Miracle-dz/UniFAR-FTUs huggingface dataset:
3D Object Super-Resolution

We consider the problem of scaling deep generative shape models to high-resolution. To this end, we introduce a novel method for the fast up-sampling of 3D objects in voxel space by super-resolution on the six orthographic depth projections. We demonstrate the training of object-specific super-resolution CNNs for depth maps and silhouettes. This allows us to efficiently generate high-resolution objects, without the cubic computational costs associated with voxel data. We evaluate our work on multiple experiments concerning high-resolution 3D objects, and show our system is capable of accurately increasing the resolution of voxelized objects by a factor of up to 16, to produce objects at resolutions as large as 512$\mathbf{\times}$512$\mathbf{\times}$512 from 32$\mathbf{\times}$32$\mathbf{\times}$32 resolution inputs. Additionally, we demonstrate our method can be easily applied in conjunction with the reconstruction of high-resolution objects from RGB images to achieve quantitative and qualitative state-of-the-art performance for this task.
Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication

Matrix multiplication is a fundamental building block in various machine learning algorithms. When the matrix comes from a large dataset, the multiplication can be split into multiple tasks which calculate the multiplication of submatrices on different nodes. As some nodes may be stragglers, coding schemes have been proposed to tolerate stragglers in such distributed matrix multiplication. However, existing coding schemes typi-cally split the matrices in only one or two dimensions, limiting their capabilities to handle large-scale matrix multiplication. Three-dimensional coding, however, does not have any code construction that achieves the optimal number of tasks required for decoding, with the best result achieved by entangled polynomial (EP) codes. In this paper, we propose dual entangled polynomial (DEP) codes that require around 25% fewer tasks than EP codes by executing two matrix multiplications on each task. With experiments in a real cloud en-vironment, we show that DEP codes can also save the decoding overhead and memory consumption of tasks.
Methodological Ambiguity and Inconsistency Constrain Unmanned Aerial Vehicles as A Silver Bullet for Monitoring Ecological Restoration

The last decade has seen an exponential increase in the application of unmanned aerial vehicles (UAVs) to ecological monitoring research, though with little standardisation or comparability in methodological approaches and research aims. We reviewed the international peer-reviewed literature in order to explore the potential limitations on the feasibility of UAV-use in the monitoring of ecological restoration, and examined how they might be mitigated to maximise the quality, reliability and comparability of UAV-generated data. We found little evidence of translational research applying UAV-based approaches to ecological restoration, with less than 7% of 2133 published UAV monitoring studies centred around ecological restoration. Of the 48 studies, > 65% had been published in the three years preceding this study. Where studies utilised UAVs for rehabilitation or restoration applications, there was a strong propensity for single-sensor monitoring using commercially available RPAs fitted with the modest-resolution RGB sensors available. There was a strong positive correlation between the use of complex and expensive sensors (e.g., LiDAR, thermal cameras, hyperspectral sensors) and the complexity of chosen image classification techniques (e.g., machine learning), suggesting that cost remains a primary constraint to the wide application of multiple or complex sensors in UAV-based research. We propose that if UAV-acquired data are to represent the future of ecological monitoring, research requires a) consistency in the proven application of different platforms and sensors to the monitoring of target landforms, organisms and ecosystems, underpinned by clearly articulated monitoring goals and outcomes; b) optimization of data analysis techniques and the manner in which data are reported, undertaken in cross-disciplinary partnership with fields such as bioinformatics and machine learning; and c) the development of sound, reasonable and multi-laterally homogenous regulatory and policy framework supporting the application of UAVs to the large-scale and potentially trans-disciplinary ecological applications of the future.
Reconstructing Cloud Contaminated Pixels Using Spatiotemporal Covariance Functions and Multitemporal Hyperspectral Imagery

