nils-herrmann/clean_scirepeval huggingface dataset:
Computer Vision ECCV 2016
Venue: Lecture Notes in Computer Science

We address the problem of robust two view correspondence estimation within the context of dynamic scene modeling. To this end, we investigate the use of local spatio temporal assumptions to both identify and refine dense low level data associations in the absence of prior dynamic content models. By developing a strictly data driven approach to correspondence search, based on bottom up local 3D motion cues of local rigidity and non local coherence, we are able to robustly address the higher order problems of video synchronization and dynamic surface modeling. Our findings suggest an important relationship between these two tasks, in that maximizing spatial coherence of surface points serves as a direct metric for the temporal alignment of local image sequences. The obtained results for these two problems on multiple publicly available dynamic reconstruction datasets illustrate both the effectiveness and generality of our proposed approach.
Computer Vision ECCV 2016 Workshops
Venue: Lecture Notes in Computer Science

This paper presents a robust multi class multi object tracking (MCMOT) formulated by a Bayesian filtering framework. Multiobject tracking for unlimited object classes is conducted by combining detection responses and changing point detection (CPD) algorithm. The CPD model is used to observe abrupt or abnormal changes due to a drift and an occlusion based spatiotemporal characteristics of track states. The ensemble of convolutional neural network (CNN) based object detector and Lucas Kanede Tracker (KLT) based motion detector is employed to compute the likelihoods of foreground regions as the detection responses of different object classes. Extensive experiments are performed using lately introduced challenging benchmark videos; ImageNet VID and MOT benchmark dataset. The comparison to state of the art video tracking techniques shows very encouraging results.
Computer Vision ECCV 2016
Venue: Lecture Notes in Computer Science

Symmetry in visual data represents repeated patterns or shapes that is easily found in natural and human made objects. Symmetry pattern on an object works as a salient visual feature attracting human attention and letting the object to be easily recognized. Most existing symmetry detection methods are based on sparsely detected local features describing the appearance of their neighborhood, which have difficulty in capturing object structure mostly supported by edges and contours. In this work, we propose a new reflection symmetry detection method extracting robust 4 dimensional Appearance of Structure descriptors based on a set of outstanding neighbourhood edge segments in multiple scales. Our experimental evaluations on multiple public symmetry detection datasets show promising reflection symmetry detection results on challenging real world and synthetic images.
Superposition of Cosmic Radiation Waves as the Source Of The Hessdalen Light Phenomena

The light phenomena of the Norwegian valley of Hessdalen are one of several unexplained light phenomena around the world. Scientists have investigated it since 1981 without a proven theory of what causes the phenomena. In this article, the superposition of cosmic radiation due to local distortion of Earths magnetic field is investigated as the source of the phenomena. Hessdalen has special characteristics in regard of its high concentration of minerals, valley formation and river containing minerals running through it and this could cause the cosmic radiation entering the valley from above to superimpose into the visible spectrum. This new theory is tested against the observed and unexplained special properties of the light phenomena, showing some promising results. Superposition of cosmic EM waves, or collisions of cosmic ray particles, should be further evaluated as the source.
Differences in Connection Strength between Mental Symptoms Might Be Explained by Differences in Variance: Reanalysis of Network Data Did Not Confirm Staging
Venue: PloS one

The network approach to psychopathology conceives mental disorders as sets of symptoms causally impacting on each other. The strengths of the connections between symptoms are key elements in the description of those symptom networks. Typically, the connections are analysed as linear associations (i.e. correlations or regression coefficients) However, there is insufficient awareness of the fact that differences in variance may account for differences in connection strength. Differences in variance frequently occur when subgroups are based on skewed data. An illustrative example is a study published in PLoS One (2013;8(3):e59559) that aimed to test the hypothesis that the development of psychopathology through "staging" was characterized by increasing connection strength between mental states. Three mental states (negative affect, positive affect, and paranoia) were studied in severity subgroups of a general population sample. The connection strength was found to increase with increasing severity in six of nine models. However, the method used (linear mixed modelling) is not suitable for skewed data.  We reanalysed the data using inverse Gaussian generalized linear mixed modelling, a method suited for positively skewed data (such as symptoms in the general population)  The distribution of positive affect was normal, but the distributions of negative affect and paranoia were heavily skewed. The variance of the skewed variables increased with increasing severity. Reanalysis of the data did not confirm increasing connection strength, except for one of nine models.  Reanalysis of the data did not provide convincing evidence in support of staging as characterized by increasing connection strength between mental states. Network researchers should be aware that differences in connection strength between symptoms may be caused by differences in variances, in which case they should not be interpreted as differences in impact of one symptom on another symptom.
Adaptive Subspace Based Inverse Projections via Division Into Multiple Sub Problems for Missing Image Data Restoration
Venue: IEEE Transactions on Image Processing

