Miracle-dz/UniFAR-FTUs huggingface dataset:
Hierarchy Denoising Recursive Autoencoders for 3D Scene Layout Prediction

Indoor scenes exhibit rich hierarchical structure in 3D object layouts. Many tasks in 3D scene understanding can benefit from reasoning jointly about the hierarchical context of a scene, and the identities of objects. We present a variational denoising recursive autoencoder (VDRAE) that generates and iteratively refines a hierarchical representation of 3D object layouts, interleaving bottom-up encoding for context aggregation and top-down decoding for propagation. We train our VDRAE on large-scale 3D scene datasets to predict both instance-level segmentations and a 3D object detections from an over-segmentation of an input point cloud. We show that our VDRAE improves object detection performance on real-world 3D point cloud datasets compared to baselines from prior work.
The cytoplasmic domain of MxiG interacts with MxiK and directs assembly of the sorting platform in the Shigella type III secretion system

Many Gram-negative bacteria use type III secretion systems (T3SSs) to inject virulence effector proteins into eukaryotic cells. The T3SS apparatus (T3SA) is structurally conserved among diverse bacterial pathogens and consists of a cytoplasmic sorting platform, an envelope-spanning basal body, and an extracellular needle with tip complex. The sorting platform is essential for effector recognition and powering secretion. Studies using bacterial “minicells” have revealed an unprecedented level of structural detail of the sorting platform; however, many of the structure-function relationships within this complex remain enigmatic. Here, we report on improved cryo-electron tomographic approaches to enhance the resolution of the Shigella T3SA sorting platform (at ≤2 nm resolution) done in concert with biochemical and genetic methods to define the sorting platform interactome and interactions with the T3SA inner membrane ring (IR). We observed that the sorting platform consists of “pods” with 6-fold symmetry that interact with the Spa47 ATPase via radial extensions comprising MxiN. Most importantly, MxiK maintained an interaction with the IR via specific interactions with the cytoplasmic domain of the IR protein MxiG (MxiGC), which is a noncanonical forkhead-associated domain, and MxiK has an elongated structure that interacts with the IR via MxiGC. T4 lysozyme-mediated insertional mutagenesis of MxiK revealed its orientation within the sorting platform and enabled disruption of interactions with its binding partners, which abolished sorting platform assembly. Finally, a comparison with the homologous interactions in the Salmonella T3SS sorting platform revealed clear differences in their IR-sorting platform interfaces that have possible mechanistic implications.
Evaluating conservation dogs in the search for rare species

Detecting rare species is important for both threatened species management and invasive species eradication programs. Conservation scent dogs provide an olfactory survey tool that has advantages over traditional visual and auditory survey techniques for some cryptic species. From the literature, we identified 5 measures important in evaluating the use of scent dogs: precision, sensitivity, effort, cost, and comparison with other techniques. We explored the scale at which performance is evaluated and examined when field testing under real working conditions is achievable. We also identified cost differences among studies. We examined 61 studies published in 1976–2018 that reported conservation dog performance, and considered the inconsistencies in the reporting of scent dog performance among these studies. The majority of studies reported some measure of performance; however, only 8 studies reported all 3 aspects necessary for performance evaluation: precision, sensitivity, and effort. Although effort was considered in 43 studies, inconsistent methods and incomplete reporting prevented meaningful evaluation of performance and comparison among studies. Differences in cost between similar studies were influenced by geographical location and how the dog and handler were sourced for the study. To develop consistent reporting for evaluation, we recommend adoption of sensitivity, precision, and effort as standard performance measures. We recommend reporting effort as the total area and total time spent searching and reporting sensitivity and precision as proportions of the sample size. Additionally, reporting of costs, survey objectives, dog training and experience, type of detection task, and human influences will provide better opportunities for comparison within and among studies.
The structure of the bacterial iron–catecholate transporter Fiu suggests that it imports substrates via a two-step mechanism

The ferric iron uptake (Fiu) transporter from Escherichia coli functions in the transport of iron–catecholate complexes across the bacterial outer membrane, providing the bacterium with iron, which is essential for growth. Recently it has become clear that Fiu also represents a liability for E. coli because its activity allows import of antimicrobial compounds that mimic catecholate. This inadvertent import suggests the potential utility of antimicrobial catechol siderophore mimetics in managing bacterial infections. However, to fully exploit these compounds, a detailed understanding of the mechanism of transport through Fiu and related transporters is required. To address this question, we determined the crystal structure of Fiu at 2.1–2.9 Å and analyzed its function in E. coli. Through analysis of the Fiuo crystal structure, in combination with in silico docking and mutagenesis, we provide insight into how Fiu and related transporters bind catecholate in a surface-exposed cavity. Moreover, through determination of the structure of Fiu in multiple crystal states, we revealed the presence of a large, selectively gated cavity in the interior of this transporter. This chamber is large enough to accommodate the Fiu substrate and may allow import of substrates via a two-step mechanism. This would avoid channel formation through the transporter and inadvertent import of toxic molecules. As Fiu and its homologs are the targets of substrate-mimicking antibiotics, these results may assist in the development of these compounds.
Deep Reinforcement Learning of Volume-Guided Progressive View Inpainting for 3D Point Scene Completion From a Single Depth Image