One of the major challenges in optical-based remote sensing is the presence of clouds, which imposes a hard constraint on the use of multispectral or hyperspectral satellite imagery for earth observation. While some studies have used interpolation models to remove cloud affected data, relatively few aim at restoration via the use of multi-temporal reference images. This paper proposes not only the use of image time-series, but also the implementation of a geostatistical model that considers the spatiotemporal correlation between them to fill the cloud-related gaps. Using Hyperion hyperspectral images, we demonstrate a capacity to reconstruct cloud-affected pixels and predict their underlying surface reflectance values. To do this, cloudy pixels were masked and a parametric family of non-separable covariance functions was automated fitted, using a composite likelihood estimator. A subset of cloud-free pixels per scene was used to perform a kriging interpolation and to predict the spectral reflectance per each cloud-affected pixel. The approach was evaluated using a benchmark dataset of cloud-free pixels, with a synthetic cloud superimposed upon these data. An overall root mean square error (RMSE) of between 0.5% and 16% of the reflectance was achieved, representing a relative root mean square error (rRMSE) of between 0.2% and 7.5%. The spectral similarity between the predicted and reference reflectance signatures was described by a mean spectral angle (MSA) of between 1° and 11°, demonstrating the spatial and spectral coherence of predictions. The approach provides an efficient spatiotemporal interpolation framework for cloud removal, gap-filling, and denoising in remotely sensed datasets.
Neural Chinese Address Parsing

This paper introduces a new task – Chinese address parsing – the task of mapping Chinese addresses into semantically meaningful chunks. While it is possible to model this problem using a conventional sequence labelling approach, our observation is that there exist complex dependencies between labels that cannot be readily captured by a simple linear-chain structure. We investigate neural structured prediction models with latent variables to capture such rich structural information within Chinese addresses. We create and publicly release a new dataset consisting of 15K Chinese addresses, and conduct extensive experiments on the dataset to investigate the model effectiveness and robustness. We release our code and data at http://statnlp.org/research/sp.
CrkII Participation in the Cellular Effects of Growth Hormone and Insulin-like Growth Factor-1

We have examined the role of CrkII in the cellular response to both human growth hormone (hGH) and human insulin-like growth factor-1 (hIGF-1). We have demonstrated that overexpression of the adaptor molecule enhances both basal phosphatidylinositol 3-kinase (PI 3-kinase) activity and also dramatically enhances the ability of both hormones to stimulate PI 3-kinase activity in the cell. Many of the effects of CrkII overexpression on hGH- and hIGF-1-stimulated cellular function can then be attributed to CrkII enhancement of PI 3-kinase stimulation by these hormones. Thus, CrkII-enhanced PI 3-kinase activity is used to enhance actin filament reorganization in response to both hGH and hIGF-1, to enhance stress activated protein kinase (SAPK) activity in response to hGH, and to diminish STAT5-mediated transcription in response to hGH. It is apparent, however, that CrkII also regulates cellular function independent of its ability to stimulate PI 3-kinase activity. This is evidenced by the ability of CrkII, in a PI 3-kinase-independent manner, to diminish the activation of p44/42 mitogen-activated protein kinase in response to both hGH and hIGF-1 and to inhibit the activation of SAPK by hIGF-1. Therefore, despite the common use of CrkII to activate PI 3-kinase, CrkII also allows hGH or hIGF-1 to selectively switch the activation of SAPK. Thus, common utilization of CrkII by hGH and hIGF-1 allows the execution of common cellular effects of these hormones, concomitant with the retention of hormonal specificity.
Addictive Auctions: Using Lucky-Draw and Gambling Addiction to Increase Participation During Auctioning

Auction theories are believed to provide a better selling opportunity for the resources to be allocated. Various organizations have taken measures to increase trust among participants towards their auction system, but trust alone cannot ensure a high level of participation. We propose a new type of auction system which takes advantage of lucky-draw and gambling addictions to increase the engagement level of candidates in an auction. Our system makes use of security features present in existing auction systems for ensuring fairness and maintaining trust among participants.
Inductive Bias of Gradient Descent based Adversarial Training on Separable Data