This paper presents adaptive subspace based inverse projections via division into multiple sub problems (ASIP DIMSs) for missing image data restoration. In the proposed method, a target problem for estimating missing image data is divided into multiple sub problems, and each sub problem is iteratively solved with the constraints of other known image data. By projection into a subspace model of image patches, the solution of each sub problem is calculated, where we call this procedure "subspace based inverse projection" for simplicity. The proposed method can use higher dimensional subspaces for finding unique solutions in each sub problem, and successful restoration becomes feasible, since a high level of image representation performance can be preserved. This is the biggest contribution of this paper. Furthermore, the proposed method generates several subspaces from known training examples and enables derivation of a new criterion in the above framework to adaptively select the optimal subspace for each target patch. In this way, the proposed method realizes missing image data restoration using ASIP DIMS. Since our method can estimate any kind of missing image data, its potential in two image restoration tasks, image inpainting and super resolution, based on several methods for multivariate analysis is also shown in this paper.
Generation of Recombinant Monoclonal Antibodies from Immunised Mice and Rabbits via Flow Cytometry and Sorting of Antigen Specific IgG+ Memory B Cells
Venue: PloS one

Single B cell screening strategies, which avoid both hybridoma fusion and combinatorial display, have emerged as important technologies for efficiently sampling the natural antibody repertoire of immunized animals and humans. Having access to a range of methods to interrogate different B cell subsets provides an attractive option to ensure large and diverse panels of high quality antibody are produced. The generation of multiple antibodies and having the ability to find rare B cell clones producing IgG with unique and desirable characteristics facilitates the identification of fit for purpose molecules that can be developed into therapeutic agents or research reagents. Here, we describe a multi parameter flow cytometry single cell sorting technique for the generation of antigen specific recombinant monoclonal antibodies from single IgG+ memory B cells. Both mouse splenocytes and rabbit PBMC from immunised animals were used as a source of B cells. Reagents staining both B cells and other unwanted cell types enabled efficient identification of class switched IgG+ memory B cells. Concurrent staining with antigen labelled separately with two spectrally distinct fluorophores enabled antigen specific B cells to be identified, i.e. those which bind to both antigen conjugates (double positive) These cells were then typically sorted at one cell per well using FACS directly into a 96 well plate containing reverse transcriptase reaction mix. Following production of cDNA, PCR was performed to amplify cognate heavy and light chain variable region genes and generate transcriptionally active PCR (TAP) fragments. These linear expression cassettes were then used directly in a mammalian cell transfection to generate recombinant antibody for further testing. We were able to successfully generate antigen specific recombinant antibodies from both the rabbit and mouse IgG+ memory B cell subset within one week. This included the generation of an anti TNFR2 blocking antibody from mice with an affinity of 90 pM.
HybriFree: a robust and rapid method for the development of monoclonal antibodies from different host species
Venue: BMC Biotechnology

BackgroundThe production of recombinant monoclonal antibodies in mammalian cell culture is of high priority in research and medical fields. A critical step in this process is the isolation of the antigen binding domain sequences of antibodies possessing the desired properties. Many different techniques have been described to achieve this goal, but all have shortcomings; most techniques have problems with robustness, are time consuming and costly, or have complications in the transfer from isolation to production phase. Here, we report a novel HybriFree technology for the development of monoclonal antibodies from different species that is robust, rapid, inexpensive and flexible and can be used for the subsequent production of antibodies in mammalian cell factories.ResultsHybriFree technology is illustrated herein via detailed examples of isolating mouse, rabbit and chicken monoclonal antibody sequences from immunized animals. Starting from crude spleen samples, antigen capturing of specific B cells is performed initially. cDNA of antibody variable domains is amplified from the captured cells and used a source material for simple and rapid restriction/ligation free cloning of expression vector library in order to produce scFv Fc or intact IgG antibodies. The vectors can be directly used for screening purposes as well as for the subsequent production of the developed monoclonal antibodies in mammalian cell culture. The antibodies isolated by the method have been shown to be functional in different immunoassays, including ELISA, immunofluorescence and Western blot. In addition, we demonstrate that by using a modified method including a negative selection step, we can isolate specific antibodies targeting the desired epitope and eliminate antibodies directed to undesired off targets.ConclusionsHybriFree can be used for the reliable development of monoclonal antibodies and their subsequent production in mammalian cells. This simple protocol requires neither the culturing of B cells nor single cell manipulations, and only standard molecular biology laboratory equipment is needed. In principle, the method is applicable to any species for which antibody cDNA sequence information is available.
Quels outils pour la reconnaissance des potentialites aquiferes des zones cotieres sous forte pression anthropique Vers une mise en exploitationdurable des ressources du plateau carbonate de Bonifacio

