Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep Reinforcement Learning Approach for Interactive Search with Sentence-level Feedback

Interactive search can provide a better experience by incorporating interaction feedback from the users. This can significantly improve search accuracy as it helps avoid irrelevant information and captures the users' search intents. Existing state-of-the-art (SOTA) systems use reinforcement learning (RL) models to incorporate the interactions but focus on item-level feedback, ignoring the fine-grained information found in sentence-level feedback. Yet such feedback requires extensive RL action space exploration and large amounts of annotated data. This work addresses these challenges by proposing a new deep Q-learning (DQ) approach, DQrank. DQrank adapts BERT-based models, the SOTA in natural language processing, to select crucial sentences based on users' engagement and rank the items to obtain more satisfactory responses. We also propose two mechanisms to better explore optimal actions. DQrank further utilizes the experience replay mechanism in DQ to store the feedback sentences to obtain a better initial ranking performance. We validate the effectiveness of DQrank on three search datasets. The results show that DQRank performs at least 12% better than the previous SOTA RL approaches. We also conduct detailed ablation studies. The ablation results demonstrate that each model component can efficiently extract and accumulate long-term engagement effects from the users' sentence-level feedback. This structure offers new technologies with promised performance to construct a search system with sentence-level interaction.
A Deep Reinforcement Learning Approach for DNN Collaborative Inference in Mobile Edge Computing

Mobile Edge Computing(MEC) has become a new paradigm to reduce the deep neural network(DNN) inference latency for resource-limited mobile devices by collaborating with edge servers. However, mobile devices' movement makes the collaborative edge site difficult to determine and the limited battery makes the device unable to work continuously. Hence, we propose a DNN collaborative inference framework in MEC, where mobile devices are equipped with energy harvesting(EH) equipment. In order to better capture energy harvesting uncertainty and task arrival randomness, we formulate the problem as a constrained Markov decision process(CMDP). Specifically, energy harvesting strategy, edge site-selection and DNN partition strategy are jointly considered to minimize the average inference latency while keeping each mobile device battery energy level stable. As for CMDP problem, general deep reinforcement learning(DRL) cannot solved it directly. We first transform the CMDP into a Markov decision process(MDP) by leveraging the Lyapunov optimization technique. Then, we decompose the MDP problem into two sub-problems: energy harvesting strategy(EHS) and DNN collaborative inference(DNNCI). We propose an algorithm based on DRL to solve DNNCI problem. Extensive simulation results are provided to demonstrate that our algorithm achieves outstanding performance over existing benchmarks and can stabilize the battery level of mobile devices.
Treatment of postmenopausal osteoporosis, patient perspectives – focus on once yearly zoledronic acid

: Oral bisphosphonates are of proven efficacy in preventing fractures in postmenopausal osteoporosis. However, poor adherence limits their real-world efficacy and clinical utility. Zoledronic acid (ZOL) is a potent bisphosphonate administered by annual intravenous infusion, effectively ensuring adherence to therapy over the following year. According to available data, 66% to 79% of patients have expressed a preference for ZOL over oral bisphosphonates. This is likely to lead to enhanced clinical outcomes, although long-term (repeat annual) adherence is currently unknown. ZOL is of proven efficacy, with hip fracture reduction of 41% and morphometric vertebral fracture reduction of 70% over 3 years in the HORIZON PFT trial. It has demonstrated a good side-effect profile with postinfusion flu-like symptoms being the most common. Additionally, it has been associated with decreased mortality in patients following surgery for hip fracture. There is no clear association between exposure and the rate of serious or nonserious atrial fibrillation. We review adherence to oral bisphosphonates, and the pharmacokinetics, efficacy, safety, and patient preference for ZOL.
Learning analytics: drivers, developments and challenges

Learning analytics is a significant area of technology-enhanced learning that has emerged during the last decade. This review of the field begins with an examination of the technological, educational and political factors that have driven the development of analytics in educational settings. It goes on to chart the emergence of learning analytics, including their origins in the 20th century, the development of data-driven analytics, the rise of learning-focused perspectives and the influence of national economic concerns. It next focuses on the relationships between learning analytics, educational data mining and academic analytics. Finally, it examines developing areas of learning analytics research, and identifies a series of future challenges.
Bioengineering the Lung : Molecules , Materials , Matrix , Morphology , and Mechanics Coming to terms with tissue engineering and regenerative medicine in the lung

