Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Evolutionary 3D Diffusion Heat Maps for Large-pose Face Alignment

As one of the fundamental vision tasks, face alignment has attracted a tremendous amount of efforts and achieved signiﬁcant improvement over the decades. While the state-of-the-art works fairly well on the lab datasets and certain face images in the wild, it may easily fail in front of large pose variation, e.g., proﬁle. In the worst case, the invisible landmarks may crash the initial models and thus limit many powerful models that only work well within a certain range using reliable features. To that end, we propose a new deep evolutionary model to integrate 3D Diffusion Heap Maps (DHM) to compensate for the invisible landmarks issue in large pose variation. Our contributions are summarized as: ﬁrst, we introduce a sparse 3D DHM to assist the initial modeling under extreme pose conditions; second, a simple yet effective CNN feature is extracted and fed to recurrent neural networks for evolutionary learning. Additionally, we propose a Recurrent HourGlass (RHG) network that boost our evolutionary learning through HourGlass and LSTM module. Extensive experiments on three popular face alignment databases demonstrate the advantage of the proposed models over the state-of-the-art, especially under large-pose conditions. We also discuss and analyze the limitations of our models and future research work.
Interaction of guidance types and the Need for Cognitive Closure in wiki-based learning

One purpose of wikis is the collaborative generation of content. During creation processes, controversies between authors emerge that they discuss on the article’s talk page. Research suggests that controversies based on opposing points of view and contradictory evidence can be fruitful to trigger individual elaboration processes. However, previous research also showed that many wikis are not necessarily suited to identify relevant discussion contents and thus users need additional support as guidance. In an experimental laboratory study (N = 181) on wiki talk pages, we investigated two guidance measures in conjunction with the need for cognitive closure: (1) visual markers to highlight controversy status (implicit guidance) and (2) a collaboration script that directs users towards discussions (explicit guidance). Effects on wiki processes and learning outcomes were analysed. The results show that both guidance types can affect user behaviours, but in interaction with the individual Need for Cognitive Closure there were no meaningful effects. With respect to learning outcomes, we found an anticipated pattern for the interaction of the Need for Cognitive Closure with both guidance principles. The data provides support for differences in the learning success depending on the provided guidance type and the individual Need for Cognitive Closure.
Comparison of the Personality Traits of Male and Female BASE Jumpers

BASE jumping is an extreme adventure sport which consists of jumping from a fixed object with specially adapted parachutes. A few studies of the personality of BASE jumpers have been conducted, but little is known about how the women in this sport compare to the men. The purpose of this study is to compare the personality traits among a sample of men and women who are experienced BASE jumpers, as this provides an interesting and important opportunity to better understand the motivation for extreme sports. Eighty-three participants completed the Temperament and Character Inventory the day before the jump at the New River Gorge Bridge Day BASE Jumping event, West Virginia, United States. The sample included 64 men and 19 women. Results show that men and women BASE jumpers shared similar personality traits both in terms of temperament and character, except for the character trait of cooperativeness on which women scored higher than men. This suggests that the basic drive for participation in extreme sports is self-regulation of personal emotional drives and needs for self-actualization, rather than to oppose social pressure or cultural bias against female participation. These findings are discussed in relation with other studies conducted among extreme athletes and in terms of congruence between personality and activity.
Software module-based 10G Optical Line Terminal for enhancing flexibility of access networks

For enhancing flexibility in optical access network services, virtualization and softwarization of optical access network equipment in a Central Office have become a hot topic in this couple of years. We try to implement the software module-based Optical Line Terminal (OLT) for further flexibility in updating its functions. However, performance degradation compared to a conventional hardware OLT, is a technical obstacle for softwarization and modularization. This article reports the influence of softwarization and modularization, and the improvement by our proposed methods, based on prototype evaluation.
Modelling Diffusion Process by Deep Neural Networks for Image Retrieval

By considering the underlying neighbourhood structure of images, diffusion process can better evaluate image similarity and has proven highly effective in improving image retrieval. Nevertheless, diffusion process stores a large neighbourhood graph, costs more online retrieval time, and requires special algorithms other than simple Euclidean search. To address these issues, this paper proposes to treat diffusion process as a “black box” and directly model it by training deep neural networks, so as to obtain better image representation that assimilates the effect of diffusion process and works with Euclidean search. We ﬁrstly put forward a kernel mapping interpretation to diffusion process, and then formulate the modelling as a deep metric learning problem. The proposed approach is unsupervised in the sense that it needs neither image labels nor external datasets, and completely avoids online diffusion process in retrieval. More interestingly, we ﬁnd that this approach could even achieve better retrieval than the original diffusion process, instead of merely approximating it. Experiments verify its effectiveness and investigate its appealing characteristics such as the generalisation to new image insertion.
Categorization of Comparative Sentences for Argument Mining

