nils-herrmann/clean_scirepeval huggingface dataset:
Sparse Bayesian Content Aware Collaborative Filtering for Implicit Feedback
Venue: IJCAI

The popularity of social media creates a large amount of user generated content, playing an important role in addressing cold start problems in recommendation. Although much effort has been devoted to incorporating this information into recommendation, past work mainly targets explicit feedback. There is still no general framework tailored to implicit feedback, such as views, listens, or visits. To this end, we propose a sparse Bayesian content aware collaborative filtering framework especially for implicit feedback, and develop a scalable optimization algorithm to jointly learn latent factors and hyperparameters. Due to the adaptive update of hyperparameters, automatic feature selection is naturally embedded in this framework. Convincing experimental results on three different implicit feedback datasets indicate the superiority of the proposed algorithm to state of the art content aware recommendation methods.
Pairwise probabilistic matrix factorization for implicit feedback collaborative filtering
Venue: Neurocomputing

Implicit feedback collaborative filtering has attracted a lot of attention in collaborative filtering, which is called one class collaborative filtering (OCCF) However, the low recommendation accuracy and the high cost of previous methods impede its generalization in real scenarios. In this paper, we develop a new model named pairwise probabilistic matrix factorization (PPMF) by using the advantages of RankRLS. PPMF model takes RankRLS integrated with PMF (probabilistic matrix factorization) to learn the relative preference for items. Different from previous works, PPMF minimizes the average number of inversions in ranking rather than maximize the gaps of the binary predicted values for OCCF problem. Meanwhile, we propose to optimize the PPMF model by the pointwise stochastic gradient descent algorithm based on bootstrap sampling, which is more effective for parameter learning than the original optimization method used in previous works. Experiments on two datasets show that PPMF model achieves satisfactory performance and outperforms the state of the art implicit feedback collaborative ranking models by using different evaluation metrics.
Fast Collaborative Filtering from Implicit Feedback with Provable Guarantees
Venue: ACML

Building recommendation algorithms is one of the most challenging tasks in Machine Learning. Although most of the recommendation systems are built on explicit feedback available from the users in terms of rating or text, a majority of the applications do not receive such feedback. Here we consider the recommendation task where the only available data is the records of user item interaction over web applications over time, in terms of subscription or purchase of items; this is known as implicit feedback recommendation. There is usually a massive amount of such user item interaction available for any web applications. Algorithms like PLSI or Matrix Factorization runs several iterations through the dataset, and may prove very expensive for large datasets. Here we propose a recommendation algorithm based on Method of Moment, which involves factorization of second and third order moments of the dataset. Our algorithm can be proven to be globally convergent using PAC learning theory. Further, we show how to extract the parameters using only three passes through the entire dataset. This results in a highly scalable algorithm that scales up to million of users even on a machine with a single core processor and 8 GB RAM and produces competitive performance in comparison with existing algorithms.
Grapefruit photonic crystal fiber sensor for gas sensing application

Use of long period gratings (LPGs) formed in grapefruit photonic crystal fiber (PCF) with thin film overlay coated on the inner surface of air holes for gas sensing is demonstrated. The finite element method was used to numerically simulate the grapefruit PCF LPG modal coupling characteristics and resonance spectral response with respect to the refractive index of thin film inside the holey region. A gas analyte induced index variation of the thin film immobilized on the inner surface of the holey region of the fiber can be observed by a shift of the resonance wavelength. As an example, we demonstrate a 2,4 dinitrotoluene (DNT) sensor using grapefruit PCF LPGs. The sensor exhibits a wavelength blue shift of ~820 pm as a result of exposure to DNT vapor with a vapor pressure of 411 ppbv at 25degC, and a sensitivity of 2 pm ppbv 1 can be achieved.
Thyroid Function Test Derangements and Mortality in Dialysis Patients: A Systematic Review and Meta analysis.
Venue: American journal of kidney diseases the official journal of the National Kidney Foundation

