nils-herrmann/clean_scirepeval huggingface dataset:
Comparison of 4 Cardiac Risk Calculators in Predicting Postoperative Cardiac Complications After Noncardiac Operations.

The 2014 American College of Cardiology/American Heart Association Perioperative Guidelines suggest using the Revised Cardiac Risk Index, myocardial infarction or cardiac arrest, or American College of Surgeons-National Surgical Quality Improvement Program calculators for combined patient-surgical risk assessment. There are no published data comparing their performance. This study compared these risk calculators and a reconstructed Revised Cardiac Risk Index in predicting postoperative cardiac complications, both during hospitalization and 30 days after operation, in a patient cohort who underwent select surgical procedures in various risk categories. Cardiac complications occurred in 14 of 663 patients (2.1%), of which 11 occurred during hospitalization. Only 3 of 663 patients (0.45%) had a myocardial infarction or cardiac arrest. Because these calculators used different risk factors, different outcomes, and different durations of observation, a true direct comparison is not possible. We found that all 4 risk calculators performed well in the setting they were originally studied but were less accurate when applied in a different manner. In conclusion, all calculators were useful in defining low-risk patients in whom further cardiac testing was unnecessary, and the myocardial infarction or cardiac arrest may be the most reliable in selecting higher risk patients.
Assembly Technologies for Integrated Transmitter/Receiver Optical Sub-Assembly Modules

We have succeeded in developing three techniques, a precise lens-alignment technique, low-loss built-in Spatial Multiplexing optics and a well-matched electrical connection for high-frequency signals, which are indispensable for realizing compact high-performance TOSAs and ROSAs employing hybrid integration technology. The lens position was controlled to within ±0.3 μm by high-power laser irradiation. All components comprising the multiplexing optics are bonded to a prism, enabling the insertion loss to be held down to 0.8 dB due to the dimensional accuracy of the prism. The addition of an FPC layer reduced the impedance mismatch at the junction between the FPC and PCB. We demonstrated a compact integrated four-lane 25 Gb/s TOSA (15.1 mm × 6.5 mm × 5.6 mm) and ROSA (17.0 mm × 12.0 mm × 7.0 mm) using the built-in spatial Mux/Demux optics with good transmission performance for 100 Gb/s Ethernet. These are respectively suitable for the QSFP28 and CFP2 form factors. key words: hybrid integration, optical sub-assembly, 100 Gb/s
EMOD2D - a program in C++ for finite difference modelling of magnetotelluric TM mode responses over 2D earth

A program, EMOD2D, in C++, is developed to compute the apparent resistivities of two-dimensional geological structures for magnetotelluric (MT) H-polarization case, using finite difference technique. Five C++ classes with member functions are designed to compute apparent resistivitiy values. The program utilizes object oriented programming features such as multiple inheritance, encapsulation to allow the user to easily deploy and modify the given classes. This program has been used to study the responses of various ore deposit models. This modelling study is relevant to: (i) search for mineral deposits underlying conductive or resistive overburden, (ii) understand the response pattern for the ore body with different depths and conductivities, and (iii) study the response pattern for the ore body when it is in contact and without contact with the overburden. The program is compiled on Borland C++. r 2006
Adaptive Task Allocation for Mobile Edge Learning

This paper aims to establish a new optimization paradigm to efficiently execute distributed learning tasks on wireless edge nodes with heterogeneous computing and communication capacities. We will refer to this new paradigm as “Mobile Edge Learning (MEL)”. The problem of adaptive task allocation for MEL is considered in this paper with the aim to maximize the learning accuracy, while guaranteeing that the total times of data distribution/aggregation over heterogeneous channels, and local computation on heterogeneous nodes, are bounded by a preset duration. The problem is first formulated as a quadraticallyconstrained integer linear problem. Being NP-hard, the paper relaxes it into a non-convex problem over real variables. We then propose a solution based on deriving analytical upper bounds on the optimal solution of this relaxed problem using KKT conditions. The merits of this proposed solution is exhibited by comparing its performances to both numerical approaches and the equal task allocation approach.
Landmark Classification With Hierarchical Multi-Modal Exemplar Feature