We present the first work on domain-independent comparative argument mining (CAM), which is the automatic extraction of direct comparisons from text. After motivating the need and identifying the widespread use of this so far under-researched topic, we present the first publicly available open-domain dataset for CAM. The dataset was collected using crowdsourcing and contains 7199 unique sentences for 217 distinct comparison target pairs selected over several domains, of which 27% contain a directed (better vs. worse) comparison. In classification experiments, we examine the impact of representations, features, and classifiers, and reach an F1-score of 88% with a gradient boosting model based on pre-trained sentence embeddings, especially reliably identifying non-comparative sentences. This paves the way for domain-independent comparison extraction from web-scale corpora for the use in result ranking and presentation for comparative queries.
Labour market outcomes of Veterans

Introduction: Employment is important to health, well-being, and adjustment from military to civilian life. Given the importance of employment, we examine Veteran labour force outcomes in Canada. Methods: We examined labour market indicators from the 2010 and 2013 Life After Service Studies cross-sectional Survey on Transition to Civilian Life, along with the 2013 Income Study for Canadian Regular Force Veterans (released since 1998). Results: In Canada, most Regular Force Veterans surveyed were employed after release and satisfied with their work – both employment and satisfaction rates grew over time. The unemployment rate did not differ from that of the general Canadian population. However, Veterans were more likely than the general Canadian population to experience activity limitations at work. Variations in outcomes were found across diverse groups of the population. For example, unemployed Veterans were younger at release, had the fewest years of service, and were more likely to have served in the Army than employed Veterans. Veterans who were not in the labour force were older and had more years of service, and many were experiencing barriers to work. Employment rates were lower among female Veterans and among medically released Veterans. Discussion: Labour market outcomes vary across sub-groups of the Veteran population, suggesting targeted approaches to improve labour market outcomes. Findings suggest that the prevention of work disability is important for improving outcomes. Best practices in preventing work disability include restructuring compensation to recognize varying degrees of earnings capacity and to encourage labour market engagement and supported employment programs.
Speech-Transformer: A No-Recurrence Sequence-to-Sequence Model for Speech Recognition

Recurrent sequence-to-sequence models using encoder-decoder architecture have made great progress in speech recognition task. However, they suffer from the drawback of slow training speed because the internal recurrence limits the training parallelization. In this paper, we present the Speech-Transformer, a no-recurrence sequence-to-sequence model entirely relies on attention mechanisms to learn the positional dependencies, which can be trained faster with more efficiency. We also propose a 2D-Attention mechanism, which can jointly attend to the time and frequency axes of the 2-dimensional speech inputs, thus providing more expressive representations for the Speech-Transformer. Evaluated on the Wall Street Journal (WSJ) speech recognition dataset, our best model achieves competitive word error rate (WER) of 10.9%, while the whole training process only takes 1.2 days on 1 GPU, significantly faster than the published results of recurrent sequence-to-sequence models.
We Usually Don’t Like Going to the Dentist: Using Common Sense to Detect Irony on Twitter

Although common sense and connotative knowledge come naturally to most people, computers still struggle to perform well on tasks for which such extratextual information is required. Automatic approaches to sentiment analysis and irony detection have revealed that the lack of such world knowledge undermines classification performance. In this article, we therefore address the challenge of modeling implicit or prototypical sentiment in the framework of automatic irony detection. Starting from manually annotated connoted situation phrases (e.g., “flight delays,” “sitting the whole day at the doctor’s office”), we defined the implicit sentiment held towards such situations automatically by using both a lexico-semantic knowledge base and a data-driven method. We further investigate how such implicit sentiment information affects irony detection by assessing a state-of-the-art irony classifier before and after it is informed with implicit sentiment information.
Deterministic Implementations for Reproducibility in Deep Reinforcement Learning

While deep reinforcement learning (DRL) has led to numerous successes in recent years, reproducing these successes can be extremely challenging. One reproducibility challenge particularly relevant to DRL is nondeterminism in the training process, which can substantially affect the results. Motivated by this challenge, we study the positive impacts of deterministic implementations in eliminating nondeterminism in training. To do so, we consider the particular case of the deep Q-learning algorithm, for which we produce a deterministic implementation by identifying and controlling all sources of nondeterminism in the training process. One by one, we then allow individual sources of nondeterminism to affect our otherwise deterministic implementation, and measure the impact of each source on the variance in performance. We find that individual sources of nondeterminism can substantially impact the performance of agent, illustrating the benefits of deterministic implementations. In addition, we also discuss the important role of deterministic implementations in achieving exact replicability of results.
Player Experience Extraction from Gameplay Video

