nils-herrmann/clean_scirepeval huggingface dataset:
Geometric calibration of a mobile C-arm for intraoperative cone-beam CT.

A geometric calibration method that determines a complete description of source-detector geometry was adapted to a mobile C-arm for cone-beam computed tomography (CBCT). The non-iterative calibration algorithm calculates a unique solution for the positions of the source (X(s), Y(s), Z(s)), detector (X(d), Y(d), Z(d)), piercing point (U(o), V(o)), and detector rotation angles (phi, theta, eta) based on projections of a phantom consisting of two plane-parallel circles of ball bearings encased in a cylindrical acrylic tube. The prototype C-arm system was based on a Siemens PowerMobil modified to provide flat-panel CBCT for image-guided interventions. The magnitude of geometric nonidealities in the source-detector orbit was measured, and the short-term (approximately 4 h) and long-term (approximately 6 months) reproducibility of the calibration was evaluated. The C-arm exhibits large geometric nonidealities due to mechanical flex, with maximum departures from the average semicircular orbit of deltaU(o) = 15.8 mm and deltaV(o) = 9.8 mm (for the piercing point), deltaX and deltaY = 6-8 mm and deltaZ = 1 mm (for the source and detector), and deltaphi approximately 2.9 degrees, deltatheta approximately 1.9 degrees, and delta eta approximately 0.8 degrees (for the detector tilt/rotation). Despite such significant departures from a semicircular orbit, these system parameters were found to be reproducible, and therefore correctable by geometric calibration. Short-term reproducibility was < 0.16 mm (subpixel) for the piercing point coordinates, < 0.25 mm for the source-detector X and Y, < 0.035 mm for the source-detector Z, and < 0.02 degrees for the detector angles. Long-term reproducibility was similarly high, demonstrated by image quality and spatial resolution measurements over a period of 6 months. For example, the full-width at half-maximum (FWHM) in axial images of a thin steel wire increased slightly as a function of the time (delta) between calibration and image acquisition: FWHM=0.62, 0.63, 0.66, 0.71, and 0.72 mm at delta = 0 s, 1 h, 1 day, 1 month, and 6 months, respectively. For ongoing clinical trials in CBCT-guided surgery at our institution, geometric calibration is conducted monthly to provide sufficient three-dimensional (3D) image quality while managing time and workflow considerations of the calibration and quality assurance process. The sensitivity of 3D image quality to each of the system parameters was investigated, as was the tolerance to systematic and random errors in the geometric parameters, showing the most sensitive parameters to be the piercing point coordinates (U(o), V(o)) and in-plane positions of the source (X(s), Y(s)) and detector (X(d), Y(d)). Errors in the out-of-plane position of the source (Z(s)) and detector (Z(d)) and the detector angles (phi, theta, eta) were shown to have subtler effects on 3D image quality.
CRISPR-Cas systems in bacteria and archaea: versatile small RNAs for adaptive defense and regulation.

Bacteria and archaea have evolved defense and regulatory mechanisms to cope with various environmental stressors, including virus attack. This arsenal has been expanded by the recent discovery of the versatile CRISPR-Cas system, which has two novel features. First, the host can specifically incorporate short sequences from invading genetic elements (virus or plasmid) into a region of its genome that is distinguished by clustered regularly interspaced short palindromic repeats (CRISPRs). Second, when these sequences are transcribed and precisely processed into small RNAs, they guide a multifunctional protein complex (Cas proteins) to recognize and cleave incoming foreign genetic material. This adaptive immunity system, which uses a library of small noncoding RNAs as a potent weapon against fast-evolving viruses, is also used as a regulatory system by the host. Exciting breakthroughs in understanding the mechanisms of the CRISPR-Cas system and its potential for biotechnological applications and understanding evolutionary dynamics are discussed.
Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework

This paper explores how the remaining useful life (RUL) can be assessed for complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. Consequently, inference and estimation techniques need to be applied on indirect measurements, anticipated operational conditions, and historical data for which a Bayesian statistical approach is suitable. Models of electrochemical processes in the form of equivalent electric circuit parameters were combined with statistical models of state transitions, aging processes, and measurement fidelity in a formal framework. Relevance vector machines (RVMs) and several different particle filters (PFs) are examined for remaining life prediction and for providing uncertainty bounds. Results are shown on battery data.
Dense Pixel Matching between Unrectified and Distorted Images using Dynamic Programming

