nils-herrmann/clean_scirepeval huggingface dataset:
Weakly supervised learning for image keypoint matching using graph convolutional networks
Venue: Knowl. Based Syst.
Year: 2020

Matching between two sets of features from a pair of images is a fundamental and critical step in most computer vision tasks. Existing attempts typically establish a set of putative correspondences with the nearest neighbor rule in feature spaces and then try to find a subset of reliable matches. However, when there are large camera angles, repetitive structures, and illumination changes existing in the two images of the same scene, recently proposed feature matching approaches do not work well to find good correspondences, especially with a higher proportion of false positive matches in the putative set. To address these problems, we propose a novel weakly supervised Graph Convolutional Siamese Network Matcher, called GCSNMatcher, to learn the correct correspondences for image feature matching. In particular, GCSNMatcher can directly work on unstructured keypoint sets and further exploit geometric information among sparse interest points by constructing dynamic neighborhood graph structures to enhance the ability of the feature representation of each keypoint. With channel wised symmetric aggregation operations in our graph convolutional neural networks, the performance of our matcher does not vary under different permutations of unordered keypoint sets. Empirical studies on Yahoo's YFCC100M benchmark dataset demonstrate that our matcher can give a more robust performance for image matching tasks than those state of the art methods, even when it is trained on small datasets.
Reward Solutions to Retention Questions
Year: 2018

Over the last few years, organisations have had difficulty facing a changing financial setting that has challenged conventional reward procedures and plans. A complete understanding of reward packages is needed including both the intrinsic and extrinsic factors that impact total reward. These factors have a key impact on performance, job satisfaction and the retention of high calibre talent; all of which contribute to organisation effectiveness. More specifically, research has shown that the different types of rewards employers offer to employees affect the recruitment and retention of top talent. Few businesses realise just how much employee turnover impacts their bottom line. Reward strategy is one of the least understood elements of retention strategies, yet one of the most frequently used. This chapter will cover the trends in remuneration options linked to staff retention.
Hollow MnO2 as a tumor microenvironment responsive biodegradable nano platform for combination therapy favoring antitumor immune responses
Venue: Nature Communications
Year: 2017

Herein, an intelligent biodegradable hollow manganese dioxide (H MnO2) nano platform is developed for not only tumor microenvironment (TME) specific imaging and on demand drug release, but also modulation of hypoxic TME to enhance cancer therapy, resulting in comprehensive effects favoring anti tumor immune responses. With hollow structures, H MnO2 nanoshells post modification with polyethylene glycol (PEG) could be co loaded with a photodynamic agent chlorine e6 (Ce6) and a chemotherapy drug doxorubicin (DOX) The obtained H MnO2 PEG/C&D would be dissociated under reduced pH within TME to release loaded therapeutic molecules, and in the meantime induce decomposition of tumor endogenous H2O2 to relieve tumor hypoxia. As a result, a remarkable in vivo synergistic therapeutic effect is achieved through the combined chemo photodynamic therapy, which simultaneously triggers a series of anti tumor immune responses. Its further combination with checkpoint blockade therapy would lead to inhibition of tumors at distant sites, promising for tumor metastasis treatment.MnO2 nanostructures are promising TME responsive theranostic agents in cancer. Here, the authors develop a nano platform based on hollow H MnO2 nanoshells able to modulate the tissue microenvironment, release a drug and inhibit tumor growth alone or in combination with check point blockade therapy.
Elevated hepcidin serum level in response to inflammatory and iron signals in exercising athletes is independent of moderate supplementation with vitamin C and E
Venue: Physiological reports
Year: 2015

Iron deficiency among endurance athletes is of major concern for coaches, physicians, and nutritionists. Recently, it has been observed that hepcidin, the master regulator of iron metabolism, was upregulated after exercise and was found to be related to interleukin 6 (IL 6) elevation. In this study performed on noniron deficient and well trained runners, we observed that hepcidin concentrations remain elevated in response to inflammatory and iron signals despite a 28 days supplementation period with vitamins C (500 mg/day) and E (400 IU/day)
Some empirical advances in matrix completion
Venue: Signal Process.
Year: 2011

Solving the matrix completion problem via rank minimization is NP hard. Recent studies have shown that this problem can be addressed as a convex nuclear norm minimization problem, albeit at an increase in the required number of samples. This paper proposes a non convex optimization problem (a variant of convex nuclear norm minimization) for the solution of matrix completion. It also develops a fast numerical algorithm to solve the optimization. This empirical study shows that significant improvement can be achieved by the proposed method compared to the previous ones.The number of required samples is also dependent on the type of sampling scheme used. This work shows that blue noise sampling schemes yield more accurate matrix completion results compared to the popular uniform random sampling.
Operant Assessment of DMTP Spatial Working Memory in Mice
Venue: Front. Behav. Neurosci.
Year: 2019

