Miracle-dz/UniFAR-FTUs huggingface dataset:
Reference Based on Adaptive Attention Mechanism for Image Captioning*

Image captioning, as a conjunction of computer vision and natural language processing, is receiving increasing attention in recent years. Most existing methods are using the attention-based CNN-RNN frameworks, which can understand images effectively and generate more natural descriptions. The recent research discovers that the expression of an event can effectively promote people's understanding and result in successful detection in objects and actions. Inspired by the textual information, in this paper, we propose a Reference based on Adaptive Attention Mechanism (R-AAM) model adding the reference sentence into the attention system to correct the area where the image is concentrated. Both in the training and testing processes, the reference sentence is selected by computing the largest consensus score among the nearest images in the training data set. The reference sentence associated with an image can help select the highlight region for the generating word in time sequence. The result has shown that the generated sentences utilizing the reference sentence express the richer semantic information and fix the wrong recognition phenomenon. Our proposed R-AAM method achieves comparable performances on the well-known public dataset MSCOCO with five popular evaluation metrics.
High-Source–Drain Voltage-Induced Reliability Issues of Sub-28-nm Node MOSFET’s Application in Resistive-Type Nonvolatile Memory Array

In this brief, we investigate the reliability issues of MOSFETs with bulk 28-nm technology for its possible application in resistive-type nonvolatile memory (RNVM). The impact of electrical stress on transistors when working in the 1T1R array cells is discussed. It is found that the high source–drain voltage bias during the 1T1R array operation is the main reason causing the MOSFET degradation. Furthermore, by comparing the electrical characteristics of MOSFETs with different channel lengths, it is confirmed that the degradation is caused by high-energy electrons drifting from the source side to the drain end under a high electric field. As a result, this issue will be more serious as the gate length continues to scale down. An ultralow operation voltage RNVM is required for a better lifetime if RNVM is embedded in sub-28-nm technologies.
Improved Single Shot Object Detector Using Enhanced Features and Predicting Heads

Object detection attracts much attention for its great value in theories and applications. The one-stage single shot object detectors outperform the two-stage methods in running speed with a comparable performance. In this paper, we propose three novel strategies, to further improve the performances of single shot detector without sacrificing their runtime efficiency. Firstly, we design the multi-scale context aggregation module to embeds the context information into the learned features. Secondly, we design the multi-path predicting head, which decouples the network layers and can easily learn the effective receptive fields of different aspect ratios, to detect objects of various aspect ratios better. Thirdly, we adopt a top-down feature map pyramid to detect objects using features of different semantic powers and resolutions. Sufficient ablation experiments are conducted to prove the efficiency of the proposed methods. We design a one stage single detector named as ISSD, using the three strategies. Experimental results on PASCAL VOC 2007 and 2012 shows ISSD achieves the new state-of-the-art on accuracy with the comparable running speed.
Dhh1 is a member of the SESA network

The correct separation of chromosomes during mitosis is necessary to prevent genetic instability and aneuploidy, which are responsible for cancer and other diseases, and it depends on proper centrosome duplication. In a recent study, we found that Smy2 can suppress the essential role of Mps2 in the insertion of yeast centrosome into the nuclear membrane by interacting with Eap1, Scp160, and Asc1 and designated this network as SESA (Smy2, Eap1, Scp160, Asc1). Detailed analysis showed that the SESA network is part of a mechanism which regulates translation of POM34 mRNA. Thus, SESA is a system that suppresses spindle pole body duplication defects by repressing the translation of POM34 mRNA. In this study, we performed a genome‐wide screening in order to identify new members of the SESA network and confirmed Dhh1 as a putative member. Dhh1 is a cytoplasmic DEAD‐box helicase known to regulate translation. Therefore, we hypothesized that Dhh1 is responsible for the highly selective inhibition of POM34 mRNA by SESA.
Ecological and Economic Survey