Landmark image classification attracts increasing research attention due to its great importance in real applications, ranging from travel guide recommendation to 3-D modelling and visualization of geolocation. While large amount of efforts have been invested, it still remains unsolved by academia and industry. One of the key reasons is the large intra-class variance rooted from the diverse visual appearance of landmark images. Distinguished from most existing methods based on scalable image search, we approach the problem from a new perspective and model landmark classification as multi-modal categorization , which enjoys advantages of low storage overhead and high classification efficiency. Toward this goal, a novel and effective feature representation, called hierarchical multi-modal exemplar (HMME) feature, is proposed to characterize landmark images. In order to compute HMME, training images are first partitioned into the regions with hierarchical grids to generate candidate images and regions. Then, at the stage of exemplar selection, hierarchical discriminative exemplars in multiple modalities are discovered automatically via iterative boosting and latent region label mining. Finally, HMME is generated via a region-based locality-constrained linear coding (RLLC), which effectively encodes semantics of the discovered exemplars into HMME. Meanwhile, dimension reduction is applied to reduce redundant information by projecting the raw HMME into lower-dimensional space. The final HMME enjoys advantages of discriminative and linearly separable. Experimental study has been carried out on real world landmark datasets, and the results demonstrate the superior performance of the proposed approach over several state-of-the-art techniques.
Proximodistal Heterogeneity of Hippocampal CA3 Pyramidal Neuron Intrinsic Properties, Connectivity, and Reactivation during Memory Recall

The hippocampal CA3 region is classically viewed as a homogeneous autoassociative network critical for associative memory and pattern completion. However, recent evidence has demonstrated a striking heterogeneity along the transverse, or proximodistal, axis of CA3 in spatial encoding and memory. Here we report the presence of striking proximodistal gradients in intrinsic membrane properties and synaptic connectivity for dorsal CA3. A decreasing gradient of mossy fiber synaptic strength along the proximodistal axis is mirrored by an increasing gradient of direct synaptic excitation from entorhinal cortex. Furthermore, we uncovered a nonuniform pattern of reactivation of fear memory traces, with the most robust reactivation during memory retrieval occurring in mid-CA3 (CA3b), the region showing the strongest net recurrent excitation. Our results suggest that heterogeneity in both intrinsic properties and synaptic connectivity may contribute to the distinct spatial encoding and behavioral role of CA3 subregions along the proximodistal axis.
The Evolution of Cognitive Bias

The Evolution of Cognitive Bias Despite widespread claims to the contrary, the human mind is not worse than rational… but may often be better than rational. On the surface, cognitive biases appear to be somewhat puzzling when viewed through an evolutionary lens. Because they depart from standards of logic and accuracy, they appear to be design flaws instead of examples of good engineering. Cognitive traits can be evaluated according to any number of performance criteria-logical sufficiency, accuracy, speed of processing, and so on. The value of a criterion depends on the question the scientist is asking. To the evolutionary psychologist, however, the evaluative task is not whether the cognitive feature is accurate or logical, but rather how well it solves a particular problem, and how solving this problem contributed to fitness ancestrally. Viewed in this way, if a cognitive bias positively impacted fitness it is not a design flaw – it is a design feature. This chapter discusses the many biases that are probably not the result of mere constraints on the design of the mind or other mysterious irrationalities, but rather are adaptations that can be studied and better understood from an evolutionary perspective. By cognitive bias, we mean cases in which human cognition reliably produces representations that are systematically distorted compared to some aspect of objective reality. We note that the term bias is used in the literature in a number of different ways (see, We do not seek to make commitments about these definitions here; rather, we use bias throughout this chapter in the relatively noncommittal sense defined above. An evolutionary psychological perspective predicts that the mind is equipped with function-specific mechanisms adapted for special purposes—mechanisms with special design for Cognitive Bias-3 solving problems such as mating, which are separate, at least in part, from those involved in solving problems of food choice, predator avoidance, and social exchange (e. demonstrating domain specificity in solving a particular problem is a part of building a case that the trait has been shaped by selection to perform that function. The evolved function of the eye, for instance, is to facilitate sight because it does this well (it exhibits proficiency), the features of the eye have the common and unique effect of facilitating sight (it exhibits specificity), and there are no plausible alternative hypotheses that account for the eye's features. Some design features that appear to be flaws when viewed in …
NFV and SDN-based cost-efficient and agile value-added video services provisioning in content delivery networks

Due to the recent surge in end-users demands, value-added video services (e.g. in-stream video advertisements) need to be provisioned in a cost-efficient and agile manner in Content Delivery Networks (CDNs). Network Function Virtualization (NFV) is an emerging technology that aims to reduce costs and bring agility by decoupling network functions from the underlying hardware. It is often used in combination with Software Defined Network (SDN), a technology to decouple control and data planes. This paper proposes an NFV and SDN-based architecture for a cost-efficient and agile provisioning of value-added video services in CDNs. In the proposed architecture, the application-level middleboxes that enable value-added video services (e.g. mixer, compressor) are provisioned as Virtual Network Functions (VNFs) and chained using application-level SDN switches. HTTP technology is used as the pillar of the implementation architecture. We have built a prototype and deployed it in an OPNFV test lab and in SAVI, a Canadian distributed test bed for future Internet applications. The performance is also evaluated.
Multilayer neural networks and Bayes decision theory