Adversarial training is a principled approach for training robust neural networks. Despite of tremendous successes in practice, its theoretical properties still remain largely unexplored. In this paper, we provide new theoretical insights of gradient descent based adversarial training by studying its computational properties, specifically on its inductive bias. We take the binary classification task on linearly separable data as an illustrative example, where the loss asymptotically attains its infimum as the parameter diverges to infinity along certain directions. Specifically, we show that when the adversarial perturbation during training has bounded $\ell_2$-norm, the classifier learned by gradient descent based adversarial training converges in direction to the maximum $\ell_2$-norm margin classifier at the rate of $\tilde{\mathcal{O}}(1/\sqrt{T})$, significantly faster than the rate $\mathcal{O}(1/\log T)$ of training with clean data. In addition, when the adversarial perturbation during training has bounded $\ell_q$-norm for some $q\ge 1$, the resulting classifier converges in direction to a maximum mixed-norm margin classifier, which has a natural interpretation of robustness, as being the maximum $\ell_2$-norm margin classifier under worst-case $\ell_q$-norm perturbation to the data. Our findings provide theoretical backups for adversarial training that it indeed promotes robustness against adversarial perturbation.
Primordial density fluctuations generated by wormholes

We studied the influence of a wormhole existing in the expanding Universe and passing through the horizon volume on the energy density of the Universe. The two-point correlation function of a free massless scalar field mimicking the inflaton was found. The revealed violation of the translation invariance during the process in question might be potentially envisaged in the anisotropy in cosmic microwave background radiation map.
Extended Expectation Cryptanalysis on Round-reduced AES

Distinguishers on round-reduced AES have attracted considerable attention in the recent years. Although the number of rounds covered in key-recovery attacks has not been increased since, subspace, yoyo, and multiple-of-n cryptanalysis advanced the understanding of properties of the cipher. Expectation cryptanalysis is an umbrella term for all forms of statistical analysis that try to identify properties whose expectation differs from that of an ideal primitive. For substitution-permutation networks, integral attacks seem a suitable target for extension since they usually end after a linear layer sums several subcomponents. Based on results by Patarin, Chen et al. already observed that the expected number of collisions differs slightly for a sum of permutations from the ideal. Though, their target remained lightweight primitives. The present work applies expectation-based distinguishers from a sum of PRPs to round-reduced AES. We show how to extend the well-known 3-round integral distinguisher to expectation distinguishers over 4 and 5 rounds. In contrast to previous expectation distinguishers by Grassi et al., our approach allows to prepend a round that starts from a diagonal subspace. We demonstrate how the prepended round can be used for key recovery. Moreover, we show how the prepended round can be integrated to form a six-round distinguisher. For all distinguishers, our results are supported by their implementations with Cid et al.’s established Small-AES version.
Multiphase quasar-driven outflows in PG 1114+445

Substantial evidence in the last few decades suggests that outflows from supermassive black holes (SMBH) may play a significant role in the evolution of galaxies. These outflows, powered by active galactic nuclei (AGN), are thought to be the fundamental mechanism by which the SMBH transfers a significant fraction of its accretion energy to the surrounding environment. Large-scale outflows known as warm absorbers (WA) and fast disk winds known as ultra-fast outflows (UFO) are commonly found in the spectra of many Seyfert galaxies and quasars, and a correlation has been suggested between them. Recent detections of low ionization and low column density outflows, but with a high velocity comparable to UFOs, challenge such initial possible correlations. Observations of UFOs in AGN indicate that their energetics may be enough to have an impact on the interstellar medium (ISM). However, observational evidence of the interaction between the inner high-ionization outflow and the ISM is still missing. We present here the spectral analysis of 12 XMM-Newton/EPIC archival observations of the quasar PG 1114+445, aimed at studying the complex outflowing nature of its absorbers. Our analysis revealed the presence of three absorbing structures. We find a WA with velocity v ∼ 530 km s−1, ionization log ξ/erg cm s−1 ∼ 0.35, and column density log NH/cm−2 ∼ 22, and a UFO with vout ∼ 0.145c, log ξ/erg cm s−1 ∼ 4, and log NH/cm−2 ∼ 23. We also find an additional absorber in the soft X-rays (E < 2 keV) with velocity comparable to that of the UFO (vout ∼ 0.120c), but ionization (log ξ/erg cm s−1 ∼ 0.5) and column density (log NH/cm−2 ∼ 21.5) comparable with those of the WA. The ionization, velocity, and variability of the three absorbers indicate an origin in a multiphase and multiscale outflow, consistent with entrainment of the clumpy ISM by an inner UFO moving at ∼15% the speed of light, producing an entrained ultra-fast outflow (E-UFO).
Segment Integrated Gradients: Better attributions through regions

