ethicalabs/Research-Intent-Collab huggingface dataset:
MegIS: High-Performance, Energy-Efficient, and Low-Cost Metagenomic Analysis with In-Storage Processing

Metagenomics has led to significant advances in many fields. Metagenomic analysis commonly involves the key tasks of determining the species present in a sample and their relative abundances. These tasks require searching large metagenomic databases. Metagenomic analysis suffers from significant data movement overhead due to moving large amounts of low-reuse data from the storage system. In-storage processing can be a fundamental solution for reducing this overhead. However, designing an in-storage processing system for metagenomics is challenging because existing approaches to metagenomic analysis cannot be directly implemented in storage effectively due to the hardware limitations of modern SSDs. We propose MegIS, the first in-storage processing system designed to significantly reduce the data movement overhead of the end-to-end metagenomic analysis pipeline. MegIS is enabled by our lightweight design that effectively leverages and orchestrates processing inside and outside the storage system. We address in-storage processing challenges for metagenomics via specialized and efficient 1) task partitioning, 2) data/computation flow coordination, 3) storage technology-aware algorithmic optimizations, 4) data mapping, and 5) lightweight in-storage accelerators. MegIS's design is flexible, capable of supporting different types of metagenomic input datasets, and can be integrated into various metagenomic analysis pipelines. Our evaluation shows that MegIS outperforms the state-of-the-art performance- and accuracy-optimized software metagenomic tools by 2.7$\times$-37.2$\times$ and 6.9$\times$-100.2$\times$, respectively, while matching the accuracy of the accuracy-optimized tool. MegIS achieves 1.5$\times$-5.1$\times$ speedup compared to the state-of-the-art metagenomic hardware-accelerated (using processing-in-memory) tool, while achieving significantly higher accuracy.
EXPLORATIVE ANALYSIS OF CORPORATE SOCIAL RESPONSIBILITY REPORTING: THE CASE OF LEADING EUROPEAN FOOD RETAILERS

The purpose of this paper is to give an insight into the corporate social responsibility (CSR) activities reported in CSR reports published on the web sites of the leading European food retailers. Paper explains the importance of the CSR, followed by the latest state-of-art on the CSR issues reported by food retailers, with special emphasis and focus on international, leading European food retailers. The findings of explorative analysis of corporate social responsibility reporting of the leading European food retailers reveal their dedication to CSR issues and consideration of their own activities. However, based on the previous style of reporting activities, we observe the positive shift from focal company view to the holistic view of the entire supply chain and consequentially, sustainable supply chain perspective. This observation confi rms that leading food retailers are power holders and trend-setters in the CSR issues.
Towards exact molecular dynamics simulations with machine-learned force fields

AbstractMolecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. However, the predictive power of these simulations is only as good as the underlying interatomic potential. Classical potentials often fail to faithfully capture key quantum effects in molecules and materials. Here we enable the direct construction of flexible molecular force fields from high-level ab initio calculations by incorporating spatial and temporal physical symmetries into a gradient-domain machine learning (sGDML) model in an automatic data-driven way. The developed sGDML approach faithfully reproduces global force fields at quantum-chemical CCSD(T) level of accuracy and allows converged molecular dynamics simulations with fully quantized electrons and nuclei. We present MD simulations, for flexible molecules with up to a few dozen atoms and provide insights into the dynamical behavior of these molecules. Our approach provides the key missing ingredient for achieving spectroscopic accuracy in molecular simulations.
Surveillance, National Security and the Right to Privacy in the Digital Era

The fast growth of digital technologies has changed the state security needs against the needs of an individual fundamentally. Mass surveillance and collection of data, as well as algorithmic monitoring, is becoming more popular among governments worldwide to help deal with terrorism, cybercrime and transnational threats. Although such procedures are usually approved within the framework of the doctrine of national security, they create serious issues concerning the loss of privacy, civil liberties and democratic accountability. The paper analyzes the legal, ethical and the policy aspects of surveillance during the digital age and specifically how state security goals can be reconciled with international human rights standards. Using comparative viewpoints, the United States, the European Union and Pakistan, the paper brings out issues in regulating surveillance in both constitutional and international contexts. Finally, it contends that surveillance should be subjected to effective oversight, transparency and proportionality in order to make sure that surveillance activities are legitimate, legal and comply with fundamental rights.
Enabling Semantic Interoperability of Disease Surveillance Data in Health Information Exchange Systems for Community Health Workers

