Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Scene Geometry for Scale Adaptive Object Tracking in Surveillance Videos

Object tracking is a key technology in video surveillance. Reliable tracker must be adaptive to the constantly changing object sizes. Most of the state-of-the-art methods estimate the object scales using their appearances. Those methods are vulnerable to occlusion, object deformation, illumination change and background clutter. In this paper, we propose to use the geometric context of the surveillance site as a strong clue for scale adaptation. With three reasonable assumptions on the video cameras and the surveillance sites, we deduce a simple geometric model for object scales. The parameters of this model are learned without any human intervention. Then we integrate this model into baseline trackers for robust scale adaptive object tracking. Experimental results on challenging surveillance videos indicate that our approach favorably improves the performance of single-scale baselines, and performs better or comparative to the state-of-the-art multi-scale trackers while significantly improve the speed.
Combining an Active Shape and Motion Models for Object Segmentation in Image Sequences

Obtaining the segmentation of an object in a sequence of images is usually achieved using a tracking methodology. However, in some applications, the whole sequence is available beforehand. This means that the segmentations can be determined simultaneously for all the frames in the sequence and taking into account the motion of the object. This paper proposes a new framework to incorporate motion information in the segmentation of image sequences using an active shape model (ASM). The motion of the object is modeled using a vector field, which is learned and refined online as the segmentation algorithm proceeds. The vector field is determined from the trajectories described by ASM points throughout the sequence. The vector field, in turn, influences the estimation of the ASM parameters by acting as a regularizer, ensuring that the segmentations are in agreement with the expected motion. The results show that coupling these models during the segmentation leads to an increase in performance, in particular by guarantee more consistent segmentations and by avoid gross errors in more challenging frames.
Infrared Image Colorization Using a S-Shape Network

This paper proposes a novel approach for colorizing near infrared (NIR) images using a S-shape network (SNet). The proposed approach is based on the usage of an encoder-decoder architecture followed with a secondary assistant network. The encoder-decoder consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. The assistant network is a shallow encoder-decoder to enhance the edge and improve the output, which can be trained end-to-end from a few image examples. The trained model does not require any user guidance or a reference image database. Furthermore, our architecture will preserve clear edges within NIR images. Our overall architecture is trained and evaluated on a real-world dataset containing a significant amount of road scene images. This dataset was captured by a NIR camera and a corresponding RGB camera to facilitate side-by-side comparison. In the experiments, we demonstrate that our SNet works well, and outperforms contemporary state-of-the-art approaches.
Color Selection and Editing for Palette-Based Photo Recoloring

We propose a method to extract palette colors from an image and edit the image for palette-based photo recoloring. A previous study proposed an editing method that used a color palette. However, that method had some constraints in changing the palette color, and it was also necessary for the user to determine the palette size. Undesirable luminance changes during color editing can be avoided by performing color editing using intrinsic color decomposition. The palette size is estimated using the Bayesian information criterion for the palette colors extracted from the reflectance image; this can later be arbitrarily changed by the user. We evaluated our approach via a user study, showing that it is more precise and faster at matching the image to the target than a previously reported method.
Influences of Fundamental Oscillation on Speaker Identification in Vocalic Utterances by Humans and Computers

We tested the influence of fundamental oscillation (fo) on human and machine speaker recognition performance in vocalic test utterances. In experiment I, we trained a Gaussian-Mixture model on 15 speakers (80 multi-word utterances each) and tested it with sustained vowel utterances (/a:/, /i:/ and /u:/) under six fo conditions, three changing (fall, rise, fall-rise) and three steady-state (high, mid, low). Results revealed better performance for the steady-state compared to the changing conditions and within the steady-state condition, performance was poorest for high fo. In experiment II, we tested 9 human listeners on a subset of 4 speakers from experiment I. They went through two training tasks (training 1: multi-word utterances; training 2: words). In the test, they recognized speakers based on the same vocalic utterances as in experiment I (for these 4 speakers). Results showed that performance was about equally high for the changing and steady-state vowels, however, in the steady-state condition performance was best for high fo vowels. The experiments suggest that (a) fo has an influence on the strength of speaker specific characteristics in vowels and (b) humans - compared to machines - pay attention to different acoustic information in vocalic utterances for speaker recognition.
Orexin-A Levels in Relation to the Risk of Metabolic Syndrome in Patients with Schizophrenia Taking Antipsychotics

