Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction, Learning, Uniform Convergence, and Scale-Sensitive Dimensions

We present a new general-purpose algorithm for learning classes of 0, 1-valued functions in a generalization of the prediction model and prove a general upper bound on the expected absolute error of this algorithm in terms of a scale-sensitive generalization of the Vapnik dimension proposed by Alon, Ben-David, Cesa-Bianchi, and Haussler. We give lower bounds implying that our upper bounds cannot be improved by more than a constant factor in general. We apply this result, together with techniques due to Haussler and to Benedek and Itai, to obtain new upper bounds on packing numbers in terms of this scale-sensitive notion of dimension. Using a different technique, we obtain new bounds on packing numbers in terms of Kearns and Schapire's fat-shattering function. We show how to apply both packing bounds to obtain improved general bounds on the sample complexity of agnostic learning. For each? >0, we establish weaker sufficient and stronger necessary conditions for a class of 0, 1-valued functions to be agnostically learnable to within? and to be an? -uniform Glivenko? Cantelli class.
Fine-grained Attention-based Video Face Recognition

This paper aims to learn a compact representation of a video for video face recognition task. We make the following contributions: first, we propose a meta attention-based aggregation scheme which adaptively and fine-grained weighs the feature along each feature dimension among all frames to form a compact and discriminative representation. It makes the best to exploit the valuable or discriminative part of each frame to promote the performance of face recognition, without discarding or despising low quality frames as usual methods do. Second, we build a feature aggregation network comprised of a feature embedding module and a feature aggregation module. The embedding module is a convolutional neural network used to extract a feature vector from a face image, while the aggregation module consists of cascaded two meta attention blocks which adaptively aggregate the feature vectors into a single fixed-length representation. The network can deal with arbitrary number of frames, and is insensitive to frame order. Third, we validate the performance of proposed aggregation scheme. Experiments on publicly available datasets, such as YouTube face dataset and IJB-A dataset, show the effectiveness of our method, and it achieves competitive performances on both the verification and identification protocols.
SinReQ: Generalized Sinusoidal Regularization for Automatic Low-Bitwidth Deep Quantized Training

Quantization of neural networks offers significant promise in reducing their compute and storage cost. Albeit alluring, without domain experts to come up with special handcrafted optimization techniques or ad-hoc manipulation of the original network architecture, deep quantization (below 8 bits) results in unrecoverable accuracy gap between the quantized model and the full-precision counterpart. We propose a novel sinusoidal regularization, dubbed SinReQ, for low precision deep quantized training. The proposed method is aimed at automatically yielding semi-quantized weights at pre-defined target bitwidths during conventional training. The proposed regularization is realized by adding a periodic function (sinusoidal regularizer) to the original objective function. We exploit the inherent periodicity with a desired convexity profile in sinusoidal functions to automatically propel weights towards target quantization levels during conventional training. Our method combines generality by providing the flexibility for arbitrary-bit quantization, and customization by optimizing different layer-wise regularizers simultaneously. Preliminary results for experiments on CIFAR10, SVHN show that integrating SinReQ within the training algorithm achieves 2.82%, and 2.11% accuracy improvements to DoReFa (Zhou et al., 2016), and WRPN (Mishra et al., 2018) methods respectively.
Behavior developing environment for the large-DOF muscle-driven humanoid equipped with numerous sensors

It is very important and hard to develop motions and behaviors for robots that have complex body and numerous actuators and sensors. The more complex a humanoid's body becomes, the more important the developing environment of the motions and behaviors will be. We have built a fully muscle-driven flexible-spine humanoid named "Kenta", which has 96 actuators and more than 400 sensors including tension sensors, posture sensors, vision sensors, and so on. This paper describes the developing environment of motions and behaviors for the robots like Kenta. A visualizable geometric model, a simulation environment transparent to the real environment, sample procedures of the motion development, and, as an example, realization of swing behavior by Kenta are presented.
FlowSAN: Privacy-Enhancing Semi-Adversarial Networks to Confound Arbitrary Face-Based Gender Classifiers

Privacy concerns in the modern digital age have prompted researchers to develop techniques that allow users to selectively suppress certain information in collected data while allowing for other information to be extracted. In this regard, semi-adversarial networks (SANs) have recently emerged as a method for imparting soft-biometric privacy to face images. SAN enables modifications of input face images so that the resulting face images can still be reliably used by arbitrary conventional face matchers for recognition purposes, while attribute classifiers, such as gender classifiers, are confounded. However, the generalizability of SANs across arbitrary gender classifiers has remained an open concern. In this paper, we propose a new method, FlowSAN, for allowing SANs to generalize to multiple gender classifiers. We propose stacking a diverse set of SAN models to compensate each other’s weaknesses, thereby forming a robust model with improved generalization capability. Extensive experiments using different unseen gender classifiers and face matchers demonstrate the efficacy of the proposed paradigm in imparting gender privacy to face images.
Magnetoencephalographic study on facial movements