Working memory (WM) is required to bridge the time between the moment of sensory perception and the usage of the acquired information for subsequent actions. Its frequent and pharmacoresistent impairment in mental health disorders urges the development of rodent paradigms through back translation of human WM tests, ideally avoiding the confounds of alternation based assays. Here we show, that mice can acquire a delayed matching to position (DMTP) operant spatial WM (SWM) paradigm that is akin to the combined attention and memory (CAM) task previously developed for rats, and that relies on a 5 choice wall [5 CSWM, 5 choice based operant testing of SWM (5 CSWM) Requiring ca. 3 months of daily training with a non illuminated operant box in the default state, mice could attain a performance level of =70% choice accuracy with short (2 s) delays in the DMTP 5 CSWM task. Performance decreased with extended delays, as expected for WM processes. Modafinil (15 and 30 mg/kg) and guanfacine (0.3 and 1 mg/kg) showed no consistent efficacy in enhancing task performance. We also found, that mice did not improve beyond chance level, when trained in the DNMTP version of the 5 CSWM. Our results outline the methodical possibility and constraints of assessing spatial WM in mice with an operant paradigm that provides high control over potentially confounding variables, such as cue directed attention, motivation or mediating strategies like body positioning.
Cerebellar premotor output neurons collateralize to innervate the cerebellar cortex
Venue: The Journal of comparative neurology
Year: 2015

Motor commands computed by the cerebellum are hypothesized to use corollary discharge, or copies of outgoing commands, to accelerate motor corrections. Identifying sources of corollary discharge, therefore, is critical for testing this hypothesis. Here we verified that the pathway from the cerebellar nuclei to the cerebellar cortex in mice includes collaterals of cerebellar premotor output neurons, mapped this collateral pathway, and identified its postsynaptic targets. Following bidirectional tracer injections into a distal target of the cerebellar nuclei, the ventrolateral thalamus, we observed retrogradely labeled somata in the cerebellar nuclei and mossy fiber terminals in the cerebellar granule layer, consistent with collateral branching. Corroborating these observations, bidirectional tracer injections into the cerebellar cortex retrogradely labeled somata in the cerebellar nuclei and boutons in the ventrolateral thalamus. To test whether nuclear output neurons projecting to the red nucleus also collateralize to the cerebellar cortex, we used a Cre dependent viral approach, avoiding potential confounds of direct red nucleus to cerebellum projections. Injections of a Cre dependent GFP expressing virus into Ntsr1 Cre mice, which express Cre selectively in the cerebellar nuclei, retrogradely labeled somata in the interposed nucleus, and putative collateral branches terminating as mossy fibers in the cerebellar cortex. Postsynaptic targets of all labeled mossy fiber terminals were identified using immunohistochemical Golgi cell markers and electron microscopic profiles of granule cells, indicating that the collaterals of nuclear output neurons contact both Golgi and granule cells. These results clarify the organization of a subset of nucleocortical projections that constitute an experimentally accessible corollary discharge pathway within the cerebellum.  2015.
SPARSITY IN CONVOLUTIONAL NEURAL NETWORKS FOR SCENE CLASSIFICATION OF REMOTE SENSING IMAGE
Year: 2019

Recently, the analysis of remote sensing images has attracted a lot of attention. In the domain of scene classification, deep learning methods, especially convolutional networks (CNNs) currently achieve the best results. Although the classification performance has reached a high level, there are still some factors limiting the improvement of classification accuracy. Based on obeservation of remote sensing scene images, we fing that some scenes are quite similar though they belong to different classes. To improve the classification performance between different scenes with similar characteristics, we propose a significant Feature Sparsity Layer that can be esaily embedded into various convolutional network architectures. The proposed layer can inhibit the confusing features meanwhile stress the discriminative features, and it is used to sparse the multi layer feature map, which is extracted by the convolutional layers. The proposed method achieves the state of the art results on three datasets UC Merced Land Use, Aerial Image Data and OPTIMAL 31, and competitive result on dataset WHU RS19.
Efficiency measurement of data centers: An elucidative review
Year: 2020

Most enterprises host their critical data in data centers to have a secure, robust environment supporting the highest uptime for their IT operations. As per industry best practices, standards, conference papers and journals; there are various ways suggested through which efficiency of data center is calculated. This review paper provides information on the efficiency calculation factors relatum to data center loads and its classification. It describes how measurement of data center efficiency is to be done and which are the crucial components which need to be considered for calculating the true values accurately for power usage efficiency (PUE) and data center infrastructure efficiency (DCiE).Basis on researchfindings working towards data center efficiency measurement, the future research scope has been identified that how various techniques can be applied on data center major components Power Cooling which further can be helpful towards improvement of Data Center efficiency.
Structure Based Virtual Screening for Drug Discovery: a Problem Centric Review
Venue: The AAPS Journal
Year: 2012

