Miracle-dz/UniFAR-FTUs huggingface dataset:
Sehvermögen und Verkehrsunfälle

Es wird heute ailgemein anerkannt, daB em gutes Sehvermogen eine der wichtigsten Voraussetzungen für em verkehrssicheres Verhalten beirn Fuhren von Kraftfahrzeugen ist. Deshalb mull sich jeder Bewerber urn eine Fahrerlaubnis zu Beginn seines ,,Kraftfahrer-Daseins" einem Sehtest unterziehen. Bekannt ist ferner, dalI die zentrale Tagessehscharfe (photopischer Bereich) ebenso wie die Sehscharfe unter nächtlichen Fahrbedingungen (rnesopischer Bereich) mit zunehmendem Lebensalter — also im Verlauf der Fahrpraxis — nachlällt und die B! endempfIndlichkeit zunimmt. Daher war es interessant, einmal der Frage nachzugehen, ob Personen mit einer in diesen Bereichen reduzierten Sehleistung in ihrer späteren Fahrgeschichte häufiger in Verkehrsunfälle verwickelt sind als Personen mit einem noch volt intakten Sehvermogen. Die Antwort darauf versucht eine Untersuchung zu geben, die in den Jahren 1982 und 1983 im Auftrag des Deutschen Grünen Kreuzes mit Unterstutzung des Kuratoriums für gutes Sehen vom Technischen Uberwachungs-Verein Bayern durchgefuhrt wurde und die nun in koniprimierter Form* berichtet werden soil.
The shape and simplicity biases of adversarially robust ImageNet-trained CNNs.

Adversarial training has been the topic of dozens of studies and a leading method for defending against adversarial attacks. Yet, it remains largely unknown (a) how adversarially-robust ImageNet classifiers (R classifiers) generalize to out-of-distribution examples; and (b) how their generalization capability relates to their hidden representations. In this paper, we perform a thorough, systematic study to answer these two questions across AlexNet, GoogLeNet, and ResNet-50 architectures. We found that while standard ImageNet classifiers have a strong texture bias, their R counterparts rely heavily on shapes. Remarkably, adversarial training induces three simplicity biases into hidden neurons in the process of 'robustifying' the network. That is, each convolutional neuron in R networks often changes to detecting (1) pixel-wise smoother patterns i.e. a mechanism that blocks high-frequency noise from passing through the network; (2) more lower-level features i.e. textures and colors (instead of objects); and (3) fewer types of inputs. Our findings reveal the interesting mechanisms that made networks more adversarially robust and also explain some recent findings.
Why is Attention Not So Interpretable

Attention-based methods have played an important role in model interpretations, where the calculated attention weights are expected to highlight the critical parts of inputs (e.g., keywords in sentences). However, recent research points out that attention-as-importance interpretations often do not work as well as we expect. For example, learned attention weights sometimes highlight less meaningful tokens like "[SEP]", ",", and ". ", and are frequently uncorrelated with other feature importance indicators like gradient-based measures. Finally, a debate on the effectiveness of attention-based interpretations has been raised. In this paper, we reveal that one root cause of this phenomenon can be ascribed to the combinatorial shortcuts, which stands for that in addition to the highlighted parts, the attention weights themselves may carry extra information which could be utilized by downstream models of attention layers. As a result, the attention weights are no longer pure importance indicators. We theoretically analyze the combinatorial shortcuts, design one intuitive experiment to demonstrate their existence, and propose two methods to mitigate this issue. Empirical studies on attention-based interpretation models are conducted, and the results show that the proposed methods can effectively improve the interpretability of attention mechanisms on a variety of datasets.
Home Range, Movements, and Habitat Use of Fishers in Wisconsin