We present a deep reinforcement learning method of progressive view inpainting for 3D point scene completion under volume guidance, achieving high-quality scene reconstruction from only a single depth image with severe occlusion. Our approach is end-to-end, consisting of three modules: 3D scene volume reconstruction, 2D depth map inpainting, and multi-view selection for completion. Given a single depth image, our method first goes through the 3D volume branch to obtain a volumetric scene reconstruction as a guide to the next view inpainting step, which attempts to make up the missing information; the third step involves projecting the volume under the same view of the input, concatenating them to complete the current view depth, and integrating all depth into the point cloud. Since the occluded areas are unavailable, we resort to a deep Q-Network to glance around and pick the next best view for large hole completion progressively until a scene is adequately reconstructed while guaranteeing validity. All steps are learned jointly to achieve robust and consistent results. We perform qualitative and quantitative evaluations with extensive experiments on the SUNCG data, obtaining better results than the state of the art.
Fisher-Bures Adversary Graph Convolutional Networks

In a graph convolutional network, we assume that the graph $G$ is generated wrt some observation noise. During learning, we make small random perturbations $\Delta{}G$ of the graph and try to improve generalization. Based on quantum information geometry, $\Delta{}G$ can be characterized by the eigendecomposition of the graph Laplacian matrix. We try to minimize the loss wrt the perturbed $G+\Delta{G}$ while making $\Delta{G}$ to be effective in terms of the Fisher information of the neural network. Our proposed model can consistently improve graph convolutional networks on semi-supervised node classification tasks with reasonable computational overhead. We present three different geometries on the manifold of graphs: the intrinsic geometry measures the information theoretic dynamics of a graph; the extrinsic geometry characterizes how such dynamics can affect externally a graph neural network; the embedding geometry is for measuring node embeddings. These new analytical tools are useful in developing a good understanding of graph neural networks and fostering new techniques.
A Tale of Two Worlds: Assessing the Vulnerability of Enclave Shielding Runtimes

This paper analyzes the vulnerability space arising in Trusted Execution Environments (TEEs) when interfacing a trusted enclave application with untrusted, potentially malicious code. Considerable research and industry effort has gone into developing TEE runtime libraries with the purpose of transparently shielding enclave application code from an adversarial environment. However, our analysis reveals that shielding requirements are generally not well-understood in real-world TEE runtime implementations. We expose several sanitization vulnerabilities at the level of the Application Binary Interface (ABI) and the Application Programming Interface (API) that can lead to exploitable memory safety and side-channel vulnerabilities in the compiled enclave. Mitigation of these vulnerabilities is not as simple as ensuring that pointers are outside enclave memory. In fact, we demonstrate that state-of-the-art mitigation techniques such as Intel's edger8r, Microsoft's "deep copy marshalling", or even memory-safe languages like Rust fail to fully eliminate this attack surface. Our analysis reveals 35 enclave interface sanitization vulnerabilities in 8 major open-source shielding frameworks for Intel SGX, RISC-V, and Sancus TEEs. We practically exploit these vulnerabilities in several attack scenarios to leak secret keys from the enclave or enable remote code reuse. We have responsibly disclosed our findings, leading to 5 designated CVE records and numerous security patches in the vulnerable open-source projects, including the Intel SGX-SDK, Microsoft Open Enclave, Google Asylo, and the Rust compiler.
SacCalib: reducing calibration distortion for stationary eye trackers using saccadic eye movements

Recent methods to automatically calibrate stationary eye trackers were shown to effectively reduce inherent calibration distortion. However, these methods require additional information, such as mouse clicks or on-screen content. We propose the first method that only requires users' eye movements to reduce calibration distortion in the background while users naturally look at an interface. Our method exploits that calibration distortion makes straight saccade trajectories appear curved between the saccadic start and end points. We show that this curving effect is systematic and the result of distorted gaze projection plane. To mitigate calibration distortion, our method undistorts this plane by straightening saccade trajectories using image warping. We show that this approach improves over the common six-point calibration and is promising for reducing distortion. As such, it provides a non-intrusive solution to alleviating accuracy decrease of eye tracker during long-term use.
Kerberoid: A Practical Android App Decompilation System with Multiple Decompilers

Decompilation is frequently used to analyze binary programs. In Android, however, decompilers all perform differently with varying apps due to their own characteristics. Obviously, there is no universal solution in all conditions. Based on this observation, we present a practical Android app decompilation system (called Kerberoid) that automatically stitches the results from multiple decompilers together to maximize the coverage and the accuracy of decompiled codes. We evaluate the performance of Kerberoid with 151 Android apps in which their corresponding source codes are publicly available. Kerberoid fully recovered all functions for 17% of the apps tested and gained a similarity score over 50% for 40% of the apps tested, increased by 7% and 9%, respectively, compared with the best existing decompiler.
Efficacy of adjuvant curcumin therapy in ulcerative colitis: A meta‐analysis of randomized controlled trials