There are many applications of multilayer neural networks to pattern classification problems in the engineering field. Recently, it has been shown that Bayes a posteriori probability can be estimated by feedforward neural networks through computer simulation. In this paper, Bayes decision theory is combined with the approximation theory on three-layer neural networks, and the two-category n-dimensional Gaussian classification problem is studied. First, we prove theoretically that three-layer neural networks with at least 2n hidden units have the capability of approximating the a posteriori probability in the two-category classification problem with arbitrary accuracy. Second, we prove that the input-output function of neural networks with at least 2n hidden units tends to the a posteriori probability as Back-Propagation learning proceeds ideally. These results provide a theoretical basis for the study of pattern classification by computer simulation.
Unified Camera Tamper Detection Based on Edge and Object Information

In this paper, a novel camera tamper detection algorithm is proposed to detect three types of tamper attacks: covered, moved and defocused. The edge disappearance rate is defined in order to measure the amount of edge pixels that disappear in the current frame from the background frame while excluding edges in the foreground. Tamper attacks are detected if the difference between the edge disappearance rate and its temporal average is larger than an adaptive threshold reflecting the environmental conditions of the cameras. The performance of the proposed algorithm is evaluated for short video sequences with three types of tamper attacks and for 24-h video sequences without tamper attacks; the algorithm is shown to achieve acceptable levels of detection and false alarm rates for all types of tamper attacks in real environments.
Non-line-of-sight Imaging with Partial Occluders and Surface Normals.

Non-line-of-sight (NLOS) imaging aims at recovering the shape and albedo of objects hidden from a camera or a light source. Using ultra-fast pulsed illumination and single photon detectors, the light transport in the scene is sampled for visible objects (left). The global illumination components of these time-resolved measurements (A,E) contain sufficient information to estimate the shape of hidden objects (B,C). Using a novel formulation for NLOS light transport that models partial occlusions of hidden objects (D) via visibility terms (F), we demonstrate higher-fidelity reconstructions (C) than previous approaches to NLOS imaging (B).
Model-Reuse Attacks on Deep Learning Systems

Many of today's machine learning (ML) systems are built by reusing an array of, often pre-trained, primitive models, each fulfilling distinct functionality (e.g., feature extraction). The increasing use of primitive models significantly simplifies and expedites the development cycles of ML systems. Yet, because most of such models are contributed and maintained by untrusted sources, their lack of standardization or regulation entails profound security implications, about which little is known thus far. In this paper, we demonstrate that malicious primitive models pose immense threats to the security of ML systems. We present a broad class of model-reuse attacks wherein maliciously crafted models trigger host ML systems to misbehave on targeted inputs in a highly predictable manner. By empirically studying four deep learning systems (including both individual and ensemble systems) used in skin cancer screening, speech recognition, face verification, and autonomous steering, we show that such attacks are (i) effective - the host systems misbehave on the targeted inputs as desired by the adversary with high probability, (ii) evasive - the malicious models function indistinguishably from their benign counterparts on non-targeted inputs, (iii) elastic - the malicious models remain effective regardless of various system design choices and tuning strategies, and (iv) easy - the adversary needs little prior knowledge about the data used for system tuning or inference. We provide analytical justification for the effectiveness of model-reuse attacks, which points to the unprecedented complexity of today's primitive models. This issue thus seems fundamental to many ML systems. We further discuss potential countermeasures and their challenges, which lead to several promising research directions.
Dog and mouse: toward a balanced view of the mammalian olfactory system

Although the most intensively studied mammalian olfactory system is that of the mouse, in which olfactory chemical cues of one kind or another are detected in four different nasal areas [the main olfactory epithelium (MOE), the septal organ (SO), Grüneberg's ganglion, and the sensory epithelium of the vomeronasal organ (VNO)], the extraordinarily sensitive olfactory system of the dog is also an important model that is increasingly used, for example in genomic studies of species evolution. Here we describe the topography and extent of the main olfactory and vomeronasal sensory epithelia of the dog, and we report finding no structures equivalent to the Grüneberg ganglion and SO of the mouse. Since we examined adults, newborns, and fetuses we conclude that these latter structures are absent in dogs, possibly as the result of regression or involution. The absence of a vomeronasal component based on VR2 receptors suggests that the VNO may be undergoing a similar involutionary process.
Automatic anatomical brain MRI segmentation combining label propagation and decision fusion