This M.S. thesis consists of 3 technical papers which present data I recorded while studying fishers (Martes pennanti) in northern Wisconsin from August 1981 to August 1982. The first paper, "Home Range Area, Overlap, and Elevation Selection of Fishers in Wisconsin", will be submitted to the American Midland Naturalist. The second and third papers, "Activity, Movements, and Track Count Analysis of Fishers in Wisconsin" and "Habitat Use of Fishers in Wisconsin", will be submitted to the Journal of Wildlife Management. Consequently, style and format of the manuscripts vary according to specifications required by the respective journals. Data recorded during the study but not presented in the papers are provided in Appendices.
Arabidopsis GEX1 Is a Nuclear Membrane Protein of Gametes Required for Nuclear Fusion During Reproduction

During the life cycle of flowering plants, nuclear fusion, or karyogamy, occurs three times: once during female gametogenesis, when the two polar nuclei fuse in the central cell, and twice during double fertilization. In Arabidopsis thaliana, nuclear fusion events during sexual reproduction proceed without the breakdown of the nuclear envelope, indicating that nuclear membrane fusion is essential for the completion of this process. Arabidopsis gamete expressed 1 (GEX1) is a membrane protein that is conserved among plant species. GEX1 shares homology with the yeast karyogamy protein Kar5, which is primarily expressed in the nuclear membrane. The GEX1 family represents a putative karyogamy factor. Herein, we show that GEX1 is required for the nuclear fusion events in Arabidopsis reproduction. GEX1-deficient mature female gametophytes were found to contain two unfused polar nuclei in close proximity within the central cell. Electron microscopy showed that the outer membrane of the polar nuclei was connected via the endoplasmic reticulum, whereas the inner membrane remained unfused. These results indicate that GEX1 is involved in polar nuclear membrane fusion following the fusion of the outer nuclear membrane. Furthermore, sperm nuclear fusion events were defective in the fertilized egg and central cell following plasmogamy in the fertilization of gex1-1 female gametophytes by gex1-1 pollen. An analysis of GEX1 localization in the female gametophyte using a transgenic line expressing GFP-tagged GEX1 driven by the GEX1 promoter showed that GEX1 is a nuclear membrane protein in the egg and central cell. Time-lapse live-cell imaging showed that in developing female gametophytes, the nuclear GFP-GEX1 signal was first detectable in the central cell shortly before the polar nuclei came in close contact, and then in the egg cell. Thus, we suggest that the GEX1-family proteins are nuclear membrane proteins involved in karyogamy in the reproduction of eukaryotes including flowering plants.
Potential Toxic Effect of Bisphenol A on the Cardiac Muscle of Adult Rat and the Possible Protective Effect of Omega-3: A Histological and Immunohistochemical Study

Bisphenol A (BPA) is intensely used in the production of polycarbonate plastics and epoxy resins. Recently, BPA has been receiving increased attention due to its link to various health problems that develop after direct or indirect human exposure. Previous studies have shown the harmful effect of high doses of BPA; however, the effect of small doses of BPA on disease development is controversial. The aim of this study was to investigate the effect of a low dose of BPA on the rat myocardium and to explore the outcome of coadministration of Omega-3 fatty acid (FA). Thirty adult male rats were divided equally into control group, BPA-treated group (1.2 mg/kg/day, intraperitoneally for 3 weeks), and BPA and Omega-3-treated group (received BPA as before plus Omega-3 at a daily dose of 300 mg/kg/day orally) for 3 weeks. Exposure to BPA resulted in structural anomalies in the rat myocardium in the form of disarrangement of myofibers, hypertrophy of myocytes, myocardial fibrosis, and dilatation of intramyocardial arterioles. On the other hand, mast cell density and media-to-lumen area ratio were not significantly altered. Interestingly, concomitant administration of Omega-3 FAs with BPA significantly reduced BPA-induced changes and provided a protective effect to the myocardium. In conclusion, exposure to a low dose of BPA could potentially lead to pathological alterations in the myocardium, which could be prevented by administration of Omega-3 FA.
ARSketch: Sketch-Based User Interface for Augmented Reality Glasses