In this review, we introduced our three studies that focused on facial movements. In the first study, we examined the temporal characteristics of neural responses elicited by viewing mouth movements, and assessed differences between the responses to mouth opening and closing movements and an averting eyes condition. Our results showed that the occipitotemporal area, the human MT/V5 homologue, was active in the perception of both mouth and eye motions. Viewing mouth and eye movements did not elicit significantly different activity in the occipitotemporal area, which indicated that perception of the movement of facial parts may be processed in the same manner, and this is different from motion in general. In the second study, we investigated whether early activity in the occipitotemporal region evoked by eye movements was influenced by the facial contour and/or features such as the mouth. Our results revealed specific information processing for eye movements in the occipitotemporal region, and this activity was significantly influenced by whether movements appeared with the facial contour and/or features, in other words, whether the eyes moved, even if the movement itself was the same. In the third study, we examined the effects of inverting the facial contour (hair and chin) and features (eyes, nose, and mouth) on processing for static and dynamic face perception. Our results showed the following: (1) In static face perception, activity in the right fusiform area was affected more by the inversion of features while that in the left fusiform area was affected more by a disruption in the spatial relationship between the contour and features; and (2) In dynamic face perception, activity in the right occipitotemporal area was affected by the inversion of the facial contour.
A Study on Combine use of Steganoghaphy and Cryptography for Data Hiding

Steganography hides the existence of message by embedding data in some other digital media like image or audio format and Cryptography converts data in to cipher text that can be in unreadable format to normal user. This paper can concentrate to make review of combine data hiding techniques useable for security of data. This paper can defines RSA algorithm for encryption and embedded encrypted data in an image using DCT based steganographic technique. The DCT based technique is better than the other techniques like LSB, Modulus arithmetic steganography.
Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competition

The Pommerman Team Environment is a recently proposed benchmark which involves a multi-agent domain with challenges such as partial observability, decentralized execution (without communication), and very sparse and delayed rewards. The inaugural Pommerman Team Competition held at NeurIPS 2018 hosted 25 participants who submitted a team of 2 agents. Our submission nn_team_skynet955_skynet955 won 2nd place of the "learning agents'' category. Our team is composed of 2 neural networks trained with state of the art deep reinforcement learning algorithms and makes use of concepts like reward shaping, curriculum learning, and an automatic reasoning module for action pruning. Here, we describe these elements and additionally we present a collection of open-sourced agents that can be used for training and testing in the Pommerman environment. Code available at: this https URL
HEMATOPOIESIS AND STEM CELLS Glypican-3 – mediated inhibition of CD 26 by TFPI : a novel mechanism in hematopoietic stem cell homing and maintenance

Hematopoietic stem cells (HSCs) are responsible for maintaining all blood cells throughout the lifetime of an individual, and are used clinically to treat various malignant and nonmalignant disorders. However, for some HSC grafts, for instance from umbilical cord blood (UCB), limited numbers of HSCs restrict their application to pediatric patients. Expanding HSCs in vitro or improving their homing efficiency would overcome this hurdle. As the HSC niche regulates HSC function in vivo, it is believed that additional insights in the regulation of HSCs by their niche may identify novel ways to manipulate HSCs and enhance their clinical use. A number of niche factors regulate HSC function by interacting with their respective receptors expressed on HSCs. These molecular interactions also play important roles in homing of the transplanted HSCs, which adhere to the vasculature through integrins and pass through the endothelium following rolling mediated by selectins. Directional migration of hematopoietic stem/progenitor cells (HSPCs) is mediated in large part by interaction of cell-surface– expressed CXCR4 with a gradient of CXCL12 or stroma-derived factor-1a expressed in the bone marrow (BM) niche. Loss of CXCR4 or annexin 2, involved in the presentation of CXCL12 to HSCs, severely reduces the number of HSCs in BM of adult mice. Incubation of murine or human HSPCs with anti-CXCR4 antibodies significantly reduces their homing and engraftment ability, while infusion of CXCR4-selective antagonists induces an increase in circulating HSPCs. CD26, a serine protease, cleaves an N-terminal dipeptide from CXCL12 thereby depleting its chemotactic activity. CD26deficient or CD26 inhibitor–treated mouse BM as well as human UCB-derived HSPCs display enhanced migration toward CXCL12, which is translated in improved engraftment. During a screen of stromal feeders from fetal sites of hematopoiesis, used to mimic the hematopoietic niche, we found that transcripts for Tfpi were . 20-fold higher in murine stromal cells that supported long-term repopulating (LTR) HSCs in noncontact cultures. Tissue-factor (TF) pathway inhibitor (TFPI) mediates the coagulation cascade. TFPI is a serine protease inhibitor that contains 3 Kunitz-type domains, 2 of which bind to factor VIIa and Xa. Although there was no evidence for a role of TFPI in hematopoiesis, other molecules involved in coagulation such as uPA and uPAR have been shown to affect HSC homeostasis. Here, we report that TFPI acts as a biological inhibitor of CD26 in murine BM as well as human UCB-derived HSPCs. Decrease in CD26 activity led to better chemotactic activity of HSPCs resulting in enhanced homing and engraftment potential. We further demonstrate that TFPI binds to heparan sulfate proteoglycan Glypican-3 (GPC3), which itself is known to inhibit CD26 activity
Enhanced Labeling of Issue Reports (with F3T)

