Miracle-dz/UniFAR-FTUs huggingface dataset:
Attend and Align: Improving Deep Representations with Feature Alignment Layer for Person Retrieval

In fine-grained recognition, object misalignment and background noise are two long-standing factors that influence the robustness of deep learning models. This paper mainly focuses on person re-identification (re-ID) and introduces a feature alignment layer (FAL) which alleviates the target misalignment and the background noise simultaneously. Through attention mechanism, FAL informs the underlying importance of each pixel on feature maps, i.e., whether the pixel is beneficial towards discriminating different persons. Then the discriminative regions relocate to the center and are stretched to fill the feature maps. Such an “attend and align” mechanism is specified into two steps: target position prediction and value assignment. In the first step, a pixel on feature maps learns to find a target position which is ID-discriminative. In the second step, the pixel is assigned with a new value using the context of the predicted position. Moreover, FAL can be easily plugged into a canonical Convolutional Neural Network (CNN) and learned in an end-to-end manner. In experiment, our method yields competitive results compared with the state-of-the-art approaches on three person re-ID datasets, Market-1501, DukeMTMC-reID and CUHK03. We also demonstrate that our method improves a competitive fine-grained recognition baseline on CUB-200-2011.
Study on Kurtosis of Ships and Sea Clutter in Complex-Valued Sentinel-1 Imagery

Complex information contained in single-channel synthetic aperture radar (SAR) imagery is often discarded due to the conventional resolution theory. However, recent researches show that noncircularity and non-Gaussianity can be of significance for ocean monitoring. This paper presents a study into kurtosis of ships and sea clutter in complex-valued Sentinel-1 imagery. Kurtosis can measure the outlier-prone of a distribution. Thus, it can identify Gaussianity and non-Gaussianity. Interferometric Wide Swath (IW) Single Look Complex (SLC) data are taken into account in the kurtosis calculation. The statistical results show that the real and imaginary parts of sea clutter present Gaussianity while ships show sup-Gaussiantiy. The kurtosis of sea clutter is approximately constant and reaches 3 while the kurtosis of ships is much larger than 3. This also implies that ships can be detected and segmented by kurtosis test efficiently. Two preliminary experiments relevant to ship detection and segmentation are then presented. These results demonstrate that kurtosis can be a vital indicator for ocean monitoring in single-channel complex-valued SAR imagery.
Perceived Psychotherapist's Empathy and Therapy Motivation as Determinants of Long-Term Therapy Success—Results of a Cohort Study of Short Term Psychodynamic Inpatient Psychotherapy

Objective: Outcome predictors and determinants for treatment outcome of inpatient psychotherapy will be assessed in a follow-up-study. Sociodemographic factors and the level of depressiveness at admission, the perceived psychotherapist's empathy rated by patients and the therapy motivation as possible moderators of treatment outcome (reduction of depressive symptoms) are analyzed. Methods: In a cohort study, the outcome of inpatient multimodal psychotherapy was examined with Beck-Depression-Inventory (BDI) at admission (T1), discharge (T2) and at follow-up (1–3 years after treatment) (T3). Inclusion criteria were: Inpatient psychotherapy between 2007 and 2010 with a duration of at least 1 week and complete data set. The influence on therapy success of (1) sociodemographic factors, (2) the perceived psychotherapist's empathy rated by patients using the Consultation and Relational Empathy Measure (CARE), and (3) the therapy motivation of the patients rated by therapists are examined by means of correlation analysis, distribution comparisons and subsequently logistic regression. Results: Ninety-two (64 females, average age 39 yrs.) of 182 eligible patients participated in the follow-up survey. Duration of inpatient psychotherapy lasted 8.7 weeks ± 3.6 [min. 1, max. 33 weeks]. The perceived psychotherapist's empathy, therapy motivation, education level and depression at baseline had a significant impact on therapy success. Gender, age, and partnership were not significant. The length between discharge and follow-up had no influence on the results. Based on these variables a multiple logistic regression explained 42% of the variation (goodness-of-fit). Conclusion: Due to the shown relevance of the psychotherapist's empathy perceived by patients and the therapy motivation of patients for therapy success, both factors should be considered already at the beginning of the therapy. Consequently, they should be recognized in the context of postgraduate training and education.
Position Sensorless Deadbeat Power Control of Doubly-Fed Induction Generator Based on MRAS Observer