The ability to extract the sequence of game events for a given player's play-through has traditionally required access to the game's engine or source code. This serves as a barrier to researchers, developers, and hobbyists who might otherwise benefit from these game logs. In this paper we present two approaches to derive game logs from game video via convolutional neural networks and transfer learning. We evaluate the approaches in a Super Mario Bros. clone, Mega Man and Skyrim. Our results demonstrate our approach outperforms random forest and other transfer baselines.
Reservoir Computing Hardware for Time Series Forecasting

Hardware implementation of Recurrent neural networks are able to increase the computing capacity in relation to software, so it can be of high interest when ultra-high speed processing is a requirement. However, the traditional hardware realization of neural networks has a cost in terms of power dissipation and circuit area due to the need of implementing a large quantity of binary multipliers as part of the synapses process. In this paper, a recurrent neural network scheme known as simple cyclic reservoir is implemented for time series processing. Synapses are implemented using single shift-add operations that maintains a similar accuracy with respect to full multipliers but with high savings in terms of area and power. The network architecture takes advantage of the fixed connectivity of the reservoir that only modifies the output layer of the network. Such design is synthesized in a digital circuitry, evaluated for a time-series benchmark prediction task and compared with previously published hardware implementation of a Reservoir Computing systems.
A carnosine analog mitigates metabolic disorders of obesity by reducing carbonyl stress

Sugar- and lipid-derived aldehydes are reactive carbonyl species (RCS) frequently used as surrogate markers of oxidative stress in obesity. A pathogenic role for RCS in metabolic diseases of obesity remains controversial, however, partly because of their highly diffuse and broad reactivity and the lack of specific RCS-scavenging therapies. Naturally occurring histidine dipeptides (e.g., anserine and carnosine) show RCS reactivity, but their therapeutic potential in humans is limited by serum carnosinases. Here, we present the rational design, characterization, and pharmacological evaluation of carnosinol, i.e., (2S)-2-(3-amino propanoylamino)-3-(1H-imidazol-5-yl)propanol, a derivative of carnosine with high oral bioavailability that is resistant to carnosinases. Carnosinol displayed a suitable ADMET (absorption, distribution, metabolism, excretion, and toxicity) profile and was determined to have the greatest potency and selectivity toward &agr;,&bgr;-unsaturated aldehydes (e.g., 4-hydroxynonenal, HNE, ACR) among all others reported thus far. In rodent models of diet-induced obesity and metabolic syndrome, carnosinol dose-dependently attenuated HNE adduct formation in liver and skeletal muscle, while simultaneously mitigating inflammation, dyslipidemia, insulin resistance, and steatohepatitis. These improvements in metabolic parameters with carnosinol were not due to changes in energy expenditure, physical activity, adiposity, or body weight. Collectively, our findings illustrate a pathogenic role for RCS in obesity-related metabolic disorders and provide validation for a promising new class of carbonyl-scavenging therapeutic compounds rationally derived from carnosine.
Noise Robust Speech Recognition on Aurora4 by Humans and Machines

Although great progress has been made in automatic speech recognition (ASR), significant performance degradation still exists in noisy environments. Based on our previous introduced very deep CNNs, this paper further integrates residual learning to evaluate very deep convolutional residual network (VDCRN) in noisy conditions, which shows more powerful robustness. Then, cluster adaptive training (CAT) is developed on the VDCRN to reduce the mismatch between the training and testing in noisy scenarios. Moreover, the advanced future-vector assisted LSTM-RNN LM is proposed to achieve a further gain. All the proposed approaches are evaluated on Aurora4 and show a significant improvement for each technology. The final system achieves 3.09% WER on Aurora4, which is approaching humans' performance on this task. This is a new milestone for noise-robust ASR on this benchmark.
Integrated inductor optimization for Power Supply on Chip

New needs in power efficiency and integration are driving the research to ever shrinking converters. One of the most crucial and hardest to optimize device inside the integrated converter is the inductor. In this paper, a computer-assisted optimization is done for the racetrack shaped microinductor used in this kind of system. The design methodology is described and the inductor model is validated using a Finite Element simulator. The results closely match the design equations and are compared with data reported in past publications. Since the inductor is only one element of the whole converter, some conclusions are drawn concerning the trade-offs when optimizing the converter as a whole.
Residual Learning for Face Sketch Synthesis

