nils-herrmann/clean_scirepeval huggingface dataset:
Extracorporeal photopheresis: what is it and when should it be used?

Extracorporeal photopheresis (ECP) is a technique that was developed > 20 years ago to treat erythrodermic cutaneous T-cell lymphoma (CTCL). The technique involves removal of peripheral blood, separation of the buffy coat, and photoactivation with a photosensitizer and ultraviolet A irradiation before re-infusion of cells. More than 1000 patients with CTCL have been treated with ECP, with response rates of 31-100%. ECP has been used in a number of other conditions, most widely in the treatment of chronic graft-versus-host disease (cGvHD) with response rates of 29-100%. ECP has also been used in several other autoimmune diseases including acute GVHD, solid organ transplant rejection and Crohn's disease, with some success. ECP is a relatively safe procedure, and side-effects are typically mild and transient. Severe reactions including vasovagal syncope or infections are uncommon. This is very valuable in conditions for which alternative treatments are highly toxic. The mechanism of action of ECP remains elusive. ECP produces a number of immunological changes and in some patients produces immune homeostasis with resultant clinical improvement. ECP is available in seven centres in the UK. Experts from all these centres formed an Expert Photopheresis Group and published the UK consensus statement for ECP in 2008. All centres consider patients with erythrodermic CTCL and steroid-refractory cGvHD for treatment. The National Institute for Health and Clinical Excellence endorsed the use of ECP for CTCL and suggested a need for expansion while recommending its use in specialist centres. ECP is safe, effective, and improves quality of life in erythrodermic CTCL and cGvHD, and should be more widely available for these patients.
The Continuous Cold-start Problem in e-Commerce Recommender Systems

Many e-commerce websites use recommender systems to recommend items to users. When a user or item is new, the system may fail because not enough information is available on this user or item. Various solutions to this ‘cold-start problem’ have been proposed in the literature. However, many real-life e-commerce applications suffer from an aggravated, recurring version of cold-start even for known users or items, since many users visit the website rarely, change their interests over time, or exhibit different personas. This paper exposes the Continuous Cold Start (CoCoS) problem and its consequences for contentand context-based recommendation from the viewpoint of typical e-commerce applications, illustrated with examples from a major travel recommendation website, Booking.com.  continuous cold start
The neurobiology of semantic memory

Semantic memory includes all acquired knowledge about the world and is the basis for nearly all human activity, yet its neurobiological foundation is only now becoming clear. Recent neuroimaging studies demonstrate two striking results: the participation of modality-specific sensory, motor, and emotion systems in language comprehension, and the existence of large brain regions that participate in comprehension tasks but are not modality-specific. These latter regions, which include the inferior parietal lobe and much of the temporal lobe, lie at convergences of multiple perceptual processing streams. These convergences enable increasingly abstract, supramodal representations of perceptual experience that support a variety of conceptual functions including object recognition, social cognition, language, and the remarkable human capacity to remember the past and imagine the future.
Role of RUNX1 in hematological malignancies.

RUNX1 is a member of the core-binding factor family of transcription factors and is indispensable for the establishment of definitive hematopoiesis in vertebrates. RUNX1 is one of the most frequently mutated genes in a variety of hematological malignancies. Germ line mutations in RUNX1 cause familial platelet disorder with associated myeloid malignancies. Somatic mutations and chromosomal rearrangements involving RUNX1 are frequently observed in myelodysplastic syndrome and leukemias of myeloid and lymphoid lineages, that is, acute myeloid leukemia, acute lymphoblastic leukemia, and chronic myelomonocytic leukemia. More recent studies suggest that the wild-type RUNX1 is required for growth and survival of certain types of leukemia cells. The purpose of this review is to discuss the current status of our understanding about the role of RUNX1 in hematological malignancies.
Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings

Machine translation is highly sensitive to the size and quality of the training data, which has led to an increasing interest in collecting and filtering large parallel corpora. In this paper, we propose a new method for this task based on multilingual sentence embeddings. Our approach uses an encoder-decoder trained over an initial parallel corpus to build multilingual sentence representations, which are then incorporated into a new margin-based method to score, mine and filter parallel sentences. In contrast to previous approaches, which rely on nearest neighbor retrieval with a hard threshold over cosine similarity, our proposed method accounts for the scale inconsistencies of this measure, considering the margin between a given sentence pair and its closest candidates instead. Our experiments show large improvements over existing methods. We outperform the best published results on the BUCC shared task on parallel corpus mining by more than 10 F1 points. We also improve the precision from 48.9 to 83.3 on the reconstruction of 11.3M English-French sentence pairs of the UN corpus. Finally, filtering the English-German ParaCrawl corpus with our approach, we obtain 31.2 BLEU points on newstest2014, an improvement of more than one point over the best official filtered version.
Auditory brainstem response latency in forward masking, a marker of sensory deficits in listeners with normal hearing thresholds