Hand gesture interaction is a key component in Augmented Reality (AR) / Mixed Reality (MR). Users usually interact with AR/MR devices, e.g., Microsoft HoloLens, etc., via hand gestures to express their intentions and the devices will recognize the gestures and respond accordingly to users. However, the use of such technique so far is limited to only a few less-expressive hand gestures, which, unfortunately, are insufficient or inadequate to input complex information. To tackle this problem, we introduce a sketch-based neural network-driven user interface for AR/MR glasses, called ARSketch, which enables drawing sketches freely in air to interact with the devices. ARSketch combines: (1) hand pose estimation that estimates the egocentric hand poses in an energy-efficient way, (2) sketch generation that generates sketches using key point positions of hand poses, and (3) sketch-photo retrieval that takes sketches as inputs to retrieve relevant photos. The evaluation results on our collected sketch dataset demonstrate the efficacy of ARSketch for user interaction.
Presence, Embodied Interaction and Motivation: Distinct Learning Phenomena in an Immersive Virtual Environment

The use of immersive virtual environments (IVEs) for educational purposes has increased in recent years, but the mechanisms through which they contribute to learning is still unclear. Popular explanations for the learning benefits brought by IVEs come from motivation, presence and embodied perspectives; either as individual benefits or through mediation effects on each other. This paper describes an experiment designed to interrogate these approaches, and provides evidence that embodied controls and presence encourage learning in immersive virtual environments, but for distinct,non-interacting reasons, which are also not explained by motivational benefits.
CFVMNet: A Multi-branch Network for Vehicle Re-identification Based on Common Field of View

Vehicle re-identification (re-ID) aims to retrieve the image of the same vehicles across multiple cameras. It has attracted wide attention in the field of computer vision owing to the deployment of surveillance system. However, some unfavorable factors restrict the retrieval accuracy of re-ID; minor inter-class difference and orientation variation are two main issues. In this study, we proposed a multi-branch network based on common field of view (CFVMNet) to address these issues. In the proposed method, we extracted and fused the global and local detail features using four branches and the Batch DropBlock (BDB) strategy to accentuate inter-class difference. We also considered some other attributes (i.e., color, type, and model) in the feature extraction process to make the final features more recognizable. For the issue of orientation variation that could lead to large intra-class difference, we learned two different metrics according to whether there is common field of view of two vehicle images, respectively, which can enable the proposed CFVMNet to focus on different regions. Extensive experiments on two public datasets, VeRi-776 and VehicleID, show that the proposed method outperformed the state-of-the-art approaches to vehicle re-ID.
How to Learn Item Representation for Cold-Start Multimedia Recommendation?

The ability of recommending cold items (that have no behavior history) is a core strength of multimedia recommendation compared with behavior-only collaborative filtering. To learn effective item representation, a key challenge lies in the discrepancy between training and testing, since the cold items only exist in the testing data. This means that the signal used to represent an item varies during training and testing --- in the training stage, we can represent an item with both collaborative embedding and content embedding; whereas in the testing stage, we represent a cold item with content embedding only. Nevertheless, existing learning frameworks omit this critical discrepancy, resulting in suboptimal item representation for multimedia recommendation. In this work, we pay special attention to cold items in multimedia recommender training. To address the discrepancy, we first represent an item with dual representation, i.e., two vectors where one follows the traditional way that combines collaborative embedding and content embedding, and the other assumes that the item is cold by replacing the collaborative embedding with zero vector. We then propose a Multi-Task Pairwise Ranking (MTPR) framework for model training, which enforces the observed interactions ranking higher than the unobserved ones even if the item is assumed to be cold. As a general learning framework, Our MTPR is agnostic to the choice of the collaborative (and/or content) encoder. We demonstrate it on VBPR, a representative multimedia recommendation model based on matrix factorization. Extensive experiments on three datasets of diverse domains validate MTPR, which leads to better representation for both cold and non-cold items in the testing stage, thus improving the overall performance of multimedia recommendation.
Deep Disturbance-Disentangled Learning for Facial Expression Recognition

