Miracle-dz/UniFAR-FTUs huggingface dataset:
Drug resistant tuberculosis in Africa: Current status, gaps and opportunities

Background The World Health Organization End TB Strategy targets for 2035 are ambitious and drug resistant tuberculosis is an important barrier, particularly in Africa, home to over a billion people. Objective We sought to review the current status of drug resistant tuberculosis in Africa and highlight key areas requiring improvement. Methods Available data from 2016 World Health Organization global tuberculosis database were extracted and analysed using descriptive statistics. Results The true burden of drug resistant tuberculosis on the continent is poorly described with only 51% of countries having a formal survey completed. In the absence of this data, modelled estimates were used and reported 92 629 drug resistant tuberculosis cases with 42% of these occurring in just two countries: Nigeria and South Africa. Of the cases estimated, the majority of patients (70%) were not notified, representing ‘missed cases’. Mortality among patients with multi-drug resistant tuberculosis was 21%, and was 43% among those with extensively drug resistant tuberculosis. Policies on the adoption of new diagnostic tools was poor and implementation was lacking. A rifampicin result was available for less than 10% of tuberculosis cases in 23 of 47 countries. Second-line drug resistance testing was available in only 60% of countries. The introduction of the short multi-drug resistant tuberculosis regimen was a welcome development, with 40% of countries having implemented it in 2016. Bedaquiline has also been introduced in several countries. Conclusion Drug resistant tuberculosis is largely missed in Africa and this threatens prospects to achieve the 2035 targets. Urgent efforts are required to confirm the true burden of drug resistant tuberculosis in Africa. Adoption of new tools and drugs is essential if the 2035 targets are to be met.
Beraprost sodium preconditioning prevents inflammation, apoptosis, and autophagy during hepatic ischemia-reperfusion injury in mice via the P38 and JNK pathways

Objective The goal of this study was to determine the effects of beraprost sodium (BPS) preconditioning on hepatic ischemia-reperfusion (IR) injury and its underlying mechanisms of action. Materials and methods Mice were randomly divided into sham, IR, IR+BPS (50 µg/kg), and IR+BPS (100 µg/kg) groups. Saline or BPS was given to the mice by daily gavage for 1 week before the hepatic IR model was established. Liver tissues and orbital blood were collected at 2, 8, and 24 hours after reperfusion for the determination of liver enzymes, inflammatory mediators, apoptosis- and autophagy-related proteins, key proteins in P38 and c-Jun N-terminal kinase (JNK) cascades, and evaluation of liver histopathology. Results BPS preconditioning effectively reduced serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels, improved pathological damage, ameliorated production of tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β), and affected expressions of Bax, Bcl-2, Caspase-3, Caspase-8, and Caspase-9, microtubule-associated protein 1 light chain 3 (LC3), Beclin-1, and P62. The protective effects of BPS preconditioning were associated with reduced P38 and JNK phosphorylation. Conclusion BPS preconditioning ameliorated hepatic IR injury by suppressing inflammation, apoptosis, and autophagy, partially via inhibiting activation of the P38 and JNK cascades.
Validation of diabetes medication self-efficacy scale in Chinese with type 2 diabetes

Aims Greater diabetes medication self-efficacy has been shown to associate with better medication adherence, which is critical for disease control for people living with type 2 diabetes (T2D). The Diabetes Medication Self-efficacy Scale (DMSS) is a 19-item instrument to measure diabetes medication self-efficacy. The aim of the current study was to translate the DMSS into Chinese and validate the Chinese version of DMSS (CDMSS) among Chinese T2D patients. Methods The CDMSS was translated using forward-backward method, and its validity and reliability were assessed among 257 Chinese T2D patients. Exploratory factor analysis, Cronbach's α and Spearman's rank correlation coefficients were used to evaluate the psychometric properties of CDMSS. Receiver operating characteristic analysis was used to assess the discrimination of CDMSS for medication adherence and glycemic control. Results Exploratory factor analysis has generated a one-factor structure of the 11-item version of CDMSS (CDMSS-11), which accounted for 63.1% of the variance. The Spearman's rank correlation coefficient between the original 19-item and 11-item scale was 0.96. The Cronbach's α for internal consistency was 0.94, and the test-retest reliability coefficient was 0.76. The CDMSS-11 score was significantly correlated with diabetes distress, medication adherence, as well as blood levels of fasting plasma glucose and hemoglobin A1c (both P<0.001). The area under receiver operating characteristic curve and its corresponding 95% CI was 0.79 (0.73-0.84) for medication adherence, and it was 0.65 (0.57-0.72) for better glycemic control. Conclusion The CDMSS-11 is a valid and reliable measure to assess medication self-efficacy among Chinese T2D patients.
The Neural Representational Space of Social Memory