Deadbeat power control (DBPC) is an effective approach to achieve accurate and quick power control for doubly-fed induction generator (DFIG) systems. The desired rotor voltage vector reference can be directly calculated based on the DFIG mathematical model in DBPC. And space vector modulation (SVM) is utilized to synthesis the rotor voltage reference. Satisfactory steady state performance and dynamic responses can be obtained using this method. However, in conventional DBPC, the information of rotor speed and position is obtained from speed transducer and position encoder, which increases the overall cost and reduces the robustness of system. This paper presents a position sensorless technique based on model reference adaptive system (MRAS) and integrates it with DBPC to achieve position sensorless operation. The proposed method is robust against the rotor speed variation, even when the rotor speed of DFIG changes from sub synchronous speed to super-synchronous speed. The effectiveness of the proposed DBPC with MRAS observer is confirmed by the presented simulation results from a 1500 W DFIG system.
Evaluation and Characterization of 3-D Printed Pyramid Horn Antennas Utilizing Different Deposition Techniques for Conductive Material

This paper evaluates different metal deposition approaches for a two-staged additive manufacturing (AM) process for 3-D horn antennas, divided into the deposition of plastic and conductive material. The demonstrated fabrication workflow allows for antennas of low cost and low weight and, therefore, demonstrates the vast benefit of AM. The comparison of a commercially available cast aluminum horn to the fabricated prototypes reveals a deviation of up to 3 dB in terms of transmission losses in copolarization over a distance of 1.85 m. Cross-polarization measurements are presented revealing a larger variation among the different metallization approaches. The results shown in this paper demonstrate a successful application of AM in radio frequency engineering. Moreover, one of the presented approaches makes use of only 3-D printing systems and is, therefore, potentially suitable for totally automated manufacturing.
Towards Augmenting Multimedia Qoe With Wearable Devices: Perspectives from an Empirical Study

Wearable technologies and devices, including fitness trackers, smart watches, glasses, headgear and smart jewelry have been on the rise and are trending in the consumer market. Nonetheless, some wearable technologies have proven to be more successful and better received than others. The reasons behind this could be how users feel about the functionalities, features, aesthetics of wearables and their overall experience that are rarely considered. To address this, we present the results of an empirical study in which the Quality of Experience (QoE) and perceived usability of two wearable devices - a haptic vest and a heart rate monitor band – whilst watching multimedia content were explored. Results show enhanced user QoE when wearable devices were employed. Moreover, the usability and comfort of the two devices received positive feedback from users. However, participants were not so keen in wearing the devices regularly and in public.
Sentiment Analysis of Indonesian News Using Deep Learning (Case Study: TVKU Broadcast)

A news sentence is a sentence that tells or does something. In fact, The comparison between positive news and negative news broadcast on TV stations in Indonesia is 1:11. One point for positive newscasting and 11 points for negative newscasting. In the TVKU dataset because the original record number is too large, then the dataset used in this study only used 2.048 data. Which 1025 Positif and 1023 negative. Then obtained accurate results between labeling and testing. By using deep learning, we can get accurate results to analyze the sentiment from TVKU news.
Inter-Symbol Differential Detection-Enabled Sampling Frequency Offset Compensation for DDO-OFDM