To achieve effective facial expression recognition (FER), it is of great importance to address various disturbing factors, including pose, illumination, identity, and so on. However, a number of FER databases merely provide the labels of facial expression, identity, and pose, but lack the label information for other disturbing factors. As a result, many methods are only able to cope with one or two disturbing factors, ignoring the heavy entanglement between facial expression and multiple disturbing factors. In this paper, we propose a novel Deep Disturbance-disentangled Learning (DDL) method for FER. DDL is capable of simultaneously and explicitly disentangling multiple disturbing factors by taking advantage of multi-task learning and adversarial transfer learning. The training of DDL involves two stages. First, a Disturbance Feature Extraction Model (DFEM) is pre-trained to perform multi-task learning for classifying multiple disturbing factors on the large-scale face database (which has the label information for various disturbing factors). Second, a Disturbance-Disentangled Model (DDM), which contains a global shared sub-network and two task-specific (i.e., expression and disturbance) sub-networks, is learned to encode the disturbance-disentangled information for expression recognition. The expression sub-network adopts a multi-level attention mechanism to extract expression-specific features, while the disturbance sub-network leverages adversarial transfer learning to extract disturbance-specific features based on the pre-trained DFEM. Experimental results on both the in-the-lab FER databases (including CK+, MMI, and Oulu-CASIA) and the in-the-wild FER databases (including RAF-DB and SFEW) demonstrate the superiority of our proposed method compared with several state-of-the-art methods.
VideoIC: A Video Interactive Comments Dataset and Multimodal Multitask Learning for Comments Generation

Live video interactive commenting, a.k. a. danmaku, is an emerging social feature on online video sites, which involves rich multimodal information interaction among viewers. In order to support various related research, we build a large scale video interactive comments dataset called VideoIC, which consists of 4951 videos spanning 557 hours and 5 million comments. Videos are collected from popular categories on the 'Bilibili' video streaming website. Comparing to other existing danmaku datasets, our VideoIC contains richer and denser comments information, with 1077 comments per video on average. High comment density and diverse video types make VideoIC a challenging corpus for various research such as automatic video comments generation. We also propose a novel model based on multimodal multitask learning for comment generation (MML-CG), which integrates multiple modalities to achieve effective comment generation and temporal relation prediction. A multitask loss function is designed to train both tasks jointly in the end-to-end manner. We conduct extensive experiments on both VideoIC and Livebot datasets. The results prove the effectiveness of our model and reveal some features of danmaku.
Hearing like Seeing: Improving Voice-Face Interactions and Associations via Adversarial Deep Semantic Matching Network

Many cognitive researches have shown that human may 'see voices' or 'hear faces', and such ability can be potentially associated by machine vision and intelligence. However, this research is still under early stage. In this paper, we present a novel adversarial deep semantic matching network for efficient voice-face interactions and associations, which can well learn the correspondence between voices and faces for various cross-modal matching and retrieval tasks. Within the proposed framework, we exploit a simple and efficient adversarial learning architecture to learn the cross-modal embeddings between faces and voices, which consists of two subnetworks, respectively, for generator and discriminator. The former subnetwork is designed to adaptively discriminate the high-level semantical features between voices and faces, in which the triplet loss and multi-modal center loss are in tandem utilized to explicitly regularize the correspondences among them. The latter subnetwork is further leveraged to maximally bridge the semantic gap between the representations of voice and face data, featuring on maintaining the semantic consistency. Through the joint exploitation of the above, the proposed framework can well push representations of voice-face data from the same person closer while pulling those representations of different person away. Extensive experiments empirically show that the proposed approach involves fewer parameters and calculations, adapts various cross-modal matching tasks for voice-face data and brings substantial improvements over the state-of-the-art methods.
Towards Clustering-friendly Representations: Subspace Clustering via Graph Filtering

Finding a suitable data representation for a specific task has been shown to be crucial in many applications. The success of subspace clustering depends on the assumption that the data can be separated into different subspaces. However, this simple assumption does not always hold since the raw data might not be separable into subspaces. To recover the "clustering-friendly" representation and facilitate the subsequent clustering, we propose a graph filtering approach by which a smooth representation is achieved. Specifically, it injects graph similarity into data features by applying a low-pass filter to extract useful data representations for clustering. Extensive experiments on image and document clustering datasets demonstrate that our method improves upon state-of-the-art subspace clustering techniques. Especially, its comparable performance with deep learning methods emphasizes the effectiveness of the simple graph filtering scheme for many real-world applications. An ablation study shows that graph filtering can remove noise, preserve structure in the image, and increase the separability of classes.
Sexual dimorphism and female reproduction in the Qinghai toad-headed lizard Phrynocephalus vlangalii

