Miracle-dz/UniFAR-FTUs huggingface dataset:
Seq2Bubbles: Region-Based Embedding Learning for User Behaviors in Sequential Recommenders

User behavior sequences contain rich information about user interests and are exploited to predict user's future clicking in sequential recommendation. Existing approaches, especially recently proposed deep learning models, often embed a sequence of clicked items into a single vector, i.e., a point in vector space, which suffer from limited expressiveness for complex distributions of user interests with multi-modality and heterogeneous concentration. In this paper, we propose a new representation model, named as Seq2Bubbles, for sequential user behaviors via embedding an input sequence into a set of bubbles each of which is represented by a center vector and a radius vector in embedding space. The bubble embedding can effectively identify and accommodate multi-modal user interests and diverse concentration levels. Furthermore, we design an efficient scheme to compute distance between a target item and the bubble embedding of a user sequence to achieve next-item recommendation. We also develop a self-supervised contrastive loss based on our bubble embeddings as an effective regularization approach. Extensive experiments on four benchmark datasets demonstrate that our bubble embedding can consistently outperform state-of-the-art sequential recommendation models.
Multiple Exemplars Learning for Fast Image Retrieval

The past decade, we have witnessed rapid progress in compact representation learning for fast image retrieval. In the unsupervised scenario, product quantization (PQ) is one of the promising methods to generate compact image representation for fast and accurate retrieval. Inspired by the great success of deep neural network (DNN) achieved in computer vision, many works attempted to integrate PQ in DNN for end-to-end supervised training. Nevertheless, in existing deep PQ methods, data samples from different classes share the same codebook. Thus, they might be entangled with each other in the feature space. Meanwhile, existing deep PQ methods relying on triplet or pairwise loss require a huge number of training triplets or pairs, which are expensive in computation and scale poorly. In this work, we propose a multiple exemplars learning (MEL) approach to improve retrieval accuracy and training efficiency. For each class, we learn a class-specific codebook consisting of multiple exemplars to partition the class-specific feature space. Since the feature space as well as the codebook is class-specific, samples of different classes are disentangled in the feature space. We incorporate the proposed MEL in a convolutional neural network, supporting end-to-end training. Moreover, we propose MEL loss which trains the network in a considerably more efficient manner than existing deep product quantization approaches based on pairwise or triplet loss. Systematic experiments conducted on two public benchmarks demonstrate the effectiveness and efficiency of our method.
CBML: A Cluster-based Meta-learning Model for Session-based Recommendation

Session-based recommendation is to predict an anonymous user's next action based on the user's historical actions in the current session. However, the cold-start problem of limited number of actions at the beginning of an anonymous session makes it difficult to model the user's behavior, i.e., hard to capture the user's various and dynamic preferences within the session. This severely affects the accuracy of session-based recommendation. Although some existing meta-learning based approaches have alleviated the cold-start problem by borrowing preferences from other users, they are still weak in modeling the behavior of the current user. To tackle the challenge, we propose a novel cluster-based meta-learning model for session-based recommendation. Specially, we adopt a soft-clustering method and design a parameter gate to better transfer shared knowledge across similar sessions and preserve the characteristics of the session itself. Besides, we apply two self-attention blocks to capture the transition patterns of sessions in both item and feature aspects. Finally, comprehensive experiments are conducted on two real-world datasets and demonstrate the superior performance of CBML over existing approaches.
Counterfactual Review-based Recommendation

Incorporating review information into the recommender system has been demonstrated to be an effective method for boosting the recommendation performance. Previous research mainly focus on designing advanced architectures to better profile the users and items. However, the review information in realities can be highly sparse and imbalanced, which poses great challenges for effective user/item representations and satisfied performance enhancement. To alleviate this problem, in this paper, we propose to improve review-based recommendation by counterfactually augmenting the training samples. We focus on a common setting --- feature-aware recommendation, and the main building block of our idea lies in the counterfactual question: "what would be the user's decision if her feature-level preference had been different? ''. When augmenting the training samples, we actively change the user preference (also called intervention), and predict the user feedback on the items based on pre-trained recommender models. Instead of changing the user preference in a random manner, we design a learning-based method to discover the samples which are more effective for model optimization. In order to improve the sample qualities, we propose two strategies --- constrained feature perturbation and frequency-based sampling --- to equip our model. Since the sample generation model can be not perfect, we theoretically analyze the relation between the model prediction error and the number of generated samples. As a byproduct, our framework can explain the user pair-wise preference, which is complementary to the traditional point-wise explanations. Extensive experiments demonstrate that our model can significantly improve the performance of the state-of-the-art methods.
Clarifying the concept of need in the organizational context