Sampling frequency offset (SFO) between digital-to-analog converters and analog-to-digital converters will seriously degrade the receiver performance of the asynchronous direct-detection optical OFDM (DDO-OFDM), due to the SFO-induced inter-symbol interference, inter-channel interference, and phase rotation. In this letter, a simple and overhead-free SFO compensation scheme enabled by inter-symbol differential detection (ISDD) is proposed and experimentally demonstrated in a short-reach and low-cost directly modulated laser-based DDO-OFDM system with an optimal fast Fourier transform size. The experimental results exhibit that the SFO up to ±200 ppm can be compensated with acceptable error vector magnitude (EVM) penalties after 20-km standard single-mode fiber transmission. Moreover, the EVM performance can be further enhanced by using a decision-aided residual phase compensation method after ISDD when SFO is large.
Evaluation Methodology for Virtual Base Station Platforms in Radio Access Networks

Virtual base stations (BS) with scalable resources and flexible functions are necessary for the fifth-generation and future mobile systems. The virtualization platform is the fundamental component of virtual BSs and plays a very important role in determining BS performance. As BS virtualization platforms can be provided by both conventional BS manufactures and third-party software vendors, it is crucial to define unified methodology to evaluate the performance of different virtualization platforms. In this paper, for the first time in literature, we design a set of comprehensive performance metrics and propose evaluation methods for BS virtualization platforms. More specifically, there are two types of metrics, i.e., micro level and macro level metrics. Micro level metrics quantify the virtualization platforms’ basic performances including real-time performance and virtualization overhead. Macro level metrics quantify the platforms ability to support BS resource and function virtualization while satisfying the radio access network requirements. Correspondingly, two-level evaluation methodology is designed to collect micro and macro level metrics from different parts of a virtual BS by running proper workloads. Finally, using the proposed methodology, two representative virtualization platforms, i.e., KVM hypervisor and Super BS virtualization platform are evaluated. These experiments verify that the proposed methodology can effectively evaluate the performance of different platforms to provide a valuable reference for the selection of virtual BS platforms. Moreover, the proposed evaluation methodology is a significant step toward constructing an open ecosystem for radio access networks.
Cayley- Klein Metric Learning with Shrinkage-Expansion Constraints

Cayley-Klein metric is a specific kind of non-Euclidean metric in projective space. Recently, it has been introduced into metric learning with encouraging performance when dealing with computer vision tasks. However, the original Cayley-Klein metric learning methods with conventional pairwise and triplet-wise constraints, which are fixed bound based constraints, may not perform well when the intra-and inter-class variations of data distribution become complex. Pairwise constraints restrict the distance between samples of a similar pair to be lower than a fixed upper bound, and the distance between samples of a dissimilar pair higher than a fixed lower bound. Triplet-wise constraints restrict the distance between samples of a similar pair to be smaller than that between a pair of samples from different classes. In this paper, we propose a novel Cayley-Klein metric learning method (CKseML) with adaptive shrinkage-expansion pairwise constraints. CKseML is very effective in learning metric from data with complex distributions. Our experimental results demonstrate that CKseML achieves better performance than the original Cayley-Klein metric learning methods.
Unsupervised Heterogeneous Domain Adaptation via Shared Fuzzy Equivalence Relations

Unsupervised domain adaptation (UDA) aims to recognize newly emerged patterns in target domains, which may be unlabeled, by leveraging knowledge from patterns learnt from source domains. However, existing UDA models and algorithms still suffer from heterogeneous domains, known as the heterogeneous unsupervised domain adaptation (HeUDA) issue. To address this issue, this paper presents a novel HeUDA model via n-dimensional fuzzy geometry and fuzzy equivalence relations, called F-HeUDA. The n-dimensional fuzzy geometry is used to propose a metric to measure the similarity between features on one domain. Then, based on this metric, shared fuzzy equivalence relations (SFER) are proposed. The SFER can allow two domains to use the same $\alpha $ to get the same number of clustering categories. Through these clustering categories, knowledge from the heterogeneous source domain can be transferred to the unlabeled target domain. Different to existing HeUDA models, the proposed F-HeUDA model does not need that two domains must have the same number of instances. As a result, the proposed model has a better ability to handle the issue of small datasets. Experiments distributed across four real datasets were conducted to validate the proposed model. This testing regime demonstrates that the proposed model outperforms the state-of-the-art models, especially when the target domain has very few instances.
Bayesian Multi-Hyperplane Machine for Pattern Recognition

