Miracle-dz/UniFAR-FTUs huggingface dataset:
Maximum margin matrix factorization for code recommendation

Code recommender systems ease the use and learning of software frameworks and libraries by recommending calls based on already present code. Typically, code recommender tools have been based on rather simple rule based systems while many of the recent advances in Recommender Systems and Collaborative Filtering have been largely focused on rating data. While many of these advances can be incorporated in the code recommendation setting this problem also brings considerable challenges of its own. In this paper, we extend state-of-the-art collaborative filtering technology, namely Maximum Margin Matrix Factorization (MMMF) to this interesting application domain and show how to deal with the challenges posed by this problem. To this end, we introduce two new loss functions to the MMMF model. While we focus on code recommendation in this paper, our contributions and the methodology we propose can be of use in almost any collaborative setting that can be represented as a binary interaction matrix. We evaluate the algorithm on real data drawn from the Eclipse Open Source Project. The results show a significant improvement over current rule-based approaches.
The evolution of Lisp

Lisp is the world's greatest programming language—or so its proponents think. The structure of Lisp makes it easy to extend the language or even to implement entirely new dialects without starting from scratch. Overall, the evolution of Lisp has been guided more by institutional rivalry, one-upsmanship, and the glee born of technical cleverness that is characteristic of the “hacker culture” than by sober assessments of technical requirements. Nevertheless this process has eventually produced both an industrial-strength programming language, messy but powerful, and a technically pure dialect, small but powerful, that is suitable for use by programming-language theoreticians. We pick up where McCarthy's paper in the first HOPL conference left off. We trace the development chronologically from the era of the PDP-6, through the heyday of Interlisp and MacLisp, past the ascension and decline of special purpose Lisp machines, to the present era of standardization activities. We then examine the technical evolution of a few representative language features, including both some notable successes and some notable failures, that illuminate design issues that distinguish Lisp from other programming languages. We also discuss the use of Lisp as a laboratory for designing other programming languages. We conclude with some reflections on the forces that have driven the evolution of Lisp.
Thermodynamic modeling

The solubility of two natural phenolic compounds (vanillic acid ( VA ) and vanillin ( V )) in four individual solvents (ethanol, butan-2-ol, ethylene glycol, and ethyl acetate) was measured using the gravimetric method from 283.15 to 323.15 K at atmospheric pressure. The optimization of the complexes between VA –solvents and V –solvents using the B3LYP theoretic technique and the 6-31+G (d, p) basis set indicated that the absolute value of interaction energy was higher, suggesting that the interaction between solute and the solvent was stronger. In addition, two excellent activity coefficient models (NRTL and Wilson) were used to correlate and evaluate the equilibrium solubility results. The highest relative average deviation and root-mean-square deviation values for VA are 4.36% and 3.09 × 10 -3 , respectively, and 3.35% and 5.52 × 10 -3 for V . Furthermore, the excess enthalpy of the solution was determined on the basis of thermodynamic relations and the Wilson model. The findings indicate that the dissolution process was endothermic.
On the in(cid:143) uence of bottom topography and the Deep Western Boundary Current on Gulf Stream separation

The Gulf Stream separates abruptly from the North American coastline at Cape Hatteras. The absenceof signi(cid:142)cant seasonaland interannualvariabilityin the separationpoint,comparedwith that of other separating boundary currents, suggests that Gulf Stream separation is locally controlled.In this paper we consider the possible in(cid:143) uence of bottom topographyand the Deep Western Boundary Current (DWBC), which descends underneaththe Gulf Stream at Cape Hatteras. The path of the DWBC is strongly constrained by bottom topography. At Cape Hatteras, the continental shelf widens and the DWBC is forced to swing offshore and pass beneath the Gulf Stream. Three possible mechanisms by which bottom topography and the DWBC can affect the separationof the Gulf Stream are proposedand investigated:(i) topographymodi(cid:142)es the background potential vorticity contours; (ii) the DWBC ‘‘advects’’ the Gulf Stream separation point southward; (iii) intense downwelling as the DWBC passes beneath the Gulf Stream induces an adverse pressure gradientin the Gulf Stream, leadingto its separation. Results from a series of idealizednumericalexperimentswith a ‘‘geostrophicvorticity’’ model are presentedto investigatethese mechanisms.Topographyalone does have an impact on the separation point, broadly consistentwith modi(cid:142) cation of the backgroundpotential vorticity.We also show that the presence of a DWBC does, indeed, push the time average separation of the Gulf Stream farther southward, consistentwith both the advectionand adverse pressure gradient mechanisms.However, the time-dependent boundary current separation is more nonlinear than suggested by each of the above mechanisms, undergoing a series of abrupt transitions between northern and southern separationstates.As the DWBC transportis increased,the southernseparationstate is occupiedmore and more frequently.
Heterogeneous Multi-Robot Dialogues for Search Tasks

