Miracle-dz/UniFAR-FTUs huggingface dataset:
Computing Robust Inverse Kinematics Under Uncertainty

Robotic tasks, like reaching a pre-grasp configuration, are specified in the end effector space or task space, whereas, robot motion is controlled in joint space. Because of inherent actuation errors in joint space, robots cannot achieve desired configurations in task space exactly. Furthermore, different inverse kinematics (IK) solutions map joint space error set to task space differently. Thus for a given task with a prescribed error tolerance, all IK solutions will not be guaranteed to successfully execute the task. Any IK solution that is guaranteed to execute a task (possibly with high probability) irrespective of the realization of the joint space error is called a robust IK solution. In this paper we formulate and solve the robust inverse kinematics problem for redundant manipulators with actuation uncertainties (errors). We also present simulation and experimental results on a 7-DoF redundant manipulator for two applications, namely, a pre-grasp positioning and a pre-insertion positioning scenario. Our results show that the robust IK solutions result in higher success rates and also allows the robot to self-evaluate how successful it might be in any application scenario.
Fraud Detection in Networks: State-of-the-art

Financial fraud detection represents the challenge of finding anomalies in networks of financial transactions. In general, anomaly detection is the problem of distinguishing between normal data samples with well defined patterns or signatures and those that do not conform to the expected profiles. The fraudulent behavior in money laundering may manifest itself through unusual patterns in financial transaction networks. Most commonly, these networks are represented as attributed graphs, with numerical features complementing relational information. We present a survey on anomaly detection techniques used for fraud detection that exploit both the graph structure underlying the data and the contextual information contained in the attributes.
Enforcing Reasoning in Visual Commonsense Reasoning

The task of Visual Commonsense Reasoning is extremely challenging in the sense that the model has to not only be able to answer a question given an image, but also be able to learn to reason. The baselines introduced in this task are quite limiting because two networks are trained for predicting answers and rationales separately. Question and image is used as input to train answer prediction network while question, image and correct answer are used as input in the rationale prediction network. As rationale is conditioned on the correct answer, it is based on the assumption that we can solve Visual Question Answering task without any error - which is over ambitious. Moreover, such an approach makes both answer and rationale prediction two completely independent VQA tasks rendering cognition task meaningless. In this paper, we seek to address these issues by proposing an end-to-end trainable model which considers both answers and their reasons jointly. Specifically, we first predict the answer for the question and then use the chosen answer to predict the rationale. However, a trivial design of such a model becomes non-differentiable which makes it difficult to train. We solve this issue by proposing four approaches - softmax, gumbel-softmax, reinforcement learning based sampling and direct cross entropy against all pairs of answers and rationales. We demonstrate through experiments that our model performs competitively against current state-of-the-art. We conclude with an analysis of presented approaches and discuss avenues for further work.
VLT/SPHERE exploration of the young multiplanetary system PDS70

Context. PDS 70 is a young (5.4 Myr), nearby (~113 pc) star hosting a known transition disk with a large gap. Recent observations with SPHERE and NACO in the near-infrared (NIR) allowed us to detect a planetary mass companion, PDS 70 b, within the disk cavity. Moreover, observations in Hα with MagAO and MUSE revealed emission associated to PDS 70 b and to another new companion candidate, PDS 70 c, at a larger separation from the star. PDS 70 is the only multiple planetary system at its formation stage detected so far through direct imaging. Aims. Our aim is to confirm the discovery of the second planet PDS 70 c using SPHERE at VLT, to further characterize its physical properties, and search for additional point sources in this young planetary system. Methods. We re-analyzed archival SPHERE NIR observations and obtained new data in Y, J, H and K spectral bands for a total of four different epochs. The data were reduced using the data reduction and handling pipeline and the SPHERE data center. We then applied custom routines (e.g., ANDROMEDA and PACO) to subtract the starlight. Results. We re-detect both PDS 70 b and c and confirm that PDS 70 c is gravitationally bound to the star. We estimate this second planet to be less massive than 5 MJup and with a Teff around 900 K. Also, it has a low gravity with logg between 3.0 and 3.5 dex. In addition, a third object has been identified at short separation (~0.12′′) from the star and gravitationally bound to the star. Its spectrum is however very blue, meaning that we are probably seeing stellar light reflected by dust and our analysis seems to demonstrate that it is a feature of the inner disk. We cannot however completely exclude the possibility that it is a planetary mass object enshrouded by a dust envelope. In this latter case, its mass should be of the order of a few tens of M⊕. Moreover, we propose a possible structure for the planetary system based on our data, and find that this structure cannot be stable on a long timescale.
A Real Indoor Navigation System Based on Variational Gaussian Process State-Space Model and Smartphone Sensory Data

