Miracle-dz/UniFAR-FTUs huggingface dataset:
Computation Resource Allocation Solution in Recommender Systems

Recommender systems rely heavily on increasing computation resources to improve their business goal. By deploying computation-intensive models and algorithms, these systems are able to inference user interests and exhibit certain ads or commodities from the candidate set to maximize their business goals. However, such systems are facing two challenges in achieving their goals. On the one hand, facing massive online requests, computation-intensive models and algorithms are pushing their computation resources to the limit. On the other hand, the response time of these systems is strictly limited to a short period, e.g. 300 milliseconds in our real system, which is also being exhausted by the increasingly complex models and algorithms. In this paper, we propose the computation resource allocation solution (CRAS) that maximizes the business goal with limited computation resources and response time. We comprehensively illustrate the problem and formulate such a problem as an optimization problem with multiple constraints, which could be broken down into independent sub-problems. To solve the sub-problems, we propose the revenue function to facilitate the theoretical analysis, and obtain the optimal computation resource allocation strategy. To address the applicability issues, we devise the feedback control system to help our strategy constantly adapt to the changing online environment. The effectiveness of our method is verified by extensive experiments based on the real dataset from Taobao.com. We also deploy our method in the display advertising system of Alibaba. The online results show that our computation resource allocation solution achieves significant business goal improvement without any increment of computation cost, which demonstrates the efficacy of our method in real industrial practice.
Integrating Column-Oriented Storage and Query Processing Techniques Into Graph Database Management Systems

Column-oriented RDBMSs, which support traditional read-heavy analytics workloads, employ a speciﬁc set of storage and query processing techniques for scalability and performance, such as positional tuple IDs, column-speciﬁc compression, and block-oriented processing. We revisit these techniques in the context of contemporary graph database management systems (GDBMSs). GDBMSs support a new set of analytics workloads, such as fraud detection in ﬁnancial transaction networks or recommendations in social networks, that are also read-heavy but have fundamentally diﬀerent access patterns than traditional analytics workloads. We ﬁrst review the data characteristics and query access patterns in GDBMS to identify components of GDBMSs where existing columnar techniques can and cannot directly be used. We then present the physical data layout of columnar data structures, new columnar compression, and query-processing techniques that are optimized for GDBMSs. Our techniques include a new compact vertex and edge ID scheme, a new null and empty list compression scheme based on preﬁx-sums, and list-based query processing. We have integrated our techniques into GraphﬂowDB, an in-memory GDBMS. Compared to uncompressed storage, our compression techniques has scaled the system by 3.55x with minimal performance overheads. Our null compression scheme outperforms existing columnar schemes in query performance, with minor loss in compression rate and achieves both higher compression rate and better query performance as compared to row-oriented storage techniques adopted by existing GDBMSs. Finally, our list-based query processor techniques improve query performance by 2.7x on a variety of path queries and signiﬁcantly outperform their corresponding conventional versions.
Understanding the Usability Challenges of Machine Learning In High-Stakes Decision Making

includes other explanation types, as described in this paper. Machine learning (ML) is being applied to a diverse and ever-growing set of domains. In many cases, domain experts — who often have no expertise in ML or data science — are asked to use ML predictions to make high-stakes decisions. Multiple ML usability challenges can appear as result, such as lack of user trust in the model, inability to reconcile human-ML disagreement, and ethical concerns about oversimplification of complex problems to a single algorithm output. In this paper, we investigate the ML usability challenges present
Multi-tracer intensity mapping: cross-correlations, line noise & decorrelation

Line intensity mapping (LIM) is a rapidly emerging technique for constraining cosmology and galaxy formation using multi-frequency, low angular resolution maps. Many LIM applications crucially rely on cross-correlations of two line intensity maps, or of intensity maps with galaxy surveys or galaxy/CMB lensing. We present a consistent halo model to predict all these cross-correlations and enable joint analyses, in 3D redshift-space and for 2D projected maps. We extend the conditional luminosity function formalism to the multi-line case, to consistently account for correlated scatter between multiple galaxy line luminosities. This allows us to model the scale-dependent decorrelation between two line intensity maps, a key input for foreground rejection and for approaches that estimate auto-spectra from cross-spectra. This also enables LIM cross-correlations to reveal astrophysical properties of the interstellar medium inacessible with LIM auto-spectra. We expose the different sources of luminosity scatter or “line noise” in LIM, and clarify their effects on the 1-halo and galaxy shot noise terms. In particular, we show that the effective number density of halos can in some cases exceed that of galaxies, counterintuitively. Using observational and simulation input, we implement this halo model for the Hα, [Oiii], Lyman-α, CO and [Cii] lines. We encourage observers and simulators to measure galaxy luminosity correlation coefficients for pairs of lines whenever possible. Our code is publicly available at https://github.com/EmmanuelSchaan/HaloGen/tree/LIM. In a companion paper, we use this halo model formalism and code to highlight the degeneracies between cosmology and astrophysics in LIM, and to compare the LIM observables to galaxy detection for a number of surveys.
Cost Optimal Planning as Satisfiability