In this paper, a novel framework for dense pixel matching based on dynamic programming is introduced. Unlike most techniques proposed in the literature, our approach assumes neither known camera geometry nor the availability of rectified images. Under such conditions, the matching task cannot be reduced to finding correspondences between a pair of scanlines. We propose to extend existing dynamic programming methodologies to a larger dimensional space by using a 3D scoring matrix so that correspondences between a line and a whole image can be calculated. After assessing our framework on a standard evaluation dataset of rectified stereo images, experiments are conducted on unrectified and non-linearly distorted images. Results validate our new approach and reveal the versatility of our algorithm.
Chiron-1: A Software Architecture for User Interface Development, Maintenance, and Run-Time Support

The Chiron-1 user interface system demonstrates key techniques that enable a strict separation of an application from its user interface. These techniques include separating the control-flow aspects of the application and user interface: they are concurrent and may contain many threads. Chiron also separates windowing and look-and-feel issues from dialogue and abstract presentation decisions via mechanisms employing a client-server architecture. To separate application code from user interface code, user interface agents called artists are attached to instances of application abstract data types (ADTs). Operations on ADTs within the application implicitly trigger user interface activities within the artists. Multiple artists can be attached to ADTs, providing multiple views and alternative forms of access and manipulation by either a single user or by multiple users. Each artist and the application run in separate threads of control. Artists maintain the user interface by making remote calls to an abstract depiction hierarchy in the Chiron server, insulting the user interface code from the specifics of particular windowing systems and toolkits. The Chiron server and clients execute in separate processes. The client-server architecture also supports multilingual systems: mechanisms are demonstrated that support clients written in programming languages other than that of the server while nevertheless supporting object-oriented server concepts. The system has been used in several universities and research and development projects. It is available by anonymous ftp.
Automated 3-D Retinal Layer Segmentation of Macular Optical Coherence Tomography Images With Serous Pigment Epithelial Detachments

Automated retinal layer segmentation of optical coherence tomography (OCT) images has been successful for normal eyes but becomes challenging for eyes with retinal diseases if the retinal morphology experiences critical changes. We propose a method to automatically segment the retinal layers in 3-D OCT data with serous retinal pigment epithelial detachments (PED), which is a prominent feature of many chorioretinal disease processes. The proposed framework consists of the following steps: fast denoising and B-scan alignment, multi-resolution graph search based surface detection, PED region detection and surface correction above the PED region. The proposed technique was evaluated on a dataset with OCT images from 20 subjects diagnosed with PED. The experimental results showed the following. 1) The overall mean unsigned border positioning error for layer segmentation is 7.87±3.36 μm, and is comparable to the mean inter-observer variability ( 7.81±2.56 μm). 2) The true positive volume fraction (TPVF), false positive volume fraction (FPVF) and positive predicative value (PPV) for PED volume segmentation are 87.1%, 0.37%, and 81.2%, respectively. 3) The average running time is 220 s for OCT data of 512 × 64 × 480 voxels.
Energy-Efficient Cloud Computing

Energy efficiency is increasingly important for future information and communication technologies (ICT), because the increased usage of ICT, together with increasing energy costs and the need to reduce green house gas emissions call for energy-efficient technologies that decrease the overall energy consumption of computation, storage and communications. Cloud computing has recently received considerable attention, as a promising approach for delivering ICT services by improving the utilization of data-centre resources. In principle, cloud computing can be an inherently energy-efficient technology for ICT provided that its potential for significant energy savings that have so far focused on hardware aspects, can be fully explored with respect to system operation and networking aspects. Thus this paper reviews the usage in the context of Cloud Computing, of methods and technologies currently used for energy-efficient operation of computer hardware and network infrastructure. After surveying some of the current best practice and relevant literature in this area, this paper identifies some of the remaining key research challenges that arise when such energy saving techniques are extended for use in Cloud Computing environments.
Deep Recurrent Convolutional Networks for Video-based Person Re-identification: An End-to-End Approach

In this paper, we present an end-to-end approach to simultaneously learn spatio-temporal features and corresponding similarity metric for video-based person re-identification. Given the video sequence of a person, features from each frame that are extracted from all levels of a deep convolutional network can preserve a higher spatial resolution from which we can model finer motion patterns. These lowlevel visual percepts are leveraged into a variant of recurrent model to characterize the temporal variation between time-steps. Features from all time-steps are then summarized using temporal pooling to produce an overall feature representation for the complete sequence. The deep convolutional network, recurrent layer, and the temporal pooling are jointly trained to extract comparable hidden-unit representations from input pair of time series to compute their corresponding similarity value. The proposed framework combines time series modeling and metric learning to jointly learn relevant features and a good similarity measure between time sequences of person. Experiments demonstrate that our approach achieves the state-of-the-art performance for video-based person re-identification on iLIDS-VID and PRID 2011, the two primary public datasets for this purpose.
SVDD-based outlier detection on uncertain data