The concept of need is embedded in economic systems, especially in their marketing and development functions. The very purpose of economy and markets is to fulfill human needs. Recognizing and understanding customers’ needs and how they change provides important opportunities for innovation, sales growth, competitiveness and profits. In the strict sense, the concept of need is psychological and linked to individuals. From the theoretical viewpoint, markets can be seen as institutional solutions of how resources are applied to solve human problems or needs (cf. Vargo, 2009). These institutions are formed as micro level activity of dyads of individuals generate higher meso and macro level structures within groups, organizations, industries and societies (Chandler and Vargo, 2011). Therefore needs are also visible at several system levels above the individual. Yet they are less understood in the organizational context, as they have been primarily discussed in individual psychology. Recently, marketing literature and the approach of service-dominant logic (Vargo and Lusch, 2004, 2008) has aimed to build bridge between individual, organizational, and market levels, suggesting the study of actor-to-actor value co-creation (Vargo and Lusch, 2011; Gummesson and Polese, 2009). The purpose of this article is to gain new insight on organizational needs and the needs of networked actors by building on the service-dominant logic discussion, the discussion of organizations and the psychological discussion of needs. This also clarifies the ways that value cocreation is embedded in social context (cf. Edvardsson et al., 2011). This paper is structured as follows. We first study the concept of need at the individual level by summarizing literature of how the construct of need is understood in psychology and marketing. We also explore the relationship of need to its neighboring constructs, like want, motivation, and value. We then use literature on the rational, natural and open systems views of organizations (Scott, 2003) and on the service-dominant logic in order to discuss the concept of need in organizational and networked actor contexts. We conclude our paper by discussing the theoretical and practical implications of our findings.
Source-code Summarization of Java Methods Using Control-Flow Graphs

Source-code summarization aims to generate natural-language summaries for software artifacts (e.g., method and class). Various research works showed the use of text-retrieval-based techniques, heuristic-based techniques, and data-driven techniques for source-code summarization. In data-driven techniques, researchers used a sequence of source-code tokens and other representations of source code (e.g., application programming interface (API) sequences and abstract syntax tree (AST)) as an input to source-code summarization models. According to the current published literature in source-code summarization, researchers have not explored the use of a sequence extracted from control-flow graph that shows a contextual relationship between program instructions based on control-flow relationships for source-code summarization models. In this work, we employ control-flow graph representations to increase the prediction accuracy of a bi-directional long-short term memory (LSTM) source-code summarization model in terms of describing the functionality of Java methods. We use an attention-based bi-directional LSTM sequence-to-sequence model to show the use of linearized control-flow graph sequences alongside a sequence of source-code tokens. We compared our model with the current state-of-the-art and with or without a linearized control-flow graph. We created a source-code summarization dataset to train and evaluate our approach and conducted expert and automatic evaluations. In the expert evaluation, the participants gave rating for summaries generated by each model in terms of correctly describing the functionality of a Java method. Our models outperformed the state-of-the-art in terms of the mean average-rating. Also, the expert evaluation showed us the model benefit from the structural information. In the automatic evaluation, we found that the use of control-flow graphs does not increase the prediction accuracy of a bi-directional LSTM model in terms of BLEU score compared to a bi-directional LSTM model that does not use control-flow graphs. However, we found our source-code summarization approach that uses a control-flow graph as an additional representation better than encoding AST in graph neural networks. Overall, we improved the state-of-the-art for method summarization with our models that take sequence of method tokens with and without a control-flow graph.
Isoquinoline Alkaloids and the Ionophore Monensin Supplemented Alone or Combined on Ruminal Fermentation and Nutrient Digestibility in Steers Fed a High-Energy Diet

Facultad de Medicina Veterinaria y Zootecnia. Universidad Autónoma de Sinaloa. Blvd. San Ángel s/n, Fraccionamiento San Benito CP 80246, Culiacán, Sinaloa, México Instituto de Investigaciones en Ciencias Veterinarias. Universidad Autónoma de Baja California. Km 4.5 carretera Mexicali-San Felipe, CP 21386, Mexicali, Baja California, México Facultad de Medicina Veterinaria y Zootecnia, Universidad Nacional Autónoma de México, Col. CU, Coyoacán, CP 04510, Cd. de México, México Department of Animal Science, University of California, Davis, United States Departamento de Ciencias Naturales y Exactas, Universidad Autónoma de Occidente, Unidad Regional Guasave, Avenida Universidad s/n, Flamingos, CP 81048, Guasave, Sinaloa
Job Search Under Low Pay, Low Security, and High Unemployment