Structure based virtual screening (SBVS) has been widely applied in early stage drug discovery. From a problem centric perspective, we reviewed the recent advances and applications in SBVS with a special focus on docking based virtual screening. We emphasized the researchers' practical efforts in real projects by understanding the ligand target binding interactions as a premise. We also highlighted the recent progress in developing target biased scoring functions by optimizing current generic scoring functions toward certain target classes, as well as in developing novel ones by means of machine learning techniques.
Defining and Evaluating Network Communities Based on Ground Truth
Venue: ICDM
Year: 2012

Nodes in real world networks organize into densely linked communities where edges appear with high concentration among the members of the community. Identifying such communities of nodes has proven to be a challenging task mainly due to a plethora of definitions of a community, intractability of algorithms, issues with evaluation and the lack of a reliable gold standard ground truth. In this paper we study a set of 230 large real world social, collaboration and information networks where nodes explicitly state their group memberships. For example, in social networks nodes explicitly join various interest based social groups. We use such groups to define a reliable and robust notion of ground truth communities. We then propose a methodology which allows us to compare and quantitatively evaluate how different structural definitions of network communities correspond to ground truth communities. We choose 13 commonly used structural definitions of network communities and examine their sensitivity, robustness and performance in identifying the ground truth. We show that the 13 structural definitions are heavily correlated and naturally group into four classes. We find that two of these definitions, Conductance and Triad participation ratio, consistently give the best performance in identifying ground truth communities. We also investigate a task of detecting communities given a single seed node. We extend the local spectral clustering algorithm into a heuristic parameter free community detection method that easily scales to networks with more than hundred million nodes. The proposed method achieves 30% relative improvement over current local clustering methods.
Probiotics and their role in gastrointestinal cancers prevention and treatment; an overview
Venue: Gastroenterology and hepatology from bed to bench
Year: 2018

Cancers of the gastrointestinal (GI) track are a serious global health problem. The human GI tract is home to trillions of microorganisms that known as gut microbiota and have established a symbiotic relationship with the host. The human intestinal microbiota plays an important role in the development of the gut immune system, metabolism, nutrition absorption, production of short chain fatty acids and essential vitamins, resistance to pathogenic microorganisms, and modulates a normal immunological response. Microbiota imbalance has been involved in many disorders including inflammatory bowel disease, obesity, asthma, psychiatric illnesses, and cancers. Oral administration of probiotics seems to play a protective role against cancer development as a kind of functional foods. Moreover, clinical application of probiotics has shown that some probiotic strains can reduce the incidence of post operative inflammation in cancer patients. In the present narrative review, we carried out update knowledge on probiotic effects and underlying mechanism to GI cancers. Currently, it is accept that most commercial probiotic products are generally safe and can used as a supplement for cancer prevention and treatment. Nevertheless, well designed, randomized, double blind, placebo controlled human studies are required to gain the acceptance of the potential probiotics as an alternative therapy for cancer control.
Stochastic PDEs and Lack of Regularity
Year: 2015

We review results on the existence and uniqueness for a surface growth model with or without space time white noise. If the surface is a graph, then this model has striking similarities to the three dimensional Navier Stokes equations in terms of energy estimates and scaling properties, and in both models the question of uniqueness of global weak solutions remains open.In the physically relevant dimension d=2$d=2$ and with the physically relevant space time white noise driving the equation, the direct fixed point argument for mild solutions fails, as there is not sufficient regularity for the stochastic forcing. The situation is the simplest case where the method of regularity structures introduced by Martin Hairer can be applied, although we follow here a significantly simpler approach to highlight the key problems. Using spectral Galerkin method or any other type of regularization of the noise, one can give a rigorous meaning to the stochastic PDE and show existence and uniqueness of local solutions in that setting. Moreover, several types of regularization seem to yield all the same solution.We finally comment briefly on possible blow up phenomena and show with a simple argument that many complex valued solutions actually do blow up in finite time. This shows that energy estimates alone are not enough to verify global uniqueness of solutions. Results in this direction are known already for the 3D Navier Stokes by Li and Sinai, treating complex valued solutions, and more recently by Tao by constructing an equation of Navier Stokes type with blow up.
Design of Low Floor Quasi Cyclic IRA Codes and Their FPGA Decoders
Year: 2007

