Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Attacks on Deep Learning Face Recognition in the Physical World

Deep neural networks (DNNs) have been increasingly used in face recognition (FR) systems. Recent studies, however, show that DNNs are vulnerable to adversarial examples, which can potentially mislead the FR systems using DNNs in the physical world. Existing attacks on these systems either generate perturbations working merely in the digital world, or rely on customized equipments to generate perturbations and are not robust in varying physical environments. In this paper, we propose FaceAdv, a physical-world attack that crafts adversarial stickers to deceive FR systems. It mainly consists of a sticker generator and a transformer, where the former can craft several stickers with different shapes and the latter transformer aims to digitally attach stickers to human faces and provide feedbacks to the generator to improve the effectiveness of stickers. We conduct extensive experiments to evaluate the effectiveness of FaceAdv on attacking 3 typical FR systems (i.e., ArcFace, CosFace and FaceNet). The results show that compared with a state-of-the-art attack, FaceAdv can significantly improve success rate of both dodging and impersonating attacks. We also conduct comprehensive evaluations to demonstrate the robustness of FaceAdv.
A Construction of Maximally Recoverable Codes With Order-Optimal Field Size

We construct maximally recoverable codes (corresponding to partial MDS codes) which are based on linearized Reed-Solomon codes. The new codes have a smaller field size requirement compared with known constructions. For certain asymptotic regimes, the constructed codes have order-optimal alphabet size, asymptotically matching the known lower bound.
Field Trial of a Flexible Real-Time Software-Defined GPU-Based Optical Receiver

We introduce a flexible, software-defined real-time multi-modulation format receiver implemented on an off-the-shelf general-purpose graphics processing unit (GPU). The flexible receiver is able to process 2 GBaud 2-, 4-, 8-, and 16-ary pulse-amplitude modulation (PAM) signals as well as 1 GBaud 4-, 16- and 64-ary quadrature amplitude modulation (QAM) signals, with the latter detected using a Kramers–Kronig (KK) coherent receiver. Experimental performance evaluation is shown for back-to-back. In addition, by using the JGN high speed R&D network testbed, performance is evaluated after transmission over 91 km field-deployed optical fiber and reconfigurable optical add-drop multiplexers (ROADMs).
Self-EMD: Self-Supervised Object Detection without ImageNet

In this paper, we propose a novel self-supervised representation learning method, Self-EMD, for object detection. Our method directly trained on unlabeled non-iconic image dataset like COCO, instead of commonly used iconic-object image dataset like ImageNet. We keep the convolutional feature maps as the image embedding to preserve spatial structures and adopt Earth Mover's Distance (EMD) to compute the similarity between two embeddings. Our Faster R-CNN (ResNet50-FPN) baseline achieves 39.8% mAP on COCO, which is on par with the state of the art self-supervised methods pre-trained on ImageNet. More importantly, it can be further improved to 40.4% mAP with more unlabeled images, showing its great potential for leveraging more easily obtained unlabeled data. Code will be made available.
The Impact of a Surprise Donation Ask

Individuals frequently exploit “flexibility” built into decision environments to give less. They use uncertainty to justify options benefiting themselves over others, they avoid information that may encourage them to give, and they avoid the ask itself. In this paper, we examine whether a reluctance to give may arise even when such explicit flexibility is absent. We investigate whether merely alerting individuals to an upcoming prosocial ask — that is neither avoided nor occurs in an environment with flexibility — results in reduced prosocial behavior. That is, we investigate whether individuals use time to quickly find ways to decline prosocial asks and thus whether surprising individuals with prosocial asks increases compliance. Results from a field study and complementary online studies provide a clear answer: yes.
An Investigation in to Virtual World Adoption

