Miracle-dz/UniFAR-FTUs huggingface dataset:
Snap and Find: Deep Discrete Cross-domain Garment Image Retrieval

With the increasing number of online stores, there is a pressing need for intelligent search systems to understand the item photos snapped by customers and search against large-scale product databases to find their desired items. However, it is challenging for conventional retrieval systems to match up the item photos captured by customers and the ones officially released by stores, especially for garment images. To bridge the customer- and store- provided garment photos, existing studies have been widely exploiting the clothing attributes (\textit{e.g.,} black) and landmarks (\textit{e.g.,} collar) to learn a common embedding space for garment representations. Unfortunately they omit the sequential correlation of attributes and consume large quantity of human labors to label the landmarks. In this paper, we propose a deep multi-task cross-domain hashing termed \textit{DMCH}, in which cross-domain embedding and sequential attribute learning are modeled simultaneously. Sequential attribute learning not only provides the semantic guidance for embedding, but also generates rich attention on discriminative local details (\textit{e.g.,} black buttons) of clothing items without requiring extra landmark labels. This leads to promising performance and 306$\times$ boost on efficiency when compared with the state-of-the-art models, which is demonstrated through rigorous experiments on two public fashion datasets.
Findings from Multipurpose IoT Solution Experimentations in Finnish SMEs: Common Expectations and Challenges

The Finnish SMEs are showing increasing interest in modern digital solutions in hopes of streamlining their work processes. At the same time, the companies are struggling with resourcing issues and find it challenging to familiarize themselves with new solutions and how those could be applied in their business environment. To support the technical transition, we provided 10 Finnish SMEs with multipurpose IoT solutions, allowing them to conduct guided experimentations with relevant objectives. The business areas of the target companies varied widely from manufacturing to transportation and accommodation, as did their individual needs and expectations towards IoT. Based on this multifaceted background, we compared the companies’ original expectations with the actualised experimentation outcomes, aiming at identifying commonly occurring challenges linked to multipurpose IoT solutions. As a result, an overview of the generalizable findings was gathered to offer further insight on how the multipurpose IoT devices could better serve the SMEs.
Modulatory Effect of Peripheral Magnetic and Neuromuscular Electrical Stimulation on Cortical Excitability: A Functional Near-Infrared Spectroscopy Study

Background: The present study was designed to investigate the effects of neuromuscular electrical stimulation (NMES) and peripheral magnetic stimulation (PMS) applied to the wrist extensor muscle on the cortical activity of healthy adults by using fNIRS. Methods: Fifteen healthy adult subjects (7 males, mean age: 27.13 ± 4.52 years) all received two different conditions of peripheral muscle stimulation in random order: (1) NMES and (2) PMS. The sessions were separated by at least 48 h as a washout period. During muscle stimulation, the motor evoked potential (MEP) of the left primary motor cortex (M1) was measured by transcranial magnetic stimulation (TMS) and the concentration of oxygenated (HbO) and deoxygenated (HbR) hemoglobin detected by fNIRS were used to evaluate the excitability and the activity of the cortex. Results: After the stimulation of the wrist extensor, the MEP amplitude in the left M1 area did not change in both conditions, and there was no difference between NMES and PMS condition. NMES reduced HbO values of several channels in the Prefrontal cortex (PFC), Somatosensory motor cortex (SMC) and Occipital cortex (OC), and HbR valus of several channels in the PFC and SMC. During the PMS stimulation period, the HbO value of all brain areas did not change significantly, while the HbR value of the SMC area decreased. The HbO and HbR value of the channels in the SMC did not differ between NMES and PMS. Inter-region of interest and inter-channel analysis between NMES and PMS showed no difference in functional connectivity. Conclusions: In the case of wrist extensor muscle stimulation, both NMES and PMS can induce cortical activation. PMS targeted to increases the activity of the contralateral SMC, while NMES increased contralateral SMC activity and negatively activated the PFC and OC.
An Analysis of the Influencing Factors of Study Engagement and Its Enlightenment to Education: Role of Perceptions of School Climate and Self-Perception