Current existing multi-hyperplane machine approach deals with high-dimensional and complex datasets by approximating the input data region using a parametric mixture of hyperplanes. Consequently, this approach requires an excessively time-consuming parameter search to find the set of optimal hyper-parameters. Another serious drawback of this approach is that it is often suboptimal since the optimal choice for the hyper-parameter is likely to lie outside the searching space due to the space discretization step required in grid search. To address these challenges, we propose in this paper BAyesian Multi-hyperplane Machine (BAMM). Our approach departs from a Bayesian perspective, and aims to construct an alternative probabilistic view in such a way that its maximum-a-posteriori (MAP) estimation reduces exactly to the original optimization problem of a multi-hyperplane machine. This view allows us to endow prior distributions over hyper-parameters and augment auxiliary variables to efficiently infer model parameters and hyper-parameters via Markov chain Monte Carlo (MCMC) method. We then employ a Stochastic Gradient Descent (SGD) framework to scale our model up with ever-growing large datasets. Extensive experiments demonstrate the capability of our proposed method in learning the optimal model without using any parameter tuning, and in achieving comparable accuracies compared with the state-of-art baselines; in the meantime our model can seamlessly handle with large-scale datasets.
Applying Deep Neural Networks to Named Entity Recognition in Portuguese Texts

There is currently few research in using deep learning (DL) applied to Named Entities Recognition (NER) in Portuguese texts. This work exposes some challenges and limitations but also the benefits of applying DL architectures to NER in Portuguese. Four different DL architectures are applied to Portuguese datasets. All architectures are heavily influenced by previous published work in NER applied to English. Annotated data is used to train and test NER models, while non-annotated data is used to train word embeddings, as well as being a key part of a bootstrapping approach, where raw textual data is used to create NER models.
Discriminative sparse representation for speech mode classification

In this paper, we propose a discriminative sparse feature for speech mode classification (SMC). We use sparse representation, to calculate efficient representations of speech signal while learning dictionary atoms for speech mode classification. In this study, we collectively consider the three broad speech modes: conversation, extempore and read speech for mode classification. The usefulness of sparse representation depends on the dictionary. Therefore, we presented a Fisher discriminative (FD) based dictionary to obtain the variation in the speech mode. The feature extraction scheme using Fisher discriminative dictionary learning (FDDL) method is shown by exploring the discriminative information present in both coding coefficients and reconstruction error. The proposed sparse feature is extensively evaluated on Bengali speech corpus, and it results in the better performance than the conventional speech features.
A Literature Study on Different Multi-Document Summarization Techniques

Currently the rate of improvement of information is developing exponentially in the World Wide Web. In this manner, removing real and significant information from tremendous data has transformed into a testing issue. Starting late text summarization is seen as one of the response for expel material information from immense documents. In view of number of documents considered for summarization, the summarization task is requested as single document or multi-document summarization. Rather than single document, multi-document summarization is all the additionally striving for the examiners to find correct abstract from multiple documents. In this paper, firstly we will discuss about the concept of multi-document summarization and then we will have an in depth analysis of various methodologies which goes under the multi-document summarization. The paper moreover contains bits of knowledge about the focal points and issues in the present methods. This would especially be helpful for researchers working in this field of text data mining. By using this data, researchers can create new or mixed based methodologies for multi-document summarization.
A 15-lncRNA signature predicts survival and functions as a ceRNA in patients with colorectal cancer