Standard automatic methods for recognizing problematic code can be greatly improved via the incremental application of human+artificial expertise. In this approach, call $F^3T$, AI tools explore software to find commits that they guess is most problematic. Humans the apply their expertise to check that guess (perhaps resulting in the AI updating the support vectors within their SVM learner). We recommend this human+AI partnership, for several reasons. When a new domain is encountered, $F^3T$ can learn better ways to label which comments refer to real problems. Further, in studies with 9 open source software projects, $F^3T$'s incremental application of human+artificial intelligence is at least an order of magnitude cheaper to use than existing methods. Lastly, $F^3T$ is very effective. For the data sets explored here, when compared to standard methods, $F^3T$ improved $P_{opt}(20)$ and G-scores performance by 26\% and 48\% on median value.
Cognitive Triaging of Phishing Attacks

In this paper we employ quantitative measurements of cognitive vulnerability triggers in phishing emails to predict the degree of success of an attack. To achieve this we rely on the cognitive psychology literature and develop an automated and fully quantitative method based on machine learning and econometrics to construct a triaging mechanism built around the cognitive features of a phishing email; we showcase our approach relying on data from the anti-phishing division of a large financial organization in Europe. Our evaluation shows empirically that an effective triaging mechanism for phishing success can be put in place by response teams to effectively prioritize remediation efforts (e.g. domain takedowns), by first acting on those attacks that are more likely to collect high response rates from potential victims.
The Game of Tetris in Machine Learning

The game of Tetris is an important benchmark for research in artificial intelligence and machine learning. This paper provides a historical account of the algorithmic developments in Tetris and discusses open challenges. Handcrafted controllers, genetic algorithms, and reinforcement learning have all contributed to good solutions. However, existing solutions fall far short of what can be achieved by expert players playing without time pressure. Further study of the game has the potential to contribute to important areas of research, including feature discovery, autonomous learning of action hierarchies, and sample-efficient reinforcement learning.
Automatic transcription of pitched and unpitched sounds from polyphonic music

Automatic transcription of polyphonic music has been an active research field for several years and is considered by many to be a key enabling technology in music signal processing. However, current transcription approaches either focus on detecting pitched sounds (from pitched musical instruments) or on detecting unpitched sounds (from drum kits). In this paper, we propose a method that jointly transcribes pitched and unpitched sounds from polyphonic music recordings. The proposed model extends the probabilistic latent component analysis algorithm and supports the detection of pitched sounds from multiple instruments as well as the detection of un-pitched sounds from drum kit components, including bass drums, snare drums, cymbals, hi-hats, and toms. Our experiments based on polyphonic Western music containing both pitched and unpitched instruments led to very encouraging results in multi-pitch detection and drum transcription tasks.
Leveraging Crowdsourced GPS Data for Road Extraction From Aerial Imagery

Deep learning is revolutionizing the mapping industry. Under lightweight human curation, computer has generated almost half of the roads in Thailand on Open- StreetMap (OSM) using high resolution aerial imagery. Bing maps are displaying 125 million computer generated building polygons in the U.S. While tremendously more efficient than manual mapping, one cannot map out everything from the air. Especially for roads, a small prediction gap by image occlusion renders the entire road useless for routing. Misconnections can be more dangerous. Therefore computer-based mapping often requires local verifications, which is still labor intensive. In this paper, we propose to leverage crowdsourced GPS data to improve and support road extraction from aerial imagery. Through novel data augmentation, GPS rendering, and 1D transpose convolution techniques, we show almost 5% improvements over previous competition winning models, and much better robustness when predicting new areas without any new training data or domain adaptation.
OVERVIEW OF SUBLINGUAL TABLETS Malay

Drug delivery via the oral mucous membrane is considered to be a promising alternative to the oral route. Sublingual route is a useful when rapid onset of action is desired with better patient compliance than orally ingested tablets. In terms of permeability, the sublingual area of the oral cavity (i.e. the floor of the mouth) is more permeable than the buccal (cheek) area, which in turn is more permeable than the palatal (roof of the mouth) area. The portion of drug absorbed through the sublingual blood vessels bypasses the hepatic first‐pass metabolic processes giving acceptable bioavailability. Various techniques can be used to formulate sublingual tablets. New sublingual technologies address many pharmaceutical and patient needs, ranging from enhanced life‐cycle management to convenient dosing for paediatric, geriatric, and psychiatric patients with dysphagia. This review highlights the different sublingual dosage forms, factors affecting the sublingual absorption, advantages, various in vitro and in vivo evaluation parameters and commercially available sublingual dosage forms.
Partial Characterization of Circulating Inhibin-B and Pro-αC During Development in the Male Rhesus Monkey1.