We investigate upper bounds on the length of cost optimal plans that are valid for problems with 0-cost actions. We employ these upper bounds as horizons for a SAT-based encoding of planning with costs. Given an initial upper bound on the cost of the optimal plan, we experimentally show that this SAT-based approach is able to compute plans with better costs, and in many cases it can match the optimal cost. Also, in multiple instances, the approach is successful in proving that a certain cost is the optimal plan cost.
A confined dynamo: Magnetic activity of the K-dwarf component in the pre-cataclysmic binary system V471 Tauri

Context. Late-type stars in close binary systems can exhibit strong magnetic activity owing to rapid rotation supported by tidal locking. On the other hand, tidal coupling may suppress the differential rotation which is a key ingredient of the magnetic dynamo. Aims. We studied the red dwarf component in the eclipsing binary system V471 Tau in order to unravel the relations between the different activity layers, from the stellar surface through the chromosphere up to the corona. Our aim is to study how the magnetic dynamo in the late-type component is affected by the close white dwarf companion. Methods. We used space photometry, high-resolution spectroscopy, and X-ray observations from different space instruments to explore the main characteristics of magnetic activity. We applied a light curve synthesis program to extract the eclipsing binary model and to analyze the residual light variations. Photometric periods were obtained using a Fourier-based period search code. We searched for flares by applying an automated flare detection code. Spectral synthesis was used to derive or specify some of the astrophysical parameters. Doppler imaging was used to reconstruct surface temperature maps, which were cross-correlated to derive surface differential rotation. We applied different conversion techniques to make it possible to compare the X-ray emissions obtained from different space instruments. Results. From the K2 photometry we found that 5–10 per cent of the apparent surface of the red dwarf is covered by cool starspots. From seasonal photometric period changes we estimated a weak differential rotation. From the flare activity we derived a cumulative flare frequency diagram which suggests that frequent flaring could have a significant role in heating the corona. Using high-resolution spectroscopy we reconstructed four Doppler images for different epochs which reveal an active longitude, that is, a permanent dominant spot facing the white dwarf. From short term changes in the consecutive Doppler images we derived a weak solar-type surface differential rotation with αDR = 0.0026 shear coefficient, similar to that provided by photometry. The long-term evolution of X-ray luminosity reveals a possible activity cycle length of ≈12.7 yr, traces of which were also discovered in the Hα spectra. Conclusions. We conclude that the magnetic activity of the red dwarf component in V471 Tau is strongly influenced by the close white dwarf companion. We confirm the presence of a permanent dominant spot (active longitude) on the red dwarf facing the white dwarf. The weak differential rotation of the red dwarf is very likely the result of tidal confinement by the companion. We find that the periodic appearance of the inter-binary Hα emission from the vicinity of the inner Lagrangian point is correlated with the activity cycle.
Reinforcement Learning Control of a Forestry Crane Manipulator

Forestry machines are heavy vehicles performing complex manipulation tasks in unstructured production forest environments. Together with the complex dynamics of the onboard hydraulically actuated cranes, the rough forest terrains have posed a particular challenge in forestry automation. In this study, the feasibility of applying reinforcement learning control to forestry crane manipulators is investigated in a simulated environment. Our results show that it is possible to learn successful actuator-space control policies for energy efficient log grasping by invoking a simple curriculum in a deep reinforcement learning setup. Given the pose of the selected logs, our best control policy reaches a grasping success rate of 97%. Including an energy-optimization goal in the reward function, the energy consumption is significantly reduced compared to control policies learned without incentive for energy optimization, while the increase in cycle time is marginal. The energy-optimization effects can be observed in the overall smoother motion and acceleration profiles during crane manipulation.
Calibration of the scintillation of cerium-doped yttrium aluminum garnet crystals irradiated by monoenergetic 4 MeV energy electrons

This paper presents the results of measurements of fluorescing cerium-doped yttrium aluminum garnet crystals after being irradiated by an accelerated electron beam with energy of around 4 MeV. The measurements were performed using the PHIL linear accelerator at LAL (France). We observe linear dependence of the crystal emission to the electron beam charge and the isotropy of the photon emission. We provide the calibration coefficients of the photon emission depending on the charge of the accelerated electron beam for two crystals originating from two different manufacturers.
Quantifying Photoinduced Polaronic Distortions in Inorganic Lead Halide Perovskite Nanocrystals