The post-Covid shutdown U.S. labor market is characterized by many open jobs and substantial unemployment. Despite numerous hypotheses regarding this paradoxical situation, existing models in labor economics lack the features necessary to adequately evaluate the potential policy remedies or causes. I construct a search model to reproduce the prevailing dynamics, which I then use to evaluate the employment impact and effect on the welfare of low-income workers from large-scale transfers, unemployment benefits, and an earnings subsidy. One-off transfers hardly affect employment in a downturn. Raising unemployment benefits by $300 per month leads to a 0.6 point increase in the unemployment rate. Adding an earnings subsidy to unemployment benefits raises employment by 7 percentage points at a 23% lower level of inequality, yet increases costs by only 16-30%. In contrast to a compensating differentials framework, the model shows that lower earners tend to be more at risk for layoffs, and that this effect is stronger following a negative aggregate shock. Following a simulated shock, mean consumption falls by 5% yet consumption at the 10th percentile drops by 16%. Although the risk borne by lower earners is often greater, their savings are often lower, because they have less consumption to seek to smooth and less opportunity to accumulate savings.
I. INTRODUCTION In 2016, Connecticut changed its high school accountability measure from the Smarter Balanced Assessment Consortium (SBAC) to the Scholastic Assessment Test (SAT) under the leadership of Governor

This paper will discuss the correlation between the SAT and the Math Inventory Test. Many school districts adopted the Math Inventory as a tool to measure student growth from grades kindergarten through high school. The Math Inventory is a computer-administered test that gives students math problems spanning from counting to high school level math. When completed, the students are given a quantile measure, much like a Lexile score for reading skill. The purpose of this study is to figure out if success on the Math Inventory is a good indicator for performing well on the SAT. For most high schools around the United States, objectives and lessons are aligned with those of the SAT. The goal of high school teachers is for students to excel on the SAT so that they can go to college, which means the tests used in middle school should be aligned with that goal. If the Math Inventory is not, then it might not be a very good use of school time and resources. Data was analyzed from the 2017-2018 school year from ten different high schools in an urban school district to determine the correlation between Math Inventory score, and the math score/sub scores of SAT/PSAT. The value of the Pearson’s correlation coefficient is used to suggest a fairly moderate positive relationship between these two variables.
Surface Modification of Poly(lactic acid) Film via Cold Plasma Assisted Grafting of Fumaric and Ascorbic Acid

Plant-based materials have found their application in the packaging with a yearly growing production rate. These naturally biodegradable polymers are obtained from renewable and sustainable natural resources with reduced environmental impact and affordable cost. These materials have found their utilization in fully-renewable plant-based packaging products, such as Tetra Pak®-like containers, by replacing commonly-used polyethylene as the polymer component. Poly(lactic acid) (PLA) is one of the representative plant-based polymers because of its eco-friendliness and excellent chemical and mechanical properties. In this work, a PLA surface was modified by various food additives, namely ascorbic acid (ASA) and fumaric acid (FA), using plasma-initiated grafting reactions in order to improve the surface and adhesion properties of PLA. Various analytical and microscopic techniques were employed to prove the grafting process. Moreover, the improved adhesion of the modified PLA foil to aluminum (Al) foil in a laminate configuration was proven by peel resistance measurements. The peel resistance of modified PLA increased by 74% and 184% for samples modified by ASA and FA, respectively, compared with untreated PLA.
Can women escape the male influence when surfing? An ethnographic study of female surfers in Southern Spain

Abstract This article explores how female recreational surfers in southern Spain experience a marginalized status within the male-dominated sport of surfing. The main objective is to determine if there is a male-legitimized criterion that describes how surfing should be and what is considered an authentic practise of this sport. The results indicate that there are certain cultural assumptions originated by men that affect not only the relationship that women build with this sport at the time of its practice but when they share it with other women. It describes a scenario in which women have been deprived of the opportunity to establish their own criteria to feel identified when surfing. The lack of female referents or the absence of women in this sport could explain this paradigm.
Controls on diagenesis of the Triassic Kurrachine Dolomite, Syria