Gel filtration chromatography and ELISAs for inhibin-B and pro-αC were used to examine the circulating forms of inhibin in the neonatal (age 2–6 weeks), juvenile (age 1–2 yr), and adult male rhesus monkey. In all samples, isoforms of inhibin-B of 26–36K and 150K were found. Both forms were significantly greater in the adult. Theα -subunit assay detected major peaks at 45–60 and 29–31K, and a minor peak of greater than 100K. As for inhibin-B, the major forms of inhibin pro-αC were highest in adulthood. Inhibin-B and pro-αC were measurable in peripheral plasma at age 1 week, increased with the neonatal rise in plasma FSH, and then decreased but remained detectable through age 1 yr. Values in adult males were higher than at any time during the first year of life. Finally, mean values of plasma inhibin-B and pro-αC in five monkeys, based on multiple blood samples drawn between age 1 week and 1 yr, were rank ordered and were found to be highly positively correlated (r = 0.96), suggesting that inhibin levels in.
Level of detail control of animation meshes

Level of detail (LOD) and character animation are both well-explored fields in computer graphics, but little has been done to combine them. Level of detail refers to the generation and use of low-detail models in place of high-detail ones where this will not be visible (for example, for far-away objects). A method that combines LOD with character animation will make it practical to render crowds of highly detailed, animated characters in real time. A good application would be a simulated battle in a game or movie. Standard LOD techniques can produce good lowdetail approximations for a single pose, but do not provide any guarantee of fidelity for other poses of an animated character. We aim to produce level of details representations that are faithful approximations in all poses of the character. Unlike some previous research [Shamir and Pascucci 2001], the proposed technique would not require prior knowledge of the animation itself. It could thus be used in situations with dynamic animation, for instance in a game.
Transfer of orbital angular momentum of light to plasmonic excitations in metamaterials

Vortex light twists electrons in a metallic disk with subwavelength corrugation. The emergence of the vortex beam with orbital angular momentum (OAM) has provided intriguing possibilities to induce optical transitions beyond the framework of the electric dipole interaction. The uniqueness stems from the OAM transfer from light to material, as demonstrated in electronic transitions in atomic systems. In this study, we report on the OAM transfer to electrons in solid-state systems, which has been elusive to date. Using metamaterials (periodically textured metallic disks), we show that multipolar modes of the surface electromagnetic excitations (so-called spoof localized surface plasmons) are selectively induced by the terahertz vortex beam. Our results reveal selection rules governed by the conservation of the total angular momentum, which is confirmed by numerical simulations. The efficient transfer of light’s OAM to elementary excitations in solid-state systems at room temperature opens up new possibilities of OAM manipulation.
Dynamic Graph Modules for Modeling Object-Object Interactions in Activity Recognition

Video action recognition, a critical problem in video understanding, has been gaining increasing attention. To identify actions induced by complex object-object interactions, we need to consider not only spatial relations among objects in a single frame, but also temporal relations among different or the same objects across multiple frames. However, existing approaches that model video representations and non-local features are either incapable of explicitly modeling relations at the object-object level or unable to handle streaming videos. In this paper, we propose a novel dynamic hidden graph module to model complex object-object interactions in videos, of which two instantiations are considered: a visual graph that captures appearance/motion changes among objects and a location graph that captures relative spatiotemporal position changes among objects. Additionally, the proposed graph module allows us to process streaming videos, setting it apart from existing methods. Experimental results on benchmark datasets, Something-Something and ActivityNet, show the competitive performance of our method.
A similarity measure for material appearance

We present a model to measure the similarity in appearance between different materials, which correlates with human similarity judgments. We first create a database of 9,000 rendered images depicting objects with varying materials, shape and illumination. We then gather data on perceived similarity from crowdsourced experiments; our analysis of over 114,840 answers suggests that indeed a shared perception of appearance similarity exists. We feed this data to a deep learning architecture with a novel loss function, which learns a feature space for materials that correlates with such perceived appearance similarity. Our evaluation shows that our model outperforms existing metrics. Last, we demonstrate several applications enabled by our metric, including appearance-based search for material suggestions, database visualization, clustering and summarization, and gamut mapping.