In rodent models, acoustic exposure too modest to elevate hearing thresholds can nonetheless cause auditory nerve fiber deafferentation, interfering with the coding of supra-threshold sound. Low-spontaneous rate nerve fibers, important for encoding acoustic information at supra-threshold levels and in noise, are more susceptible to degeneration than high-spontaneous rate fibers. The change in auditory brainstem response (ABR) wave-V latency with noise level has been shown to be associated with auditory nerve deafferentation. Here, we measured ABR in a forward masking paradigm and evaluated wave-V latency changes with increasing masker-to-probe intervals. In the same listeners, behavioral forward masking detection thresholds were measured. We hypothesized that 1) auditory nerve fiber deafferentation increases forward masking thresholds and increases wave-V latency and 2) a preferential loss of low-spontaneous rate fibers results in a faster recovery of wave-V latency as the slow contribution of these fibers is reduced. Results showed that in young audiometrically normal listeners, a larger change in wave-V latency with increasing masker-to-probe interval was related to a greater effect of a preceding masker behaviorally. Further, the amount of wave-V latency change with masker-to-probe interval was positively correlated with the rate of change in forward masking detection thresholds. Although we cannot rule out central contributions, these findings are consistent with the hypothesis that auditory nerve fiber deafferentation occurs in humans and may predict how well individuals can hear in noisy environments.
DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection

In this paper, we propose multi-stage and deformable deep convolutional neural networks for object detection. This new deep learning object detection diagram has innovations in multiple aspects. In the proposed new deep architecture, a new deformation constrained pooling (defpooling) layer models the deformation of object parts with geometric constraint and penalty. With the proposed multistage training strategy, multiple classifiers are jointly o ptimized to process samples at different difficulty levels. A ne w pre-training strategy is proposed to learn feature represe ntations more suitable for the object detection task and with good generalization capability. By changing the net structures, training strategies, adding and removing some key components in the detection pipeline, a set of models with large diversity are obtained, which significantly improves the effectiveness of modeling averaging. The proposed approach ranked #2 in ILSVRC 2014. It improves the mean averaged precision obtained by RCNN, which is the stateof-the-art of object detection, from31% to 45%. Detailed component-wise analysis is also provided through extensiv experimental evaluation.
A Comparative Study of Maintainability Index of Open Source Software

The development of Open Source Software (OSS) is fundamentally different from the proprietary software. In the OSS development scenario a single developer or group of developers writes the source code for the first version of the software and make it freely available over the internet. Then other developers are invited to contribute to the existing code for its next release. Making the source code of the software available on the internet allows developers around the world to contribute code, add new functionality, improvement of the existing source code and submitting bug fixes to the current release. In such a software development scenario the maintenance of the open source software is a perpetual task. Developing an OSS system implies a series of frequent maintenance efforts for debugging existing functionality and adding new functionality to the software system. The process of making the modifications to software systems after their first release is known as maintenance process. The term maintainability is closely related to the software maintenance because maintainability means the easiness to perform maintenance of the system. The most widely used software metric to quantify the maintainability is known as Maintainability Index (MI). In this study the MI of four most popular OSS namely Apache, Mozilla Firefox, MySql and FileZilla for fifty successive releases was empirically investigated. The MI in terms of software metrics namely Lines of Code (LOC), Cyclomatic Complexity (CC), and Halstead Volume (V) was computed for all the fifty successive versions of four OSS. The software metrics were calculated using Resource Standard Metrics (RSM) tool and Crystal Flow tool. It was observed from the results that the MI value was the highest in case of Mozilla Firefox and was the lowest in the case of Apache OSS.
A decision-theoretic approach to detection-based target search with a UAV

Search and rescue missions and surveillance require finding targets in a large area. These tasks often use unmanned aerial vehicles (UAVs) with cameras to detect and move towards a target. However, common UAV approaches make two simplifying assumptions. First, they assume that observations made from different heights are deterministically correct. In practice, observations are noisy, with the noise increasing as the height used for observations increases. Second, they assume that a motion command executes correctly, which may not happen due to wind and other environmental factors. To address these, we propose a sequential algorithm that determines actions in real time based on observations, using partially observable Markov decision processes (POMDPs). Our formulation handles both observations and motion uncertainty and errors. We run offline simulations and learn a policy. This policy is run on a UAV to find the target efficiently. We employ a novel compact formulation to represent the coordinates of the drone relative to the target coordinates. Our POMDP policy finds the target up to 3.4 times faster when compared to a heuristic policy.
Counterfactual Estimation and Optimization of Click Metrics in Search Engines: A Case Study