Low density parity check (LDPC) codes have been intensively studied in past decade for their capacity approaching performance. LDPC code implementation complexity and the error rate floor are still two significant unsolved issues which prevent their application in some important communication systems. In this dissertation, we make efforts toward solving these two problems by introducing the design of a class of LDPC codes called structured irregular repeat accumulate (SIRA) codes. These S IRA codes combine several advantages of other types of LDPC codes, including low encoder and decoder complexities, flexibility in design, and good performance on different channels. It is also demonstrated in this dissertation that the S IRA codes are suitable for rate compatible code family design and a multi rate code family has been designed which may be implemented with a single encoder/decoder. The study of the error floor problem of LDPC codes is very difficult because simulating LDPC codes on a computer at very low error rates takes an unacceptably long time. To circumvent this difficulty, we implemented a universal quasi cyclic LDPC decoder on a field programmable gate array (FPGA) platform. This hardware platform accelerates the simulations by more than 100 times as compared to software simulations. We implemented two types of decoders with partially parallel architectures on the FPGA: a circulant based decoder and a protograph based decoder. By focusing on the protograph based decoder, different soft iterative decoding algorithms were implemented. It provides us with a platform for quickly evaluating and analyzing different quasi cyclic LDPC codes, including the S IRA
A Domain Knowledge Enhanced LSTM CRF Model for Disease Named Entity Recognition.
Venue: AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Year: 2019

Disease named entity recognition (NER) is a critical task for most biomedical natural language processing (NLP) applications. For example, extracting diseases from clinical trial text can be helpful for patient profiling and other downstream applications such as matching clinical trials to eligible patients. Similarly, disease annotation in biomedical articles can help information search engines to accurately index them such that clinicians can easily find relevant articles to enhance their knowledge. In this paper, we propose a domain knowledge enhanced long short term memory network conditional random field (LSTM CRF) model for disease named entity recognition, which also augments a character level convolutional neural network (CNN) and a character level LSTM network for input embedding. Experimental results on a scientific article dataset show the effectiveness of our proposed models compared to state of the art methods in disease recognition.
Findings of the WMT 2020 Shared Task on Automatic Post Editing
Venue: WMT
Year: 2020

We present the results of the 6th round of the WMT task on MT Automatic Post Editing. The task consists in automatically correcting the output of a "black box" machine translation system by learning from existing human corrections of different sentences. This year, the challenge consisted of fixing the errors present in English Wikipedia pages translated into German and Chinese by state ofthe art, not domain adapted neural MT (NMT) systems unknown to participants. Six teams participated in the English German task, submitting a total of 11 runs. Two teams participated in the English Chinese task submitting 2 runs each. Due to i) the different source/domain of data compared to the past (Wikipedia vs Information Technology) ii) the different quality of the initial translations to be corrected and iii) the introduction of a new language pair (English Chinese) this year's results are not directly comparable with last year's round. However, on both language directions, participants' submissions show considerable improvements over the baseline results. On English German, the top ranked system improves over the baseline by 11.35 TER and +16.68 BLEU points, while on EnglishChinese the improvements are respectively up to 12.13 TER and +14.57 BLEU points. Overall, coherent gains are also highlighted by the outcomes of human evaluation, which confirms the effectiveness of APE to improve MT quality, especially in the new generic domain selected for this year's round.
Document Level Neural Machine Translation Using BERT as Context Encoder
Venue: AACL
Year: 2020

Large scale pre trained representations such as BERT have been widely used in many natural language understanding tasks. The methods of incorporating BERT into document level machine translation are still being explored. BERT is able to understand sentence relationships since BERT is pre trained using the next sentence prediction task. In our work, we leverage this property to improve document level machine translation. In our proposed model, BERT performs as a context encoder to achieve document level contextual information, which is then integrated into both the encoder and decoder. Experiment results show that our proposed method can significantly outperform strong document level machine translation baselines on BLEU score. Moreover, the ablation study shows our method can capture document level context information to boost translation performance.
Market and Labour Control in Digital Capitalism 1
Year: 2016

Theorists of post capitalism have recently argued for a more or less inevitable end to capitalism. They assume that private accumulation is systematically blocked by the inability of capitalist corporations to create revenues by setting prices as they lose control over the reproduction of their commodities and that in this process, capitalist labour will eventually disappear. Drawing on a case study of Amazon and thoughts on the policies of other leading digital corporations, we challenge these assumptions. Key corporate players of digitization are trying to become powerful monopolies and have partly succeeded in doing so, using the network effects and scaling opportunities of digital goods and building socio technical ecosystems. These strategies have led to the development of in part isomorphic structures, hence creating a situation of oligopolistic market competition. We draw on basic assumptions of monopoly capital theory to argue that in this situation labour process rationalization becomes key to the corporation's competitive strategies. We see the expansion of digital control and the organizational structures applied by key corporate players of the digital economy as evidence for the expansion of capitalist labour, not its reduction.