A study of medicinal and aromatic plants (MAPs) was conducted from September, 2007 to April, 2008 in different sites of District Chitral, Pakistan. The objectives of the study were to identify alternate livelihood opportunity for the local people and to investigate linkages in the market channel starting from collectors to consumers. Information was collected through semi-structured questionnaire. The survey revealed 60 species of plants with high therapeutic and economic value. These plants were used for the curing of various diseases in traditional system of medicines. The study showed that neither men nor women were aware of the vast array of herbs with medicinal properties that exist in their locality. It was also noted that the trade of MAP is highly uncoordinated and complex, involving many players. Out of 60 MAP, total 13 species of MAPs are collected for sale purposes, which are mostly collected by children and women. The collectors are not properly trained and do not have any marketing skills or access to larger markets and are often not aware of the high market value. As a result, most of collected materials are sold to local middlemen at a very low price.
Measuring Habitual Arm Use Post-stroke With a Bilateral Time-Constrained Reaching Task

Background: Spontaneous use of the more-affected arm is a meaningful indicator of stroke recovery. The Bilateral Arm Reaching Test (BART) was previously developed to quantify arm use by measuring arm choice to targets projected over a horizontal hemi-workspace. In order to improve clinical validity, we constrained the available movement time, thereby promoting more spontaneous decision making when selecting between the more-affected and less affected arm during the BART. Methods: Twenty-two individuals with mild to moderate hemiparesis were tested with the time-based BART in three time-constraint conditions: no-time constraint, medium, and fast conditions. Arm use was measured across three sessions with a 2-week interval in a spontaneous choice block, in which participants were instructed to use either the more-affected or the less-affected arm to reach targets. We tested the effect of time-constraint condition on the more-affected arm use, external validity of the BART with the Actual Amount of Use Test (AAUT), and test-retest reliability across the three test sessions. Results: The fast condition in the time-based BART showed reduced use of the more-affected arm compared to the no-time constraint condition P < 0.0001) and the medium condition P = 0.0006; Tukey post hoc analysis after mixed-effect linear regression). In addition, the fast condition showed strong correlation with the AAUT r = 0.829, P < 0.001), and excellent test-retest reliability (ICC = 0.960, P < 0.0001). Conclusion: The revised BART with a time-restricted fast condition provides an objective, accurate, and repeatable measure of spontaneous arm use in individuals with chronic stroke hemiparesis.
Sequenced-Replacement Sampling for Deep Learning

We propose sequenced-replacement sampling (SRS) for training deep neural networks. The basic idea is to assign a fixed sequence index to each sample in the dataset. Once a mini-batch is randomly drawn in each training iteration, we refill the original dataset by successively adding samples according to their sequence index. Thus we carry out replacement sampling but in a batched and sequenced way. In a sense, SRS could be viewed as a way of performing "mini-batch augmentation". It is particularly useful for a task where we have a relatively small images-per-class such as CIFAR-100. Together with a longer period of initial large learning rate, it significantly improves the classification accuracy in CIFAR-100 over the current state-of-the-art results. Our experiments indicate that training deeper networks with SRS is less prone to over-fitting. In the best case, we achieve an error rate as low as 10.10%.
Bidirectional Mode Slicing and Re-Combining for Mode Conversion in Planar Waveguides

