A Restart Local Search for Solving Diversified Top-k Weight Clique Search Problem

Diversified top-k weight clique (DTKWC) search problem is an important generalization of the diversified top-k clique (DTKC) search problem with practical applications. The diversified top-k weight clique search problem aims to search k maximal cliques that can cover the maximum weight in a vertex weighted graph. In this work, we propose a novel local search algorithm called TOPKWCLQ for the DTKWC search problem which mainly includes two strategies. First, a restart strategy is adopted, which repeated the construction and updating processes of the maximal weight clique set. Second, a scoring heuristic is designed by giving different priorities for maximal weight cliques in candidate set. Meanwhile, a constraint model of the DTKWC search problem is constructed such that the research concerns can be evaluated. Experimental results show that the proposed algorithm TOPKWCLQ outperforms than the comparison algorithm on large-scale real-world graphs.

Quantitative analysis of the orientation of white stork nestlings’ parents from video monitoring

Video-monitoring has become in the last decades common practice for animal observation and conservation purposes. In Ornithology, it is mostly used for tracking predators and nest surveillance, but, with the rapid development and spreading of webcams on nests for educational purposes, new opportunities arise for behavioral investigation, through citizen science for instance. In this article, we use video-monitoring from a public webcam on a White stork (Ciconia ciconia) nest and perform systematic image analysis to record the positioning and orientation of the guarding parent on the nest, during the nestling period over 60 days. From this data of 450 orientation samples, correlations with weather parameters are drawn. Our results suggest that the sun is responsible for most of the orientation with an hourly dependence, while the wind has prevalence during rainy days. A change in the parent behavior is also observed around the time the nestlings are known to attain their maximal weight. These preliminary findings provide new insights on weather influence on parental care behavior likely linked with the parent’s sensing. The versatility of the proposed method allows for behavioral studies on a wide variety of species.

PERCEIVED SELF-REGULATORY SUCCESS IN DIETING AND ITS CORRELATES AMONG WOMEN WITH FOOD ADDICTION

"Background: Several psychological features are implicated in the dieting success. Better understanding of these features may allow reducing dieting failure of both surgical and non-surgical weight loss interventions, especially for individuals with food addiction (FA). In non-clinical population, low perceived self-regulatory success (PSRS) in dieting is associated with higher BMI (body mass index), FA, food craving and impulsivity. PSRS could partially explain weight gain in FA, but no study investigated this association in the specific FA population. Method: To diagnose FA, 288 women recruited online completed The Yale Food Addiction Scale 2.0. They also completed the following self-administrated questionnaires: the French adaptation of the PSRS in dieting scale, the Food Craving Questionnaire Trait-Reduced, and the Barratt Impulsivity Scale-11. They specified their height, current and lifetime maximal weight, and if they were in a current diet. Mean age was 26.1±10.3 years. Mean current BMI was 23.4±5.5 kg/m². Results: 79 women met criteria for FA (27.4%), indicating significant less PSRS in dieting and higher probability to be in a current diet to lose weight. In the whole population (n=288), PSRS in dieting was negatively correlated with current and lifetime maximal BMI, food craving, FA, attentional and non-planning impulsivity. In a multiple linear regression conducted in the subgroup of women with FA, PSRS score was predicted by age, current BMI, food addiction and food craving. More, results suggested food craving enable the association between food addiction and PSRS. Conclusion: These results showed the high preoccupation about food intake and weight gain in the FA population. Psychological features associated with FA such as food craving and impulsivity, seem to impact the PSRS in dieting, increasing psychological vulnerability."

Multi-particle reconstruction in the High Granularity Calorimeter using object condensation and graph neural networks

The high-luminosity upgrade of the LHC will come with unprecedented physics and computing challenges. One of these challenges is the accurate reconstruction of particles in events with up to 200 simultaneous protonproton interactions. The planned CMS High Granularity Calorimeter offers fine spatial resolution for this purpose, with more than 6 million channels, but also poses unique challenges to reconstruction algorithms aiming to reconstruct individual particle showers. In this contribution, we propose an end-to-end machine-learning method that performs clustering, classification, and energy and position regression in one step while staying within memory and computational constraints. We employ GravNet, a graph neural network, and an object condensation loss function to achieve this task. Additionally, we propose a method to relate truth showers to reconstructed showers by maximising the energy weighted intersection over union using maximal weight matching. Our results show the efficiency of our method and highlight a promising research direction to be investigated further.

Arbitrage Portfolios

We propose a new methodology for forming arbitrage portfolios that utilizes the information contained in firm characteristics for both abnormal returns and factor loadings. The methodology gives maximal weight to risk-based interpretations of characteristics’ predictive power before any attribution is made to abnormal returns. We apply the methodology to simulated economies and to a large panel of U.S. stock returns. The methodology works well in our simulation and when applied to stocks. Empirically, we find the arbitrage portfolio has (statistically and economically) significant alphas relative to several popular asset pricing models and annualized Sharpe ratios ranging from 1.31 to 1.66.