Miracle-dz/UniFAR-FTUs huggingface dataset:
Compositional Convolutional Networks For Robust Object Classification under Occlusion

Deep convolutional neural networks (DCNNs) are powerful models that yield impressive results at object classification. However, recent work has shown that they do not generalize well to partially occluded objects and to mask attacks. In contrast to DCNNs, compositional models are robust to partial occlusion, however, they are not as discriminative as deep models. In this work, we integrate DCNNs and compositional object models to retain the best of both approaches: a discriminative model that is robust to partial occlusion and mask attacks. Our model is learned in two steps. First, a standard DCNN is trained for image classification. Subsequently, we cluster the DCNN features into dictionaries. We show that the dictionary components resemble object part detectors and learn the spatial distribution of parts for each object class. We propose mixtures of compositional models to account for large changes in the spatial activation patterns (e.g. due to changes in the 3D pose of an object). At runtime, an image is first classified by the DCNN in a feedforward manner. The prediction uncertainty is used to detect partially occluded objects, which in turn are classified by the compositional model. Our experimental results demonstrate that such compositional convolutional networks resolve a fundamental problem of current deep learning approaches to computer vision: They recognize occluded objects with exceptional performance, even when they have not been exposed to occluded objects during training, while at the same time maintaining high discriminative performance for non-occluded objects.
Fast human motion prediction for human-robot collaboration with wearable interface

In this paper, we propose a novel human-robot interface capable to anticipate the user intention while performing reaching movements on a working bench in order to plan the action of a collaborative robot. The system integrates two levels of prediction: motion intention prediction, to detect movements onset and offset; motion direction prediction, based on Gaussian Mixture Model (GMM) trained with IMU and EMG data following an evidence accumulation approach. Novel dynamic stopping criteria have been proposed to flexibly adjust the trade-off between early anticipation and accuracy. Results show that our system outperforms previous methods, achieving a real-time classification accuracy of 94.3±2.9% after 160.0msec±80.0msec from movement onset. The proposed interface can find many applications in the Industry 4.0 framework, where it is crucial for autonomous and collaborative robots to understand human movements as soon as possible to avoid accidents and injuries.
Adaptive Reduced Rank Regression

Low rank regression has proven to be useful in a wide range of forecasting problems. However, in settings with a low signal-to-noise ratio, it is known to suffer from severe overfitting. This paper studies the reduced rank regression problem and presents algorithms with provable generalization guarantees. We use adaptive hard rank-thresholding in two different parts of the data analysis pipeline. First, we consider a low rank projection of the data to eliminate the components that are most likely to be noisy. Second, we perform a standard multivariate linear regression estimator on the data obtained in the first step, and subsequently consider a low-rank projection of the obtained regression matrix. Both thresholding is performed in a data-driven manner and is required to prevent severe overfitting as our lower bounds show. Experimental results show that our approach either outperforms or is competitive with existing baselines.
Multisensory Facial Stimulation Implicitly Improves Evaluations of the Goodness of Attractive Others

It has been well demonstrated that shared multisensory experiences between the self and others can influence the social perception of out-group members. Previous research has shown that the illusion of ownership over a dark-skinned rubber hand or full virtual body generated less negative implicit bias against people with dark skin. However, less is known about how perceived attractiveness difference between self and other affects social perception toward those others after shared multisensory experience. The present study assessed whether shared multisensory experience between the self and attractive others would affect the implicit evaluation of goodness of others. Seventy-three women participated in the study. After the visuotactile multisensory stimulation procedure, participants were administered the Single Category Implicit Association Test (SC-IAT), which presents two attributes (good and bad) and one concept (other). Results showed that the more attractive the faces are, the more positive their implicit evaluation becomes after the synchronous tactile stimulation. This result suggests that shared multisensory experience makes people feel more positive toward others who have positive attribute. This finding suggests that self-other blurring in social contexts might be a compelling factor in evaluating other people positively.
Optimizing Collagen Scaffolds for Bone Engineering: Effects of Cross-linking and Mineral Content on Structural Contraction and Osteogenesis