Abstract Background The role of orexin-A in regulating metabolic homeostasis has been recognized, but its association with antipsychotic-induced metabolic abnormalities remains unclear. We investigated the association between orexin-A levels and metabolic syndrome in patients with schizophrenia treated with clozapine or less obesogenic antipsychotics compared with nonpsychiatric controls. Methods Plasma orexin-A levels and metabolic parameters were determined in 159 patients with schizophrenia: 109 taking clozapine; 50 taking aripiprazole, amisulpride, ziprasidone, or haloperidol; and 60 nonpsychiatric controls. Results Orexin-A levels were significantly higher in the group taking less obesogenic antipsychotics, followed by the clozapine group and the controls (F=104.6, P<.01). Higher orexin-A levels were correlated with better metabolic profiles in the patient groups but not in the controls. Regression analyses revealed that the patients with higher orexin-A levels had significantly lower risk of metabolic syndrome (adjusted odds ratio [OR]=0.04, 95% CI: 0.01–0.38 for the 2nd tertile; OR=0.04, 95% CI: 0.01–0.36 for the 3rd tertile, compared with the first tertile), after adjustment for age, sex, smoking history, types of antipsychotics (clozapine vs less obesogenic antipsychotics), duration of antipsychotic treatment, and disease severity. Conclusions Our results revealed that the orexin-A level was upregulated in patients with schizophrenia treated with antipsychotics, especially for the group taking less obesogenic antipsychotics. Furthermore, higher orexin-A levels were independently associated with better metabolic profiles. These observations suggest that an upregulation of orexin-A has a protective effect against the development of metabolic abnormalities in patients with schizophrenia receiving antipsychotic treatment.
Gestalt Interest Points with a Neural Network for Makeup-Robust Face Recognition

In this paper, we propose a novel approach for the domain of makeup-robust face recognition. Most face recognition schemes usually fail to generalize well on these data where there is a large difference between the training and testing sets, e.g., makeup changes. Our method focuses on the problem of determining whether face images before and after makeup refer to the same identity. The work on this fundamental research topic benefits various real-world applications, for example automated passport control, security in general, and surveillance. Experiments show that our method is highly effective in comparison to state-of-the-art methods.
Cyclopean Image Based Stereoscopic Image Quality Assessment by Using Sparse Representation

3D image quality assessment confronts more difficulties than 2D image quality assessment. In this paper a 3D image quality assessment metric based on sparse representation was proposed. The contributions of the proposed method mainly include the following points: a color cyclopean image is used to better simulate the process of image processing in human brain, which is also very suitable for evaluating the quality of asymmetric distortion image. Meanwhile, for during sparse reconstruction some important information will be lost, we use the corresponding color cyclopean image to do compensation before feature extracting. And the paper creatively extracts spatial and spectral entropy feature of the distortion color cyclopean image and the corresponding reconstruction cyclopean image, respectively. Finally, we uses SVR to evaluate the quality of stereoscopic image. Experimental results show that the proposed method is very much in line with human visual perception.
Low Light Image Denoising Based on Poisson Noise Model and Weighted TV Regularization

Since the signal-to-noise ratio (SNR) is low in a dark environment, the captured images are seriously corrupted by sensor noise. Moreover, the sensor noise is signal-dependent, and has different characteristics from Gaussian distribution. In this paper, we propose low light image denoising based on Poisson noise model and weighted total variation (TV) regularization. In the data fidelity term, we adopt Poisson noise model to consider characteristics of sensor noise. In the regularization term, we present TV regularization based on two weights: Ratio between intensity mean and variance, and edge directionality. The ratio between intensity mean and variance adjusts the smoothing degree, while the edge directionality preserves image details. Experimental results demonstrate that the proposed method effectively removes sensor noise in low light images as well as outperforms the-state-of-the-arts in terms of the naturalness image quality evaluator (NIQE).
Traditional Norwegian Kveik Are a Genetically Distinct Group of Domesticated Saccharomyces cerevisiae Brewing Yeasts

The widespread production of fermented food and beverages has resulted in the domestication of Saccharomyces cerevisiae yeasts specifically adapted to beer production. While there is evidence beer yeast domestication was accelerated by industrialization of beer, there also exists a farmhouse brewing culture in western Norway which has passed down yeasts referred to as kveik for generations. This practice has resulted in ale yeasts which are typically highly flocculant, phenolic off flavor negative (POF-), and exhibit a high rate of fermentation, similar to previously characterized lineages of domesticated yeast. Additionally, kveik yeasts are reportedly high-temperature tolerant, likely due to the traditional practice of pitching yeast into warm (>28°C) wort. Here, we characterize kveik yeasts from 9 different Norwegian sources via PCR fingerprinting, whole genome sequencing of selected strains, phenotypic screens, and lab-scale fermentations. Phylogenetic analysis suggests that kveik yeasts form a distinct group among beer yeasts. Additionally, we identify a novel POF- loss-of-function mutation, as well as SNPs and CNVs potentially relevant to the thermotolerance, high ethanol tolerance, and high fermentation rate phenotypes of kveik strains. We also identify domestication markers related to flocculation in kveik. Taken together, the results suggest that Norwegian kveik yeasts are a genetically distinct group of domesticated beer yeasts with properties highly relevant to the brewing sector.
Mothers and Children Exposed to Intimate Partner Violence: A Review of Treatment Interventions

