Miracle-dz/UniFAR-FTUs huggingface dataset:
Interactive Optimal Teaching with Unknown Learners

This paper introduces a new approach for machine teaching that partly addresses the (unavoidable) mismatch between what the teacher assumes about the learning process of the student and the actual process. We analyze several situations in which such mismatch takes place, including when the student? s learning algorithm is known but the corresponding parameters are not, and when the learning algorithm itself is not known. Our analysis is focused on the case of a Bayesian Gaussian learner, and we show that, even in this simple case, the lack of knowledge regarding the student? s learning process significantly deteriorates the performance of machine teaching: while perfect knowledge of the student ensures that the target is learned after a finite number of samples, lack of knowledge thereof implies that the student will only learn asymptotically (i.e., after an infinite number of samples). We introduce interactivity as a means to mitigate the impact of imperfect knowledge and show that, by using interactivity, we are able to recover finite learning time, in the best case, or significantly faster convergence, in the worst case. Finally, we discuss the extension of our analysis to a classification problem using linear discriminant analysis, and discuss the implications of our results in single- and multi-student settings.
Deep Propagation Based Image Matting

In this paper, we propose a deep propagation based image matting framework by introducing deep learning into learning an alpha matte propagation principal. Our deep learning architecture is a concatenation of a deep feature extraction module, an affinity learning module and a matte propagation module. These three modules are all differentiable and can be optimized jointly via an end-to-end training process. Our framework results in a semantic-level pairwise similarity of pixels for propagation by learning deep image representations adapted to matte propagation. It combines the power of deep learning and matte propagation and can therefore surpass prior state-of-the-art matting techniques in terms of both accuracy and training complexity, as validated by our experimental results from 243K images created based on two benchmark matting databases.
Regenerative and Protective Actions of the GHK-Cu Peptide in the Light of the New Gene Data

The human peptide GHK (glycyl-l-histidyl-l-lysine) has multiple biological actions, all of which, according to our current knowledge, appear to be health positive. It stimulates blood vessel and nerve outgrowth, increases collagen, elastin, and glycosaminoglycan synthesis, as well as supports the function of dermal fibroblasts. GHK’s ability to improve tissue repair has been demonstrated for skin, lung connective tissue, boney tissue, liver, and stomach lining. GHK has also been found to possess powerful cell protective actions, such as multiple anti-cancer activities and anti-inflammatory actions, lung protection and restoration of chronic obstructive pulmonary disease (COPD) fibroblasts, suppression of molecules thought to accelerate the diseases of aging such as NFκB, anti-anxiety, anti-pain and anti-aggression activities, DNA repair, and activation of cell cleansing via the proteasome system. Recent genetic data may explain such diverse protective and healing actions of one molecule, revealing multiple biochemical pathways regulated by GHK.
Language to Action: Towards Interactive Task Learning with Physical Agents

Language communication plays an important role in human learning and knowledge acquisition. With the emergence of a new generation of cognitive robots, empowering these robots to learn directly from human partners becomes increasingly important. This paper gives a brief introduction to interactive task learning where humans can teach physical agents new tasks through natural language communication and action demonstration. It discusses research challenges and opportunities in language and communication grounding that are critical in this process. It further highlights the importance of commonsense knowledge, particularly the very basic physical causality knowledge, in grounding language to perception and action.
ABClass : Une approche d'apprentissage multi-instances pour les séquences(ABClass: A multiple instance learning approach for sequence data)

Dans le cas du probleme de l'apprentissage multi-instances (MI) pour les sequences, les donnees d'apprentissage consistent en un ensemble de sacs ou chaque sac contient un ensemble d'instances/sequences. Dans certaines applications du monde reel, comme la bioinformatique, comparer un couple aleatoire de sequences n'a aucun sens. En fait, chaque instance de chaque sac peut avoir une relation structurelle et/ou fonctionnelle avec d'autres instances dans d'autres sacs. Ainsi, la tâche de classification doit prendre en compte la relation entre les instances semantiquement liees a travers les sacs. Dans cet article, nous presentons ABClass, une nouvelle approche de classification MI des sequences. Chaque sequence est representee par un vecteur d'attributs extraits a partir de l'en-semble des instances qui lui sont liees. Pour chaque sequence du sac a predire, un classifieur discriminant est applique afin de calculer un resultat de classification partiel. Ensuite, une methode d'agregation est appliquee afin de generer le resultat final. Nous avons applique ABClass pour resoudre le probleme de la prediction de la resistance aux rayonnements ionisants (RRI) chez les bacteries. Les resultats experimentaux sont satisfaisants.
A Deep Framework for Cross-Domain and Cross-System Recommendations