Predominantly, a school provides pedagogical support to its students, though perceptions about the school’s internal environment can incessantly influence a student’s aggregated learning experience. The present study aims to investigate the impact of the campus environment on students’ study engagement and the roles of self-concept clarity and intentional self-regulation on this association. A hypothetical model about the relationship between campus perception, self-concept clarity, intentional self-regulation, and study engagement was tested. A total of 1597 students from Gansu Province, China, selected through a convenience sampling technique, were surveyed utilizing self-rated scales. Data were collected utilizing the Study Engagement Scale, Perceptions of School Climate Scale, Intentional Self-Regulation Scale and Self-Concept Clarity Scale. Results showed lower to moderate positive correlations between study variables. Students’ campus environment perceptions positively predicted adolescents’ study engagement. This association was also mediated by self-concept clarity and intentional self-regulation. All of these factors explained 41.26% variability of the study engagement. These findings show how the learning environment can impact students. Specifically, these results help demonstrate that self-concept clarity and intentional self-regulation have academic importance to optimize students’ study engagement that in turn promotes quality learning.
ContextDesc: Local Descriptor Augmentation With Cross-Modality Context

Most existing studies on learning local features focus on the patch-based descriptions of individual keypoints, whereas neglecting the spatial relations established from their keypoint locations. In this paper, we go beyond the local detail representation by introducing context awareness to augment off-the-shelf local feature descriptors. Specifically, we propose a unified learning framework that leverages and aggregates the cross-modality contextual information, including (i) visual context from high-level image representation, and (ii) geometric context from 2D keypoint distribution. Moreover, we propose an effective N-pair loss that eschews the empirical hyper-parameter search and improves the convergence. The proposed augmentation scheme is lightweight compared with the raw local feature description, meanwhile improves remarkably on several large-scale benchmarks with diversified scenes, which demonstrates both strong practicality and generalization ability in geometric matching applications.
DFT Simulation of the Water Molecule Interaction with the (00l) Surface of Montmorillonite

Montmorillonite is one of the principal mineralogical phases in clay minerals, where its interaction with water and other molecules represents one of the most important aspects and properties for basic science and specific applications. In fact, montmorillonite has many uses in various scientific and technological fields, ranging from environmental remediation to ceramics, food science, and construction/building materials. Several efforts have characterized its structure and physico-chemical properties, especially at the Tetrahedral-Octahedral-Tetrahedral TOT surface. For this purpose, in this work, the authors investigated the structural and electrostatic potential features of the (00l) surface of montmorillonite and the water adsorption process by first principle methods (density functional theory, DFT), considering both static and molecular dynamics approaches. The provided data further extend the knowledge of the modulation of the water molecule adsorption with this important clay mineral.
The impact of alternative feeding strategies on total factor productivity growth of pig farming: Empirical evidence from EU countries

Aim of study: To investigate the impact of adopting new feeding precision technology on pig production. Area of study: Four EU countries (Germany, France, Poland and Spain) during the period 2010–2015. Material and methods: The Färe-Primont index was used to estimate total factor productivity change and its components, technological change and efficiency change. Main results: German, French and Spanish farms experienced total factor productivity (TFP) progress, while Polish farms did not for both feeding strategies. Our empirical findings suggest a high impact on the productivity of ad libitum feeding technique compared to the restricted one for all countries. Research highlights: Precision feeding strategies provide another avenue to more sustainable livestock production and further evidence that implementing individual ad libitum feeding systems for pigs could enhance farm’s productivity
Generative Adversarial Networks for Text Using Word2vec Intermediaries