Face sketch synthesis plays an important role in both digital entertainment and law enforcement. It can bridge the great texture discrepancy between face photos and sketches. Most of the current face sketch synthesis approaches directly learn the relationship between the photos and sketches, and it is very difficult for them to generate the individual specific details, which we call rare features. To address this problem, in this paper we propose a novel face sketch synthesis through residual learning. In contrast the traditional approaches, which try to construct the sketch image directly, we aim at predicting the residual image (between the photo and sketch), given the photo observation. In addition, we also introduce a couple dictionary learning algorithm through preserving the local geometry structure of data space, which is usually ignored by existing methods. Our proposed method shows impressive results on the face sketch synthesis task, when compared with some state-of-the-arts including some recent proposed deep learning based approaches.
Impact of KDIGO-Defined Acute Kidney Injury on Mortality after Percutaneous Coronary Intervention for Acute Myocardial Infarction

Background: There are limited data regarding the incidence and long-term impact of acute kidney injury (AKI) according to the KDIGO guidelines on the outcome in patients with myocardial infarction (MI) treated with percutaneous coronary intervention (PCI). The aim of the study was to evaluate the prevalence of AKI, as classified by the KDIGO criteria, and its association with long-term mortality. Methods: Data from 5,859 MI patients undergoing PCI at our institution were analyzed. We compared the group without and with AKI according to the KDIGO criteria in relation to long-term mortality. Results: AKI was documented in 499 (8.5%) patients. AKI stage 1 occurred in 6.2% of patients, AKI stage 2 in 0.9% of patients, and AKI stage 3 in 1.5% of patients. Patients with AKI had a higher long-term mortality (57.3 vs. 20.6%; p < 0.0001). The mortality was 50.3% in AKI stage 1, 56.9% in AKI stage 2, and 87.2% in AKI stage 3. The hazard ratios for all-cause mortality for AKI stages 1–3 were 1.77, 1.85, and 6.30 compared to patients with no AKI. Cardiogenic shock, bleeding, heart failure, age, renal dysfunction, diabetes, hyperlipidemia, ST-elevation MI, contrast volume/glomerular filtration ratio, P2Y12 receptor antagonists, and radial access were associated with the development of AKI. Conclusion: A slight increase in serum creatinine was associated with a progressive increase in long-term mortality in patients with AKI according to the KDIGO definition.
Triple Verification Network for Generalized Zero-Shot Learning

Conventional zero-shot learning approaches often suffer from severe performance degradation in the generalized zero-shot learning (GZSL) scenario, i.e., to recognize test images that are from both seen and unseen classes. This paper studies the Class-level Over-fitting (CO) and empirically shows its effects to GZSL. We then address ZSL as a triple verification problem and propose a unified optimization of regression and compatibility functions, i.e., two main streams of existing ZSL approaches. The complementary losses mutually regularizes the same model to mitigate the CO problem. Furthermore, we implement a deep extension paradigm to linear models and significantly outperform state-of-the-art methods in both GZSL and ZSL scenarios on the four standard benchmarks.
Macroscopic Modeling of MnZn Ferrites for the Calculation of Eddy-Current Losses in the Frequency- and Time-Domain

The present trend towards miniaturization of passive components in the field of power electronics requires increased switching frequencies. At higher operating frequencies, however, the eddy-current losses in MnZn ferrites significantly contribute to the energy dissipation, especially in large specimens. This frequency-dependent behavior is correlated to the inhomogeneous micro-structure of the material. Ferrites are composed of grains separated by thin nearly insulating boundary layers. Due to the capacitive behavior of the boundary layers, eddy-currents are not confined within the grains but can flow across the whole sample cross-section. The first part of this paper will identify the contributions to losses of eddy-currents circulating inside the grains and eddy-currents circulating within the entire sample cross-section. For this investigation, the microscopic structure of the ferrite is modeled including the individual grains and grain boundaries. Based on the results, formulas for macroscopic material properties are derived, which describe the frequency-dependent behavior of MnZn ferrites. As a characterization of ferrites in the frequency-domain is only valid for small excitations, the macroscopic modeling of the material is extended to the time-domain. The derived time-domain model is valid for any excitation. Hence, the heterogeneous structure in combination with the nonlinear and hysteretic magnetic behavior of ferrites can be included in time-domain simulations.
Performance of Linear and Widely Linear Equalizers for FBMC/OQAM modulation

In this paper, we compare BER performance of linear (MMSE-LE) and widely linear (MMSE-WLE) equalizers applied on a filter bank multi-carrier modulation with Offset-QAM modulation (FBMC-OQAM). Theoretical expressions of bit error rate (BER) are derived with very good agreement with simulations. An equivalent model using $M$-ary pulse amplitude modulation (M-PAM) is derived from the FBMC modulator/demodulator. Theoretical BER expressions are based on this model. We show that BER for the LE is highly dependent on the fact that adjacent sub-carriers are switched on or off while WLE gives always the best possible performance.