Cross-Domain Recommendation (CDR) and Cross-System Recommendations (CSR) are two of the promising solutions to address the long-standing data sparsity problem in recommender systems. They leverage the relatively richer information, e.g., ratings, from the source domain or system to improve the recommendation accuracy in the target domain or system. Therefore, finding an accurate mapping of the latent factors across domains or systems is crucial to enhancing recommendation accuracy. However, this is a very challenging task because of the complex relationships between the latent factors of the source and target domains or systems. To this end, in this paper, we propose a Deep framework for both Cross-Domain and Cross-System Recommendations, called DCDCSR, based on Matrix Factorization (MF) models and a fully connected Deep Neural Network (DNN). Specifically, DCDCSR first employs the MF models to generate user and item latent factors and then employs the DNN to map the latent factors across domains or systems. More importantly, we take into account the rating sparsity degrees of individual users and items in different domains or systems and use them to guide the DNN training process for utilizing the rating data more effectively. Extensive experiments conducted on three real-world datasets demonstrate that DCDCSR framework outperforms the state-of-the-art CDR and CSR approaches in terms of recommendation accuracy.
Dynamic Complexities in a Supply Chain System with Lateral Transshipments

The horizontal interaction between retailers, coupled with replenishment rules and time delays, makes the dynamics in supply chain systems highly complicated. This paper aims to explore the impacts of lateral transshipments on the stability, bullwhip effect, and other performance measurements in the context of a two-tiered supply chain system composed of one supplier and two retailers. In particular, we developed a unified discrete-time state space model to address two different scenarios of placing orders. Analytical stability results are derived, through which we found that inappropriate lateral transshipment policies readily destabilize the supply chain system. Moreover, the lead time of lateral transshipments further complicates the stability problem. Theoretical results are validated through simulation experiments and the influences of system parameters on performance measures are investigated numerically. Numerical simulations show that lateral transshipments help improve the customer service level for both retailers. It is also interesting to observe that the demand of the two retailers can be satisfied even if only one retailer places orders from the upstream supplier.
A Hybrid Tabu Search Algorithm for a Real-World Open Vehicle Routing Problem Involving Fuel Consumption Constraints

Outsourcing logistics operation to third-party logistics has attracted more attention in the past several years. However, very few papers analyzed fuel consumption model in the context of outsourcing logistics. This problem involves more complexity than traditional open vehicle routing problem (OVRP), because the calculation of fuel emissions depends on many factors, such as the speed of vehicles, the road angle, the total load, the engine friction, and the engine displacement. Our paper proposed a green open vehicle routing problem (GOVRP) model with fuel consumption constraints for outsourcing logistics operations. Moreover, a hybrid tabu search algorithm was presented to deal with this problem. Experiments were conducted on instances based on realistic road data of Beijing, China, considering that outsourcing logistics plays an increasingly important role in China’s freight transportation. Open routes were compared with closed routes through statistical analysis of the cost components. Compared with closed routes, open routes reduce the total cost by 18.5% with the fuel emissions cost down by nearly 29.1% and the diver cost down by 13.8%. The effect of different vehicle types was also studied. Over all the 60- and 120-node instances, the mean total cost by using the light-duty vehicles is the lowest.
Genetic analysis of social-class mobility in five longitudinal studies

Significance Genome-wide association study (GWAS) discoveries about educational attainment have raised questions about the meaning of the genetics of success. These discoveries could offer clues about biological mechanisms or, because children inherit genetics and social class from parents, education-linked genetics could be spurious correlates of socially transmitted advantages. To distinguish between these hypotheses, we studied social mobility in five cohorts from three countries. We found that people with more education-linked genetics were more successful compared with parents and siblings. We also found mothers’ education-linked genetics predicted their children’s attainment over and above the children’s own genetics, indicating an environmentally mediated genetic effect. Findings reject pure social-transmission explanations of education GWAS discoveries. Instead, genetics influences attainment directly through social mobility and indirectly through family environments. A summary genetic measure, called a “polygenic score,” derived from a genome-wide association study (GWAS) of education can modestly predict a person’s educational and economic success. This prediction could signal a biological mechanism: Education-linked genetics could encode characteristics that help people get ahead in life. Alternatively, prediction could reflect social history: People from well-off families might stay well-off for social reasons, and these families might also look alike genetically. A key test to distinguish biological mechanism from social history is if people with higher education polygenic scores tend to climb the social ladder beyond their parents’ position. Upward mobility would indicate education-linked genetics encodes characteristics that foster success. We tested if education-linked polygenic scores predicted social mobility in >20,000 individuals in five longitudinal studies in the United States, Britain, and New Zealand. Participants with higher polygenic scores achieved more education and career success and accumulated more wealth. However, they also tended to come from better-off families. In the key test, participants with higher polygenic scores tended to be upwardly mobile compared with their parents. Moreover, in sibling-difference analysis, the sibling with the higher polygenic score was more upwardly mobile. Thus, education GWAS discoveries are not mere correlates of privilege; they influence social mobility within a life. Additional analyses revealed that a mother’s polygenic score predicted her child’s attainment over and above the child’s own polygenic score, suggesting parents’ genetics can also affect their children’s attainment through environmental pathways. Education GWAS discoveries affect socioeconomic attainment through influence on individuals’ family-of-origin environments and their social mobility.
A MapReduce-Based Parallel Frequent Pattern Growth Algorithm for Spatiotemporal Association Analysis of Mobile Trajectory Big Data