Photonics integrated circuits and planar lightwave circuits are crucial in future high capacity optical communication networks. Such circuits can perform complicated and sophisticated signal processing functions’ on-chip level. However, they should have high bandwidth in order to handle ultrafast data flow and thus achieve high-capacity transmission. One way to achieve such high bandwidth is using few-mode waveguides. Therefore, it becomes mandatory to find a way to convert from current conventional single-mode planar waveguides into few-mode waveguides, and moreover to selectively excite the desired modes within the converted multimode waveguides. In this paper, a novel silica-glass planar waveguide converter is numerically demonstrated for the first time. This device can convert in a bidirectional fashion between single-mode and two, three, or four modes planar waveguides, in addition, to selectively excite the desired mode. The device principle of operation mainly depends on spatial modes slicing and re-combining to achieve the desired higher or lower order modes. That could be accomplished by cascading stages of V-shape and M-shape graded-index planar waveguides. The graded-indices allow for flexible and simple geometrical designs that can split or combine fundamental as well as higher order modes. The finite-difference time-domain numerical method is utilized to verify the device operation and evaluate its performance for each excited mode in bidirectional directions. The device shows good performance over the entire C-band wavelength range with the worst-case magnitude of insertion-loss ≅ 3 dB, polarization-dependent loss ≅ 0.6 dB, and return-loss ≅ 21 dB. It also shows a worst-case cross-talk of ≅ −24.1 dB among the excited high-order modes in the forward direction, and a worst-case mode-rejection ratio of ≅ −23 dB for non-excited modes in the reverse direction. Moreover, the device shows reasonable performance tolerance to variations in V-shape and M-shape waveguide design parameters.
A Novel Dynamic Power Routing Scheme to Maximize Loadability of Islanded Hybrid AC/DC Microgrids Under Unbalanced AC Loading

This paper proposes a novel dynamic power routing (DPR) scheme for hybrid ac/dc microgrids operating in islanded mode, where unlike in grid-connected microgrids, local generation adequacy is crucial for proper system operation. The unbalanced nature of ac distribution networks limits the microgrid loadability in the sense that loads must be shed from heavily loaded phases, even if the connected distributed generators (DGs) have not reached their total three-phase capacity limits. The main challenge is to exploit the available resources by routing the power between the ac subgrid phases, thereby minimizing load shedding. The proposed method utilizes the interlinking converters between the ac and dc sides of hybrid ac/dc microgrids to provide this functionality. A supervisory controller implements a DPR-based optimal power flow (OPF) algorithm to allow full loadability of the islanded network. The formulated OPF problem is solved analytically using an interior point method that has proved to be computationally cost-effective. Many case studies are conducted to address the unbalance problem and to validate the effectiveness of the proposed strategy against conventional methods, which are based solely on optimal DG droop settings.
Repairing Process Models With Logical Concurrent and Casual Relations via Logical Petri Nets

Process mining techniques can build process models according to event logs generated by enterprise information systems. However, some event logs cannot be correctly replayed by the model, which needs to be repaired. When new activities are found in event logs, the existing models repair methods cannot accurately find the positions where to add them. By considering relations between new and original activities, this paper proposes an approach to repair logical Petri net-based models. We first define two new kinds of relations between new and original activities called logical concurrent and casual relations, respectively. Then, a ladder matrix is constructed, and the differences between an original process model and event logs can be obtained. Next, a deviation matrix is used to record these differences. New activities are added to construct a concurrent relation with original activities, or inserted behind the original activities to construct a casual relation. Through performing experiments on diagnosis data in a hospital, the correctness and effectiveness of the repaired model has higher fitness and precision than those by the previous approaches.
Ablation of ErbB4 in parvalbumin‐positive interneurons inhibits adult hippocampal neurogenesis through down‐regulating BDNF/TrkB expression

Parvalbumin (PV) positive interneurons in the subgranular zone (SGZ) can regulate adult hippocampal neurogenesis. ErbB4 is mainly expressed in PV neurons in the hippocampus and is crucial for keeping normal function of PV neurons. However, whether ErbB4 in PV interneurons affects the adult hippocampal neurogenesis remains unknown. In the present study, we deleted ErbB4 specifically in PV neurons by crossing PV‐Cre mice with ErbB4f/f mice. Results of BrdU labeling and NeuN staining revealed that the proliferation of neural progenitors was increased but the survival and maturation of newborn neurons were decreased in the hippocampus of mice after deleting ErbB4 in PV neurons, suggesting that ErbB4 in PV neurons is closely associated with the process of adult hippocampal neurogenesis. Interestingly, the expression of brain‐derived neurotrophic factor (BDNF) and its receptor, tropomyosin‐related kinase B (TrkB), was significantly decreased in the hippocampus of ErbB4‐deleted mice. Together, our data suggested that ErbB4 in PV neurons might modulate adult hippocampal neurogenesis by affecting BDNF–TrkB signaling pathway.
Smart Classrooms Aided by Deep Neural Networks Inference on Mobile Devices