The Integrating the Healthcare Enterprise (IHE) advocates for the integration of distributed and heterogeneous health information systems. This is achieved through the development of standards that specify protocols through which the integrated systems can communicate as profiles. In Namibia, healthcare services are extended to communities through community health workers (CHW). Most CHW are sent to the field to educate and raise awareness of diseases in the communities. However, there is no platform for them to communicate the disease surveillance information to the regional and Head offices in real-time. The IHE, through its Information Technology Infrastructure Technical Framework Volume 2b Transaction B provides a Cross-Gateway Patient Discovery (XCPD) profile that can support the means to locate communities which hold patient-relevant health data as well as the translation of patient identifiers across communities that hold similar patient data. The XCPD profile can be adapted to support communication between the CHW, the DHIS-2 in the Ministry of Health and Social Services (MoHSS) and silo HIS in the regional hospitals, for them to share and also to exchange information within a health information exchange ecosystem. At the present moment, the DHIS-2 and the silo health information systems work in isolation simply because these HIS are heterogeneous, they are not interfaced and also not distributed. The study sought to develop a framework to enable the semantic interoperability of the DHIS-2, silo systems and CHWs for data semantic interoperability so that they can exchange disease-surveillance information.
Independent root-cause analysis of contributing factors, including dismantling of 2 duodenoscopes, to investigate an outbreak of multidrug-resistant Klebsiella pneumoniae

Background and Aims: Worldwide, an increasing number of duodenoscope-associated outbreaks are reported. The high prevalence rate of contaminated duodenoscopes puts patients undergoing ERCP at risk of exogenous transmission of microorganisms. The contributing factors of the duodenoscope design to contamination are not well understood. This article reports on the investigation after the outbreak of a multidrug-resistant Klebsiella pneumoniae (MRKP) related to 2 Olympus TJF-Q180V duodenoscopes. Methods: We conducted a contact patient screening and microbiologic laboratory database search. Reprocessing procedures were audited, and both duodenoscopes were fully dismantled to evaluate all potential contamination factors. Outcomes were reviewed by an experienced independent expert. Results: In total, 102 patients who had undergone an ERCP procedure from January to August 2015 were invited for screening. Cultures were available of 81 patients, yielding 27 MRKP-infected or -colonized patients. Ten patients developed an MRKP-related active infection. The 2 duodenoscopes had attack rates (the number of infected or colonized cases/number of exposed persons) of 35% (17/49) and 29% (7/24), respectively. Identical MRKP isolates were cultured from channel flushes of both duodenoscopes. The review revealed 4 major abnormalities: miscommunication about reprocessing, undetected damaged parts, inadequate repair of duodenoscope damage, and duodenoscope design abnormalities, including the forceps elevator, elevator lever, and instrumentation port sealing. Conclusions: Outbreaks are associated with a combination of factors, including duodenoscope design issues, repair issues, improper cleaning, and systemic monitoring of contamination. To eliminate future duodenoscope-associated infections, a multipronged approach is required, including clear communication by all parties involved, a reliable servicing market, stringent surveillance measures, and eventually new duodenoscope designs and reprocessing procedures with a larger margin of safety.
Satellite Image Classification with Deep Learning

Satellite imagery is important for many applications including disaster response, law enforcement, and environmental monitoring. These applications require the manual identification of objects and facilities in the imagery. Because the geographic expanses to be covered are great and the analysts available to conduct the searches are few, automation is required. Yet traditional object detection and classification algorithms are too inaccurate and unreliable to solve the problem. Deep learning is a family of machine learning algorithms that have shown promise for the automation of such tasks. It has achieved success in image understanding by means of convolutional neural networks. In this paper we apply them to the problem of object and facility recognition in high-resolution, multi-spectral satellite imagery. We describe a deep learning system for classifying objects and facilities from the IARPA Functional Map of the World (fMoW) dataset into 63 different classes. The system consists of an ensemble of convolutional neural networks and additional neural networks that integrate satellite metadata with image features. It is implemented in Python using the Keras and TensorFlow deep learning libraries and runs on a Linux server with an NVIDIA Titan X graphics card. At the time of writing the system is in 2nd place in the fMoW TopCoder competition. Its total accuracy is 83%, the F1 score is 0.797, and it classifies 15 of the classes with accuracies of 95% or better.
Reviewing the evidence used in cost effectiveness models in health technology assessment: a qualitative investigation of current concerns

INTRODUCTION Health technology assessment (HTA) reports involve the development of a cost-effectiveness model in addition to a systematic review of the clinical effectiveness evidence. The purpose of modelling is to draw together all relevant evidence and bring this to bear on the decision problem. By its very nature, the development of the economic model requires additional information beyond clinical efficacy to inform its parameters. The way in which this is done has a fundamental impact on the results of the model and ultimately the decision outcome. Whilst there are accepted methods for reviewing efficacy within the framework of a systematic review the same is not true for the scope of evidence required for models. A systematic approach is required but it is unclear exactly what that means. Several issues need to be considered when reviewing evidence to use in populating cost-effectiveness models. Where timelines are stringent, rapid methods are needed. At the same time the approach needs to be transparent, reproducible, and systematic with precautions for minimising bias. Multiple sources of evidence will be required including: randomised controlled trials and other clinical studies, registry databases, elicitation of expert clinical judgement, industry submissions, routine costing datasets, health valuation studies, grey literature and other sources. Some of the issues around reviewing for model parameters have been highlighted in detail,1,2,3,4 yet there remains very little guidance in the literature with regard to best practice in this area. This study used qualitative methods to explore these issues more fully.
Examination of a Proposed Technique for the Economical Detection and Analysis of Ultra-high Energy Cosmic Ray Showers