Prakash YS, Tschumperlin DJ, Stenmark KR. Coming to terms with tissue engineering and regenerative medicine in the lung. Am J Physiol Lung Cell Mol Physiol 309: L625–L638, 2015. First published August 7, 2015; doi:10.1152/ajplung.00204. 2015. —Lung diseases such as emphysema, interstitial fibrosis, and pulmonary vascular diseases cause significant morbidity and mortality, but despite substantial mechanistic understanding, clinical management options for them are limited, with lung transplantation being implemented at end stages. However, limited donor lung availability, graft rejection, and long-term problems after transplantation are major hurdles to lung transplantation being a panacea. Bioengineering the lung is an exciting and emerging solution that has the ultimate aim of generating lung tissues and organs for transplantation. In this article we capture and review the current state of the art in lung bioengineering, from the multimodal approaches, to creating anatomically appropriate lung scaffolds that can be recellularized to eventually yield functioning, transplant-ready lungs. Strategies for decellularizing mammalian lungs to create scaffolds with native extracellular matrix components vs. de novo generation of scaffolds using biocompatible materials are discussed. Strengths vs. limitations of recellularization using different cell types of various pluripotency such as embryonic, mesenchymal, and induced pluripotent stem cells are highlighted. Current hurdles to guide future research toward achieving the clinical goal of transplantation of a bioengineered lung are discussed.
Bioengineering the Lung : Molecules , Materials , Matrix , Morphology , and Mechanics Isolation of individual cellular components from lung tissues of patients with lymphangioleiomyomatosis

Katsutoshi Ando, Naoya Fujino, Keiko Mitani, Chiharu Ota, Yoshinori Okada, Takashi Kondo, Teruaki Mizobuchi, Masatoshi Kurihara, Kenji Suzuki, Yoshito Hoshika, Hiroki Ebana, Etsuko Kobayashi, Kazuhisa Takahashi, Hiroshi Kubo, and Kuniaki Seyama Division of Respiratory Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan; Department of Advanced Preventive Medicine for Infectious Disease, Tohoku University Graduate School of Medicine, Sendai, Japan; Division of Human Pathology, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan; Department of Thoracic Surgery, Institute of Development, Aging, and Cancer, Tohoku University Graduate School of Medicine, Miyagi, Japan; Pneumothorax Research Center and Division of Thoracic Surgery, Nissan Tamagawa Hospital, Tokyo, Japan; and Division of Thoracic Surgery, Juntendo University Faculty of Medicine and Graduate School of Medicine, Bunkyo-Ku, Tokyo, Japan
An Energy-Aware and Beaconless Geographic Routing for Mobile Ad Hoc Network

Geographic routing has been widely hailed owning to its efficiency and scalability,and it is one of research hotspots for routing algorithms of Mobile Ad Hoc Network.Since node energy in such network is limited and cannot be supplemented easily in many cases,balancing energy consumption is one of the most important method to prolong network lifetime.To solve the problem of the energy-consumption imbalance of greedy routing and perimeter routing,we present an Energy-Aware and Beaconless Geographic Routing(EBGR). It operates in two strategies:contend-based greedy-forwarding strategy and void handling strategy.In the contend-based greedy-forwarding strategy,nodes forward data packets by competition.When receiving a data packet from upstream node,intermediate nodes within its forwarding area contend to relay it by waiting for a dynamic forwarding delay(obtained by energy and location information and called DFD). The intermediate node with minimum DFD transmits the data packet first and other nodes cancel their scheduled transmission for the same data packet by overhearing its packet forwarding.When there are no neighbor nodes in the forwarding area,a void handling strategy is employed.Applying angle progress and energy information to DFD,we can allow data packet to traverse the void and to balance energy consumption.Finally,simulation results show that EBGR exhibits 2%-4% packet delivery ratio higher and 10%-20% network lifetime longer than BLR and GEAR.
The effects of topographic correction and gap filling in imagery on the detection of tropical forest disturbances using a Landsat time series in Myanmar

ABSTRACT In this study, we evaluated the effects of topographic correction and gap filling of Landsat Enhanced Thematic Mapper Plus (ETM+) images on the accuracy of forest change detection through a trajectory-based approach. Four types of Landsat time series stacks (LTSS) were generated. These stacks resulted from combinations of topographically corrected and uncorrected imagery combined with gap-filled and unfilled stacks. These combinations of stacks were then used as input into a trajectory-based change detection. The results of change detection from trajectory-based analysis using these LTSS were compared in order to assess the effects of both topographic correction and gap-filling procedures on the ability to detect forest disturbances. The results showed that overall accuracies of change detection were improved after gap filling (10.5% and 7.5%), but were only slightly improved after topographic correction (3.6% and 0.6%). Although the gap-filling process introduced some uncertainty that might have caused false change detection, the number of pixels whose detection of disturbance was enhanced after gap filling exceeded those detecting false change. The results also showed that the topographic correction did not contribute much to improve the change detection in this study area. However, topographic correction has a potential to increase the accuracy of change detection in areas of more rugged terrain and steep slopes. This is because a direct relationship between the slope of the topography with topographic correction and an enhanced detection of disturbance in pixels from year to year was observed in this study. For robust change detection, we recommend that a gap-filling process should be included in the trajectory-based analysis procedures such as the one used in this study where a single image per year is used to characterize change. We also recommend that in areas of rugged terrain, a topographic correction in the image pre-processing should be implemented.
A Systematic Review and Meta-Analysis of Efficacy, Cost-Effectiveness, and Safety of Selected Complementary and Alternative Medicine for Neck and Low-Back Pain