Saliency methods can aid understanding of deep neural networks. Recent years have witnessed many improvements to saliency methods, as well as new ways for evaluating them. In this paper, we 1) present a novel region-based attribution method, Segment-Integrated Gradients (SIG), that builds upon integrated gradients (Sundararajan et al. 2017), 2) introduce evaluation methods for empirically assessing the quality of image-based saliency maps (Performance Information Curves (PICs)), and 3) contribute an axiom-based sanity check for attribution methods. Through empirical experiments and example results, we show that SIG produces better results than other saliency methods for common models and the ImageNet dataset.
Reliable Classification Explanations via Adversarial Attacks on Robust Networks

Neural Networks (NNs) have been found vulnerable to a class of imperceptible attacks, called adversarial examples, which arbitrarily alter the output of the network. These attacks have called the validity of NNs into question, particularly on sensitive problems such as medical imaging or fraud detection. We further argue that the fields of explainable AI and Human-In-The-Loop (HITL) algorithms are impacted by adversarial attacks, as attacks result in perturbations outside of the salient regions highlighted by state-of-the-art techniques such as LIME or Grad-CAM. This work accomplishes three things which greatly reduce the impact of adversarial examples, and pave the way for future HITL workflows: we propose a novel regularization technique inspired by the Lipschitz constraint which greatly improves an NN's resistance to adversarial examples; we propose a collection of novel network and training changes to complement the proposed regularization technique, including a Half-Huber activation function and an integrator-based controller for regularization strength; and we demonstrate that networks trained with this technique may be deliberately attacked to generate rich explanations. Our techniques led to networks more robust than the previous state of the art: using the Accuracy-Robustness Area (ARA), our most robust ImageNet classification network scored 42.2% top-1 accuracy on unmodified images and demonstrated an attack ARA of 0.0053, an ARA 2.4x greater than the previous state-of-the-art at the same level of accuracy on clean data, achieved with a network one-third the size. A far-reaching benefit of this technique is its ability to intuitively demonstrate decision boundaries to a human observer, allowing for improved debugging of NN decisions, and providing a means for improving the underlying model.
Analyzing Traffic Signal Performance Measures to Automatically Classify Signalized Intersections

Traffic signals are installed at road intersections to control the flow of traffic. An optimally operating traffic signal improves the efficiency of traffic flow while maintaining safety. The effectiveness of traffic signals has a significant impact on travel time for vehicular traffic. There are several measures of effectiveness (MOE) for traffic signals. In this paper, we develop a work-flow to automatically score and rank the intersections in a region based on their performance, and group the intersections that show similar behavior, thereby highlighting patterns of similarity. In the process, we also detect potential bottlenecks in the region of interest.
A new approach for open‐end sequential change point monitoring

We propose a new sequential monitoring scheme for changes in the parameters of a multivariate time series. In contrast to procedures proposed in the literature which compare an estimator from the training sample with an estimator calculated from the remaining data, we suggest to divide the sample at each time point after the training sample. Estimators from the sample before and after all separation points are then continuously compared calculating a maximum of norms of their differences. For open‐end scenarios our approach yields an asymptotic level α procedure, which is consistent under the alternative of a change in the parameter. By means of a simulation study it is demonstrated that the new method outperforms the commonly used procedures with respect to power and the feasibility of our approach is illustrated by analyzing two data examples.
Local and global well-posedness for a quadratic Schr\"odinger system on spheres and Zoll manifolds