Purpose Colorectal cancer (CRC) is one of the most common malignant tumors worldwide. This study aimed to explore the prognostic value of lncRNAs in CRC. Material and methods We performed gene expression profiling to identify differentially expressed lncRNAs between 51 normal and 646 tumor tissues from The Cancer Genome Atlas database. Cox regression and robust likelihood-based survival models were used to find prognosis-related lncRNAs. A lncRNA signature was developed to predict the overall survival of patients with CRC. In addition, a receiver operating characteristic curve analysis was performed to identify the optimal cutoff with the best Youden index to divide patients into different groups based on risk level. Results Eighty survival-related lncRNAs were identified and a 15-lncRNA signature was developed on the basis of a risk score to comprehensively predict the overall survival of patients with CRC. The prognostic value of the 15-lncRNA risk score was validated using the internal testing set and total set. The risk indicator was shown to be an independent prognostic factor (hazard ratio =2.92; 95% CI: 1.73–4.94; P<0.001). Notably, all 15 lncRNAs (AC024581.1, FOXD3-AS1, AC012531.1, AC003101.2, LINC01219, AC083967.1, AL590483.1, AC105118.1, AC010789.1, AC067930.5, AC105219.2, LINC01354, LINC02474, LINC02257, and AC079612.1) were newly found to correlate with the prognosis of patients with CRC. Furthermore, the function of 15 lncRNAs was explored through the ceRNA network. These lncRNAs regulated coding genes that were involved in many key cancer pathways. Conclusion A 15-lncRNA expression signature was discovered as a prognostic indicator for patients with CRC, which may act as competing endogenous RNA (ceRNAs) to play a crucial role in the modulation of cancer-related pathways. These findings may allow a better understanding of the prognostic value of lncRNAs.
Integrated Optics DFB Lasers On Glass For High Radio-Frequency Generation

In this paper, we study the capacity of glass integrated optics DFB lasers to generate millimeter wave and terahertz frequencies. We present the different technological parameters that can be adjusted to generate frequencies up to 8.8THz by photomixing two free running DFB lasers on glass. The impact of each parameter is explicitly demonstrated using simulations and experimental measurements.
Wireless Channel Models for Maritime Communications

Recently, broadband maritime communication has attracted much attention due to the rapid development of blue economy. In addition to the conventional MF/HF/VHF bands, there has been increasing interests in the utilization of higher frequency bands to provide broadband data service to the sea area. To design efficient maritime communication systems, the first and a fundamental requirement is to develop a framework to understand the wireless channels. In an integrated air-ground-sea communications network, there are two major type of channels to be investigated, namely the air-to-sea channel (e.g., for communication links from the aircraft-based base stations or relays) and the near-sea-surface channel (for land-to-ship/ship-to-land or ship-to-ship communications). Due to the unique features of the maritime propagation environment such as sparse scattering, sea wave movement, and the ducting effect over the sea surface, the modeling of these maritime channel links differs from conventional terrestrial wireless channels in many aspects and, consequently, will result in significant impact on the transceiver design. In this survey, we highlight the most notable differences from the modeling perspective as well as the channel characteristics for the air-to-sea and near-sea-surface channel links, with more focus on the latter. After a thorough review of existing modeling approaches and measurement campaigns, we conclude that the sparse and the location-dependent properties constitute the most important and distinctive characteristics of the maritime wireless channels. As such, we further remark on the challenges and research topics for future development of maritime communications.
An Improved Polar Formation Imaging Algorithm for Squinted TOPS SAR

For squinted terrain observation by progressive scans (TOPS) synthetic aperture radar (SAR) imaging mode, the generalized polar formation algorithm (PFA) is improved and an imaging algorithm, which firstly carries out linear range walk correction (LRWC) and then applies the PFA interpolation, is proposed in this paper. The LRWC can effectively mitigate the range-azimuth coupling effect and simplify range cell migration (RCM) correction. Besides, after performing LRWC, the azimuth sampling interval is still uniform, as such, the azimuth interpolation can be completed by Chirp-Z transform. For the azimuth-variation of Doppler rate induced by the antenna beam steering and LRWC, the azimuth nonlinear chirp scaling (ANCS) is implemented. Therefore, the azimuth focus depth is improved greatly and the scene size which can be well-focused is increased. Both simulation and real data results are provided to demonstrate the effectiveness of the proposed method.