Virtual worlds are emerging in importance as more multinational firms are investing heavily in these emerging communities. Although much excitement has been built up around the idea of virtual worlds, a gap exists between those who sign up and those who engage in the virtual world. Our analysis of the gap between those who initiate an action and have signed up to join a virtual world and those who follow through and ultimately engage the community on a regular basis derives from a lack of adoption. Through the use of a subject matter expert study, we identified 35 factors to explain adoption, which then explain through the use of seven psychological theories. After discussing and integrating these seven factors, we test our model through a test of 223 new users of Second Life. The results from our empirical test of these seven theories are presented, and we conclude by discussing the theoretical and practical implications of understanding virtual world adoption.
Babesia spp . in Dogs from Córdoba , Colombia

Canine babesiosis is a tick-borne disease with worldwide distribution, caused by intra-erythrocytic protozoa of the genus Babesia. The present study describes the clinical features and molecular detection of B. canis vogeli in dogs in Córdoba, Colombia. Blood samples were taken from dogs with clinical signs compatible with tick-borne diseases. Peripheral blood samples were examined microscopically for the presence of Babesia spp. merozoites and trophozoites. Blood sample obtained by jugular or cephalic venipuncture were used for DNA extraction. PCR assay was carried out using primers for the 18S rRNA gene of Babesia spp. of the 42 dogs suspected of babesiosis, 23 were females and 19 males. Peripheral blood examination demonstrated intraerythrocytic piroplasms compatible with Babesia spp. in 23 of 42 (54.7%) dogs. The morphometric study showed that 73% (17/23) were large babesias (2.4x5 μm) and 26% (6/23) were small Babesia (1x3.2 μm). Eleven of 42 (26%) blood samples were positive for B. canis vogeli. Clinical signs found in positive dogs were: anorexia 63% (7/11), lethargy and apathy 63% (7/11), fever 54% (6/11), pale mucous membranes 54% (6/11), lymphadenomegaly 18% (2/11), vomiting 9% (1/11) and diarrhea 9% (1/11). Blood counts showed that 70% (8/11) dogs had anemia. Leukocyte disorders were variable; 27% (3/11) had leukocytosis and 46% (5/11) had leukopenia. Sequenced samples of the 18S rRNA gene showed 99% identity with B. canis vogeli sequences from several countries.
Autopsy Evaluation of Homicidal Deaths in Western Mumbai Region-2 Years Prospective Study

DOI: 10.21276/sjams.2017. 5.12. 13 Abstract: Killing of an individual is the highest level of aggression found in all cultures. Though the pattern and method of homicide is different in different cultures and geographical regions the motive and intention behind remains same such as property dispute, revenge, infidelity arguments and scuffle. This prospective study is conducted for the purpose of the evaluation incidence of homicide, socio-demographic profile of victims and pattern of injuries on body region in the Dept of Forensic Medicine at H.B. T. Medical College, Mumbai from the period of Jan 2015 to Dec.2016 The study reveals homicidal deaths accounted for 2.11% of autopsies and the predominance of male victims 54.5% cases. 60% victims were in the age group 2140 years. Maximum homicides took place at victim‘s residence 45.45%. Friends of victims were involved in 30.3% cases. Arguments were main motive 45.45%. Most of cases were brought dead 58%. Hard and blunt weapon is most common type of weapon used 39.4%. head was the commonest body part targeted for homicide and head injury is most common cause of death 39.39%. Defence wound was seen in 42.4 % case and incised wound is the most common type of defence injury 42.8%.
Transformer-Based Models for Automatic Identification of Argument Relations: A Cross-Domain Evaluation

Argument mining is defined as the task of automatically identifying and extracting argumentative components (e.g., premises, claims, etc.) and detecting the existing relations among them (i.e., support, attack, rephrase, no relation). One of the main issues when approaching this problem is the lack of data, and the size of the publicly available corpora. In this work, we use the recently annotated US2016 debate corpus. US2016 is the largest existing argument annotated corpus, which allows exploring the benefits of the most recent advances in natural language processing in a complex domain like argument (relation) mining. We present an exhaustive analysis of the behavior of transformer-based models (i.e., BERT, XLNET, RoBERTa, DistilBERT, and ALBERT) when predicting argument relations. Finally, we evaluate the models in five different domains, with the objective of finding the less domain-dependent model. We obtain a macro F1-score of 0.70 with the US2016 evaluation corpus, and a macro F1-score of 0.61 with the Moral Maze cross-domain corpus.
VOXLINGUA107: A Dataset for Spoken Language Recognition