Frequent pattern mining is an effective approach for spatiotemporal association analysis of mobile trajectory big data in data-driven intelligent transportation systems. While existing parallel algorithms have been successfully applied to frequent pattern mining of large-scale trajectory data, two major challenges are how to overcome the inherent defects of Hadoop to cope with taxi trajectory big data including massive small files and how to discover the implicitly spatiotemporal frequent patterns with MapReduce. To conquer these challenges, this paper presents a MapReduce-based Parallel Frequent Pattern growth (MR-PFP) algorithm to analyze the spatiotemporal characteristics of taxi operating using large-scale taxi trajectories with massive small file processing strategies on a Hadoop platform. More specifically, we first implement three methods, that is, Hadoop Archives (HAR), CombineFileInputFormat (CFIF), and Sequence Files (SF), to overcome the existing defects of Hadoop and then propose two strategies based on their performance evaluations. Next, we incorporate SF into Frequent Pattern growth (FP-growth) algorithm and then implement the optimized FP-growth algorithm on a MapReduce framework. Finally, we analyze the characteristics of taxi operating in both spatial and temporal dimensions by MR-PFP in parallel. The results demonstrate that MR-PFP is superior to existing Parallel FP-growth (PFP) algorithm in efficiency and scalability.
A Novel Cell Penetrating Peptide for the Differentiation of Human Neural Stem Cells

Retinoic acid (RA) is a bioactive lipid that has been shown to promote neural stem cell differentiation. However, the highly hydrophobic molecule needs to first solubilize and translocate across the cell membrane in order to exert a biological response. The cell entry of RA can be aided by cell penetrating peptides (CPPs), which are short amino acid sequences that are able to carry bioactive cargo past the cell membrane. In this work, a novel cell penetrating peptide was developed to deliver RA to human neural stem cells and, subsequently, promote neuronal differentiation. The novel CPP consists of a repeating sequence, whose number of repeats is proportional to the efficiency of cell penetration. Using fluorescence microscopy, the mode of translocation was determined to be related to an endocytic pathway. The levels of β-III tubulin (Tubb3) and microtubule associated protein 2 (MAP2) expression in neural stem cells treated with RA conjugated to the CPP were assessed by quantitative immunocytochemistry.
Mining Outlier Data in Mobile Internet-Based Large Real-Time Databases

Mining outlier data guarantees access security and data scheduling of parallel databases and maintains high-performance operation of real-time databases. Traditional mining methods generate abundant interference data with reduced accuracy, efficiency, and stability, causing severe deficiencies. This paper proposes a new mining outlier data method, which is used to analyze real-time data features, obtain magnitude spectra models of outlier data, establish a decisional-tree information chain transmission model for outlier data in mobile Internet, obtain the information flow of internal outlier data in the information chain of a large real-time database, and cluster data. Upon local characteristic time scale parameters of information flow, the phase position features of the outlier data before filtering are obtained; the decision-tree outlier-classification feature-filtering algorithm is adopted to acquire signals for analysis and instant amplitude and to achieve the phase-frequency characteristics of outlier data. Wavelet transform threshold denoising is combined with signal denoising to analyze data offset, to correct formed detection filter model, and to realize outlier data mining. The simulation suggests that the method detects the characteristic outlier data feature response distribution, reduces response time, iteration frequency, and mining error rate, improves mining adaptation and coverage, and shows good mining outcomes.
Geriatric Assessment in Older Patients with Acute Myeloid Leukemia

The incidence of acute myeloid leukemia (AML) increases with age, but the outcomes for older adults with AML are poor due to underlying tumor biology, poor tolerance to aggressive treatment, and the physiologic changes of aging. Because of the underlying heterogeneity in health status, treatment decisions are difficult in this population. A geriatric assessment (GA) refers to the use of various validated tools to assess domains that are important in older adults including physical function, cognition, comorbidities, polypharmacy, social support, and nutritional status. In older patients with cancer, a GA can guide treatment decision-making, predict treatment toxicity, and guide supportive care interventions. Compared to solids tumors, there is a relative lack of studies evaluating the use of a GA in older patients with AML. In this review, we will discuss the principles, common domains, feasibility, and benefits of GA, with a focus on older patients with AML that includes practical applications for clinical management.
What's on YouTube? A Case Study on Food and Beverage Advertising in Videos Targeted at Children on Social Media.