The development of next-generation perovskite-based optoelectronic devices relies critically on the understanding of the interaction between charge carriers and the polar lattice in out-of-equilibrium conditions. While it has become increasingly evident for CsPbBr3 perovskites that the Pb–Br framework flexibility plays a key role in their light-activated functionality, the corresponding local structural rearrangement has not yet been unambiguously identified. In this work, we demonstrate that the photoinduced lattice changes in the system are due to a specific polaronic distortion, associated with the activation of a longitudinal optical phonon mode at 18 meV by electron–phonon coupling, and we quantify the associated structural changes with atomic-level precision. Key to this achievement is the combination of time-resolved and temperature-dependent studies at Br K and Pb L3 X-ray absorption edges with refined ab initio simulations, which fully account for the screened core-hole final state effects on the X-ray absorption spectra. From the temporal kinetics, we show that carrier recombination reversibly unlocks the structural deformation at both Br and Pb sites. The comparison with the temperature-dependent XAS results rules out thermal effects as the primary source of distortion of the Pb–Br bonding motif during photoexcitation. Our work provides a comprehensive description of the CsPbBr3 perovskites’ photophysics, offering novel insights on the light-induced response of the system and its exceptional optoelectronic properties.
TaskAllocator: A Recommendation Approach for Role-based Tasks Allocation in Agile Software Development

In this paper, we propose a recommendation approach – TaskAllocator – in order to predict the assignment of incoming tasks to potential befitting roles. The proposed approach, identifying team roles rather than individual persons, allows project managers to perform better tasks allocation in case the individual developers are over-utilized or moved on to different roles/projects. We evaluated our approach on ten agile case study projects obtained from the Taiga. io repository. In order to determine the TaskAllocator’s performance, we have conducted a benchmark study by comparing it with contemporary machine learning models. The applicability of the TaskAllocator was assessed through a plugin that can be integrated with JIRA and provides recommendations about suitable roles whenever a new task is added to the project. Lastly, the source code of the plugin and the dataset employed have been made public.
A Proactive Context-Aware Recommender System for In-Vehicle Use

In the last years, the amount of in-vehicle functionalities has drastically increased. Consequently, users frequently have to navigate through a complex menu structure to control a desired functionality. This information overload is typically the cause of distracted driving, which can have unwelcome consequences. The main contribution of this paper is to provide a user behavioral model to represent the recurrent preferences of users for in-vehicle functionalities and to employ it to construct a proactive context-aware recommender system. This work is based on the assumption that most drivers tend to use a given feature within similar contextual environments (i.e. have routines). Therefore, the proposed method aims to reduce the cognitive workload by automatically executing the regularly used features. We believe, this system will not only minimize the number of driver interactions, and hence, the distractions, but also let passengers have a more comfortable and personalized experience. The viability and efficacy of the proposed algorithm are justified by theory and demonstrated on a user study.
The p Curve Method for Illumination Estimation

When taking an image with a camera, the illumination can have a lot of influence on the colors of objects in the image. This influence can have adverse effects on different computer vision tasks. This is solved by preprocessing the image using computational color constancy. In this paper we propose a new method for illumination estimation, one of the parts of color constancy, using low level image statistics and optimization approaches, the p curve method. Experimental results are presented comparing both the newly proposed method to already established methods as well as comparing different variants of the proposed method.
A System to Retrofit Existing Infrastructure to be Smart and IoT Ready

With this paper, the aim was to establish an ecosystem where an existing infrastructure could be retrofitted with the smart technologies and various appliances in a house could communicate with each other. This enables the existing infrastructure consisting of fans, lights and devices like refrigerator, geysers and air-conditioners to be communication ready with upcoming technologies. The project was aimed to build systems that made homes safe, reliable and self-sustained. New processes were set up that increased the efficiency of day to day tasks. One key goal along with the rest was to reduce the total carbon footprint of the house on the environment. The key functionalities included were to build secure mechanisms and logic for access control system and temperature monitoring and control system. Along with this, a system comprising of various sensors was built which could monitor water level in tanks, control valves and gas leakage which makes a house safe to live in.
Electron Traps and Excess Current Induced by Hot‐Hole Injection into Thin SiO2 Films

Electron capture and excess current after substrate hot-hole injection into 60 and 131 A silicon dioxides have been studied. After the hole injection into 131 A oxides, a transient excess current appears in the gate current-oxide field characteristics and electrons are captured even at low oxide fields for the positive gate polarity. The low field electron capture is explained based on the tunneling of electrons from the substrate into the positive charge and neutral trap centers created near the substrate-SiO 2 interface. The transient excess current is suggested to be due to the two current components: the displacement current component due to the electron capture by both the positive charge and the neutral trap centers, and the tunneling current component enhanced by the positive charge located near the interface. In 60 A oxides, excess currents appear for both positive and negative gate polarities after the hole injection, and consist of the steady-state leakage current component and the transient current component. The leakage current induced by the hole injection is increased as the oxide thickness is decreased.
Fostering big data analytics capability through process innovation: Is management innovation the missing link?