Regions in three-dimensional magnetic resonance (MR) brain images can be classified using protocols for manually segmenting and labeling structures. For large cohorts, time and expertise requirements make this approach impractical. To achieve automation, an individual segmentation can be propagated to another individual using an anatomical correspondence estimate relating the atlas image to the target image. The accuracy of the resulting target labeling has been limited but can potentially be improved by combining multiple segmentations using decision fusion. We studied segmentation propagation and decision fusion on 30 normal brain MR images, which had been manually segmented into 67 structures. Correspondence estimates were established by nonrigid registration using free-form deformations. Both direct label propagation and an indirect approach were tested. Individual propagations showed an average similarity index (SI) of 0.754+/-0.016 against manual segmentations. Decision fusion using 29 input segmentations increased SI to 0.836+/-0.009. For indirect propagation of a single source via 27 intermediate images, SI was 0.779+/-0.013. We also studied the effect of the decision fusion procedure using a numerical simulation with synthetic input data. The results helped to formulate a model that predicts the quality improvement of fused brain segmentations based on the number of individual propagated segmentations combined. We demonstrate a practicable procedure that exceeds the accuracy of previous automatic methods and can compete with manual delineations.
Improvement of GPR tracking by using inertial and GPS combined data

In most applications of Ground Penetrating Radar (GPR), it is very important to combine the radar with an accurate positioning system. This allows solving errors in the localisation of buried objects, which may be generated by measurement conditions such as the soil slope, in the case of a ground-coupled GPR, and the aerial vehicle altitude, in the case of a GPR mounted on a drone or helicopter. This paper presents the implementation of a low-cost system for positioning, tracking and trimming of GPR data. The proposed system integrates Global Positioning System (GPS) data with those of an Inertial Measurement Unit (IMU). So far, the electronic board including GPS and IMU was designed, developed and tested in the laboratory. As a next step, GPR results will be collected in outdoor scenarios of practical interest and the accuracy of data measured by using our positioning system will be compared to the accuracy of data measured without using it.
A review of antennas and propagation for MIMO wireless communications

Multiple-input-multiple-output (MIMO) wireless systems use multiple antenna elements at transmit and receive to offer improved capacity over single antenna topologies in multipath channels. In such systems, the antenna properties as well as the multipath channel characteristics play a key role in determining communication performance. This paper reviews recent research findings concerning antennas and propagation in MIMO systems. Issues considered include channel capacity computation, channel measurement and modeling approaches, and the impact of antenna element properties and array configuration on system performance. Throughout the discussion, outstanding research questions in these areas are highlighted.
Additive spanners and (alpha, beta)-spanners

An (α, β)-spanner of an unweighted graph <i>G</i> is a subgraph <i>H</i> that distorts distances in <i>G</i> up to a multiplicative factor of α and an additive term β. It is well known that any graph contains a (multiplicative) (2<i>k</i>−1, 0)-spanner of size <i>O</i>(<i>n</i><sup>1+1/<i>k</i></sup>) and an (additive) (1,2)-spanner of size <i>O</i>(<i>n</i><sup>3/2</sup>). However no other additive spanners are known to exist.
 In this article we develop a couple of new techniques for constructing (α, β)-spanners. Our first result is an additive (1,6)-spanner of size <i>O</i>(<i>n</i><sup>4/3</sup>). The construction algorithm can be understood as an economical agent that assigns <i>costs</i> and <i>values</i> to paths in the graph, <i>purchasing</i> affordable paths and ignoring expensive ones, which are intuitively well approximated by paths already purchased. We show that this <i>path buying</i> algorithm can be parameterized in different ways to yield other sparseness-distortion tradeoffs. Our second result addresses the problem of which (α, β)-spanners can be computed efficiently, ideally in linear time. We show that, for any <i>k</i>, a (<i>k</i>,<i>k</i>−1)-spanner with size <i>O</i>(<i>kn</i><sup>1+1/<i>k</i></sup>) can be found in linear time, and, further, that in a distributed network the algorithm terminates in a constant number of rounds. Previous spanner constructions with similar performance had roughly twice the multiplicative distortion.
Kinematics and Application of a Hybrid Industrial Robot – Delta-RST 1

Serial robots and parallel robots have their own pros and cons. While hybrid robots consisting of both of them are possible and expected to retain their merits and minimize the disadvantages. The Delta-RST presented here is such a hybrid robot built up by integrating a 3-DoFs traditional Delta parallel structure and a 3-DoFs RST robotic wrist. In this paper, we focus on its kinematics analysis and its applications in industry. Firstly, the robotic system of the Delta-RST will be described briefly. Then the complete and systemic kinematics of this kind of robot will be presented in detail, followed by simulations and applications to demonstrate the correctness of the analysis, as well as the effectiveness of the developed robotic system. The closed-form kinematic analysis results are universal for similar hybrid robots constructing with the Delta parallel mechanism and serial chains.
AN EXHAUSTIVE STUDY ON ASSOCIATION RULE MINING

Association Rule Mining is a data mining method that enables to find out frequent item set, interesting pattern, interesting correlation among set of items in a transactional database or data repositories. This paper establishes the preparatory of basic concept about Association Rule Mining. Broadly, Association Rule Mining can be classified into Apriori, Partitioning, and Frequent Pattern Tree Algorithms. This study is to show the Benefits and Limitations.  Data Mining, Association rule, Apriori, FP Tree, and Partitioning