Outlier detection is an important problem that has been studied within diverse research areas and application domains. Most existing methods are based on the assumption that an example can be exactly categorized as either a normal class or an outlier. However, in many real-life applications, data are uncertain in nature due to various errors or partial completeness. These data uncertainty make the detection of outliers far more difficult than it is from clearly separable data. The key challenge of handling uncertain data in outlier detection is how to reduce the impact of uncertain data on the learned distinctive classifier. This paper proposes a new SVDD-based approach to detect outliers on uncertain data. The proposed approach operates in two steps. In the first step, a pseudo-training set is generated by assigning a confidence score to each input example, which indicates the likelihood of an example tending normal class. In the second step, the generated confidence score is incorporated into the support vector data description training phase to construct a global distinctive classifier for outlier detection. In this phase, the contribution of the examples with the least confidence score on the construction of the decision boundary has been reduced. The experiments show that the proposed approach outperforms state-of-art outlier detection techniques.
Graph-Based Named Entity Linking with Wikipedia

Named entity linking (NEL) grounds entity mentions to their corresponding Wikipedia article. State-of-the-art supervised NEL systems use features over the rich Wikipedia document and link-graph structure. Graph-based measures have been effective over WordNet for word sense disambiguation (WSD). We draw parallels between NEL and WSD, motivating our unsupervised NEL approach that exploits the Wikipedia article and category link graphs. Our system achieves 85.5% accuracy on the TAC 2010 shared task — competitive with the best supervised and unsupervised systems.
Knowledge and Common Knowledge in a Distributed Environment

We argue that the right way to understand distributed protocols is by considering how messages change the state of knowledge of a system. We present a hierarchy of knowledge states that a system may be in, and discuss how communication can move the system's state of knowledge of a fact up the hierarchy. Of special interest is the notion of common knowledge. Common knowledge is an essential state of knowledge for reaching agreements and coordinating action. We show that in practical distributed systems, common knowledge is not attainable. We introduce various relaxations of common knowledge that are attainable in many cases of interest. We describe in what sense these notions are appropriate, and discuss their relationship to each other. We conclude with a discussion of the role of knowledge in distributed systems.
What Is a Paraphrase?

Paraphrases are sentences or phrases that convey the same meaning using different wording. Although the logical definition of paraphrases requires strict semantic equivalence, linguistics accepts a broader, approximate, equivalence—thereby allowing far more examples of “quasi-paraphrase.” But approximate equivalence is hard to define. Thus, the phenomenon of paraphrases, as understood in linguistics, is difficult to characterize. In this article, we list a set of 25 operations that generate quasi-paraphrases. We then empirically validate the scope and accuracy of this list by manually analyzing random samples of two publicly available paraphrase corpora. We provide the distribution of naturally occurring quasi-paraphrases in English text.
Millimeter-Wave EBG-Based Aperture-Coupled Dielectric Resonator Antenna

Design, fabrication and testing of millimeter-wave (MMW) dielectric resonator antenna (DRA) surrounded by electromagnetic band-gap (EBG) structure are presented. For this purpose, MMW mushroom-like, circular patch EBG (CP-EBG) cell is designed and fabricated. The propagation characteristics of the proposed CP-EBG structure are measured using the asymmetric microstrip line method. A cylindrical DRA incorporating the developed CP-EBG structure is then designed and its performance is evaluated with and without the CP-EBG around the 60 GHz bandwidth. Measurements show a significant improvement in the antenna radiation characteristics when it is surrounded by CP-EBG structure. A gain increase of up to 3.2 dBi is obtained while preserving the gain flatness over the suggested bandwidth ( ± 0.7 dB). An additional backlobe suppression of up to 6.5 dBi is achieved. Moreover, radiation toward substrate edges is significantly reduced.
Searchable Symmetric Encryption: Designs and Challenges

Searchable Symmetric Encryption (SSE) when deployed in the cloud allows one to query encrypted data without the risk of data leakage. Despite the widespread interest, existing surveys do not examine in detail how SSE’s underlying structures are designed and how these result in the many properties of a SSE scheme. This is the gap we seek to address, as well as presenting recent state-of-the-art advances on SSE. Specifically, we present a general framework and believe the discussions may lead to insights for potential new designs. We draw a few observations. First, most schemes use index table, where optimal index size and sublinear search can be achieved using an inverted index. Straightforward updating can only be achieved using direct index, but search time would be linear. A recent trend is the combinations of index table, and tree, deployed for efficient updating and storage. Secondly, mechanisms from related fields such as Oblivious RAM (ORAM) have been integrated to reduce leakages. However, using these mechanisms to minimise leakages in schemes with richer functionalities (e.g., ranked, range) is relatively unexplored. Thirdly, a new approach (e.g., multiple servers) is required to mitigate new and emerging attacks on leakage. Lastly, we observe that a proposed index may not be practically efficient when implemented, where I/O access must be taken into consideration.
Comparative analysis of axial flux SRM topologies for electric vehicle application