Generative adversarial networks (GANs) have shown considerable success, especially in the realistic generation of images. In this work, we apply similar techniques for the generation of text. We propose a novel approach to handle the discrete nature of text, during training, using word embeddings. Our method is agnostic to vocabulary size and achieves competitive results relative to methods with various discrete gradient estimators.
Barriers of Knowledge Transfer between Globally Distributed Teams in ICT Product Development

Increased globalization has generated a growth of globally distributed teams, which are characterized by geographical distance and used by organizations to increase innovation. However, to be able to collaborate effectively, teams are obligated to continuously conduct knowledge transfer (KT) between different geographical locations. Thus, this paper focused on identifying the main barriers that globally distributed teams face in conducting KT and how these barriers are unraveled. Based on an interpretative case study of a product development unit (PDU) in research and development (R&D) department of a telecom company, we identified eight main barriers that hinder the KT between globally distributed teams. These barriers are; knowledge embeddedness, knowledge accessibility and documentation gaps, knowledge complexity, knowledge problematic articulability, ICT tools reliance vs. face to face, inefficient ICT tools utilization, inefficient IT support, and lack of formal processes and guidelines.
AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning

Multi-task learning (MTL) has achieved success over a wide range of problems, where the goal is to improve the performance of a primary task using a set of relevant auxiliary tasks. However, when the usefulness of the auxiliary tasks w.r. t. the primary task is not known a priori, the success of MTL models depends on the correct choice of these auxiliary tasks and also a balanced mixing ratio of these tasks during alternate training. These two problems could be resolved via manual intuition or hyper-parameter tuning over all combinatorial task choices, but this introduces inductive bias or is not scalable when the number of candidate auxiliary tasks is very large. To address these issues, we present AutoSeM, a two-stage MTL pipeline, where the first stage automatically selects the most useful auxiliary tasks via a Beta-Bernoulli multi-armed bandit with Thompson Sampling, and the second stage learns the training mixing ratio of these selected auxiliary tasks via a Gaussian Process based Bayesian optimization framework. We conduct several MTL experiments on the GLUE language understanding tasks, and show that our AutoSeM framework can successfully find relevant auxiliary tasks and automatically learn their mixing ratio, achieving significant performance boosts on several primary tasks. Finally, we present ablations for each stage of AutoSeM and analyze the learned auxiliary task choices.
Empirical Bayes Regret Minimization

Most existing bandit algorithms are designed to attain low regret on any problem instance. While celebrated, this approach is often conservative because it ignores many intricate properties of actual problem instances. In this work, we pioneer the idea of minimizing an empirical approximation to the Bayes regret, the expected regret with respect to a distribution over problems. This approach can be viewed as an instance of learning-to-learn, it is conceptually straightforward, and easy to implement. We present a theoretical analysis to upper bound the Bayes regret of this approach. We then conduct a comprehensive empirical study in a wide range of bandit problems, from Bernoulli bandits to structured problems, such as linear and Gaussian process bandits. We report significant improvements over state-of-the-art bandit algorithms, often by an order of magnitude, by simply optimizing over a sample from the distribution.
Quasi‐Periodic Variation of the Lower Equatorial Cloud Induced by Atmospheric Waves on Venus

A quasi‐periodic variation of the lower cloud amount induced by atmospheric waves was reproduced in our Venus general circulation model for the first time. This result agrees well with previous 2.3‐μm infrared nightside measurements. The cloud amount variation is mainly caused by a temperature fluctuation with a period of ∼5.5 Earth days near the cloud base, which is associated with a Kelvin‐like gravity wave with a zonal wavenumber‐1 structure. Our result might be able to explain the mechanism of the periodical variation of the brightness in the Venusian low‐latitudes shown by the previous 2.3‐μm infrared nightside measurements. It is also suggested that the temporal variation of the mass loading due to the temperature fluctuation is related to the abrupt appearance and disappearance of the large cloud particle (Mode 3) and explains the sharp discontinuity of the lower cloud found by the recent Akatsuki and ground‐based infrared nightside measurements.
Dynamics and Entropy Analysis for a New 4-D Hyperchaotic System with Coexisting Hidden Attractors