We evaluated current evidence associating thyroid function test result derangements with risk for mortality in patients with chronic kidney failure treated by long term dialysis.  Systematic review and meta analysis of cohort studies.  Dialysis patients.  We searched PubMed, Web of Science, Science Citation Index, Cochrane Library, and Embase databases from inception through December 2015. PREDICTORS Hypothyroidism (thyrotropin level greater than reference range) and low triiodothyronine (T3) and thyroxine (T4) levels.  All cause and cardiovascular mortality.  involving 14,766 participants (4,450 deaths) were identified. Of those, 6 studies provided data for cardiovascular mortality (2,772 participants with 327 cardiovascular deaths) Overall, confidence in the available evidence was moderate. Pooled adjusted HRs for all cause mortality associated with hypothyroidism, low T3 level, and low T4 level were 1.24 (95% CI, 1.14 1.34) 1.67 (95% CI, 1.23 2.27) and 2.40 (95% CI, 1.47 3.93) respectively. Pooled adjusted HRs for cardiovascular mortality associated with low T3 and T4 levels were 1.84 (95% CI, 1.24 2.74) and 3.06 (95% CI, 1.29 7.24) respectively.  Fewer studies reporting on T4 and thyrotropin outcomes.  In patients treated with long term dialysis, (cardiovascular) mortality is consistently higher in the presence of thyroid function test result derangements.
A new saccadic indicator of peripheral vestibular function based on the video head impulse test
Venue: Neurology

While compensatory saccades indicate vestibular loss in the conventional head impulse test paradigm (HIMP) in which the participant fixates an earth fixed target, we investigated a complementary suppression head impulse paradigm (SHIMP) in which the participant is fixating a head fixed target to elicit anticompensatory saccades as a sign of vestibular function.  HIMP and SHIMP eye movement responses were measured with the horizontal video head impulse test in patients with unilateral vestibular loss, patients with bilateral vestibular loss, and in healthy controls.  Vestibulo ocular reflex gains showed close correlation (R2 0.97) with slightly lower SHIMP than HIMP gains (mean gain difference 0.06 0.05 SD, p 0.001) However, the 2 paradigms produced complementary catch up saccade patterns: HIMP elicited compensatory saccades in patients but rarely in controls, whereas SHIMP elicited large anticompensatory saccades in controls, but smaller or no saccades in bilateral vestibular loss. Unilateral vestibular loss produced covert saccades in HIMP, but later and smaller saccades in SHIMP toward the affected side. Cumulative HIMP and SHIMP saccade amplitude differentiated patients from controls with high sensitivity and specificity.  While compensatory saccades indicate vestibular loss in conventional HIMP, anticompensatory saccades in SHIMP using a head fixed target indicate vestibular function. SHIMP saccades usually appear later than HIMP saccades, therefore being more salient to the naked eye and facilitating vestibulo ocular reflex gain measurements. The new paradigm is intuitive and easy to explain to patients, and the SHIMP results complement those from the standard video head impulse test.  This case control study provides Class III evidence that SHIMP accurately identifies patients with unilateral or bilateral vestibulopathies.
Phagonaute: A web based interface for phage synteny browsing and protein function prediction.
Venue: Virology

Distant homology search tools are of great help to predict viral protein functions. However, due to the lack of profile databases dedicated to viruses, they can lack sensitivity. We constructed HMM profiles for more than 80,000 proteins from both phages and archaeal viruses, and performed all pairwise comparisons with HHsearch program. The whole resulting database can be explored through a user friendly "Phagonaute" interface to help predict functions. Results are displayed together with their genetic context, to strengthen inferences based on remote homology. Beyond function prediction, this tool permits detections of co occurrences, often indicative of proteins completing a task together, and observation of conserved patterns across large evolutionary distances. As a test, Herpes simplex virus I was added to Phagonaute, and 25% of its proteome matched to bacterial or archaeal viral protein counterparts. Phagonaute should therefore help virologists in their quest for protein functions and evolutionary relationships.
Sequencing and cloning of antigen specific antibodies from mouse memory B cells
Venue: Nature Protocols