A study has been made of a recently suggested scheme for the economical detection and analysis of ultra-high energy cosmic ray showers. It is demonstrated that the scheme should be feasible, particularly if detectors are used to sample the y-ray component of the showers.
Transformers in Time Series: A Survey

Transformers have achieved superior performances in many tasks in natural language processing and computer vision, which also triggered great interest in the time series community. Among multiple advantages of Transformers, the ability to capture long-range dependencies and interactions is especially attractive for time series modeling, leading to exciting progress in various time series applications. In this paper, we systematically review Transformer schemes for time series modeling by highlighting their strengths as well as limitations. In particular, we examine the development of time series Transformers in two perspectives. From the perspective of network structure, we summarize the adaptations and modifications that have been made to Transformers in order to accommodate the challenges in time series analysis. From the perspective of applications, we categorize time series Transformers based on common tasks including forecasting, anomaly detection, and classification. Empirically, we perform robust analysis, model size analysis, and seasonal-trend decomposition analysis to study how Transformers perform in time series. Finally, we discuss and suggest future directions to provide useful research guidance. To the best of our knowledge, this paper is the first work to comprehensively and systematically summarize the recent advances of Transformers for modeling time series data. We hope this survey will ignite further research interests in time series Transformers.
Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks

We propose Unified-IO, a model that performs a large variety of AI tasks spanning classical computer vision tasks, including pose estimation, object detection, depth estimation and image generation, vision-and-language tasks such as region captioning and referring expression, to natural language processing tasks such as question answering and paraphrasing. Developing a single unified model for such a large variety of tasks poses unique challenges due to the heterogeneous inputs and outputs pertaining to each task, including RGB images, per-pixel maps, binary masks, bounding boxes, and language. We achieve this unification by homogenizing every supported input and output into a sequence of discrete vocabulary tokens. This common representation across all tasks allows us to train a single transformer-based architecture, jointly on over 90 diverse datasets in the vision and language fields. Unified-IO is the first model capable of performing all 7 tasks on the GRIT benchmark and produces strong results across 16 diverse benchmarks like NYUv2-Depth, ImageNet, VQA2.0, OK-VQA, Swig, VizWizGround, BoolQ, and SciTail, with no task-specific fine-tuning. Code and demos for Unified-IO are available at: https://unified-io.allenai.org.
Appropriate and inappropriate statistical mechanical treatments for phase transitions at electrified interfaces

Abstract   The prerequisites for a statistical mechanical treatment of phase transitions of adsorbed layers on electrode surfaces to give results compatible with both classical thermodynamics and experiment are studied. It is shown that the use of the charge density as the independent electrical variable, either in the canonical ensemble or the grand ensemble partition function of simple models, leads to artifacts which are at variance with thermodynamics. In contrast, the generalised ensemble Δ, presented in Nikitas et al. (J. Electroanal. Chem., 317 (1991) 43), which uses the potential drop across the layer which undergoes the transition as independent variable, gives results compatible with both experiment and thermodynamics under all circumstances.  The results obtained are compared with the predictions of Guidelli and Aloisi's three-dimensional lattice model of TIP4P water molecules. It is shown that in this case the choice of the charge density as independent variable leads to an incomplete description of the two-dimensional phase transition properties, since the vertical steps obtained in the adsorption isotherms at constant charge density may either indicate a new type of phase transition which has not been observed experimentally yet or they are artifacts which do not represent the properties of the phase transition.
Carbon Emission Reduction Strategies in Logistics Transportation: A Case Study of Truck Fleet Route Optimization using the Vehicle Routing Problem in Denpasar City

Efficient logistics distribution is a key factor in strengthening business competitiveness, particularly in urban areas such as Denpasar, Bali, which face congestion and infrastructure limitations. This study aims to optimize logistics distribution routes by applying the Vehicle Routing Problem method supported by a Geographic Information System. The study evaluates strategies to reduce carbon emissions in Denpasar’s logistics operations through route optimization using the Vehicle Routing Problem approach integrated with Geographic Information System tools. The data include existing routes of PT X, vehicle characteristics, fuel consumption, travel distance, and route elevation. Carbon emissions are calculated using a diesel emission factor of 2.7 kilograms of CO? per liter. The analysis uses ORS Tools in QGIS to generate optimal routes that consider distance and elevation gain. The results indicate a strong influence of topography on fuel consumption. The Ubud route shows substantial elevation gain, which increases energy demand and emissions. Route optimization reduces emissions by 11.61 percent on the Ubud to Ngurah Rai route and by 8.97 percent on the Mengwi to Ngurah Rai route. These findings highlight the importance of terrain based route modeling to improve logistics distribution efficiency and reduce carbon emissions.
Strengthening women engagement in post-Conflict Peace-building in North-East Nigeria: An interrogation of the UN Resolution 1325