We implement a wireless indoor navigation system based on the variational Gaussian process state-space model (GPSSM) with low quality sensory data collected by smartphone. The proposed system explores both the expressive power of the non-parametric Gaussian process model and its natural mechanism for integrating the state-of-the-art navigation techniques designed upon state-space model. We adapt the existing variational GPSSM framework to practical wireless navigation scenarios. Experimental results obtained from a real office environment validate the outstanding performance of the variational GPSSM in comparison with the traditional parametric state-space model in terms of navigation accuracy.
Analyzing the impact of speaker localization errors on speech separation for automatic speech recognition

We investigate the effect of speaker localization on the performance of speech recognition systems in a multispeaker, multichannel environment. Given the speaker location information, speech separation is performed in three stages. In the first stage, a simple delay-and-sum (DS) beamformer is used to enhance the signal impinging from the speaker location which is then used to estimate a time-frequency mask corresponding to the localized speaker using a neural network. This mask is used to compute the second order statistics and to derive an adaptive beamformer in the third stage. We generated a multichannel, multispeaker, reverberated, noisy dataset inspired from the well studied WSJ0-2mix and study the performance of the proposed pipeline in terms of the word error rate (WER). An average WER of 29.4% was achieved using the ground truth localization information and 42.4% using the localization information estimated via GCC-PHAT. Though higher signal-to-interference ratio (SIR) between the speakers was found to positively impact the speech separation performance, equivalent performances were obtained for mixtures with lower SIR values when the speakers are well separated in space.
Cross-Lingual Vision-Language Navigation

Vision-Language Navigation (VLN) is the task where an agent is commanded to navigate in photo-realistic environments with natural language instructions. Previous research on VLN is primarily conducted on the Room-to-Room (R2R) dataset with only English instructions. The ultimate goal of VLN, however, is to serve people speaking arbitrary languages. To do this, we collect a cross-lingual R2R dataset, extending the original benchmark with corresponding Chinese instructions. But it is impractical to collect human-annotated instructions for every existing language. Based on the newly introduced dataset, we propose a general cross-lingual VLN framework to enable instruction-following navigation for different languages. We first explore the possibility of building a cross-lingual agent when no training data of the target language is available. The cross-lingual agent is equipped with a meta-learner to aggregate cross-lingual representations and with a visually grounded cross-lingual alignment module to align textual representations of different languages. Under the zero-shot learning scenario, our model shows competitive results even compared to a model trained with all target language instructions. Besides, we introduce an adversarial domain adaption loss to improve the transferring ability of our model when given a certain amount of target language data. Our dataset and methods demonstrate potentials of building scalable cross-lingual agents to serve speakers with different languages.
Evading Real-Time Person Detectors by Adversarial T-shirt

It is known that deep neural networks (DNNs) could be vulnerable to adversarial attacks. The so-called physical adversarial examples deceive DNN-based decision makers by attaching adversarial patches to real objects. However, most of the existing works on physical adversarial attacks focus on static objects such as glass frame, stop sign and image attached to a cardboard. In this work, we proposed adversarial T-shirt, a robust physical adversarial example for evading person detectors even if it suffers from deformation due toa moving person's pose change. To the best of our knowledge, the effect of deformation is first modeled for designing physical adversarial examples with respect to non-rigid objects such as T-shirts. We show that the proposed method achieves 79% and 63% attack success rates in digital and physical worlds respectively against YOLOv2. In contrast, the state-of-the-art physical attack method to fool a person detector only achieves 27% attack success rate. Furthermore, by leveraging min-max optimization, we extend our method to the ensemble attack setting against object detectors YOLOv2 and Faster R-CNN simultaneously.
Anomalously Narrow Line Widths of Compact Massive Star-forming Galaxies at z ∼ 2.3: A Possible Inclination Bias in the Size–Mass Plane