Machine Learning over edge computing devices is levering up embedded and IoT intelligence and is expected to grow even more. Today, Machine Learning applications are mainly driven by Cloud Computing, but a recent trend towards on-device ML execution is taken over. We can expect that in coming years there will be smart homes and buildings populated by much more smart devices able to assist human activities more naturally. We present a work in progress, a mobile edge computing prototype built to explore and validate how ready are smartphones for on-device execution of deep neural networks (DNNs) using as a test bed a light control of a smart classroom via object recognition using three pre-trained non-optimized DNN models embedded in one mobile app. The prototype was successfully accomplished for recognition and control tasks using only smartphone's CPU/GPU processing units to run DNN models under 670ms. Finally, on-device DNN inference performance is discussed and some future work issues are presented.
Assisted Shifting of Electricity Use

Shifting is an energy-conserving interaction strategy for moving energy consumption to times where it is sustainably favorable. This interaction strategy is attracting increasing interest within sustainable HCI studies. While most of these consider how interactive technology can change household behavior, only a few report on how shifting is experienced in everyday life when assisted by automation. In this study, we investigate an interactive technology that assists households to shift electricity consumption to times when electricity is cheap or more sustainable. Our study was conducted as a long-term field deployment for 6--18 months with eight households, each living with an interactive prototype that shifts running times for a heat pump within user-defined boundaries. Our findings show that managing heat pumps toward assisted shifting was well-received by all households because it was a convenient way to shift electricity consumption. Shifting electricity use facilitated price savings of 6.8–16.9%. Nevertheless, our findings also reveal a conflict between the system design, and how householders actually interact with their heating system and experience assisted shifting. Based on the eight households’ experiences, we present three overall themes of convenience, control, and complexity that each describes different aspects of long-term real-life use of automatic technology assisting households to shift electricity use. We discuss the broader implications of these findings and the role of design and future sustainability technology in everyday life.
An Ultra-Lightweight and Secure Scheme for Communications of Smart Meters and Neighborhood Gateways by Utilization of an ARM Cortex-M Microcontroller

Modernization of the traditional electrical grid has become achievable by the emergence of innovative smart grid technology. The main contribution of this new technology is the bidirectional flow of information. The smart meters (SMs) send the usage reports to the power operator and also some instructions are sent from the power operator in order to be executed by the SMs. In between, there exist some gateways which are responsible for data aggregation. Here, we aim at the communications of SMs and neighborhood gateways. On one hand, the presented communication scheme must consider the need for consumption reports transmission in short time intervals, and on the other hand, it must consider both security and the constrained resources of SMs. To this end, in this paper, using only the hash and Exclusive-OR operations, we propose an extremely lightweight communication scheme that not only provides the security requirements, but also can cope with one-minute or even less time intervals of data transmission while putting the least burden on the SMs resources. Our comparative analysis demonstrates substantial reduction in storage space and computational complexity. More importantly, our formal verification using the ProVerif tool and implementation on an ARM microcontroller confirm our claim.
An Improved Framework of Affine Motion Compensation in Video Coding

Affine motion compensation (AMC) is a promising coding tool in Joint Exploration Model (JEM) developed by the Joint Video Exploration Team. AMC in JEM employs a 4-parameter affine model between the current block and its reference block. With this model, the motion vectors (MV) of each sub-block can be derived from the MVs at two control points. In this paper, we present a practical framework to further improve the AMC in JEM. First, we introduce a multi-model AMC approach, which allows the encoder to select either the 4-parameter affine model or the 6-parameter affine model adaptively. Second, we improve the affine inter-mode in two aspects. For the normative part, we present an efficient affine motion coding method, which replaces the affine MV Prediction (MVP) candidates in JEM with more accurate but simpler ones, and employs a second-order MVP. For the non-normative part, we enhance the motion estimation process for AMC, by regulating the optimization algorithm. Finally, we propose to unify the affine merge-mode and the normal merge-mode into a unified merge-mode, which combine affine merge candidates and normal merge candidates in a single merge candidate list. Partial of these methods have been adopted into the next generation video coding standard named Versatile Video Coding. Simulation results show that the proposed methods can achieve 1.67% BD rate savings in average for the random access configurations.
DVD: Constructing a Discriminative Video Descriptor by Convolving Frame Features