Social functioning involves learning about the social networks in which we live and interact; knowing not just our friends, but also who is friends with our friends. This study utilized an incidental learning paradigm and representational similarity analysis (RSA), a functional MRI multivariate pattern analysis technique, to examine the relationship between learning social networks and the brain’s response to the faces within the networks. We found that accuracy of learning face pair relationships through observation is correlated with neural similarity patterns to those pairs in the left temporoparietal junction (TPJ), the left fusiform gyrus, and the subcallosal ventromedial prefrontal cortex (vmPFC), all areas previously implicated in social cognition. This model was also significant in portions of the cerebellum and thalamus. These results show that the similarity of neural patterns represent how accurately we understand the closeness of any two faces within a network. Our findings indicate that these areas of the brain not only process knowledge and understanding of others, but also support learning relations between individuals in groups.
Stimulus- and Response-locked Posterior Contralateral Negativity Bisect Cognitive Operations in Visual Search

Abstract We explored the flow of information during visual search by examining activity indexing visual attention (N2pc) and the subsequent processing of the selected objects in visual short-term memory (SPCN) time-locked to stimulus presentation and to the motor response. We measured event-related activity at posterior sites (PO7/PO8) for 96 participants during a simple visual search task. A response-locked posterior contralateral negativity (RLpcN) was observed with a scalp distribution similar to that of the N2pc and SPCN. The RLpcN was compared with the stimulus-locked activity (N2pc and SPCN) across experimental manipulations (targets were either closer or farther from fixation in visual space, and the response was either more frequent [75%] or less frequent [25%]) and across response speeds (EEG data were separated into tertiles by RT both within-subjects and between-subjects). The leading edge and early portion of the RLpcN appeared to reflect the initial deployment of attention (N2pc), whereas the later portion (up to peak amplitude) reflected subsequent processing of visual information (SPCN). SPCN and RLpcN also had similar modulations in amplitude for both analyses. Moreover, whereas very small N2pc and SPCN onset latency differences were observed when data were separated into tertiles by RT, there were large onset differences for the RLpcN, with earlier RLpcN onsets for longer RTs, suggesting that RT variance is in large determined by processing after the initial deployment of attention. The results show how we can bisect processing responsible for variations in RT relative to the onset of visual spatial attention.
Sparse Cost Volume for Efficient Stereo Matching

Stereo matching has been solved as a supervised learning task with convolutional neural network (CNN). However, CNN based approaches basically require huge memory use. In addition, it is still challenging to find correct correspondences between images at ill-posed dim and sensor noise regions. To solve these problems, we propose Sparse Cost Volume Net (SCV-Net) achieving high accuracy, low memory cost and fast computation. The idea of the cost volume for stereo matching was initially proposed in GC-Net. In our work, by making the cost volume compact and proposing an efficient similarity evaluation for the volume, we achieved faster stereo matching while improving the accuracy. Moreover, we propose to use weight normalization instead of commonly-used batch normalization for stereo matching tasks. This improves the robustness to not only sensor noises in images but also batch size in the training process. We evaluated our proposed network on the Scene Flow and KITTI 2015 datasets, its performance overall surpasses the GC-Net. Comparing with the GC-Net, our SCV-Net achieved to: (1) reduce 73.08 % GPU memory cost; (2) reduce 61.11 % processing time; (3) improve the 3PE from 2.87 % to 2.61 % on the KITTI 2015 dataset.
In-Vehicle Voice Interface with Improved Utterance Classification Accuracy Using Off-the-Shelf Cloud Speech Recognizer

SUMMARY For voice-enabled car navigation systems that use a multi-purpose cloud speech recognition service (cloud ASR), utterance classiﬁ-cation that is robust against speech recognition errors is needed to realize a user-friendly voice interface. The purpose of this study is to improve the accuracy of utterance classiﬁcation for voice-enabled car navigation systems when inputs to a classiﬁer are error-prone speech recognition results obtained from a cloud ASR. The role of utterance classiﬁcation is to predict which car navigation function a user wants to execute from a sponta-neous utterance. A cloud ASR causes speech recognition errors due to the noises that occur when traveling in a car, and the errors degrade the accuracy of utterance classiﬁcation. There are many methods for reducing the number of speech recognition errors by modifying the inside of a speech recognizer. However, application developers cannot apply these methods to cloud ASRs because they cannot customize the ASRs. In this paper, we propose a system for improving the accuracy of utterance classiﬁcation by modifying both speech-signal inputs to a cloud ASR and recognized-sentence outputs from an ASR. First, our system performs speech enhancement on a user’s utterance and then sends both enhanced and non-enhanced speech signals to a cloud ASR. Speech recognition results from both speech signals are merged to reduce the number of recognition errors. Second, to reduce that of utterance classiﬁcation errors, we propose a data augmentation method, which we call “optimal doping,” where not only accurate transcriptions but also error-prone recognized sentences are added to training data. An evaluation with real user utterances spoken to car navigation products showed that our system reduces the number of utterance classi-ﬁcation errors by 54% from a baseline condition. Finally, we propose a semi-automatic upgrading approach for classiﬁers to beneﬁt from the improved performance of cloud ASRs.
Enhancing Job Scheduling on Inter-Rackscale Datacenters with Free-Space Optical Links