Dialogue agents are often designed with the tacit assumption that at any one time, there is but one agent and one human, and that their communication channel is exclusive. We are interested in examining situations in which multiple heterogeneous dialogue agents need to interact with a human interlocutor, and where the communication channel becomes necessarily shared. To this end we have constructed a multi-agent dialogue test-bed on which to study dialogue coordination issues in multi-
Inﬂuencer Engagement on Social Media: A Conceptual Model, the Development and Validation of a Measurement Scale

: This research aims to conceptualize, develop, and validate a speciﬁc instrument for measuring the engagement of followers towards inﬂuencers on social media, and more speciﬁcally, in this ﬁrst research, on Instagram. We surveyed (in-depth interviews, and questionnaires) 32 marketing experts and 1170 Instagram followers. Based on the applications of factor analysis and structural equation modelling, we determined 21 valid items. The scale assesses the cognitive, affective, and behavioral characteristics of follower’s engagement across ﬁve dimensions. The results provide insight into the interactive, personal, and social aspects of this type of virtual engagement. It is the ﬁrst scale to measure this engagement in a multidimensional framework, which advances future research. Additionally, it will help managers identify the strongest dimensions of their inﬂuencers’ engagement and thus be able to adjust marketing communication strategies to foster multidimensional follower engagement and subsequent partnerships.
Strong Preservation by Model Deformation

Reliable and secure system design requires an increasing number of methods, algorithms, and tools for automatic program manipulation. Any program change corresponds to a transformation that affects the semantics at some given level of abstraction. We call these techniques model deformations. In this paper we propose a mathematical foundation for completeness-driven deformations of transition systems w.r. t. a given abstraction, and we introduce an algorithm for systematic deformation of Kripke structures for inducing strong preservation in abstract model checking. We prove that our model deformations are deeply related with must and may transitions in modal transition systems.
Mechanisms of the Carcinogenic Chromium(VI)-induced DNA-Protein Cross-linking and Their Characterization in Cultured Intact Human Cells*

DNA-protein complexes (DPCs) were induced in human leukemic T-lymphocyte MOLT4 cells by treatment with potassium chromate. DPCs were isolated by ultracentrifugal sedimentation in the presence of 2% SDS and 5 M urea. The complexes were analyzed by two-dimensional SDS-polyacrylamide gel electrophoresis. Three acidic proteins of 74, 44, and 42 kDa and a basic protein of 51 kDa were primarily complexed to DNA following 25 μM chromate treatment. Higher concentrations of chromate cross-linked many other proteins to DNA. Amino acid sequencing and immunoblotting studies indicated that the acidic 44-kDa protein could be nuclear β-actin. Lectin and aminoglycoside nucleotidyltransferase were also found to cross-link with DNA by chromate treatment. The composition and stability of the DPCs were studied using nucleases, proteinase K, and disruptive chemicals. Pretreatment of cells with antioxidants inhibited the formation of DPCs, measured as K+-SDS precipitable DPCs, indicating the involvement of oxidative mechanisms. Because chromate causes certain nuclear proteins to form complexes with DNA and the complexes are resistant to treatments such as 2% SDS and 5 M urea, but disruptable under gel electrophoretic conditions, chromium could be used as a cross-linking agent for the identification of other proteins, such as transcription factors, that transiently interact with DNA.
Pro-angiogenic potential of human chorion-derived stem cells: in vitro and in vivo evaluation

Human chorion‐derived stem cells (hCDSC) were previously shown to demonstrate multipotent properties with promising angiogenic characteristics in monolayer‐cell culture system. In our study, we investigated the angiogenic capability of hCDSC in 3‐dimensional (3D) in vitro and in vivo angiogenic models for the purpose of future application in the treatment of ischaemic diseases. Human CDSC were evaluated for angiogenic and endogenic genes expressions by quantitative PCR. Growth factors secretions were quantified using ELISA. In vitro and in vivo vascular formations were evaluated by histological analysis and confocal microscopic imaging. PECAM‐1+ and vWF+ vascular‐like structures were observed in both in vitro and in vivo angiogenesis models. High secretions of VEGF and bFGF by hCDSC with increased expressions of angiogenic and endogenic genes suggested the possible angiogenic promoting mechanisms by hCDSC. The cooperation of hCDSC with HUVECS to generate vessel‐like structures in our systems is an indication that there will be positive interactions of hCDSC with existing endothelial cells when injected into ischaemic tissues. Hence, hCDSC is suggested as the novel approach in the future treatment of ischaemic diseases.
Analog circuits for modeling biological neural networks: design and applications

Computational neuroscience is emerging as a new approach in biological neural networks studies. In an attempt to contribute to this field, the authors present here a modeling work based on the implementation of biological neurons using specific analog integrated circuits. They first describe the mathematical basis of such models, then present analog emulations of different neurons. Each model is compared to its biological real counterpart as well as its numerical computation. Finally, the authors demonstrate the possible use of these analog models to interact dynamically with real cells through artificial synapses within hybrid networks. This method is currently used to explore neural networks dynamics.
Does “Clock” Matter in Prostate Cancer?