This paper investigates the use of automatically collected web audio data for the task of spoken language recognition. We generate semi-random search phrases from language-specific Wikipedia data that are then used to retrieve videos from YouTube for 107 languages. Speech activity detection and speaker diarization are used to extract segments from the videos that contain speech. Post-filtering is used to remove segments from the database that are likely not in the given language, increasing the proportion of correctly labeled segments to 98%, based on crowd-sourced verification. The size of the resulting training set (VoxLingua107) is 6628 hours (62 hours per language on the average) and it is accompanied by an evaluation set of 1609 verified utterances. We use the data to build language recognition models for several spoken language identification tasks. Experiments show that using the automatically retrieved training data gives competitive results to using hand-labeled proprietary datasets. The dataset is publicly available1.
Towards a better understanding of supernova environments: a study of SNe 2004dg and 2012P in NGC 5806 with HST and MUSE

Core-collapse supernovae (SNe) are the inevitable fate of most massive stars. Since most stars form in groups, SN progenitors can be constrained with information of their environments. It remains challenging to accurately analyse the various components in the environment and to correctly identify their relationships with the SN progenitors. Using a combined dataset of VLT/MUSE spatially-resolved integral-field-unit (IFU) spectroscopy and HST/ACS+WFC3 high-spatial resolution imaging, we present a detailed investigation of the environment of the Type II-P SN 2004dg and Type IIb SN 2012P. The two SNe occurred in a spiral arm of NGC 5806, where a star-forming complex is apparent with a giant H II region. By modelling the ionised gas, a compact star cluster and the resolved stars, we derive the ages and extinctions of stellar populations in the vicinity of the SNe. The various components are consistent with a sequence of triggered star formation as the spiral density wave swept through their positions. For SNe 2004dg and 2012P, we identify their host stellar populations and derive initial masses of $10.0^{+0.3}_{-0.2}~M_\odot$ and $15.2^{+2.0}_{-1.0}~M_\odot$ for their progenitors, respectively. Both results are consistent with those from pre-explosion images or nebular-phase spectroscopy. SN 2012P is spatially coincident but less likely to be coeval with the star-forming complex. As in this case, star formation bursts on small scales may appear correlated if they are controlled by any physical processes on larger scales; this may lead to a high probability of chance alignment between older SN progenitors and younger stellar populations.
Decoding and Diversity in Machine Translation

Neural Machine Translation (NMT) systems are typically evaluated using automated metrics that assess the agreement between generated translations and ground truth candidates. To improve systems with respect to these metrics, NLP researchers employ a variety of heuristic techniques, including searching for the conditional mode (vs. sampling) and incorporating various training heuristics (e.g., label smoothing). While search strategies significantly improve BLEU score, they yield deterministic outputs that lack the diversity of human translations. Moreover, search tends to bias the distribution of translated gender pronouns. This makes human-level BLEU a misleading benchmark in that modern MT systems cannot approach human-level BLEU while simultaneously maintaining human-level translation diversity. In this paper, we characterize distributional differences between generated and real translations, examining the cost in diversity paid for the BLEU scores enjoyed by NMT. Moreover, our study implicates search as a salient source of known bias when translating gender pronouns.
Analyzing Unaligned Multimodal Sequence via Graph Convolution and Graph Pooling Fusion