Background. Back pain is a common problem and a major cause of disability and health care utilization. Purpose. To evaluate the efficacy, harms, and costs of the most common CAM treatments (acupuncture, massage, spinal manipulation, and mobilization) for neck/low-back pain. Data Sources. Records without language restriction from various databases up to February 2010. Data Extraction. The efficacy outcomes of interest were pain intensity and disability. Data Synthesis. Reports of 147 randomized trials and 5 nonrandomized studies were included. CAM treatments were more effective in reducing pain and disability compared to no treatment, physical therapy (exercise and/or electrotherapy) or usual care immediately or at short-term follow-up. Trials that applied sham-acupuncture tended towards statistically nonsignificant results. In several studies, acupuncture caused bleeding on the site of application, and manipulation and massage caused pain episodes of mild and transient nature. Conclusions. CAM treatments were significantly more efficacious than no treatment, placebo, physical therapy, or usual care in reducing pain immediately or at short-term after treatment. CAM therapies did not significantly reduce disability compared to sham. None of the CAM treatments was shown systematically as superior to one another. More efforts are needed to improve the conduct and reporting of studies of CAM treatments.
Instruments for Mountainous Areas

Because of the difficulty of data collection, little is known about the climate and hydrology of high mountain basins. Following the development of an automatic weather station (AWS) in the 1960s for use in upland Britain, the Institute of Hydrology is now engaged in developing an AWS for operation in more mountainous areas. To this end, and using the summit of Cairn Gorm in Scotland as a proving-ground, investigations are being carried out into methods of preventing ice accretion, new ways of deploying temperature sensors, alternative ways of exposing radiation sensors consistent with the deicing techniques, the sensing of wind by static sensors, the choice of the most appropriate method of sensing humidity, and the measurement of rain and snowmelt. This paper describes these new techniques.
An Application Of The Aftershock Probability Evaluation Methods For Recent Albania Earthquakes Based On Gutenberg-Richter And Modified Omori Models

In this study, an assessment on the aftershock occurrence probability based on the combination of Gutenberg-Richter and modified Omori formulae is made in order to forecast how many large aftershocks should follow small main shocks and in order to evaluate aftershock probability that a randomly chosen event is greater than or equal to a certain magnitude of aftershock. For this purpose, we made an application of aftershock probability evaluation methods to nine aftershock sequences in which occurred Albania between 2004 and 2009 with magnitude level between 4.5 and 5.0.
Personalized Question Answering : A Use Case for Business Analysis

In this paper we describe a powerful use case application in the area of emergency situations management in which to illustrate the benefits of a system based on Semantic Web Services (SWS), through the automation of the business processes involved. After creating Web services to provide spatial data to third parties through the Internet, semantics and domain ontologies were added to represent the business processes involved, allowing: ease of access and combination of heterogeneous data from different providers; and automatic discovery, access and composition to perform more complex tasks. In this way, our prototype contributes to better management of emergency situations by those responsible. The work described is supported by the DIP (Data, Information and Process Integration with Semantic Web Services) project. DIP (FP6 – 507483), an Integrated Project funded under the European Union’s IST programme.
The Journal of General Physiology

On further examination of the data obtained from the three-dimensional reconstructions we realized that in the two places where we took the data as being derived from a single channel there was more than one channel present. Although the conclusions reached from the data would not be altered, we want to inform the readers of this error. For the data shown in Fig. 2 on page 265, although only one fluorescence transient was detected, there was a second caffeine-activated channel that opened before and closed after the fluorescence transient. Any fluorescence increase due to Ca influx through that channel was out of the image field and, therefore, would not contribute to the fluorescence measurements obtained for the figure. However, the brief closures in the current trace could have come from either channel. When constructing the figure , we had assumed all of the brief closures were from the channel of interest. To estimate how much of an error this assumption would produce, we recalculated the slope of ⌬ F total (or signal mass) versus charge (⌬ Q) assuming the opposite situation (all of the brief closures being from the other channel). The average difference in the slopes for all six planes remained nearly the same (Ϫ 1.8 Ϯ 0.7% versus Ϫ 1.3 Ϯ 0.7% for original versus recalculated data, respectively). The latter value is the same as that indicated in the original text by plotting ⌬ F total versus time instead of charge. Therefore, the brief closures did not contribute significantly to the differences among the slopes of ⌬ F total versus ⌬ Q as viewed from each optical plane. The stretch-activated channel current (referred to at the top of the second column on page 266), recorded in a cell-attached patch, could have been derived from more than one channel. However, unlike for the caffeine-activated channels, the opening of more than one channel in the cell-attached patch would not have altered the slope of ⌬ F total versus ⌬ Q because both measurements were obtained from the same patch with the channels essentially in the same plane. Also, there was essentially the same average difference in the slopes of ⌬ F total versus ⌬ Q or versus time (Ϫ 4.5 Ϯ 4.9% and Ϫ 3.3 Ϯ 5%, respectively). Both values were also in the original text. We regret we did not catch these before the publication of the paper and …
Message from the Director