Les eaux souterraines des aquiferes cotiers constituent une ressource de premiere importance pour les besoins d'approvisionnement en eau potable, l'agriculture, l'industrie ou le tourisme. Toutefois, dans la mesure ou elles sont en continuite hydraulique avec la mer, leur exploitation requiert des precautions particulieres afin de ne pas perturber l'equilibre fragile entre ces deux masses d'eau. Les reservoirs cotiers d'eau souterraine sont donc tres vulnerables face aux activites humaines tant du point de vue de la qualite que de la quantite.L'etude du fonctionnement de ces hydrosystemes et la mise en place d'outils de gestion adaptes apparaissent donc essentiels pour garantir la dimension renouvelable des ressources en eau souterraine,enjeu fondamental pour un developpement socio economique et territorial durable.Cette problematique est particulierement bien illustree en Corse, principalement dans l'extreme sud. Bonifacio est en effet l'une des villes les plus visitees de l'ile.
Transatlantic Environment and Energy Politics: Comparative and International Perspectives

Expanding Transatlantic relations: implications for environment and energy politics, Miranda A. Schreurs, Henrik Selin and Stacy D. VanDeveer Part I Governing Within and Beyond the State: Comparative Environmental Governance and Its Implications for Policy Development: governance for sustainable development: the United States and the European Union compared, Elizabeth Bomberg Intergovernmental management of environmental policy in the United States and the EU, Sonja WAlti. Part II Governing Risk: Chemical Regulations, Asbestos Bans, Product Standards, and Genetically Modified Organisms: Transatlantic politics of chemicals management, Henrik Selin Oceans apart? Policy reversals, transatlantic politics, and the EU asbestos ban, Marcus Carson Targeting consumer product environmental impacts across the Atlantic, Alastair Iles Transatlantic food fights in an era of globalization: when menus, rules and choices collide, Patricia M. Keilbach Implications of the transatlantic biotech dispute for developing countries, Thomas Bernauer and Philipp Aerni. Part III Governing Carbon: Renewable Energy and Climate Change: Promotion of renewable energy in the United States and the European Union: policy progress and prospects, Ian H. Rowlands Conflict and cooperation in transatlantic climate politics: different stories at different levels, Miranda A. Schreurs, Henrik Selin and Stacy D. VanDeveer. Part IV Governing Global Markets: Environmental Standards and Certification Approaches: Export promotion, trade, and the environment: negotiating environmental standards for export credit agencies across the Atlantic, Marcus Schaper The emergence of non state environmental governance in European and North American forest sectors, Benjamin Cashore, Graeme Auld, Deanna Newsom and Elizabeth Egan Mad cows and ailing hens: the transatlantic relationship and livestock diseases, Kate O'Neill.  Transatlantic environmental relations: implications for the global community, Miranda A. Schreurs, Henrik Selin and Stacy D. VanDeveer References Index.
Occupational Stress and Work Life Balance: A Study of Female Faculties of Central Universities in Delhi, India

This study specifically investigates the degree of and the relationship between occupational stress and work life balance amid female faculties in central universities of Delhi, India. The study is based on primary data collected from 120 female faculties from two central universities in Delhi, India. The data are analyzed using various statistical tools and methods. The results of the study reveal that there exists a moderate level of occupational stress and moderate level of work life balance amid female faculties in central universities of Delhi, India. The results of the study further reveal that there is a strong negative relationship between Occupational Stress and Work Life Balance. This study can prove to be of great help to the management of educational set up to increase the level of work life balance and decrease the level of occupational stress among their faculty members; so as to create a work environment that everyone feels proud to be the part of. This, in turn, will increase the organizational efficiency and effectiveness in academic set ups.
Do You Look Like Me? How Bias Affects Affirmative Action in Hiring

In today's multicultural work environment, a better understanding of how bias affects hiring and promotion decisions, and what can be done to reduce its effects, is a major concern for hospitality industry human resource professionals and academics. Past research on racial bias and social identity theory shows contradictory effects; we propose that by examining additional boundary conditions we can clarify the "same race" bias effect in hiring and candidate evaluation. We propose that perceptions of competence regarding job applicants provided to HR managers, and their attitudes toward affirmative action programs, can help reduce bias in the hiring process. Using an experimental design, we investigated these effects in two samples involving business college students and practicing HR managers at a full service national hotel chain. We found that job candidate competence and raters' attitudes toward affirmative action reduced the same race effect, with some important differences between the two samples. Affirmative action attitudes enhance same race bias among African American evaluators in both samples, the study shows, while information on candidate competence has the opposite effect in the student sample.
"You don't have anorexia, you just want to look like a celebrity" perceived stigma in individuals with anorexia nervosa*
Venue: Journal of mental health