The activism towards increased engagement of women participation in peacebuilding process dates back to 1975, from the UN Women conference in Mexico, the 1980 Women Conference in Copenhagen, to the unanimous passage of the UNSCR1325. The fact that women are the most vulnerable in times of war earns them the right to be included in counter-insurgency management and the post-conflict peace process. In North-eastern Nigeria, women suffer from Boko-haram insurgencies and despicable acts, including forced marriages, rapes, displacement, destruction of livelihood and widowed. The study argues that by domesticating the UNSCR1325, the strategies for mitigating the human suffering inflicted on the people by the insurgency would be sufficiently engendered to address the needs of women who are mostly impacted by the insurgencies as mothers, wives’ sisters and daughters.
Cosmic-Ray Modulation: An Empirical Relation with Solar and Heliospheric Parameters

Long-term variations of galactic cosmic rays were compared with the behavior of various solar activity indices and heliospheric parameters during the current solar cycle. This study continues previous works where the cosmic-ray intensity for the solar cycles 20, 21, and 22 was well simulated from the linear combination of the sunspot number, the number of grouped solar flares, and the geomagnetic index Ap. The application of this model to the current solar cycle characterized by many peculiarities and extreme solar events led us to study more empirical relations between solar-heliospheric variables, such as the interplanetary magnetic field, coronal mass ejections, and the tilt of the heliospheric current sheet, and cosmic-ray modulation. By analyzing monthly cosmic-ray data from the Neutron Monitor Stations of Oulu (cutoff rigidity 0.81 GV) and Moscow (2.42 GV) the contribution of these parameters in the ascending, maximum, and descending phases of the cycle was investigated and it is shown that a combination of these parameters reproduces the majority of the modulation potential variations during this cycle. The approach applied makes it possible to better describe the behavior of cosmic rays in the epochs of the solar maxima, which could not be done before. An extended study of the time profiles, the correlations, and the time lags of the cosmic-ray intensity against these parameters using the method of minimizing RMS over all the considered period 1996 – 2006 determines characteristic properties of this cycle as being an odd cycle. Moreover, the obtained hysteresis curves and a correlative analysis during the positive polarity (qA>0, where q is the particle charge) and during the negative polarity (qA<0) intervals of the cycle result in significantly different behavior between solar and heliospheric parameters. The time lag and the correlation coefficient of the cosmic-ray intensity are higher for the solar indices in comparison to the heliospheric ones. A similar behavior also appears in the case of the intervals with positive and negative polarity of the solar magnetic field.
Simulation of physisorption and chemisorption of cysteine on gold substrates in water solutions: classical molecular dynamics based on a reactive force field (ReaxFF)

Classical molecular dynamics simulations based on a reactive force field (Reaxff), parametrized on quantum mechanics and dynamics calculations to describe accurately the interactions of the molecule with gold, are used to explore and characterize in detail the adsorption of cysteine on different types of gold supports in water solution. Various binding modes of the molecules are identified and possible adsorption reaction routes are proposed.
Joint Neural Collaborative Filtering for Recommender Systems

We propose a Joint Neural Collaborative Filtering (J-NCF) method for recommender systems. The J-NCF model applies a joint neural network that couples deep feature learning and deep interaction modeling with a rating matrix. Deep feature learning extracts feature representations of users and items with a deep learning architecture based on a user-item rating matrix. Deep interaction modeling captures non-linear user-item interactions with a deep neural network using the feature representations generated by the deep feature learning process as input. J-NCF enables the deep feature learning and deep interaction modeling processes to optimize each other through joint training, which leads to improved recommendation performance. In addition, we design a new loss function for optimization that takes both implicit and explicit feedback, point-wise and pair-wise loss into account.           Experiments on several real-world datasets show significant improvements of J-NCF over state-of-the-art methods, with improvements of up to 8.24% on the MovieLens 100K dataset, 10.81% on the MovieLens 1M dataset, and 10.21% on the Amazon Movies dataset in terms of HR@10. NDCG@10 improvements are 12.42%, 14.24%, and 15.06%, respectively. We also conduct experiments to evaluate the scalability and sensitivity of J-NCF. Our experiments show that the J-NCF model has a competitive recommendation performance with inactive users and different degrees of data sparsity when compared to state-of-the-art baselines.