Big data is heralded as the next big thing for organizations to gain competitive advantages. New data-driven firms need to control key resources in order to develop the new data-driven capabilities they need. The present paper analyzes the relationships between process innovation capability, management innovation and big data analytics capability, covering aspects related to a better understanding of how firms can obtain benefit from their investments in big data. PLS-SEM models with data from 195 firms are used. The main results suggest that management innovation and process innovation capabilities have an important role in the development of big data analytics capability. Big data analytics capability is much more than just investing in technology, collecting vast amounts of data, and allowing the technology department to experiment with analytics. The outcomes of this study present evidence on how innovative managers who promote innovations in process as well as innovations in different aspects of the organization favor the development of capabilities in big data analytics.
Trends in Health and Wellness Peer Educator Training: A Five-Year Analysis

This study explored trends in health and wellness peer educator training using five years of data from a multi-institutional study. Findings indicated training increased among 17 health and wellness topics over five years, and peer educators utilized the knowledge gained in their training to facilitate programs and engage in educational conversations. Peer educators without training though are also engaged in these outreach activities. Implications for student affairs practice are included.
‘Data must fall’: mobile data pricing, regulatory paralysis and citizen action in South Africa

ABSTRACT South Africa’s mobile telecommunications industry boasts four major players, namely Vodacom, MTN, CELL C and Telkom. On the face of it, this suggests a diverse market with healthy competition. However, the costs of mobile services, including data and voice calls remains among the highest in the world, suggesting a distorted and uncompetitive market. Despite increasing awareness of this reality, and growing public outcry, change has been slow and piecemeal, with both policymakers and regulatory authorities not taking decisive action. Using institutional policy analysis, document analysis and interviews with sampled actors from the telecommunication industry, civic society and the regulatory authority, this paper argues that a combination of regulatory paralysis, collusive behaviour by oligopolistic operators, indecisive government policy, and to some degree citizen inaction have led to sustained overpricing of mobile data in South Africa. It highlights the corporate dominance in the policing of this sector, which has implications for citizens’ capacity to participate in various spheres of life, and hence for democracy. The paper is premised on the public interest theory and theories of universal (internet) access which are germane to understanding access to information as a human rights issue.
Estimation of DNA base composition by high performance liquid chromatography of its nuclease P1 hydrolysate.

In order to develop a new method for determination of the G+C content of a DNA, preparations from 26 bacterial strains and salmon sperm were digested to form 5'-deoxyribonucleotide-monophosphates (dNMP) with nuclease P1 and subjected to high performance liquid chromatography (HPLC). Chromatograms indicated excellent separation of the components, and the data analysis suggested sufficient reproducibility and reasonable A/T and G/C ratios over a wide range (39 to 72mol%) of G+C values.
Coherence Between Brain Activation and Speech Envelope at Word and Sentence Levels Showed Age-Related Differences in Low Frequency Bands

Abstract Speech perception is dynamic and shows changes across development. In parallel, functional differences in brain development over time have been well documented and these differences may interact with changes in speech perception during infancy and childhood. Further, there is evidence that the two hemispheres contribute unequally to speech segmentation at the sentence and phonemic levels. To disentangle those contributions, we studied the cortical tracking of various sized units of speech that are crucial for spoken language processing in children (4.7–9.3 years old, N = 34) and adults (N = 19). We measured participants’ magnetoencephalogram (MEG) responses to syllables, words, and sentences, calculated the coherence between the speech signal and MEG responses at the level of words and sentences, and further examined auditory evoked responses to syllables. Age-related differences were found for coherence values at the delta and theta frequency bands. Both frequency bands showed an effect of stimulus type, although this was attributed to the length of the stimulus and not the linguistic unit size. There was no difference between hemispheres at the source level either in coherence values for word or sentence processing or in evoked response to syllables. Results highlight the importance of the lower frequencies for speech tracking in the brain across different lexical units. Further, stimulus length affects the speech–brain associations suggesting methodological approaches should be selected carefully when studying speech envelope processing at the neural level. Speech tracking in the brain seems decoupled from more general maturation of the auditory cortex.