity. Nowadays, quince is used as an ornamental plant, and as a rootstock for pear trees, with its fruit being appreciated mainly for production of jam and sweets rather than for raw consumption. Quince leaves contain compounds with antioxidant, antimicrobial and anticancerous properties that have been the focus of recent research on pharmaceutical and medical uses as well as for food preservatives. An orchard has been established in Greece, composed of quince varieties (Cydonia oblonga, N = 49) collected from different sites of the country (mainly from home gardens), constituting a unique quince gene bank collection for southeast Europe. We made a phenotypic analysis using 26 morphological plus seven agronomical descriptors coupled with molecular techniques in order to examine the genetic diversity within the collection. Principal component analysis using the 33 descriptors identified 10 components explaining the existence of more than 70% of the total variation. Subsequent cluster analysis classified most of the previously identified productive varieties of the quince orchard in the same clade of a dendrogram. Molecular analysis generated by 13 inter-simple sequence repeat primers amplified 139 bands, including 109 polymorphic bands, indicating a level of polymorphism of 79%; mean gene diversity was calculated to be 0.309. Using stepwise multiple regression analysis, a number of markers significantly associated with fire blight susceptibility, yield, mean fruit weight, citric acid content, soluble solid content, and fruit drop were identified. Hence, data extracted by multiple regression analysis could be useful in marker-assisted breeding programs, especially when no previous genetic information is available.
Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory

Graph theory is a valuable framework to study the organization of functional and anatomical connections in the brain. Its use for comparing network topologies, however, is not without difficulties. Graph measures may be influenced by the number of nodes (N) and the average degree (k) of the network. The explicit form of that influence depends on the type of network topology, which is usually unknown for experimental data. Direct comparisons of graph measures between empirical networks with different N and/or k can therefore yield spurious results. We list benefits and pitfalls of various approaches that intend to overcome these difficulties. We discuss the initial graph definition of unweighted graphs via fixed thresholds, average degrees or edge densities, and the use of weighted graphs. For instance, choosing a threshold to fix N and k does eliminate size and density effects but may lead to modifications of the network by enforcing (ignoring) non-significant (significant) connections. Opposed to fixing N and k, graph measures are often normalized via random surrogates but, in fact, this may even increase the sensitivity to differences in N and k for the commonly used clustering coefficient and small-world index. To avoid such a bias we tried to estimate the N,k-dependence for empirical networks, which can serve to correct for size effects, if successful. We also add a number of methods used in social sciences that build on statistics of local network structures including exponential random graph models and motif counting. We show that none of the here-investigated methods allows for a reliable and fully unbiased comparison, but some perform better than others.
Job scheduling using Ant Colony Optimization in grid environment

Grid computing proposes a dynamic and earthly distributed organization of resources that harvest ideal CPU cycle to drift advance computing demands and accommodate user's prerequisites. Heterogeneous gridsdemand efficient allocation and scheduling strategies to cope up with the expanding grid automations. In order to obtain optimal scheduling solutions, primary focus of research has shifted towards metaheuristic techniques. The paper uses different parameters to provide analytical study of variants of Ant Colony Optimization for scheduling sequential jobs in grid systems. Based on the literature analysis, one can summarize that ACO is the most convincing technique for schedulingproblems. However, incapacitation of ACO to fix up a systematized startup and poor scattering capability cast down its efficiency. To overpower these constraints researchers have proposed different hybridizations of ACO that manages to sustain more effective results than standalone ACO.
Management of Large Erupting Complex Odontoma in Maxilla

We present the unusual case of a large complex odontoma erupting in the maxilla. Odontomas are benign developmental tumours of odontogenic origin. They are characterized by slow growth and nonaggressive behaviour. Complex odontomas, which erupt, are rare. They are usually asymptomatic and are identified on routine radiograph but may present with erosion into the oral cavity with subsequent cellulitis and facial asymmetry. This present paper describes the presentation and management of an erupting complex odontoma, occupying the maxillary sinus with extension to the infraorbital rim. We also discuss various surgical approaches used to access this anatomic area.
Peak-to-mean power control in OFDM, Golay complementary sequences, and Reed-Muller codes

We present a range of coding schemes for OFDM transmission using binary, quaternary, octary, and higher order modulation that give high code rates for moderate numbers of carriers. These schemes have tightly bounded peak-to-mean envelope power ratio (PMEPR) and simultaneously have good error correction capability. The key theoretical result is a previously unrecognized connection between Golay complementary sequences and second-order Reed–Muller codes over alphabets 2 . We obtain additional flexibility in trading off code rate, PMEPR, and error correction capability by partitioning the second-order Reed–Muller code into cosets such that codewords with large values of PMEPR are isolated. For all the proposed schemes we show that encoding is straightforward and give an efficient decoding algorithm involving multiple fast Hadamard transforms. Since the coding schemes are all based on the same formal generator matrix we can deal adaptively with varying channel constraints and evolving system requirements.