Methods to identify genes encoding immunoglobulin heavy and light chains from single B lymphocytes vary in efficiency, error rate and practicability. Here we describe a protocol to sequence and clone the variable antibody region of single antigen specific mouse memory B cells for antibody production. After purification, antigen specific mouse memory B cells are first single cell sorted by fluorescence activated cell sorting (FACS) and V(D)J transcripts are amplified by RT PCR. Fragments are then combined with linearized expression vectors, assembled in vitro as part of a sequence and ligation independent cloning (SLIC) reaction and then transformed into Escherichia coli. Purified vectors can then be used to produce monoclonal antibodies in HEK293E suspension cells. This protocol improves the amplification efficiency of antibody variable genes and accelerates the cloning workflow. Antibody sequences will be available in 3 4 d, and microgram to milligram amounts of antibodies are produced within 14 d. The new protocol should be useful for addressing fundamental questions about antigen specific memory B cell responses, as well as for characterizing antigen specific antibodies.
Impact des aides a la decision informatisee sur la qualite des soins

Le recours aux aides a la decision (ADI) dans les systemes de prescription optimise la securisation electronique des prescriptions medicamenteuses et limite la iatrogenie medicamenteuse.Cette these de doctorat portant sur le theme des ADI dans la generation des ordres medicaux, s'est focalisee sur deux axes principaux l'etat des lieux des ADI implementees (elaboration d'une classification des ADI, recensement dans les etablissements de sante suisses et exploration de leur valeur predictive positive) et l'identification de cibles preferentielles (contextualisation pharmacologique, complexite de dematerialisation des interventions d'un pharmacien clinicien, identification de profils de patients a risque d'intervention pharmaceutique) De maniere generale, les etudes conduites dans cette these ont permis de demontrer que le developpement des ADI, qui commence tout juste a se deployer, necessite encore de nombreux efforts, mais ces travaux devraient contribuer au developpement futur de nouveaux types d'ADI et de nouvelles manieres de les utiliser dans le systeme d'information.
Comparing molecules and solids across structural and alchemical space.
Venue: Physical chemistry chemical physics PCCP

Evaluating the (dis)similarity of crystalline, disordered and molecular compounds is a critical step in the development of algorithms to navigate automatically the configuration space of complex materials. For instance, a structural similarity metric is crucial for classifying structures, searching chemical space for better compounds and materials, and driving the next generation of machine learning techniques for predicting the stability and properties of molecules and materials. In the last few years several strategies have been designed to compare atomic coordination environments. In particular, the smooth overlap of atomic positions (SOAPs) has emerged as an elegant framework to obtain translation, rotation and permutation invariant descriptors of groups of atoms, underlying the development of various classes of machine learned inter atomic potentials. Here we discuss how one can combine such local descriptors using a regularized entropy match (REMatch) approach to describe the similarity of both whole molecular and bulk periodic structures, introducing powerful metrics that enable the navigation of alchemical and structural complexities within a unified framework. Furthermore, using this kernel and a ridge regression method we can predict atomization energies for a database of small organic molecules with a mean absolute error below 1 kcal mol( 1) reaching an important milestone in the application of machine learning techniques for the evaluation of molecular properties.
Recognition of persisting emotional valence from EEG using convolutional neural networks
Venue: 2016 IEEE 9th International Workshop on Computational Intelligence and Applications (IWCIA)