Although a growing field, much is still unknown about how different clinical and social care services might improve outcomes for female victims of intimate partner violence (IPV) and their children who are indirectly exposed to it. This review sought to characterize the structure of programs that have been tested and documented in existing literature, and the mechanisms by which change, if any, may occur. Seventeen individual interventions and two follow-ups (n = 19) were included in the review. Findings suggest that a multileveled program of mothers and children working both separately and jointly together across sessions might generate the most successful psychosocial recovery for mothers and children who have experienced violence in the home. The mechanism by which this happens is likely a collaborative one, focused on enhancing the dyadic interaction. This article adds to the growing evidence base on IPV and confirms the positive impact on well-being that programs for IPV victims can have. The evidence-base overall could benefit from testing and replicating a combination of the results found in this review.
3D Cnn-Based Soma Segmentation from Brain Images at Single-Neuron Resolution

Neuron segmentation is an important task for automatic analyses of brain images that are of huge volume. Previous methods for neuron segmentation rely on handcrafted image features, and have difficulty in coping with high-resolution, low signal-to-noise-ratio brain images. Convolutional neural network (CNN) has achieved remarkable success in natural image segmentation, but CNN requires accurately labeled data for training that are difficult to achieve on brain images of huge volume. In this paper, we present a weakly supervised learning strategy to deal with the inaccurate training data problem, and thus adopt 3D CNN to perform automatic soma segmentation from brain images. We test our method on our own collected mouse brain images that are of single-neuron resolution, and results show that 3D CNN-based method outperforms the traditional methods by a significant margin.
Multi-Representations Encoding Framework for Adaptive Http Streaming

Adaptive HTTP streaming requires a video to be encoded at multiple representations of different target bitrates. To achieve both smooth streaming and good quality, the representations need to be encoded with accurate rate control and the best quality possible for the target bitrates. However, in practical applications, accurate rate control and good video quality are very difficult to achieve at the same time with one-pass and real-time encoding required by low latency applications. In this paper, we proposed a multi-representation encoding framework that reused the encoding information from low-quality representations to accelerate and optimize higher bitrate encodings. The proposed framework is implemented on two different rate control models, namely $R-\lambda$ model in HM-16.3 and complexity model in x264, to demonstrate the universality. To be best of our knowledge, the optimization in compression performance of multi - representation is first proposed in this paper. The proposed algorithm can achieve better video quality with only small latency in video coding. Results show that up to 49.6% BDRate savings and 4.43dB BDPSNR improvement are achieved in HM as compared with independent one-pass encodings. Meanwhile, 12.5% time and 14.65% BDRate savings were observed in x264.
The return of Pfeiffer’s bacillus: Rising incidence of ampicillin resistance in Haemophilus influenzae

Haemophilus influenzae, originally named Pfeiffer’s bacillus after its discoverer Richard Pfeiffer in 1892, was a major risk for global health at the beginning of the 20th century, causing childhood pneumonia and invasive disease as well as otitis media and other upper respiratory tract infections. The implementation of the Hib vaccine, targeting the major capsule type of H. influenzae, almost eradicated the disease in countries that adapted the vaccination scheme. However, a rising number of infections are caused by non-typeable H. influenzae (NTHi), which has no capsule and against which the vaccine therefore provides no protection, as well as other serotypes equally not recognised by the vaccine. The first line of treatment is ampicillin, but there is a steady rise in ampicillin resistance. This is both through acquired as well as intrinsic mechanisms, and is cause for serious concern and the need for more surveillance. There are also increasing reports of new modifications of the intrinsic ampicillin-resistance mechanism leading to resistance against cephalosporins and carbapenems, the last line of well-tolerated drugs, and ampicillin-resistant H. influenzae was included in the recently released priority list of antibiotic-resistant bacteria by the WHO. This review provides an overview of ampicillin resistance prevalence and mechanisms in the context of our current knowledge about population dynamics of H. influenzae.
High‐Value MRI

The facts behind unsustainably rising healthcare costs are complex and daunting. Although MRI exams represent a small fraction of these costs, the tremendous versatility of MR may allow it to have an outsized, positive effect on healthcare value. Realizing this potential requires innovation, purposeful communication, an increased understanding of what drives diagnostic decisions, improved efforts to better define and assess clinical impact, and a commitment to identify and address the inefficiencies in MRI use. Towards this objective, the ISMRM and RSNA recently co-provided a workshop on High-Value MRI. The tone of the workshop was highly positive, with broad agreement that action by our field is needed, accompanied by new frameworks and processes. There was a strong belief that a renewed focus on innovation and novel utilization of MRI can both address the economics of value (reducing costs) while simultaneously improving patient care. The following specific goals towards achieving “High-Value MRI” are synthesized from the workshop talks and discussion.
Learning Local and Deep Features for Efficient Cell Image Classification Using Random Forests