In this paper, we study the task of multimodal sequence analysis which aims to draw inferences from visual, language and acoustic sequences. A majority of existing works generally focus on aligned fusion, mostly at word level, of the three modalities to accomplish this task, which is impractical in real-world scenarios. To overcome this issue, we seek to address the task of multimodal sequence analysis on unaligned modality sequences which is still relatively underexplored and also more challenging. Recurrent neural network (RNN) and its variants are widely used in multimodal sequence analysis, but they are susceptible to the issues of gradient vanishing/explosion and high time complexity due to its recurrent nature. Therefore, we propose a novel model, termed Multimodal Graph, to investigate the effectiveness of graph neural networks (GNN) on modeling multimodal sequential data. The graph-based structure enables parallel computation in time dimension and can learn longer temporal dependency in long unaligned sequences. Specifically, our Multimodal Graph is hierarchically structured to cater to two stages, i.e., intra- and inter-modal dynamics learning. For the first stage, a graph convolutional network is employed for each modality to learn intra-modal dynamics. In the second stage, given that the multimodal sequences are unaligned, the commonly considered word-level fusion does not pertain. To this end, we devise a graph pooling fusion network to automatically learn the associations between various nodes from different modalities. Additionally, we define multiple ways to construct the adjacency matrix for sequential data. Experimental results suggest that our graph-based model reaches state-of-the-art performance on two benchmark datasets.
The Importance of Assortment, Pricing, and Retail Site Location for Competition in Food Retailing - Results from Marketing Research

fessor of Retailing at GeorgAugust-University of Göttingen, Platz der Göttinger Sieben 3, 37073 Göttingen, Germany, Phone +49 (0)551 39-4447, Fax +49 (0)551 39-4446 E-Mail: wtoporo@uni-goettingen.de, corresponding author. Rainer Lademann is Honorary Professor at Georg-August-University of Göttingen and Executive Partner of Lademann & Associates GmbH, Friedrich-EbertDamm 311, 22159 Hamburg, Phone: +49 (0)40 64557710, Fax: +49 (0)40 64557733, E-Mail: lademann@lademannassociates.com. The Importance of Assortment, Pricing, and Retail Site Location for Competition in Food Retailing – Results from Marketing Research
Score-Based Generative Modeling through Stochastic Differential Equations

Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the reverse-time SDE depends only on the time-dependent gradient field (\aka, score) of the perturbed data distribution. By leveraging advances in score-based generative modeling, we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples. We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling, allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE. We also derive an equivalent neural ODE that samples from the same distribution as the SDE, but additionally enables exact likelihood computation, and improved sampling efficiency. In addition, we provide a new way to solve inverse problems with score-based models, as demonstrated with experiments on class-conditional generation, image inpainting, and colorization. Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model.
Functional and Evolutionary Ecology of Fleas: A Model for Ecological Parasitology

Fleas are one of the most interesting and fascinating taxa of ectoparasites. All species in this relatively small order are obligatory haematophagous (blood-feeding) parasites of higher vertebrates. This book examines how functional, ecological and evolutionary patterns and processes of host—parasite relationships are realized in this particular system. As such it provides an in-depth case study of a host—parasite system, demonstrating how fleas can be used as a model taxon for testing ecological and evolutionary hypotheses. The book moves from basic descriptive aspects, to functional issues and finally to evolutionary explanations. It extracts several general principles that apply equally well to other host—parasite systems, so will appeal not only to flea biologists but also to mainstream parasitologists and ecologists.
Effect of phytohormones and genotype on meristem and shoot tip culture of Telfairia occidentalis Hook