The core to organize, classify, search, compare and retrieve videos is comparing the video descriptors. In this paper, we propose a Discriminative Video Descriptor (DVD) which is a general way to build the video descriptors on top of various frame features. We built the DVD on top of the HSV-color distribution and evaluated its performance for the Near-Duplicate Video Detection task by using the CC_WEB_VIDEOS dataset. The average detection accuracy achieved 94.4%. We also evaluated the DVD for Human Action Recognition task by building the DVD on top of the 3D-SIFT with Weizmann human action dataset. The average recognition accuracy achieved 97.84%. In practice, the DVD only introduce slightly computational overhead. The average time to build the DVD on top of the HSV-color distribution and 3D-SIFT for a single video was 0.128 $s$ (average 11 frames) and 0.04 $s$ (200 interest points), respectively.
Temporal Action Detection by Joint Identification-Verification

Temporal action detection aims at not only recognizing action category but also detecting start time and end time for each action instance in an untrimmed video. The key challenge of this task is to accurately classify the actions and determine the temporal boundaries of each action instance. In temporal action detection benchmark: THUMOS 2014, large variations exist in the same action category while many similarities exist in different action categories, which always limit the performance of temporal action detection. To address this problem, we propose to use joint Identification-Verification network to reduce the intra-action variations and enlarge inter-action differences. The joint Identification-Verification network is a siamese network based on 3D ConvNets, which can simultaneously predict the action categories and the similarity scores for the input pairs of video proposal segments. Extensive experimental results on the challenging THUMOS 2014 dataset demonstrate the effectiveness of our proposed method compared to the existing state-of-art methods for temporal action detection in untrimmed videos. We further demonstrate that our model is a general framework by evaluating our approach on Charades dataset.
Prognostic Value of the Burden of Cytomegalovirus Colonic Reactivation Evaluated by Immunohistochemical Staining in Patients with Active Ulcerative Colitis

BACKGROUND Colonic cytomegalovirus [CMV] reactivation has been involved in steroid refractoriness in patients with active ulcerative colitis [UC]. The benefits of antiviral therapy in this clinical setting are still under debate, but the burden of viral reactivation has been associated with a poorer outcome in some studies. Our aim was to assess whether the burden of CMV reactivation measured by the number of viral inclusions by immunohistochemistry [IHC-CMV] is associated with a risk of colectomy. METHODS Biopsy sets of UC patients with positive IHC-CMV were identified from the Pathology departments of three university hospitals. All biopsies were reviewed by expert pathologists, and the maximum number of IHC-CMV-positive cells in each biopsy set was re-assessed. Epidemiological and clinical features and clinical outcomes were recorded. RESULTS Forty-six positive IHC-CMV cases with UC were included. At the time of CMV reactivation, 70% were receiving corticosteroids, 33% azathioprine, and 24% anti-tumour necrosis factor [TNF] agents. Thirty-two patients [70%] were treated with antiviral therapy. The median number of IHC-CMV-positive cells was 2 cells/biopsy [IQR 1-4]. Fourteen patients [30%] underwent colectomy, and 4 of them [29%] showed persistence of CMV in the surgical specimen. In the multivariate analysis, colectomy was only associated with >2 positive cells/biopsy [p = 0.048] and younger age [p = 0.023]. CONCLUSIONS The burden of CMV colonic reactivation in patients with active UC, as measured by IHC, seems to be related to the risk of colectomy, and more data is needed to understand whether antiviral therapy guided by CMV burden will alter the clinical outcome.