This paper presents a new no-equilibrium 4-D hyperchaotic multistable system with coexisting hidden attractors. One prominent feature is that by varying the system parameter or initial value, the system can generate several nonlinear complex attractors: periodic, quasiperiodic, multiple topology chaotic, and hyperchaotic. The dynamics and complexity of the proposed system were investigated through Lyapunov exponents (LEs), a bifurcation diagram, a Poincaré map, and spectral entropy (SE). The simulation and calculation results show that the proposed multistable system has very rich and complex hidden dynamic characteristics. Additionally, the circuit of the chaotic system is designed to verify the physical realizability of the system. This study provides new insights into uncovering the dynamic characteristics of the coexisting hidden attractors system and provides a new choice for nonlinear control or chaotic secure communication technology.
Offloading for Mobile Device Performance Improvement

Mobile devices are increasingly becoming part of everyday life. These include smart phones, tablets, wearable devices etc. Due to their mobility aspect, they are always constrained in their size and weight, which limits their resource capacity, e.g. processing power, and battery life. One possible solution for augmentation of such resource-constrained devices is through efficient usage of their surrounding resources, i.e. using some offloading technique. This paper studies how offloading of tasks to the surrounding resources affects on both the performance of task execution as well as the battery life of the mobile device. Two mobile phones and two tablets (from two different manufacturers) are studied in the experiments to find out the impact of the device characteristics. Two computationally demanding tasks, namely image processing and encryption/decryption, are used in these experiments. These results are compared to our earlier results on mobile devices of previous generations. We assumed that the increased computing power of new devices would make offloading obsolete. Our results show gains both in energy saving and in computational performance with these mobile devices. The comparison to our earlier results show that the performance increase of newer mobile device generations has not diminished the benefits of offloading. These results are in line with results presented in literature and they show that the offloading could offer a viable approach for resource augmentation of mobile devices towards edge/fog resources emphasized by the new 5G technology.
Performance and Upskilling: Indian IT Sector Temporary Workers’ Job Insecurity Coping Strategies Identified Through Grounded Theory

Globally, the demand for temporary workers is rising across organizations and sectors, driven by benefits of lower cost and workforce flexibility. Flexibility for organizations, however, means job insecurity for the temporary worker. This study focuses on understanding how the Indian IT sector’s temporary agency workers (TAWs), that is, temporary workers recruited by staffing agencies to work with client organizations, cope with their job insecurity. Grounded theory-based analysis of 36 interviews revealed that TAWs rely on enhancing their employability as the primary strategy to combat job insecurity. In addition, to enhance employability, TAWs make proactive efforts to perform consistently and upskill continuously. TAWs also expect support, largely in the form of learning and development opportunities, from the staffing agency and client to augment employability. The findings and recommendations in this article to help TAWs improve employability hold significance for TAWs, staffing agencies as well as clients. With enhanced employability, TAWs overcome job insecurity—an impediment to job performance, which is of direct relevance to the worker and the client—and also affects the staffing agency’s long-term relationship with the client.
A New Dictionary Construction Based Multimodal Medical Image Fusion Framework

Training a good dictionary is the key to a successful image fusion method of sparse representation based models. In this paper, we propose a novel dictionary learning scheme for medical image fusion. First, we reinforce the weak information of images by extracting and adding their multi-layer details to generate the informative patches. Meanwhile, we introduce a simple and effective multi-scale sampling to implement a multi-scale representation of patches while reducing the computational cost. Second, we design a neighborhood energy metric and a multi-scale spatial frequency metric for clustering the image patches with a similar brightness and detail information into each respective patch group. Then, we train the energy sub-dictionary and detail sub-dictionary, respectively by K-SVD. Finally, we combine the sub-dictionaries to construct a final, complete, compact and informative dictionary. As a main contribution, the proposed online dictionary learning can not only obtain an informative as well as compact dictionary, but can also address the defects, such as superfluous patch issues and low computation efficiency, in traditional dictionary learning algorithms. The experimental results show that our algorithm is superior to some state-of-the-art dictionary learning based techniques in both subjective visual effects and objective evaluation criteria.
Entropic Uncertainty Relations via Direct-Sum Majorization Relation for Generalized Measurements