The Qinghai toad-headed lizard Phrynocephalus vlangalii is a viviparous agamid lizard.We collected lizards in early May 2005 from a population in Qinghai(northwestern China)to study sexual dimorphism in morphological traits and female reproductive traits.The examined morphological traits included coloration,body size,abdomen length,tail length,head length and head width.Newly ovulated females were maintained on the laboratory thermal gradients until parturition.Sexual dimorphism was evident in adults but not in sexually immature individuals.The largest male was 70.2 mm SVL(snout-vent length)and the largest female was 82.8 mm SVL.Adult males were larger in head size and tail length,whereas females were larger in body size and abdomen length.A principal component analysis on adults resolved two components(eigenvalues ≥0.5)from four SVL-free morphological variables,accounting for 83.9% of variation in the original data.The first component(57.8% of variance explained)had higher positive loading for SVL-free values of tail length,head length and head width.The second component had higher negative loading for SVL-free values of abdomen length(26.1% of variance explained). Females on the laboratory thermal gradients produced a single litter per breeding season stretching from late June to mid-July,with a litter size of 2-6 young.Litter size and litter mass were both positively correlated with maternal SVL,and neonate size(mass)was not.We did not find the trade-off between size and number of offspring in P.vlangalii.
LIGHTEN: Learning Interactions with Graph and Hierarchical TEmporal Networks for HOI in videos

Analyzing the interactions between humans and objects from a video includes identification of the relationships between humans and the objects present in the video. It can be thought of as a specialized version of Visual Relationship Detection, wherein one of the objects must be a human. While traditional methods formulate the problem as inference on a sequence of video segments, we present a hierarchical approach, LIGHTEN, to learn visual features to effectively capture spatio-temporal cues at multiple granularities in a video. Unlike current approaches, LIGHTEN avoids using ground truth data like depth maps or 3D human pose, thus increasing generalization across non-RGBD datasets as well. Furthermore, we achieve the same using only the visual features, instead of the commonly used hand-crafted spatial features. We achieve state-of-the-art results in human-object interaction detection (88.9% and 92.6%) and anticipation tasks of CAD-120 and competitive results on image based HOI detection in V-COCO dataset, setting a new benchmark for visual features based approaches. Code for LIGHTEN is available at https://github.com/praneeth11009/LIGHTEN-Learning-Interactions-with-Graphs-and-Hierarchical-TEmporal-Networks-for-HOI
The Unified Protocol for Transdiagnostic Treatment of Perinatal Depression: A Case Study

This case study aims to present the application of the Unified Protocol (UP) for the Transdiagnostic Treatment of Emotional Disorders in the case of a 38-year-old pregnant woman with a peripartum onset of major depressive disorder and anxiety symptoms. The UP is a cognitive-behavioral intervention based on the shared etiological and maintenance mechanisms of emotional disorders (i.e., mood, anxiety, and related disorders). The UP helps to modify maladaptive emotion-regulation strategies and enhance tolerance of unpleasant and intense emotions (sadness, anxiety, anger, etc.). Whereas previous studies demonstrate the efficacy of the UP in different emotional disorders, to our knowledge, none of them have used the UP to treat perinatal emotional disorders. After the eight UP modules of individual treatment, a significant decrease in anxiety, depression, negative affect, and anxiety sensitivity, and also an increase in positive affect were observed. These results were maintained at the 12-month follow-up. In addition to its clinical utility and acceptance, these results indicate that the UP can be adapted and personalized for the treatment of perinatal emotional disorders. Future studies with larger samples and controlled experimental designs should be developed to demonstrate its efficacy. Furthermore, as the UP seems to be useful in a group format, future efforts should be focused on the UP with perinatal women in a group format.