Compact, massive star-forming galaxies at z ∼ 2.5 are thought to be building the central regions of giant elliptical galaxies today. However, a significant fraction of these objects were previously shown to have much smaller Hα line widths than expected. A possible interpretation is that Hα emission from their central regions, where the highest velocities are expected, is typically obscured by dust. Here we present Atacama Large Millimeter/submillimeter Array observations of the CO(3–2) emission line of three compact, massive galaxies with Hα line widths of FWHM(Hα) ∼ 125–260 km s−1 to test this hypothesis. Surprisingly, in all three galaxies, the CO line width is similar to the Hα line width: we find FWHM(CO) ∼ 165 km s−1 for all three galaxies whereas FWHM(CO) ∼ 450–700 km s−1 was expected from a simple virial estimator. These results show that the narrow Hα line widths of many compact massive star-forming galaxies are not due to preferential obscuration of the highest velocity gas. An alternative explanation for the narrow line widths is that the galaxies are disks that are viewed nearly face-on. We suggest that there may be an inclination bias in the size–mass plane, such that the apparent rest-frame optical sizes of face-on galaxies are smaller than those of edge-on galaxies. Although not conclusive, this hypothesis is supported by an observed anti-correlation between size and axis ratio of massive galaxies.
Efficient parameterization of flamelet manifolds via mixtures of deep experts: an a priori study

This work describes and validates an approach for autonomously bifurcating turbulent combustion manifolds to divide regression tasks amongst specialized artificial neural networks (ANNs). This approach relies on the mixture of experts (MoE) framework, where each neural network is trained to be specialized in a given portion of the input space. The assignment of different input regions to the experts is determined by a gating network, which is a neural network classifier. In some previous studies, it has been demonstrated that bifurcation of a complex combustion manifold and fitting different ANNs for each part leads to better fits or faster inference speeds. However, the manner of bifurcation in these studies was based on heuristic approaches or clustering techniques. In contrast, the proposed technique enables automatic bifurcation using non-linear planes in high-dimensional turbulent combustion manifolds that are often associated with complex behavior due to different dominating physics in various zones. The proposed concept is validated using 4-dimensional (4D) and 5D flamelet tables, showing that the errors obtained with a given network size, or conversely the network size required to achieve a given accuracy, is considerably reduced. The effect of the number of experts on inference speed is also investigated, showing that by increasing the number of experts from 1 to 8, the inference time can be approximately reduced by a factor of two. Moreover, it is shown that the MoE approach divides the input manifold in a physically intuitive manner, suggesting that the MoE framework can elucidate high-dimensional datasets in a physically meaningful way.
Molecular Gas in the Outflow of the Small Magellanic Cloud

We report the first evidence of molecular gas in two atomic hydrogen (H i) clouds associated with gas outflowing from the Small Magellanic Cloud (SMC). We used the Atacama Pathfinder Experiment to detect and spatially resolve individual clumps of 12CO(2 → 1) emission in both clouds. CO clumps are compact (∼10 pc) and dynamically cold (line widths ). Most CO emission appears to be offset from the peaks of the H i emission, some molecular gas lies in regions without a clear H i counterpart. We estimate a total molecular gas mass of in each cloud and molecular gas fractions up to 30% of the total cold gas mass (molecular + neutral). Under the assumption that this gas is escaping the galaxy, we calculated a cold gas outflow rate of and mass loading factors of at a distance larger than 1 kpc. These results show that relatively weak starburst-driven winds in dwarf galaxies like the SMC are able to accelerate significant amounts of cold and dense matter and inject it into the surrounding environment.
Sidebar: Scratchpad Based Communication Between CPUs and Accelerators

Hardware accelerators for neural networks have shown great promise for both performance and power. These accelerators are at their most efficient when optimized for a fixed functionality. But this inflexibility limits the longevity of the hardware itself as the underlying neural network algorithms and structures undergo improvements and changes. We propose and evaluate a flexible design paradigm for accelerators with a close coordination with host processors. The relatively static matrix operations are implemented in specialized accelerators while fast-evolving functions, such as activations, are computed on the host processor. This architecture is enabled by a low latency shared buffer we call Sidebar. Sidebar memory is shared between the accelerator and host, exists outside of program address space and holds intermediate data only. We show that a generalised DMA dependent flexible accelerator design performs poorly in both perf and energy as compared to an equivalent fixed function accelerator. Sidebar based accelerator design achieves near identical performance and energy to equivalent fixed function accelerator while still providing all the flexibility of computing activations on the host processor.
Stellar Feedback and Resolved Stellar IFU Spectroscopy in the Nearby Spiral Galaxy NGC 300