Optimizing an interactive system against a predefined online metric is particularly challenging, especially when the metric is computed from user feedback such as clicks and payments. The key challenge is the counterfactual nature: in the case of Web search, any change to a component of the search engine may result in a different search result page for the same query, but we normally cannot infer reliably from search log how users would react to the new result page. Consequently, it appears impossible to accurately estimate online metrics that depend on user feedback, unless the new engine is actually run to serve live users and compared with a baseline in a controlled experiment. This approach, while valid and successful, is unfortunately expensive and time-consuming. In this paper, we propose to address this problem using causal inference techniques, under the contextual-bandit framework. This approach effectively allows one to run potentially many online experiments offline from search log, making it possible to estimate and optimize online metrics quickly and inexpensively. Focusing on an important component in a commercial search engine, we show how these ideas can be instantiated and applied, and obtain very promising results that suggest the wide applicability of these techniques.
Ngram2vec: Learning Improved Word Representations from Ngram Co-occurrence Statistics

The existing word representation methods mostly limit their information source to word co-occurrence statistics. In this paper, we introduce ngrams into four representation methods: SGNS, GloVe, PPMI matrix, and its SVD factorization. Comprehensive experiments are conducted on word analogy and similarity tasks. The results show that improved word representations are learned from ngram cooccurrence statistics. We also demonstrate that the trained ngram representations are useful in many aspects such as finding antonyms and collocations. Besides, a novel approach of building co-occurrence matrix is proposed to alleviate the hardware burdens brought by ngrams.
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy. However, contemporary experience is that the sparse architectures produced by pruning are difficult to train from the start, which would similarly improve training performance. We find that a standard technique for pruning weights naturally uncovers subnetworks whose initializations made them capable of training effectively. Based on these results, we articulate the lottery ticket hypothesis: dense, randomly-initialized feed-forward networks contain subnetworks (winning tickets) that—when trained in isolation—arrive at comparable test accuracy in a comparable number of iterations. The winning tickets we find have won the initialization lottery: their connections have initial weights that make training particularly effective. We present an algorithm to identify winning tickets and a series of experiments that support the lottery ticket hypothesis and the importance of these fortuitous initializations. We consistently find winning tickets that are less than 10-20% of the size of several fully-connected and convolutional feed-forward architectures for MNIST and CIFAR10. Furthermore, the winning tickets we find above that size learn faster than the original network and exhibit higher test accuracy.
Social context summarization

We study a novel problem of social context summarization for Web documents. Traditional summarization research has focused on extracting informative sentences from standard documents. With the rapid growth of online social networks, abundant user generated content (e.g., comments) associated with the standard documents is available. Which parts in a document are social users really caring about? How can we generate summaries for standard documents by considering both the informativeness of sentences and interests of social users? This paper explores such an approach by modeling Web documents and social contexts into a unified framework. We propose a dual wing factor graph (DWFG) model, which utilizes the mutual reinforcement between Web documents and their associated social contexts to generate summaries. An efficient algorithm is designed to learn the proposed factor graph model.Experimental results on a Twitter data set validate the effectiveness of the proposed model. By leveraging the social context information, our approach obtains significant improvement (averagely +5.0%-17.3%) over several alternative methods (CRF, SVM, LR, PR, and DocLead) on the performance of summarization.
An Intelligent System for Traffic Control in Smart Cities : A Case Study

Current traffic light systems use a fixed time delay for different traffic directions and do follow a particular cycle while switching from one signal to another. This creates unwanted congestion during peak hours, loss of man-hours and eventually decline in productivity. In addition to this, the current traffic light systems encourage extortion by corrupt traffic officials as commuters often violate traffic rules because of the insufficient time allocated to their lanes or may want to avoid a long waiting period for their lanes to come up. This research is aimed at tackling the afore-mentioned problems by adopting a density based traffic control approach using Jakpa Junction, one of the busiest junctions in Delta State, Nigeria as a case study. The developed system uses a microcontroller of PIC89C51 microcontroller duly interfaced with sensors. The signal timing changes automatically based on the traffic density at the junction, thereby, avoiding unnecessary waiting time at the junction. The sensors used in this project were infra-red (IR) sensors and photodiodes which were placed in a Line of Sight configuration across the loads to detect the density of the traffic signal. The density of the vehicles is measured in three zones i.e., low, medium and high based on which timings were allotted accordingly. The developed system has proven to be smart and intelligent and capable of curbing incidences of traffic malpractices and inefficiencies that have been the bane of current traffic congestion control systems in emerging cities of the third world.
GPS-less low-cost outdoor localization for very small devices