Automatic image classification systems for indirect immunofluorescence (IIF) labeling of human epithelial (HEp-2) cell specimens are needed to improve the efficient management of autoimmune diseases. In this paper, we propose to classify HEp-2 cell specimen imagery using a combination of local features and deep learning features extracted from the IIF images. Two local descriptors are used to capture texture information, namely: Rotation Invariant Co-occurrence among Local Binary Patterns (RIC-LBP) extending the LBP descriptor and Joint Motif Labels (JML) based on the Peano scan motif concept. Deep learning features are then extracted using the VGG-19 image classification network. Finally, all descriptors are combined using a late fusion approach with a Random Forests (RF) classifier with seven output classes. Experimental results show that our proposed framework achieves a mean class accuracy of 92.11% with five-fold cross validation using the RF classifier with 1000 trees on the HEp-2 specimen benchmark dataset, which outperforms the state-of-the-art accuracy on this dataset.
What Do Classifiers Actually Learn? a Case Study on Emotion Recognition Datasets

In supervised learning, a typical method to ensure that a classi-ﬁer has desirable generalization properties, is to split the available data into training, validation and test subsets. Given a proper data split, we typically then trust our results on the test data. But what do classiﬁers actually learn? In this case study we show how important it is to analyze precisely the available data, its inherent dependencies w.r. t. class labels, and present an example of a popular database for speech emotion recognition, where a minor change of the data split results in an accuracy decrease of about 55 % absolute, leading to the conclusion that linguistic content has been learned instead of the desired speech emotions.
Speech Emotion Recognition from Variable-Length Inputs with Triplet Loss Function

Automatic emotion recognition is a crucial element on understanding human behavior and interaction. Prior works on speech emotion recognition focus on exploring various feature sets and models. Compared with these methods, we propose a triplet framework based on Long Short-Term Memory Neural Network (LSTM) for speech emotion recognition. The system learns a mapping from acoustic features to discriminative embedding features, which are regarded as basis of testing with SVM. The proposed model is trained with triplet loss and supervised loss simultaneously. The triplet loss makes intra-class distance shorter and inter-class distance longer, and supervised loss incorporates class label information. In view of variable-length inputs, we explore three different strategies to handle this problem, and meanwhile make better use of temporal dynamic process information. Our experimental results on the Interactive Emotional Motion Capture (IEMOCAP) database reveal that the proposed methods are beneficial to performance improvement. We demonstrate promise of triplet framework for speech emotion recognition and present our analysis.
Role of a Community Pharmacy Service in Care of Bronchial Asthma Patients in Lithuania

Bronchial asthma is one of the most common chronic respiratory diseases, and its care is often complex. In this research, we tested the proposal that participation of pharmacists in the management of bronchial asthma can improve patient outcomes. A two-stage study was constructed consisting of a training element and a service element, using the Asthma Control Test and a structured questionnaire about the patients' disease condition (based on the results of a qualitative study). The study was conducted in 21 pharmacies in Lithuania and involved 338 asthmatic patients (age 18–88 years). It was found that before the pharmacy service was provided, the average number of mistakes patients made in administration of asthma medications was 2.03; this number decreased to 1.12 after the service was provided (p < 0.05). Disease control paralleled the improvement in number of mistakes: 26.1% of patients who previously exerted no control over the disease symptoms began to exert sufficient control over their asthma symptoms (Asthma Control Test >20) after the service was provided (p < 0.05). The reduced number of mistakes probably can be attributed to the positive effects of the provided services. By reducing the number of patient mistakes, pharmacists may improve the outcomes of asthmatic patients.
Stream Attention for Distributed Multi-Microphone Speech Recognition

Exploiting multiple microphones has been a widely-used strategy for robust automatic speech recognition (ASR). Particularly, in a general hands-free scenario, acquisition of speech usually happens using a set of distributed microphones or arrays simultaneously. Each microphone or array (deﬁned as a stream) carries a different quality of information. The technique of stream fusion is beneﬁcial to provide the best distant recognition performance against the effects of potential disturbances such as noise, reverberation, as well as the speaker movement. In this work, we propose a stream attention framework to improve the far-ﬁeld ASR performance in the distributed multi-microphone conﬁguration. Frame-level attention vectors have been derived by predicting the ASR performance of the acoustic modeling of individual streams using the posterior probabilities from the classiﬁer. They are used to characterize the amount of useful information each stream contributes, for the purpose of an efﬁcient and better-performing decoding scheme. In this paper, we investigate the ASR performance measures using our proposed stream attention system on real recorded datasets, Mixer-6 and DIRHA-WSJ. The experimental results show that the proposed framework yields substantial improvements in word error rate (WER) compared to conventional strategies.