Introduction: Osseous defects of the craniofacial skeleton occur frequently in congenital, posttraumatic, and postoncologic deformities. The field of scaffold-based bone engineering emerged to address the limitations of using autologous bone for reconstruction of such circumstances. In this work, the authors evaluate 2 modifications of three-dimensional collagen-glycosaminoglycan scaffolds in an effort to optimize structural integrity and osteogenic induction. Methods: Human mesenchymal stem cells (hMSCs) were cultured in osteogenic media on nonmineralized collagen-glycosaminoglycan (C-GAG) and nanoparticulate mineralized collagen-glycosaminoglycan (MC-GAG) type I scaffolds, in the absence and presence of cross-linking. At 1, 7, and 14 days, mRNA expression was analyzed using quantitative real-time -reverse-transcriptase polymerase chain reaction for osteocalcin (OCN) and bone sialoprotein (BSP). Structural contraction was measured by the ability of the scaffolds to maintain their original dimensions. Mineralization was detected by microcomputed tomographic (micro-CT) imaging at 8 weeks. Statistical analyses were performed with Student t-test. Results: Nanoparticulate mineralization of collagen-glycosaminoglycan scaffolds increased expression of both OCN and BSP. Cross-linking of both C-GAG and MC-GAG resulted in decreased osteogenic gene expression; however, structural contraction was significantly decreased after cross-linking. Human mesenchymal stem cells-directed mineralization, detected by micro-CT, was increased in nanoparticulate mineralized scaffolds, although the density of mineralization was decreased in the presence of cross-linking. Conclusions: Optimization of scaffold material is an essential component of moving toward clinically translatable engineered bone. Our current study demonstrates that the combination of nanoparticulate mineralization and chemical cross-linking of C-GAG scaffolds generates a highly osteogenic and structurally stable scaffold.
Gene expression and development of early pig embryos produced by serial nuclear transfer

During nuclear transfer, reprogramming makes the donor nucleus capable of directing development of the reconstructed embryo. In most cases reprogramming is incomplete, which leads to abnormal expression of early embryonic genes and subsequently, to reduced developmental potential. In the present study, we monitored the expression of Oct4, Nanog, and Sox2 in cloned porcine embryos and evaluated whether serial nuclear transfer, the transfer of nuclei of cloned embryos into enucleated oocytes, has the potential to provide a more complete reprogramming of the donor genome. The data suggested that Nanog and Sox2 expression is properly reactivated after nuclear transfer, but the relative abundance of Oct4 transcripts is abnormally low in cloned porcine blastocysts compared to control embryos produced by in vitro fertilization. When the nuclei of 8‐ to 16‐cell stage cloned embryos were introduced into enucleated oocytes to expose the chromosomes repeatedly to the ooplasmic factors, the resulting embryos showed poor developmental potential: a significantly lower percentage of embryos developed to the 4‐cell (12.0% vs. 31.8%), 8‐cell (3.1% vs. 15.0%) and blastocyst (0% vs. 8.7%) stages compared to those produced following a single round of nuclear transfer (P < 0.05). The additional time for reprogramming also did not improve gene expression. By the late 4‐cell stage, Oct4 and Sox2 expression levels were low in serial nuclear transfer embryos compared to those in embryos generated by in vitro fertilization or nuclear transfer. Overall, both developmental and gene expression data indicated that reprogramming of the donor nucleus could not be improved by serial nuclear transfer in the pig. Mol. Reprod. Dev. 76: 555–563, 2009. © 2008 Wiley‐Liss, Inc.
Evaluating the artifacts of SIGCOMM papers

A growing fraction of the papers published by CCR and at SIGCOMM-sponsored conferences include artifacts such as software or datasets. Besides CCR, these artifacts were rarely evaluated. During the last months of 2018, we organised two different Artifacts Evaluation Committees to which authors could submit the artifacts of their papers for evaluation. The first one evaluated the papers accepted by Conext’18 shortly after the TPC decision. It assigned ACM reproducibility badges to 12 different papers. The second one evaluated papers accepted by CCR and any SIGCOMM-sponsored conference. 28 papers received ACM reproducibility badges. We report on the results of a short survey among artifacts authors and reviewers and provide some suggestions for future artifacts evaluations.
Direct and inverse approximation theorems in the weighted Orlicz-type spaceswith a variable exponent

In weighted Orlicz-type spaces Sp,μ with a variable summation exponent, the direct and inverse approximation theorems are proved in terms of best approximations of functions and moduli of smoothness of fractional order. It is shown that the constant obtained in the inverse approximation theorem is the best in a certain sense. Some applications of the results are also proposed. In particular, the constructive characteristics of functional classes defined by such moduli of smoothness are given. Equivalence between moduli of smoothness and certain Peetre K-functionals is shown in the spaces Sp,μ.
Implementation of a GMSK communication system on FPGA