The evaluation of major diagenetic processes affecting the main hydrocarbon-producing interval of the Triassic Kurrachine Dolomite, in the central part of Syria, has proved to be of importance in understanding its reservoir characteristics. To describe the paragenetic sequence, core samples from two wells were studied by conventional petrographic analysis. In the first stage, pervasive replacement dolomitisation occurred. The dolomite petrography can be best explained by evaporative dolomitisation in the case of tidal flat facies and by the hypersaline brine reflux model in the case of subtidal facies. During relatively longer periods of subaerial exposure dolocrete sequences were developed. Dolomitisation of subtidal sediments in a more open-marine setting was completed during further burial by rising temperature. As a result, pervasively dolomitised rocks, with minor intercrystalline porosity in subtidal deposits and supposedly in dolocretes, were formed. In the deep burial realm, solution seams and stylolites acted as conduits for migrating hydrocarbon. Overmature hydrocarbon was observed as insoluble residue. Following fracturing and leaching by sulphate-bearing warm fluids, significant vuggy porosity was created but was subsequently occluded by precipitated anhydrite cement. The effective vuggy and fracture porosity of the Kurrachine Dolomite was created by latest stage leaching, which was controlled by the previous deep burial diagenetic processes and fracturing. The selective leaching, on the evidence of dissolution of anhydrite cement, improved the reservoir potential. Taken all together, the Kurrachine Dolomite exhibits evidence of important porosity development in burial conditions. Accordingly, the analysed core porosity and permeability data within the formation varies by location.
cis-Bromidobis(ethylene-1,2-diamine)(2-methylpropan-1-amine)cobalt(III) dibromide

In the title compound, [CoBr(C2H8N2)2(C4H11N)]Br2, the cobalt(III) ion has a distorted octahedral coordination environment and is ligated by three N atoms and a bromine ion in the equatorial plane, and by two N atoms occupying the axial positions. In the crystal, the complex cation and the two Br− counter-anions are linked by N—H.Br hydrogen bonds, forming a supramolecular framework.
Distributed Reinforcement Learning Containment Control for Multiple Nonholonomic Mobile Robots

In this paper, the distributed optimal containment control problem for multiple nonholonomic mobile robots (NHMRs) differential game is studied via reinforcement learning. An approximation-based optimal control strategy is developed to ensure the optimal performance index and avoid the potential collision among agents. Firstly, the collision avoidance problem considered in this paper is addressed by exploiting a consensus-like interconnection on a directed graph and an error transformation function. Then, on the basis of the optimal backstepping technique, a single critic neural network is adopted to obtain the solution of the coupled Hamilton-Jacobi (HJ) equation, in which an improved learning mechanism is constructed to relax the requirement on initial control conditions. In addition, based on the Lyapunov stability theory, it is proved that all signals in the closed-loop optimal control are uniformly ultimately bounded. Finally, the proposed control protocol is applied to NHMRs system, which verifies that the solution of the coupled HJ equation solves the containment problem of differential game.
Barriers and Facilitators to Staying Smoke-Free after Having a Baby, a Qualitative Study: Women’s Views on Support Needed to Prevent Returning to Smoking Postpartum

Background: Postpartum return to smoking (PPRS) is a common and important public health problem. Interventions to prevent PPRS have not been shown to be effective. We aimed to qualitatively explore the barriers and facilitators to staying smoke-free after having a baby, and women’s views on the support needed to avoid PPRS to inform future intervention development. Methods: We conducted semi-structured telephone interviews (n = 26) with pregnant women who quit smoking (n = 9), and postpartum women who were abstinent at delivery and returned to smoking (n = 7) or stayed smoke-free (n = 10). Inductive thematic analysis was used. Results: Five overarching themes were identified: (i) smoking intentions; (ii) facilitators to staying smoke-free; (iii) barriers to staying smoke-free; (iv) support to avoid relapse; and (v) e-cigarettes, nicotine replacement therapy, and varenicline. Facilitators to staying smoke-free were the health benefits to their baby, whilst barriers included stress, cravings, and being in environments where they would previously have smoked. Women wanted continuous offers of support to stay smoke-free throughout the extended postpartum period, with a particular interest in support for partners to quit smoking and self-help support. Women expressed safety concerns for e-cigarettes, nicotine replacement therapy, and varenicline. Conclusions: Offers of support to stay smoke-free should continue throughout the postpartum and engage with partners or other household members who smoke. Reassuring women about the relative safety of nicotine replacement therapy and e-cigarettes by a health professional, particularly for those who are breastfeeding, could be beneficial.
Factors Affecting Knowledge Transfer between Project Managers: A Conceptual Framework

: The purpose of this study is to identify factors from the literature review that influence knowledge transfer among project managers at all levels. Findings indicate nine factors to potentially benefit knowledge transfer amongst project managers. They can be used by project managers involved in government agencies and private sector organizations as a preliminary guidance for the practice, improvement, and growth of project managers in order to achieve a competitive edge in business.