Big changes are coming to NE-CAT over the next couple of years. As detailed in the last newsletter, we are working on installing a secondary focus on 24-ID-E. In this newsletter, you will read about new Vertical Focusing Mirrors which increase the brilliance of our beamlines and a new kilohertz framing detector, the EIGER. Though we strive to make the changes and improvements seamless for our users, much preparatory work goes into laying the foundation for each change. NE-CAT beamline scientists are joining others in the crystallographic community to learn, discuss and drive the direction of change as NE-CAT moves toward integration of the new generation of ultra-fast detectors.
Message from the Director

Device-to-Device (D2D) communication allows direct communication of a device to another device without traversing a base station. It takes advantage of the physical proximity of communication devices to achieve high bit rate, low delay, low power consumption, and dense frequency reuse. In the current cellular system, the D2D operation lets D2D users and cellular users use orthogonal radio resources (see Fig. 1) [1]. There is no interference between D2D and cellular links, but the efficiency of frequency reuse is low.
Near-Infrared Spectroscopy : Theory and Applications

N 'EAR-INFRARED spectroscopy (NIRS) theoretically offers a technique for continuous, nonlnvaslve, bedside monitoring of cerebral metabohsm. Like pulse oximetry and mixed venous oxameters, these instruments use principles of light transmission and absorption to nonlnvasively measure the concentrations of oxygenated (HbO2) and deoxygenated (Hb) hemoglobin in tissue, most often cerebral tissue. The introduction of pulse oxlmetry in 1985 made it possible to continuously and nonanvasively monitor the oxygenation of arterial blood. Although this monitoring modality dramatically improved the physician's ability to recognize and treat arterial hypoxemia, circumstances exist where tissue hypoxla, especially cerebral hypoxia, may develop m the presence of appropriate arterial and venous oxygen saturations NIRS offers the possibility of monitoring the most important oxygenation end point, that of tissue oxygen utilization Ninety percent of the oxygen that leaves the vascular space enters the mItochondria where it acts as the final electron acceptor in the electron transport chain. Transfer of electrons to oxygen occurs via the enzyme cytochrome aa3 (Cytaa3), also known as cytochrome C oxidase. Anoxia quickly results in complete reduction of this enzyme/ In 1977, Jobsis described a spectroscopic technique to determine the oxidation status of Cytaa3 in intact cat brain and rat heart using light transmission through the tissue m a manner analogous to using oximetry for hemoglobin saturation. 2 Continuous, noninvasive monitoring of cerebral oxygenation would provide information not currently available by any other modahty. Such a monitor could be used during deep hypothermic circulatory arrest or during carotid endarterectomy to diagnose the onset of global or regional cerebral hypoxia. A continuous cerebral oxygenation monitor could have both research and clinical applications for patients recovering from cerebrovascular accidents (embolic or hemorrhagic). For nearly any disease state in which
Explainable Artificial Intelligence for Cotton Yield Prediction With Multisource Data

Cotton is under the threat of climate and ecosystem change and has an essential role in the global textile industry. This makes its yield prediction essential for both economics and sustainability. The potential cotton yield can be predicted by integrating climatic factors, soil parameters, and biophysical parameters observed by high temporal and spatial resolution remote sensing satellites. This study used a multisource dataset to create an explainable and accurate predictive model for cotton yield prediction over the continental United States (CONUS). A recently proposed glass-box method called explainable boosting machine (EBM), which provides transparency, reliability, and ease of interpretation, was implemented. Accuracy performance was compared with the common machine learning (ML) methods for predicting cotton yields. The EBM showed higher accuracy against other glass-box methods and competitive results with black-box models. With the help of the EBM, the importance of individual features and their pairwise interactions was revealed without applying any post hoc methods. The study findings showed that precipitation (P), enhanced vegetation index (EVI), and leaf area index (LAI) are the three most important dynamic features. The dynamic features are the driver of the created model with 78% of the overall feature importance, followed by pairwise interactions of the features with 16% contribution. Finally, static features contribute 6% to the overall feature importance. The study highlights the importance of using multisource data and interactions of the input features and providing an interpretable model to understand the inner dynamics of cotton yield predictions.