This paper presents the design and implementation of a complete communication system using the GMSK modulation scheme. The hardware is described in VHDL and implemented on Altera FPGA. Additionally, each block used to perform the implementation is completely described accomplishing all the requirements of this kind of modulation. Mueller & Müller algorithm is also performed to ensure timing synchronization between transmitter and receiver. This communication system was developed with educational purposes.
Research on Ship Collision Avoidance Method Based on Track Prediction

This paper proposes a ship collision avoidance method based on ship track prediction. The track prediction uses improved Sage-Husa adaptive filter algorithm with the real-time data of target ships and own ship. Then the track prediction result is applied to the collision avoidance method to identify dangerous situation in advance. The risk of ship collision is obtained by the basis of fuzzy theory using the major factor for ship collision impact, and then the method will determine the effective collision avoidance actions to support decision making for the officers on watch. This method is already implemented in embedded system which can easily be installed on ship, which can pre-warn the ship officer of the incoming threat and provide collision avoidance suggestions by showing the collision risk indices (CRI) and the collision avoidance measures. The results show that the method and the already implemented system can satisfy the demand of pre-warn and auxiliary decision support for ship collision avoidance operation.
Semantic Fisher Scores for Task Transfer: Using Objects to Classify Scenes

The transfer of a neural network (CNN) trained to recognize objects to the task of scene classification is considered. A Bag-of-Semantics (BoS) representation is first induced, by feeding scene image patches to the object CNN, and representing the scene image by the ensuing bag of posterior class probability vectors (semantic posteriors). The encoding of the BoS with a Fisher vector (FV) is then studied. A link is established between the FV of any probabilistic model and the $Q$Q-function of the expectation-maximization (EM) algorithm used to estimate its parameters by maximum likelihood. This enables 1) immediate derivation of FVs for any model for which an EM algorithm exists, and 2) leveraging efficient implementations from the EM literature for the computation of FVs. It is then shown that standard FVs, such as those derived from Gaussian or even Dirichlet mixtures, are unsuccessful for the transfer of semantic posteriors, due to the highly non-linear nature of the probability simplex. The analysis of these FVs shows that significant benefits can ensue by 1) designing FVs in the natural parameter space of the multinomial distribution, and 2) adopting sophisticated probabilistic models of semantic feature covariance. The combination of these two insights leads to the encoding of the BoS in the natural parameter space of the multinomial, using a vector of Fisher scores derived from a mixture of factor analyzers (MFA). A network implementation of the MFA Fisher Score (MFA-FS), denoted as the MFAFSNet, is finally proposed to enable end-to-end training. Experiments with various object CNNs and datasets show that the approach has state-of-the-art transfer performance. Somewhat surprisingly, the scene classification results are superior to those of a CNN explicitly trained for scene classification, using a large scene dataset (Places). This suggests that holistic analysis is insufficient for scene classification. The modeling of local object semantics appears to be at least equally important. The two approaches are also shown to be strongly complementary, leading to very large scene classification gains when combined, and outperforming all previous scene classification approaches by a sizable margin.
Reconstruction of Fri Signals Using Deep Neural Network Approaches

Finite Rate of Innovation (FRI) theory considers sampling and reconstruction of classes of non-bandlimited continuous signals that have a small number of free parameters, such as a stream of Diracs. The task of reconstructing FRI signals from discrete samples is often transformed into a spectral estimation problem and solved using Prony’s method and matrix pencil method which involve estimating signal subspaces. They achieve an optimal performance given by the Cramer-Rao bound yet break down at a certain peak signal-to-´ noise ratio (PSNR). This is probably due to the so-called subspace swap event. In this paper, we aim to alleviate the subspace swap problem and investigate alternative approaches including directly estimating FRI parameters using deep neural networks and utilising deep neural networks as denoisers to reduce the noise in the samples. Simulations show significant improvements on the breakdown PSNR over existing FRI methods, which still outperform learning-based approaches in medium to high PSNR regimes.
Budgeted Reinforcement Learning in Continuous State Space

A Budgeted Markov Decision Process (BMDP) is an extension of a Markov Decision Process to critical applications requiring safety constraints. It relies on a notion of risk implemented in the shape of a cost signal constrained to lie below an - adjustable - threshold. So far, BMDPs could only be solved in the case of finite state spaces with known dynamics. This work extends the state-of-the-art to continuous spaces environments and unknown dynamics. We show that the solution to a BMDP is a fixed point of a novel Budgeted Bellman Optimality operator. This observation allows us to introduce natural extensions of Deep Reinforcement Learning algorithms to address large-scale BMDPs. We validate our approach on two simulated applications: spoken dialogue and autonomous driving.
Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction

An effective content recommendation in modern social media platforms should benefit both creators to bring genuine benefits to them and consumers to help them get really interesting content. In this paper, we propose a model called Social Explorative Attention Network (SEAN) for content recommendation. SEAN uses a personalized content recommendation model to encourage personal interests driven recommendation. Moreover, SEAN allows the personalization factors to attend to users' higher-order friends on the social network to improve the accuracy and diversity of recommendation results. Constructing two datasets from a popular decentralized content distribution platform, Steemit, we compare SEAN with state-of-the-art CF and content based recommendation approaches. Experimental results demonstrate the effectiveness of SEAN in terms of both Gini coefficients for recommendation equality and F1 scores for recommendation performance.
Large Polaron Generation and Dynamics in 3D Metal-Halide Perovskites

In recent years, metal halide perovskites have generated tremendous interest for optoelectronic applications and their underlying fundamental properties. Due to the large electron-phonon coupling characteristic of soft lattices, self-trapping phenomena are expected to dominate hybrid perovskite photoexcitation dynamics. Yet, while the photogeneration of small polarons was proven in low dimensional perovskites, the nature of polaron excitations in technologically relevant 3D perovskites, and their influence on charge carrier transport, remain elusive. In this study, we used a combination of first principle calculations and advanced spectroscopy techniques spanning the entire optical frequency range to pin down polaron features in 3D metal halide perovskites. Mid-infrared photoinduced absorption shows the photogeneration of states associated to low energy intragap electronic transitions with lifetime up to the ms time scale, and vibrational mode renormalization in both frequency and amplitude. Density functional theory supports the assignment of the spectroscopic features to large polarons leading to new intra gap transitions, hardening of phonon mode frequency, and renormalization of the oscillator strength. Theory provides quantitative estimates of the charge carrier masses and mobilities increase upon polaron formation, confirming experimental results. Overall, this work contributes to complete the scenario of elementary photoexcitations in metal halide perovskites and highlights the importance of polaronic transport in perovskite-based optoelectronic devices.
Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix

This paper studies how to sketch element-wise functions of low-rank matrices. Formally, given low-rank matrix A = [Aij] and scalar non-linear function f, we aim for finding an approximated low-rank representation of the (possibly high-rank) matrix [f(Aij)]. To this end, we propose an efficient sketching-based algorithm whose complexity is significantly lower than the number of entries of A, i.e., it runs without accessing all entries of [f(Aij)] explicitly. The main idea underlying our method is to combine a polynomial approximation of f with the existing tensor sketch scheme for approximating monomials of entries of A. To balance the errors of the two approximation components in an optimal manner, we propose a novel regression formula to find polynomial coefficients given A and f. In particular, we utilize a coreset-based regression with a rigorous approximation guarantee. Finally, we demonstrate the applicability and superiority of the proposed scheme under various machine learning tasks.
The Hierarchy of Stable Distributions and Operators to Trade Off Stability and Performance

Recent work addressing model reliability and generalization has resulted in a variety of methods that seek to proactively address differences between the training and unknown target environments. While most methods achieve this by finding distributions that will be invariant across environments, we will show they do not necessarily find the same distributions which has implications for performance. In this paper we unify existing work on prediction using stable distributions by relating environmental shifts to edges in the graph underlying a prediction problem, and characterize stable distributions as those which effectively remove these edges. We then quantify the effect of edge deletion on performance in the linear case and corroborate the findings in a simulated and real data experiment.
Attacker Behaviour Profiling using Stochastic Ensemble of Hidden Markov Models

Cyber threat intelligence is one of the emerging areas of focus in information security. Much of the recent work has focused on rule-based methods and detection of network attacks using Intrusion Detection algorithms. In this paper we propose a framework for inspecting and modelling the behavioural aspect of an attacker to obtain better insight predictive power on his future actions. For modelling we propose a novel semi-supervised algorithm called Fusion Hidden Markov Model (FHMM) which is more robust to noise, requires comparatively less training time, and utilizes the benefits of ensemble learning to better model temporal relationships in data. This paper evaluates the performances of FHMM and compares it with both traditional algorithms like Markov Chain, Hidden Markov Model (HMM) and recently developed Deep Recurrent Neural Network (Deep RNN) architectures. We conduct the experiments on dataset consisting of real data attacks on a Cowrie honeypot system. FHMM provides accuracy comparable to deep RNN architectures at significant lower training time. Given these experimental results, we recommend using FHMM for modelling discrete temporal data for significantly faster training and better performance than existing methods.