Objectives: The aim of this work was to investigate the effect of plant growth regulators (PGR), genotype and explant type on in vitro shoot induction, elongation, and multiplication in Telfairia occidentalis. Methodology and Results: In this study attempts were made to induce shoots from meristem and shoot tip culture of Telfairia occidentalis Hook F. Shoot apical meristems with 1-2 leaf primordia and shoot tip explants were aseptically isolated and cultured on 1⁄2 N6 medium supplemented with different combinations of 2mg/l BAP, IAA and kinetin. Explants were derived from seedlings of two T. occidentalis genotypes grown in sterilized soils inside vials in the laboratory. Regeneration response were examined based on five parameters – callus formation, shoot length, number of shoots per explant, number of leaves per explant and number of nodes. Shoot induction, elongation and multiplication were most effectively promoted by the medium supplemented with 2.0 mg/lBAP + 2.0 mg/l IAA for shoot tip culture, while 2.0 mg/l BAP was most effective for shoot regeneration from meristem culture. Genotype I was significantly higher than Genotype II in shoot bud regeneration response (p<0.05). Culture of explants from shoot tip led to better shoot regeneration in comparison to explants from meristem. Callus formation/induction was also influenced by explants and media interaction. Conclusion and Application of Findings: In vitro shoot induction from meristem and shoot tip culture of T. occidentalis has been demonstrated. These in vitro culture procedures would be useful for developing uniform clones or micropropagation and could also form the basis for in vitro storage of explants and subsequent regeneration of plantlets after long term conservation in this species. The recalcitrant nature its seeds makes alternative means of genetic resources conservation very necessary. More importantly, meristem culture technique is useful for developing virus-free clones and avoids the limitations imposed by conventional mode of planting. Calli produced could also be excellent targets for genetic transformation and improvement of this species.
Effects of dietary supplementation of methionine and lysine on milk production and nitrogen utilization in dairy cows 1

The effect of the content of lysine and methionine in metabolizable protein (MP) on lactation performance and N utilization in Chinese Holstein cows was determined. A control diet (C) was formulated to be adequate in energy but slightly limiting in MP. The concentration of Met and Lys in MP was 1.87 and 5.93%, respectively. The treatments were as follows (% of Met or Lys in MP): L = diet C supplemented with l-lysine-HCl at 0.49% on a dry matter (DM) basis (Met, 1.87; Lys, 7.00); M = diet C supplemented with 2-hydroxy-4-(methylthio)butanoic acid (HMB) at 0.15% (Met, 2.35; Lys, 5.93); ML = diet C supplemented with 0.49% l-lysine HCl and 0.15% HMB (Met, 2.39; Lys, 7.10). The diets were fed to 60 Chinese Holsteins in mid-lactation (average days in milk = 120, and milk yield = 32.0 kg/d) for 8 wk. Milk yield was increased by supplementation of either Lys (1.5 kg/d) or Met (2.0 kg/d), and supplementation of both Lys and Met further increased milk yield (3.8 kg/d). There was no significant difference in dry matter intake across treatment groups. Cows on treatments M (3.95%) and ML (3.90%) had higher milk fat content than those on C (3.60%) and L (3.67%), but there were no significant differences in milk protein and lactose contents or somatic cell count among treatments. Supplementation of Met or Lys significantly increased Met or Lys concentration in arterial plasma. Treatment ML had a higher conversion of intake N to milk N and lower urea N concentrations in serum, urine, and milk than did treatment C. Supplementing HMB and l-lysine-HCl to provide approximately 2.3% Met and 7.0% Lys of the MP in diets slightly limiting in MP increased milk production, milk protein yield, and N utilization efficiency.
A Novel Multi-Model Decision Fusion Network for Object Detection in Remote Sensing Images

Object detection in optical remote sensing images is still a challenging task because of the complexity of the images. The diversity and complexity of geospatial object appearance and the insufficient understanding of geospatial object spatial structure information are still the existing problems. In this paper, we propose a novel multi-model decision fusion framework which takes contextual information and multi-region features into account for addressing those problems. First, a contextual information fusion sub-network is designed to fuse both local contextual features and object-object relationship contextual features so as to deal with the problem of the diversity and complexity of geospatial object appearance. Second, a part-based multi-region fusion sub-network is constructed to merge multiple parts of an object for obtaining more spatial structure information about the object, which helps to handle the problem of the insufficient understanding of geospatial object spatial structure information. Finally, a decision fusion is made on all sub-networks to improve the stability and robustness of the model and achieve better detection performance. The experimental results on a publicly available ten class data set show that the proposed method is effective for geospatial object detection.