The ancient adaptation of a 24-hour circadian clock has profound effect on our daily biochemical, physiologic, and behavioral processes, including the monitoring of sex hormone levels. Although the disruption of the circadian cycle has been implicated in the etiology of hormone-related female breast cancer, few studies have been undertaken to determine if a link exists in the development of the most common cancer type among men whose etiology remains largely unknown: hormone-related prostate cancer. Here, we hypothesize that both altered-lighted environments and genetic variations in genes responsible for maintaining circadian rhythms may result in deregulation of clock-associated biological processes, such as androgen expression, and consequently influence an individual's risk of prostate cancer. There is also a potential for the interaction of genetic variants and exposures, such as evening shift work. Confirmation of this hypothesis will add to our understanding of the role of the circadian clock in prostate tumorigenesis and further facilitate the development of novel risk and prognostic biomarkers for prostate cancer. (Cancer Epidemiol Biomarkers Prev 2006;15(1):3–5)
Lattice interference mitigation with mixed channel state information

We study the effect of mixed partial channel state information (CSI) on the performance of lattice interference mitigation schemes. The considered lattice scheme was shown to be equivalent to dirty paper coding when both transmitter and receiver share the same (perfect or imperfect) CSI. We derive a novel lower bound for the case that the transmitter and the receiver have different versions of imperfect CSI. The novel bound reveals that lattice interference mitigation is robust for various CSI error models. The bound is demonstrated for two scenarios of practical interest: frequency division duplex (FDD) and time division duplex (TDD). Both cases result in simple lower bounds, and also give insights on the system optimization. The novel bound is also demonstrated using simulation results for the FDD case.
Combating emerging ﬁnancial risks in the big data era: A perspective review

Big data technology has had a signiﬁcant impact on new business and ﬁnancial services: for example, GPS and Bluetooth inspire location-based services, and search and web technologies motivate online shopping, reviews, and payments. These business services have become more connected than ever, and as a result, ﬁnancial frauds have become a signiﬁcant challenge. Therefore, combating ﬁnancial risks in the big data era requires breaking the borders of traditional data, algorithms, and systems. An increasing number of studies have addressed these challenges and proposed new methods for risk detection, assessment, and forecasting. As a key contribution, we categorize these works in a rational framework: ﬁrst, we identify the data that can be used to identify risks. We then discuss how big data can be combined with the emerging tools to eﬀectively learn or analyze ﬁnancial risk. Finally, we highlight the eﬀectiveness of these methods in real-world applications. Furthermore, we stress on the importance of utilizing multi-channel information, graphs, and networks of long-range dependence for the eﬀective identiﬁcation of ﬁnancial risks. We conclude our survey with a discussion on the new challenges faced by the ﬁnancial sector, namely, deep fake technology, adversaries, causal and interpretable inference, privacy protection, and microsimulations.
Self-critical perfectionism and daily affect: dispositional and situational influences on stress and coping.

This study of university students (64 men and 99 women) examined both dispositional and situational influences of self-critical (SC) perfectionism on stress and coping, which explain its association with high negative affect and low positive affect. Participants completed questionnaires at the end of the day for 7 consecutive days. Structural equation modeling indicated that the relation between SC perfectionism and daily affect could be explained by several maladaptive tendencies associated with SC perfectionism (e.g., hassles, avoidant coping, low perceived social support). Multilevel modeling indicated that SC perfectionists were emotionally reactive to stressors that imply possible failure, loss of control, and criticism from others. As well, certain coping strategies (e.g., problem-focused coping) were ineffective for high-SC perfectionists relative to low-SC perfectionists.
The Role of Tenure Security in Farmers’ Decision-Making on Investment in Improved Seeds: Insights from Mental Models

This paper applies a mental model approach to study the role of tenure security in farmers’ decision-making on agricultural investment in Uganda. We investigate the role that both perceived tenure security and formal land rights, measured by the possession of land certificates, play. Our focus is on investment in improved seeds, a widely applied strategy in agricultural development and climate change adaptation. The study design leverages the roll-out of a large land demarcation and registration project, which creates exogenous variation in farmers’ tenure security. Results show that, in contrast to expectations derived from economic theory, tenure security plays only a minor role in farmers’ decision-making process to invest in improved seeds. Out of 15 potential factors determining a farmer’s investment decision, both perceived tenure security and possession of a land certificate are among the least chosen factors, regardless of whether or not households participated in the land registration project. A heterogeneity analysis reveals that female-headed households value formalized land rights more than male-headed households.
How Evolution May Work Through Curiosity-Driven Developmental Process