Recently there has been considerable interest in EEG based emotion recognition (EEG ER) which is one of the utilization of BCI. However, it is not easy to realize the EEG ER system which can recognize emotions with high accuracy because of the tendency for important information in EEG signals to be concealed by noises. Deep learning is the golden tool to grasp the features concealed in EEG data and enable highly accurate EEG ER because deep neural networks (DNNs) may have higher recognition capability than humans' The publicly available dataset named DEAP, which is for emotion analysis using EEG, was used in the experiment. The CNN and a conventional model used for comparison are evaluated by the tests according to 11 fold cross validation scheme. EEG raw data obtained from 16 electrodes without general preprocesses were used as input data. The models classify and recognize EEG signals according to the emotional states "positive" or "negative" which were caused by watching music videos. The results show that the more training data are, the much higher the accuracies of CNNs are (by over 20% It also suggests that the increased training data need not to belong to the same person's EEG data as the test data so as to get the CNN recognizing emotions accurately. The results indicate that there are not only the considerable amount of the interpersonal difference but also commonality of EEG properties.
Search for $2\beta$ decay of {106}$Cd with an enriched {106}$CdWO$_4$ crystal scintillator in coincidence with four HPGe detectors

A radiopure cadmium tungstate crystal scintillator, enriched in Cd106 to 66% with mass of 216 g (Cd106WO4) was used to search for double b decay processes in Cd106 in coincidence with four ultra low background high purity germanium detectors in a single cryostat. Improved limits on the double b processes in Cd106 have been set on the level of 1020 1021 yr after 13 085 h of data taking. In particular, the half life limit on the two neutrino electron capture with positron emission, T2neb+1/2>=1.1x1021 yr, has reached the region of theoretical predictions. With this half life limit the effective nuclear matrix element for the 2neb+ decay is bounded as M2neb+eff<=1.1. The resonant neutrinoless double electron captures to the 2718 2741 and 2748 keV excited states of Pd106 are restricted at the level of T1/2>(8.5x1020 1.4x1021) yr.
Relative Rates of Surface and Volume Synthesis Set Bacterial Cell Size
Venue: Cell

Many studies have focused on the mechanisms underlying length and width determination in rod shaped bacteria. Here, we focus instead on cell surface area to volume ratio (SA/V) and demonstrate that SA/V homeostasis underlies size determination. We propose a model whereby the instantaneous rates of surface and volume synthesis both scale with volume. This model predicts that these relative rates dictate SA/V and that cells approach a new steady state SA/V exponentially, with a decay constant equal to the volume growth rate. To test this, we exposed diverse bacterial species to sublethal concentrations of a cell wall biosynthesis inhibitor and observed dose dependent decreases in SA/V. Furthermore, this decrease was exponential and had the expected decay constant. The model also quantitatively describes SA/V alterations induced by other chemical, nutritional, and genetic perturbations. We additionally present evidence for a surface material accumulation threshold underlying division, sensitizing cell length to changes in SA/V requirements.
Cell size and growth regulation in the Arabidopsis thaliana apical stem cell niche
Venue: Proceedings of the National Academy of Sciences

How does a cell decide when to divide or initiate DNA replication? How does it regulate its own growth? These fundamental questions are not well understood in most organisms; this lack of understanding is particularly true for multicellular eukaryotes. Following classical studies in yeast, we have quantified the key aspects of cell growth and division dynamics in the Arabidopsis apical stem cell niche. Our results disprove various theories for plant stem cell size/cell cycle regulation, such as that cell cycle progression is triggered when a prefixed critical size is attained, and constitute the necessary first step in the development of integrative mechanistic theories for the coordinated regulation of cell cycle progression, cell growth, and cell size in plants. Cell size and growth kinetics are fundamental cellular properties with important physiological implications. Classical studies on yeast, and recently on bacteria, have identified rules for cell size regulation in single cells, but in the more complex environment of multicellular tissues, data have been lacking. In this study, to characterize cell size and growth regulation in a multicellular context, we developed a 4D imaging pipeline and applied it to track and quantify epidermal cells over 3 4 d in Arabidopsis thaliana shoot apical meristems. We found that a cell size checkpoint is not the trigger for G2/M or cytokinesis, refuting the unexamined assumption that meristematic cells trigger cell cycle phases upon reaching a critical size. Our data also rule out models in which cells undergo G2/M at a fixed time after birth, or by adding a critical size increment between G2/M transitions. Rather, cell size regulation was intermediate between the critical size and critical increment paradigms, meaning that cell size fluctuations decay by ~75% in one generation compared with 100% (critical size) and 50% (critical increment) Notably, this behavior was independent of local cell cell contact topologies and of position within the tissue. Cells grew exponentially throughout the first >80% of the cell cycle, but following an asymmetrical division, the small daughter grew at a faster exponential rate than the large daughter, an observation that potentially challenges present models of growth regulation. These growth and division behaviors place strong constraints on quantitative mechanistic descriptions of the cell cycle and growth control.
Making Neural Networks Robust to Label Noise: a Loss Correction Approach
Venue: ArXiv