We derive an entropic uncertainty relation for generalized positive-operator-valued measure (POVM) measurements via a direct-sum majorization relation using Schur concavity of entropic quantities in a finite-dimensional Hilbert space. Our approach provides a significant improvement of the uncertainty bound compared with previous majorization-based approaches (Friendland, S.; Gheorghiu, V.; Gour, G. Phys. Rev. Lett. 2013, 111, 230401; Rastegin, A.E.; Życzkowski, K. J. Phys. A, 2016, 49, 355301), particularly by extending the direct-sum majorization relation first introduced in (Rudnicki, Ł.; Puchała, Z.; Życzkowski, K. Phys. Rev. A 2014, 89, 052115). We illustrate the usefulness of our uncertainty relations by considering a pair of qubit observables in a two-dimensional system and randomly chosen unsharp observables in a three-dimensional system. We also demonstrate that our bound tends to be stronger than the generalized Maassen–Uffink bound with an increase in the unsharpness effect. Furthermore, we extend our approach to the case of multiple POVM measurements, thus making it possible to establish entropic uncertainty relations involving more than two observables.
Information sharing, collaboration, and decision-making during disease outbreaks: the experience of Fiji

ABSTRACT With disease outbreaks rising, responsive and resilient systems are needed. Understanding how disease outbreaks are managed, and how governments, non-governmental organisations (NGOs), and international governmental organisations (IGOs) work together to respond is essential. Fifteen key informant interviews and one group interview were conducted with individuals in water and health sectors from the Fijian government, NGOs, and IGOs at the water catchment, divisional, and national levels about decision-making during outbreaks. Thematic analysis found changes in communication, collaboration, and coordination during an outbreak. Communication becomes open, transparent, frequent, and utilises informal pathways including social media. Collaboration increases and becomes more flexible, leading to changes in roles, responsibilities and decision-making. The flexibility helps resource and fund redistribution. Coordination guided by government priorities, funding, laws, adaptability, and flexibility, enables targeted actions including resource prioritisation. Maximising the communication, collaboration, and coordination amongst sectors during disease outbreaks and non-outbreak periods can strengthen system resilience and responsiveness.
Valorization of Agri-Food Waste from Pistachio Hard Shells: Extraction of Polyphenols as Natural Antioxidants

The agricultural processing industry usually generates a remarkable amount of by-products rich in bioactive compounds, which can be exploited for agri-food or nutraceutical applications. Pistachio’s hard shell is one of the major by-products from pistachio industrial processing. The aim of this work was the evaluation of pistachio shells as a potential source of natural antioxidants. We evaluated different extraction procedures by measuring total phenolic content, total flavonoid content and antioxidative activity (DPPH•, TEAC and ORAC). The microwave-assisted ethanol extract turned out to be the most promising and was fractionated by XAD-16 column chromatography, affording six fractions analyzed through HPLC/ESI-MS/MS and 1H-NMR to identify the main antioxidative constituents. Fractions Fr4–Fr6 demonstrated the highest antioxidant activity. Gallic acid and a monogalloylglusose isomer are the main phenolic constituents of Fr4. Both simple and complex phenolics, such as flavonoids and hydrolysable tannins, were identified in fractions Fr5 and Fr6; pentagalloylglucose and kaempferol, well-known for their antioxidant activity, are the most abundant constituents. The results highlighted that the proposed methodology can be an effective way to recover bioactive phenolic compounds from pistachio hard shell, making this by-product a promising source of compounds with potential applications in food and healthcare sectors.