We present MUSE integral field unit (IFU) observations of five individual H ii regions in two giant star-forming complexes in the low-metallicity, nearby dwarf spiral galaxy NGC 300. In combination with high spatial resolution Hubble Space Telescope photometry, we demonstrate the extraction of stellar spectra and classification of individual stars from ground-based IFU data at the distance of 2 Mpc. For the two star-forming complexes, in which no O-type stars had previously been identified, we find a total of 13 newly identified O-type stars and 4 Wolf-Rayet stars (two already-known sources and two Wolf-Rayet star candidates that this work has now confirmed). We use the derived massive stellar content to analyze the impact of stellar feedback on the H ii regions. As already found for H ii regions in the Magellanic Clouds, the dynamics of the analyzed NGC 300 H ii regions are dominated by a combination of the pressure of the ionized gas and stellar winds. Moreover, we analyze the relation between the star formation rate and the pressure of the ionized gas as derived from small (<100 pc) scales, both quantities being systematically overestimated when derived on galactic scales. With the wealth of upcoming IFU instruments and programs, this study serves as a pathfinder for the systematic investigation of resolved stellar feedback in nearby galaxies, delivering the necessary analysis tools to enable massive stellar content and feedback studies sampling an unprecedented range of H ii region properties across entire galaxies in the nearby universe.
Cutting an alignment with Ockham's razor

In this article, we investigate different parsimony-based approaches towards finding recombination breakpoints in a multiple sequence alignment. This recombination detection task is crucial in order to avoid errors in evolutionary analyses caused by mixing together portions of sequences which had a different evolution history. Following an overview of the field of recombination detection, we formulate four computational problems for this task with different objective functions. The four problems aim to minimize (1) the total homoplasy of all blocks (2) the maximum homoplasy per block (3) the total homoplasy ratio of all blocks and (4) the maximum homoplasy ratio per block. We describe algorithms for each of these problems, which are fixed-parameter tractable (FPT) when the characters are binary. We have implemented and tested the algorithms on simulated data, showing that minimizing the total homoplasy gives, in most cases, the most accurate results. Our implementation and experimental data have been made publicly available. Finally, we also consider the problem of combining blocks into non-contiguous blocks consisting of at most p contiguous parts. Fixing the homoplasy h of each block to 0, we show that this problem is NP-hard when p >= 3, but polynomial-time solvable for p = 2. Furthermore, the problem is FPT with parameter h for binary characters when p = 2. A number of interesting problems remain open.
Fast and Differentiable Message Passing for Stereo Vision

Despite the availability of many Markov Random Field (MRF) optimization algorithms, their widespread usage is currently limited due to imperfect MRF modelling arising from hand-crafted model parameters. In addition to differentiability, the two main aspects that enable learning these model parameters are the forward and backward propagation time of the MRF optimization algorithm and its parallelization capabilities. In this work, we introduce two fast and differentiable message passing algorithms, namely, Iterative Semi-Global Matching Revised (ISGMR) and Parallel Tree-Reweighted Message Passing (TRWP) which are greatly sped up on GPU by exploiting massive parallelism. Specifically, ISGMR is an iterative and revised version of the standard SGM for general second-order MRFs with improved optimization effectiveness, whereas TRWP is a highly parallelizable version of Sequential TRW (TRWS) for faster optimization. Our experiments on standard stereo benchmarks demonstrate that ISGMR achieves much lower energies than SGM and TRWP is two orders of magnitude faster than TRWS without losing effectiveness in optimization. Furthermore, our CUDA implementations are at least 7 and 650 times faster than PyTorch GPU implementations in the forward and backward propagation, respectively, enabling efficient end-to-end learning with message passing.
A Comparative Study of Neural Network Compression

There has recently been an increasing desire to evaluate neural networks locally on computationally-limited devices in order to exploit their recent effectiveness for several applications; such effectiveness has nevertheless come together with a considerable increase in the size of modern neural networks, which constitute a major downside in several of the aforementioned computationally-limited settings. There has thus been a demand of compression techniques for neural networks. Several proposal in this direction have been made, which famously include hashing-based methods and pruning-based ones. However, the evaluation of the efficacy of these techniques has so far been heterogeneous, with no clear evidence in favor of any of them over the others. The goal of this work is to address this latter issue by providing a comparative study. While most previous studies test the capability of a technique in reducing the number of parameters of state-of-the-art networks , we follow [CWT + 15] in evaluating their performance on basic ar-chitectures on the MNIST dataset and variants of it, which allows for a clearer analysis of some aspects of their behavior. To the best of our knowledge, we are the first to directly compare famous approaches such as HashedNet, Optimal Brain Damage (OBD), and magnitude-based pruning with L1 and L2 regularization among them and against equivalent-size feed-forward neural networks with simple (fully-connected) and structural (convolutional) neural networks. Rather surprisingly, our experiments show that (iterative) pruning-based methods are substantially better than the HashedNet architecture, whose compression doesn't appear advantageous to a carefully chosen convolutional network. We also show that, when the compression level is high, the famous OBD pruning heuristics deteriorates to the point of being less efficient than simple magnitude-based techniques.