BACKGROUND Unhealthy food marketing to children is a key risk factor for childhood obesity. Online video platforms have surpassed television as the primary choice for screen viewing among children but the extent of food marketing through such media is relatively unknown. We aimed to examine food and beverage advertisements (ads) encountered in YouTube videos targeting children in Malaysia. METHODS The social media analytics site SocialBlade.com was used to identify the most popular YouTube videos (n = 250) targeting children. Ads encountered while viewing these videos were recorded and analyzed for type of product promoted and ad format (video vs. overlay). Food and beverage ads were further coded based on food category and persuasive marketing techniques used. RESULTS In total 187 ads were encountered in sampled videos. Food and beverage ads were the most common at 38% (n=71), among which 56.3% (n = 40) promoted noncore foods. Ads for noncore foods were more commonly delivered as video rather than overlay ads. Among ads promoting noncore foods, the most commonly employed persuasive marketing techniques found were taste appeal (42.3%), uniqueness/novelty (32.4%), the use of animation (22.5%), fun appeal (22.5%), use of promotional characters (15.5%), price (12.7%), and health and nutrition benefits (8.5%). CONCLUSIONS Similar to television, unhealthy food ads predominate in content aimed toward children on YouTube. Policies regulating food marketing to children need to be extended to cover online content in line with a rapidly-evolving digital media environment. Service providers of social media can play a part in limiting unhealthy food advertising to children.
When Rigging a Tournament, Let Greediness Blind You

A knockout tournament is a standard format of competition, ubiquitous in sports, elections and decision making. Such a competition consists of several rounds. In each round, all players that have not yet been eliminated are paired up into matches. Losers are eliminated, and winners are raised to the next round, until only one winner exists. Given that we can correctly predict the outcome of each potential match (modelled by a tournament D), a seeding of the tournament deterministically determines its winner. Having a favorite player v in mind, the Tournament Fixing Problem (TFP) asks whether there exists a seeding that makes v the winner. Aziz et al. [AAAI’14] showed that TFP is NP-hard. They initiated the study of the parameterized complexity of TFP with respect to the feedback arc set number k of D, and gave an XP-algorithm (which is highly inefficient). Recently, Ramanujan and Szeider [AAAI’17] showed that TFP admits an FPT algorithm, running in time 2^{ O(k^2 log k)} n ^{O(1)}. At the heart of this algorithm is a translation of TFP into an algebraic system of equations, solved in a black box fashion (by an ILP solver). We present a fresh, purely combinatorial greedy solution. We rely on new insights into TFP itself, which also results in the better running time bound of 2^{ O(k log k)} n^{ O(1)} . While our analysis is intricate, the algorithm itself is surprisingly simple.
Methods for the Study of Regeneration in Stentor

Cells need to be able to regenerate their parts to recover from external perturbations. The unicellular ciliate Stentor coeruleus is an excellent model organism to study wound healing and subsequent cell regeneration. The Stentor genome became available recently, along with modern molecular biology methods, such as RNAi. These tools make it possible to study single-cell regeneration at the molecular level. The first section of the protocol covers establishing Stentor cell cultures from single cells or cell fragments, along with general guidelines for maintaining Stentor cultures. Culturing Stentor in large quantities allows for the use of valuable tools like biochemistry, sequencing, and mass spectrometry. Subsequent sections of the protocol cover different approaches to inducing regeneration in Stentor. Manually cutting cells with a glass needle allows studying the regeneration of large cell parts, while treating cells with either sucrose or urea allows studying the regeneration of specific structures located at the anterior end of the cell. A method for imaging individual regenerating cells is provided, along with a rubric for staging and analyzing the dynamics of regeneration. The entire process of regeneration is divided in three stages. By visualizing the dynamics of the progression of a population of cells through the stages, the heterogeneity in regeneration timing is demonstrated.
Upregulation of long noncoding RNA MALAT1 in papillary thyroid cancer and its diagnostic value.

AIM To investigate the expression level of lncRNA MALAT1 in papillary thyroid cancer (PTC) and evaluate its clinical diagnostic value as a biomarker in PTC. METHODS MALAT1 lncRNA expression in tissues was detected by qRT-PCR. The diagnostic value of MALAT1 as a biomarker in PTC was evaluated with receiver operating characteristics. RESULTS MALAT1 expression was upregulated in PTC tissues compared with paired corresponding noncancerous tissues. We also found that upregulated MALAT1 expression was correlated with tumor size, lymph node metastases (p = 0.011) and WHO disease stage. The area under the curve was 0.6320, 0.7192, 0.7089 and 0.7000 for PTC, lymph node metastasis, extrathyroidal extension and WHO disease stage prediction, respectively. CONCLUSION Our finding suggests that MALAT1 may exert oncogenic function in PTC and may be a potential diagnostic marker for this cancer.