SUMMARY Datacenter growth in tra ﬃ c and scale is driving innovations in constructing tightly-coupled facilities with low-latency communication for di ﬀ erent speciﬁc applications. A famous custom design is rackscale (RS) computing by gathering key server resource components into di ﬀ erent resource pools. Such a resource-pooling implementation requires a new software stack to manage resource discovery, resource allocation and data communication. The reconﬁguration of interconnection networks on their components is potentially needed to support the above demand in RS. In this context as an evolution of the original RS architecture the inter-rackscale (IRS) architecture, which disaggregates hardware components into di ﬀ erent racks according to their own areas, has been proposed. The heart of IRS is to use a limited number of free-space optics (FSO) channels for wireless connections between di ﬀ erent resource racks, via which selected pairs of racks can communicate directly and thus resource-pooling requirements are met without additional software management. In this study we evaluate the inﬂuences of FSO links on IRS networks. Evaluation results show that FSO links reduce average communication hop count for user jobs, which is close to the best possible value of 2 hops and thus provides comparable benchmark performance to that of the counterpart RS architecture. In addition, if four FSO terminals per rack are allowed, the CPU / SSD (GPU) interconnection latency is reduced by 25.99% over Fat-tree and by 67.14% over 2-D Torus. We also present the advantage of an FSO-equipped IRS system in average turnaround time of dispatched jobs for given sets of benchmark workloads.
Extracting Individual Bricks from a Laser Scan Point Cloud of an Unorganized Pile of Bricks

Bricks are the vital component of most masonry structures. Their maintenance is critical to the protection of masonry buildings. Terrestrial Light Detection and Ranging (TLidar) systems provide massive point cloud data in an accurate and fast way. TLidar enables us to sample and store the state of a brick surface in a practical way. This article aims to extract individual bricks from an unorganized pile of bricks sampled by a dense point cloud. The method automatically segments and models the individual bricks. The methodology is divided into five main steps: Filter needless points, brick boundary points removal, coarse segmentation using 3D component analysis, planar segmentation and grouping, and brick reconstruction. A novel voting scheme is used to segment the planar patches in an effective way. Brick reconstruction is based on the geometry of single brick and its corresponding nominal size (length, width and height). The number of bricks reconstructed is around 75%. An accuracy assessment is performed by comparing 3D coordinates of the reconstructed vertices to the manually picked vertices. The standard deviations of differences along x, y and z axes are 4.55 mm, 4.53 mm and 4.60 mm, respectively. The comparison results indicate that the accuracy of reconstruction based on the introduced methodology is high and reliable. The work presented in this paper provides a theoretical basis and reference for large scene applications in brick-like structures. Meanwhile, the high-accuracy brick reconstruction lays the foundation for further brick displacement estimation.
Tropical Cyclone Rainfall Estimates from FY-3B MWRI Brightness Temperatures Using the WS Algorithm

A rainfall retrieval algorithm for tropical cyclones (TCs) using 18.7 and 36.5 GHz of vertically and horizontally polarized brightness temperatures (Tbs) from the Microwave Radiation Imager (MWRI) is presented. The beamfilling effect is corrected based on ratios of the retrieved liquid water absorption and theoretical Mie absorption coefficients at 18.7 and 36.5 GHz. To assess the performance of this algorithm, MWRI measurements are matched with the National Snow and Ice Data Center (NSIDC) precipitation for six TCs. The comparison between MWRI and NSIDC rain rates is relatively encouraging, with a mean bias of −0.14 mm/h and an overall root-mean-square error (RMSE) of 1.99 mm/h. A comparison of pixel-to-pixel retrievals shows that MWRI retrievals are constrained to reasonable levels for most rain categories, with a minimum error of −1.1% in the 10–15 mm/h category; however, with maximum errors around −22% at the lowest (0–0.5 mm/h) and highest rain rates (25–30 mm/h). Additionally, Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) Tbs are applied to retrieve rain rates to assess the sensitivity of this algorithm, with a mean bias and RMSE of 0.90 mm/h and 3.11 mm/h, respectively. For the case study of TC Maon (2011), MWRI retrievals underestimate rain rates over 6 mm/h and overestimate rain rates below 6 mm/h compared with Precipitation Radar (PR) observations on storm scales. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rainfall data provided by the Remote Sensing Systems (RSS) are applied to assess the representation of mesoscale structures in intense TCs, and they show good consistency with MWRI retrievals.
Block-Punctured Binary Simplex Codes for Local and Parallel Repair in Distributed Storage Systems