Instrumenting the physical world through large networks of wireless sensor nodes, particularly for applications like marine biology, requires that these nodes be very small, light, un-tethered and unobtrusive, imposing substantial restrictions on the amount of additional hardware that can be placed at each node. Practical considerations such as the small size, form factor, cost and power constraints of nodes preclude the use of GPS(Global Positioning System) for all nodes in these networks. The problem of localization, i.e., determining where a given node is physically located in a network is a challenging one, and yet extremely crucial for many applications of very large device networks. It needs to be solved in the absence of GPS on all the nodes in outdoor environments. In this paper, we propose a simple connectivity-metric based method for localization in outdoor environments that makes use of the inherent radiofrequency(RF) communications capabilities of these devices. A fixed number of reference points in the network transmit periodic beacon signals. Nodes use a simple connectivity metric to infer proximity to a given subset of these reference points and then localize themselves to the centroid of the latter. The accuracy of localization is then dependent on the separation distance between two adjacent reference points and the transmission range of these reference points. Initial experimental results show that the accuracy for 90% of our data points is within one-third of the separation distance. localization, radio, wireless, GPS-less, connectivity, sensor networks.
Analysis of the microflora in Tibetan kefir grains using denaturing gradient gel electrophoresis.

The microflora of Tibetan kefir grains was investigated by culture- independent methods. Denaturing gradient gel electrophoresis (DGGE) of partially amplified 16S rRNA for bacteria and 26S rRNA for yeasts, followed by sequencing of the most intense bands, showed that the dominant microorganisms were Pseudomonas sp., Leuconostoc mesenteroides, Lactobacillus helveticus, Lactobacillus kefiranofaciens, Lactococcus lactis, Lactobacillus kefiri, Lactobacillus casei, Kazachstania unispora, Kluyveromyces marxianus, Saccharomyces cerevisiae, and Kazachstania exigua. The bacterial communities between three kinds of Tibetan kefir grains showed 78-84% similarity, and yeasts 80-92%. The microflora is held together in the matrix of fibrillar material composed largely of a water-insoluble polysaccharide.
The anatomy of memory: an interactive overview of the parahippocampal–hippocampal network

Converging evidence suggests that each parahippocampal and hippocampal subregion contributes uniquely to the encoding, consolidation and retrieval of declarative memories, but their precise roles remain elusive. Current functional thinking does not fully incorporate the intricately connected networks that link these subregions, owing to their organizational complexity; however, such detailed anatomical knowledge is of pivotal importance for comprehending the unique functional contribution of each subregion. We have therefore developed an interactive diagram with the aim to display all of the currently known anatomical connections of the rat parahippocampal–hippocampal network. In this Review, we integrate the existing anatomical knowledge into a concise description of this network and discuss the functional implications of some relatively underexposed connections.
My Smart Home is Under Attack

Home Automation is becoming more and more common in everyday life. In order to permit a remote control of domestic devices a bridge between the Internet network and the Home Network is necessary. This function is commonly operated by the Smart Home gateway which thus results a critical point for the security of the network. Recently several general purpose devices exist that are capable of connecting both to the Home Network and the Internet network, leading to have additional, dynamic and unpredictable, bridges between the two networks. This possibility makes such devices additional attack vectors for the Smart Home. In this work we show the risks resulting from such devices. By using an emulated scenario we also demonstrate the implementation of a real attack exploiting a general purpose device connected to both the Internet and the Home Network. Preliminary experimental results are presented confirming the success of our attack.
Comparison of Graph Cuts with Belief Propagation for Stereo, using Identical MRF Parameters

Recent stereo algorithms have achieved impressive results by modelling the disparity image as a Markov Random Field (MRF). An important component of an MRF-based approach is the inference algorithm used to find the most likely setting of each node in the MRF. Algorithms have been proposed which use Graph Cuts or Belief Propagation for inference. These stereo algorithms differ in both the inference algorithm used and the formulation of the MRF. It is unknown whether to attribute the responsibility for differences in performance to the MRF or the inference algorithm. We address this through controlled experiments by comparing the Belief Propagation algorithm and the Graph Cuts algorithm on the same MRF’s, which have been created for calculating stereo disparities. We find that the labellings produced by the two algorithms are comparable. The solutions produced by Graph Cuts have a lower energy than those produced with Belief Propagation, but this does not necessarily lead to increased performance relative to the ground-truth.