In-wheel motors for electric vehicles (EVs) have a high outer diameter (D) to axial length (L) ratio. In such applications, axial flux machines are preferred over radial flux machines due to high power density. Moreover, permanent magnet (PM)-less machines are gaining interest due to increase in cost of rare-earth PM materials. In view of this, axial flux switched reluctance motor (AFSRM) is considered as a possible option for EV propulsion. Two topologies namely, toothed and segmented rotor AFSRM are designed and compared for the same overall volume. These topologies have a three-phase, 12/16 pole single-stator, dual outer-rotor configuration along with non-overlapping winding arrangement. Analytical expressions for phase inductance and average torque are derived. To verify the performance of both the topologies a finite element method (FEM) based simulation study is carried out and its results are verified with the analytical values. It is observed from simulation that the average torque is 16.2% higher and torque ripple is 17.9% lower for segmented rotor AFSRM as compared to toothed rotor AFSRM.
Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring

Deep Neural Networks have recently gained lots of success after enabling several breakthroughs in notoriously challenging problems. Training these networks is computationally expensive and requires vast amounts of training data. Selling such pre-trained models can, therefore, be a lucrative business model. Unfortunately, once the models are sold they can be easily copied and redistributed. To avoid this, a tracking mechanism to identify models as the intellectual property of a particular vendor is necessary. In this work, we present an approach for watermarking Deep Neural Networks in a black-box way. Our scheme works for general classification tasks and can easily be combined with current learning algorithms. We show experimentally that such a watermark has no noticeable impact on the primary task that the model is designed for and evaluate the robustness of our proposal against a multitude of practical attacks. Moreover, we provide a theoretical analysis, relating our approach to previous work on backdooring.
Loss factorization, weakly supervised learning and label noise robustness

We prove that the empirical risk of most wellknown loss functions factors into a linear term aggregating all labels with a term that is label free, and can further be expressed by sums of the same loss. This holds true even for non-smooth, non-convex losses and in any RKHS. The first term is a (kernel) mean operator — the focal quantity of this work — which we characterize as the sufficient statistic for the labels. The result tightens known generalization bounds and sheds new light on their interpretation. Factorization has a direct application on weakly supervised learning. In particular, we demonstrate that algorithms like SGD and proximal methods can be adapted with minimal effort to handle weak supervision, once the mean operator has been estimated. We apply this idea to learning with asymmetric noisy labels, connecting and extending prior work. Furthermore, we show that most losses enjoy a data-dependent (by the mean operator) form of noise robustness, in contrast with known negative results.
Solid-State Transformers: On the Origins and Evolution of Key Concepts

During the past two decades, solid-state transformers (SSTs) have evolved quickly and have been considered for replacing conventional low-frequency (LF) transformers in applications such as traction, where weight and volume savings and substantial efficiency improvements can be achieved, or in smart grids because of their controllability. As shown in this article, all main modern SST topologies realize the common key characteristics of these transformers-medium-frequency (MF) isolation stage, connection to medium voltage (MV), and controllability-by employing combinations of a very few key concepts, which have been described or patented as early as the 1960s. But still, key research challenges concerning protection, isolation, and reliability remain.
Global food demand and the sustainable intensification of agriculture.

Global food demand is increasing rapidly, as are the environmental impacts of agricultural expansion. Here, we project global demand for crop production in 2050 and evaluate the environmental impacts of alternative ways that this demand might be met. We find that per capita demand for crops, when measured as caloric or protein content of all crops combined, has been a similarly increasing function of per capita real income since 1960. This relationship forecasts a 100-110% increase in global crop demand from 2005 to 2050. Quantitative assessments show that the environmental impacts of meeting this demand depend on how global agriculture expands. If current trends of greater agricultural intensification in richer nations and greater land clearing (extensification) in poorer nations were to continue, ~1 billion ha of land would be cleared globally by 2050, with CO(2)-C equivalent greenhouse gas emissions reaching ~3 Gt y(-1) and N use ~250 Mt y(-1) by then. In contrast, if 2050 crop demand was met by moderate intensification focused on existing croplands of underyielding nations, adaptation and transfer of high-yielding technologies to these croplands, and global technological improvements, our analyses forecast land clearing of only ~0.2 billion ha, greenhouse gas emissions of ~1 Gt y(-1), and global N use of ~225 Mt y(-1). Efficient management practices could substantially lower nitrogen use. Attainment of high yields on existing croplands of underyielding nations is of great importance if global crop demand is to be met with minimal environmental impacts.