We consider the initial value problem (IVP) associated to a quadratic Schrodinger system \begin{equation*} \begin{cases} i \partial_{t} v \pm \Delta_{g} v - v = \epsilon_{1} u \bar{v}, & t \in \mathbb{R},\; x \in M, \\[2ex] i \sigma \partial_{t} u \pm \Delta_{g} u - \alpha u = \frac{\epsilon_{2}}{2} v^{2}, & \sigma > 0, \;\alpha \in \mathbb{R},\; \epsilon_{i} \in \mathbb{C}\, (i = 1, 2),\\[2ex] (v(0), u(0)) = (v_0, u_0), \end{cases} \end{equation*} posed on a $d$-dimensional sphere $ \mathbb{S}^{d}$ or a compact Zoll manifold $M$. Considering $\sigma=\frac{\theta}{\beta}$ with $\theta, \beta\in \{n^2:n\in\mathbb{Z}\}$ we derive a bilinear Strichartz type estimate and use it to prove the local well-posedness results for given data $(v_0, u_0)\in H^s(M)\times H^s(M)$ whenever $s>\frac{1}{4}$ in the case $M = \mathbb{S}^{2}$ or a Zoll manifold, and $s > \frac{d - 2}{2}$ in the case $M = \mathbb{S}^{d}$ ($d \geq 3$) induced with the canonical metric. Moreover, in dimensions $2$ and $3$, we use a Gagliardo-Nirenberg type inequality to prove that the local solution can be extended globally in time whenever $s \geq 1$.
Residual Flows for Invertible Generative Modeling

Flow-based generative models parameterize probability distributions through an invertible transformation and can be trained by maximum likelihood. Invertible residual networks provide a flexible family of transformations where only Lipschitz conditions rather than strict architectural constraints are needed for enforcing invertibility. However, prior work trained invertible residual networks for density estimation by relying on biased log-density estimates whose bias increased with the network's expressiveness. We give a tractable unbiased estimate of the log density using a "Russian roulette" estimator, and reduce the memory required during training by using an alternative infinite series for the gradient. Furthermore, we improve invertible residual blocks by proposing the use of activation functions that avoid derivative saturation and generalizing the Lipschitz condition to induced mixed norms. The resulting approach, called Residual Flows, achieves state-of-the-art performance on density estimation amongst flow-based models, and outperforms networks that use coupling blocks at joint generative and discriminative modeling.
Multimodal End-to-End Autonomous Driving

A crucial component of an autonomous vehicle (AV) is the artificial intelligence (AI) is able to drive towards a desired destination. Today, there are different paradigms addressing the development of AI drivers. On the one hand, we find modular pipelines, which divide the driving task into sub-tasks such as perception and maneuver planning and control. On the other hand, we find end-to-end driving approaches that try to learn a direct mapping from input raw sensor data to vehicle control signals. The later are relatively less studied, but are gaining popularity since they are less demanding in terms of sensor data annotation. This paper focuses on end-to-end autonomous driving. So far, most proposals relying on this paradigm assume RGB images as input sensor data. However, AVs will not be equipped only with cameras, but also with active sensors providing accurate depth information (e.g., LiDARs). Accordingly, this paper analyses whether combining RGB and depth modalities, i.e. using RGBD data, produces better end-to-end AI drivers than relying on a single modality. We consider multimodality based on early, mid and late fusion schemes, both in multisensory and single-sensor (monocular depth estimation) settings. Using the CARLA simulator and conditional imitation learning (CIL), we show how, indeed, early fusion multimodality outperforms single-modality.
Heterogeneous Model Reuse via Optimizing Multiparty Multiclass Margin

Nowadays, many problems require learning a model from data owned by different participants who are restricted to share their examples due to privacy concerns, which is referred to as multiparty learning in the literature. In conventional multiparty learning, a global model is usually trained from scratch via a communication protocol, ignoring the fact that each party may already have a local model trained on her own dataset. In this paper, we define a multiparty multiclass margin to measure the global behavior of a set of heterogeneous local models, and propose a general learning method called HMR (Heterogeneous Model Reuse) to optimize the margin. Our method reuses local models to approximate a global model, even when data are non-i.i. d distributed among parties, by exchanging few examples under predefined budget. Experiments on synthetic and real-world data covering different multiparty scenarios show the effectiveness of our proposal.