We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class dependent label noise. Our method only performs a correction on the loss function, and is agnostic to both the application domain and network architecture. We propose two procedures for loss correction: they simply amount to at most a matrix inversion and multiplication, provided that we know the probability of each class being corrupted into another. We further show how one can estimate these probabilities, adapting a recent technique for noise estimation to the multi class setting, and thus providing an end to end framework. Extensive experiments on MNIST, IMDB, CIFAR 10, CIFAR 100 employing a diversity of architectures stacking dense, convolutional, pooling, dropout, batch normalization, word embedding, LSTM and residual layers demonstrate the noise robustness of our proposals. Incidentally, we also prove that, when ReLU is the only non linearity, the loss curvature is immune to class dependent label noise.
The costs and benefits of brain dopamine for cognitive control.
Venue: Wiley interdisciplinary reviews. Cognitive science

Cognitive control helps us attain our goals by resisting distraction and temptations. Dopaminergic drugs are well known to enhance cognitive control. However, there is great variability in the effects of dopaminergic drugs across different contexts, with beneficial effects on some tasks but detrimental effects on other tasks. The mechanisms underlying this variability across cognitive task demands remain unclear. I aim to elucidate this across task variability in dopaminergic drug efficacy by going beyond classic models that emphasize the importance of dopamine in the prefrontal cortex for cognitive control and working memory. To this end, I build on recent advances in cognitive neuroscience that highlight a role for dopamine in cost benefit decision making. Specifically, I reconceptualize cognitive control as involving not just prefrontal dopamine but also modulation of cost benefit decision making by striatal dopamine. This approach will help us understand why we sometimes fail to (choose to) exert cognitive control while also identifying mechanistic factors that predict dopaminergic drug effects on cognitive control.
The neural circuitry supporting goal maintenance during cognitive control: a comparison of expectancy AX CPT and dot probe expectancy paradigms
Venue: Cognitive, affective behavioral neuroscience

Goal maintenance is an aspect of cognitive control that has been identified as critical for understanding psychopathology according to criteria of the NIMH sponsored CNTRICS (Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia) and Research Domain Criteria (RDoC) initiatives. CNTRICS proposed the expectancy AX CPT, and its visual spatial parallel the dot probe expectancy (DPX) as valid measures of the cognitive and neural processes thought to be relevant for goal maintenance. The goal of this study was to specifically examine the functional neural correlates and connectivity patterns of both goal maintenance tasks in the same subset of subjects to further validate their neural construct validity and clarify our understanding of the nature and function of the neural circuitry engaged by the tasks. Twenty six healthy control subjects performed both the letter (AX) and dot pattern (DPX) variants of the CPT during fMRI. Behavioral performance was similar between tasks. The 2 tasks engaged the same brain networks including dorsolateral prefrontal cortex (DLPFC) and dorsal parietal regions, supporting their validity as complementary measures of the goal maintenance construct. Interestingly there was greater engagement of the frontal opercular insula region during the expectancy AX CPT (letter) and greater functional connectivity between the PFC and medial temporal lobe in the DPX (dot pattern) These differences are consistent with differential recruitment of phonological and visual spatial processes by the two tasks and suggest that additional long term memory systems may be engaged by the dot probe version.