Minimal research has been conducted on how individuals with Anorexia nervosa (AN) undergoing treatment perceive public stigma.  Explore how affected individuals with AN believe the general public perceives AN.  Using thematic analysis, 19 participants with AN were interviewed at the beginning of treatment.  Three interrelated themes were ascertained: (1) AN is not an illness; (2) eating disorder behaviours are volitional and the public endorses a socio cultural explanation for the disorder; 3) affected individuals delay disclosing their illness and experience shame engaging in intensive treatment for AN as a result of their perception of stigma.  Individuals with AN viewed the public as trivializing AN by viewing behaviours as within their control and by attributing eating disorder behaviours solely to socio cultural factors. Participants believed that the public minimizes the challenges associated with treatment. Findings suggest that clinical interventions targeting stigma are required to counteract perceptions held by service users regarding how others view their illness. "Mental health literacy" interventions are needed for health professionals working with high risk groups likely to avoid seeking help due to fears of stigmatization.
Memristor circuits and systems for future computing and bio inspired information processing
Venue: 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS)

Memristors can be used in mimicking synaptic plasticity of biological neuronal systems. In addition, memristor crossbars can be realized in 3 dimensional architecture like human brain. This possibility of 3 dimensional integration is crucial in implementing the full scale electronic neuron synapse system in future. One more thing to note here is that memristor based neuromorphic systems can be more energy efficient than the conventional Von Neumann ones in some applications such as bio inspired pattern processing. This is because they are more suitable to brain like parallel processing. Based on these advantages of memristor based neuromorphic systems, this paper reviews the memristor logics, where the computation and memory can be merged together. Then, we introduce neuromorphic memristor crossbars which can mimic the brain's pattern recognition of speech and image. The simulation results of neuromorphic crossbars strongly highlight the future possibility of memristor circuits in brain mimicking pattern processing. In Cellular Nanoscale Network (CNN) memristors can be used in analog multiplication that is essential to perform CNN pixel calculation with low power consumption and high area density.
Sequential Neural Models with Stochastic Layers
Venue: NIPS

How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks? This paper introduces stochastic recurrent neural networks which glue a deterministic recurrent neural network and a state space model together to form a stochastic and sequential neural generative model. The clear separation of deterministic and stochastic layers allows a structured variational inference network to track the factorization of the model's posterior distribution. By retaining both the nonlinear recursive structure of a recurrent neural network and averaging over the uncertainty in a latent path, like a state space model, we improve the state of the art results on the Blizzard and TIMIT speech modeling data sets by a large margin, while achieving comparable performances to competing methods on polyphonic music modeling.
Designing smoothing functions for improved worst case competitive ratio in online optimization
Venue: NIPS

Online optimization covers problems such as online resource allocation, online bipartite matching, adwords (a central problem in e commerce and advertising) and adwords with separable concave returns. We analyze the worst case competitive ratio of two primal dual algorithms for a class of online convex (conic) optimization problems that contains the previous examples as special cases defined on the positive orthant. We derive a sufficient condition on the objective function that guarantees a constant worst case competitive ratio (greater than or equal to \frac{1}{2} for monotone objective functions. We provide new examples of online problems on the positive orthant and the positive semidefinite cone that satisfy the sufficient condition. We show how smoothing can improve the competitive ratio of these algorithms, and in particular for separable functions, we show that the optimal smoothing can be derived by solving a convex optimization problem. This result allows us to directly optimize the competitive ratio bound over a class of smoothing functions, and hence design effective smoothing customized for a given cost function.
On the best possible competitive ratio for the multislope ski rental problem
Venue: J. Comb. Optim.

The multislope ski rental problem is an extension of the classical ski rental problem, where the player has several lease options besides the pure rent and buy options. In this problem the hardness of an instance, which is the setting of options, significantly affects the player's performance. There is an algorithm that for a given instance, computes the best possible strategy. However, the output is given as numerical values and therefore the relational nature between an instance and the best possible performance for it has not been known. In this paper we prove that even for the easiest instance, a competitive ratio smaller than $e/(e 1) \approx 1.58$$e/(e 1)1.58 cannot be achieved. More precisely, a tight lower bound on the best possible performance is obtained in a closed form parametrized by the number of options. Furthermore, we establish a matching upper and lower bound on the competitive ratio each for the 3 option and 4 option problems.