Infants' own activities create and actively select their learning experiences. Here we review recent models of embodied information seeking and curiosity-driven learning and show that these mechanisms have deep implications for development and evolution. We discuss how these mechanisms yield self-organized epigenesis with emergent ordered behavioral and cognitive developmental stages. We describe a robotic experiment that explored the hypothesis that progress in learning, in and for itself, generates intrinsic rewards: The robot learners probabilistically selected experiences according to their potential for reducing uncertainty. In these experiments, curiosity-driven learning led the robot learner to successively discover object affordances and vocal interaction with its peers. We explain how a learning curriculum adapted to the current constraints of the learning system automatically formed, constraining learning and shaping the developmental trajectory. The observed trajectories in the robot experiment share many properties with those in infant development, including a mixture of regularities and diversities in the developmental patterns. Finally, we argue that such emergent developmental structures can guide and constrain evolution, in particular with regard to the origins of language.
Sparse regularization of tensor decompositions

Multi-linear techniques using tensor decompositions provide a unifying framework for the high-dimensional data analysis. Sparsity in tensor decompositions clearly improves the analysis and inference of multi-dimensional data. Other than non-negative tensor factorizations, the literature on tensor estimation using sparsity is limited. In this paper, we introduce sparse regularization methods for tensor decompositions which are useful for dimensionality reduction, feature selection as well as signal recovery. One major challenge in most of the tensor decomposition algorithms is their heavy dependence on good initializations. To alleviate such a critical problem we propose a reliable method based on the ridge regression to provide good starting values taking advantage of sparsity. Combined with such initializations our sparse regularization methods show highly improved performance over the conventional methods in the demonstrated simulation studies.
Personalized Image Annotation Using Deep Architecture

In image-centric social networks, such as Instagram and Pinterest, users tend to share photos with several tags. These tags describe the content of the image or provide additional contextual information, and therefore may not be necessarily tied to image content and usually carry personal preference. Annotating images in social networks in a personalized manner is in demand. However, the existing image annotation models, which rely only on image content information, cannot capture the user’s tagging preference. In this paper, we propose a deep architecture for personalized image annotation by leveraging the wealth of information in user’s tagging history. The proposed architecture consists of three components: two components for learning features of the image content and user’s history tags and the other one for combining the two learned features to predict the tags. We also explore two ways to model user’s history tags: 1) simply average the embeddings of user’s history tags and 2) model user’s history tags with a sequence model by long short-term memory recurrent neural network. We evaluate our proposed deep architecture on a large-scale and realistic data set, consisting of ~22.8 million public images uploaded by ~4.69 million users. Experimental results show that our proposed deep architecture is effective on a personalized image annotation task.
Potentially inappropriate medications in hospitalized older patients: a cross-sectional study using the Beers 2015 criteria versus the 2012 criteria

Aim Polypharmacy and potentially inappropriate medications (PIMs) are prominent prescribing issues in elderly patients. The purpose of the study was to investigate the prevalence of PIMs identified by the Beers 2015 and 2012 criteria in older patients in China and identify the correlates of PIMs. Methods This retrospective, cross-sectional study was conducted at Peking University First Hospital. The Beers 2015 and 2012 criteria were applied to evaluate PIMs among hospitalized patients. The associations between PIM use and independent variables were analyzed by logistic regression. The differences between PIM use according to Beers 2012 and 2015 criteria were calculated using chi-squared and kappa tests. Results A total of 456 patients were analyzed; 244 (53.5%) and 204 (44.7%) patients had at least one PIM identified by the Beers 2015 and 2012 criteria, respectively. The most frequent PIMs were proton-pump inhibitors (PPIs), benzodiazepines, and benzodiazepine receptor agonists according to the Beers 2015 criteria. PIMs identified by the Beers 2015 criteria were associated with excessive polypharmacy (OR 1.864, 95% CI 1.210–2.871), a Barthel index ≤60 (OR 1.935, 95% CI 1.056–3.546), and the length of stay (OR 1.066, 95% CI 1.037–1.097). PIM use increased significantly between two criteria (chi-squared test, P<0.001), but good accordance was found between the previous and updated criteria (kappa test 0.782, P<0.001). Conclusion Our study showed a high prevalence of PIM use in China, which was associated with various correlates. The Beers 2015 criteria detected significantly more PIMs than the 2012 criteria due to the inclusion of PPIs.