SUMMARY In this paper, we investigate how to obtain binary locally repairable codes (LRCs) with good locality and availability from binary Simplex codes. We ﬁrst propose a Combination code having the generator matrix with all the columns of positive weights less than or equal to a given value. Such a code can be also obtained by puncturing all the columns of weights larger than a given value from a binary Simplex Code. We call by block-puncturing such puncturing method. Furthermore, we suggest a heuristic puncturing method, called subblock-puncturing, that punctures a few more columns of the largest weight from the Combination code. We determine the minimum distance, locality, availability, joint information locality, jointinformationavailabilityofCombinationcodesinclosed-form. We also demonstrate the optimality of the proposed codes with certain choices of parameters in terms of some well-known bounds.
Improving Thai Word and Sentence Segmentation Using Linguistic Knowledge

SUMMARY Word boundary ambiguity in word segmentation has long been a fundamental challenge within Thai language processing. The Conditional Random Fields (CRF) model is among the best-known methods to have achieved remarkably accurate segmentation. Nevertheless, current advancements appear to have left the problem of compound words unac-counted for. Compound words lose their meaning or context once segmented. Hence, we introduce a dictionary-based word-merging algorithm, which merges all kinds of compound words. Our evaluation shows that the algorithm can accomplish a high-accuracy of word segmentation, with compound words being preserved. Moreover, it can also restore some in-correctly segmented words. Another problem involving a di ﬀ erent word-chunking approach is sentence boundary ambiguity. In tackling the problem, utilizing the part of speech (POS) of a segmented word has been found previously to help boost the accuracy of CRF-based sentence segmentation. However, not all segmented words can be tagged. Thus, we propose a POS-based word-splitting algorithm, which splits words in order to increase POS tags. We found that with more identiﬁable POS tags, the CRF model performs better in segmenting sentences. To demonstrate the contributions of both methods, we experimented with three of their applications. With the word merging algorithm, we found that intact compound words in the product of topic extraction can help to preserve their intended meanings, o ﬀ ering more precise information for human interpretation. The algorithm, together with the POS-based word-splitting algorithm, can also be used to amend word-level Thai-English translations. In addition, the word-splitting algorithm improves sentence segmentation, thus enhancing text summarization.
Identifying Collapsed Buildings Using Post-Earthquake Satellite Imagery and Convolutional Neural Networks: A Case Study of the 2010 Haiti Earthquake

Earthquake is one of the most devastating natural disasters that threaten human life. It is vital to retrieve the building damage status for planning rescue and reconstruction after an earthquake. In cases when the number of completely collapsed buildings is far less than intact or less-affected buildings (e.g., the 2010 Haiti earthquake), it is difficult for the classifier to learn the minority class samples, due to the imbalance learning problem. In this study, the convolutional neural network (CNN) was utilized to identify collapsed buildings from post-event satellite imagery with the proposed workflow. Producer accuracy (PA), user accuracy (UA), overall accuracy (OA), and Kappa were used as evaluation metrics. To overcome the imbalance problem, random over-sampling, random under-sampling, and cost-sensitive methods were tested on selected test A and test B regions. The results demonstrated that the building collapsed information can be retrieved by using post-event imagery. SqueezeNet performed well in classifying collapsed and non-collapsed buildings, and achieved an average OA of 78.6% for the two test regions. After balancing steps, the average Kappa value was improved from 41.6% to 44.8% with the cost-sensitive approach. Moreover, the cost-sensitive method showed a better performance on discriminating collapsed buildings, with a PA value of 51.2% for test A and 61.1% for test B. Therefore, a suitable balancing method should be considered when facing imbalance dataset to retrieve the distribution of collapsed buildings.
Semantic Interpretation of Mobile Laser Scanner Point Clouds in Indoor Scenes Using Trajectories

The data acquisition with Indoor Mobile Laser Scanners (IMLS) is quick, low-cost and accurate for indoor 3D modeling. Besides a point cloud, an IMLS also provides the trajectory of the mobile scanner. We analyze this trajectory jointly with the point cloud to support the labeling of noisy, highly reflected and cluttered points in indoor scenes. An adjacency-graph-based method is presented for detecting and labeling of permanent structures, such as walls, floors, ceilings, and stairs. Through occlusion reasoning and the use of the trajectory as a set of scanner positions, gaps are discriminated from real openings in the data. Furthermore, a voxel-based method is applied for labeling of navigable space and separating them from obstacles. The results show that 80% of the doors and 85% of the rooms are correctly detected, and most of the walls and openings are reconstructed. The experimental outcomes indicate that the trajectory of MLS systems plays an essential role in the understanding of indoor scenes.