Aminosalicylic acids are recognized to be the first‐line treatment options for ulcerative colitis. Currently, the effectiveness of curcumin as an adjuvant treatment in ulcerative colitis has been investigated, which was still controversial. This study aimed to systematically review and meta‐analyze the efficacy and safety of curcumin as an adjuvant treatment in ulcerative colitis.
Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces

We present a new algorithm ASEBO for conducting optimization of high-dimensional blackbox functions. ASEBO adapts to the geometry of the function and learns optimal sets of sensing directions, which are used to probe it, on-the-fly. It addresses the exploration-exploitation trade-off of blackbox optimization, where each single function query is expensive, by continuously learning the bias of the lower-dimensional model used to approximate gradients of smoothings of the function with compressed sensing and contextual bandits methods. To obtain this model, it uses techniques from the emerging theory of active subspaces in the novel ES blackbox optimization context. As a result, ASEBO learns the dynamically changing intrinsic dimensionality of the gradient space and adapts to the hardness of different stages of the optimization without external supervision. Consequently, it leads to more sample-efficient blackbox optimization than state-of-the-art algorithms. We provide rigorous theoretical justification of the effectiveness of our method. We also empirically evaluate it on the set of reinforcement learning policy optimization tasks as well as functions from the recently open-sourced Nevergrad library, demonstrating that it consistently learns optimal inputs with fewer queries to a blackbox function than other methods.
Partial Inlining Using Local Graph Rewriting

Inlining is an important optimization technique in any modern compiler, though the description of this technique in the literature is informal and vague. We describe a technique for inlining, designed to work on a flow graph of instructions of intermediate code. Our technique uses local graph rewriting, making the semantic correctness of this technique obvious. In addition, we prove that the algorithm terminates. As a direct result of the preservation of the semantics of the program after each local rewriting step, the algorithm can stop after any iteration, resulting in a partial inlining of the called function. Such partial inlining can be advantageous in order to avoid the inlining of code that is not performance critical, in particular for creating arguments and calls to error-signaling functions.
Group-Wise Correlation Stereo Network

Stereo matching estimates the disparity between a rectified image pair, which is of great importance to depth sensing, autonomous driving, and other related tasks. Previous works built cost volumes with cross-correlation or concatenation of left and right features across all disparity levels, and then a 2D or 3D convolutional neural network is utilized to regress the disparity maps. In this paper, we propose to construct the cost volume by group-wise correlation. The left features and the right features are divided into groups along the channel dimension, and correlation maps are computed among each group to obtain multiple matching cost proposals, which are then packed into a cost volume. Group-wise correlation provides efficient representations for measuring feature similarities and will not lose too much information like full correlation. It also preserves better performance when reducing parameters compared with previous methods. The 3D stacked hourglass network proposed in previous works is improved to boost the performance and decrease the inference computational cost. Experiment results show that our method outperforms previous methods on Scene Flow, KITTI 2012, and KITTI 2015 datasets.
Automatic Ontology Learning from Domain-Specific Short Unstructured Text Data

Ontology learning is a critical task in industry, dealing with identifying and extracting concepts captured in text data such that these concepts can be used in different tasks, e.g. information retrieval. Ontology learning is non-trivial due to several reasons with limited amount of prior research work that automatically learns a domain specific ontology from data. In our work, we propose a two-stage classification system to automatically learn an ontology from unstructured text data. We first collect candidate concepts, which are classified into concepts and irrelevant collocates by our first classifier. The concepts from the first classifier are further classified by the second classifier into different concept types. The proposed system is deployed as a prototype at a company and its performance is validated by using complaint and repair verbatim data collected in automotive industry from different data sources.
Singing Voice Conversion with Non-parallel Data

Singing voice conversion is a task to convert a song sang by a source singer to the voice of a target singer. In this paper, we propose using a parallel data free, many-to-one voice conversion technique on singing voices. A phonetic posterior feature is first generated by decoding singing voices through a robust Automatic Speech Recognition Engine (ASR). Then, a trained Recurrent Neural Network (RNN) with a Deep Bidirectional Long Short Term Memory (DBLSTM) structure is used to model the mapping from person-independent content to the acoustic features of the target person. F0 and aperiodic are obtained through the original singing voice, and used with acoustic features to reconstruct the target singing voice through a vocoder. In the obtained singing voice, the targeted and sourced singers sound similar. To our knowledge, this is the first study that uses non parallel data to train a singing voice conversion system. Subjective evaluations demonstrate that the proposed method effectively converts singing voices.
Semi-Blind Channel-and-Signal Estimation for Uplink Massive MIMO With Channel Sparsity

This paper considers the transceiver design for uplink massive multiple-input multiple-output (MIMO) systems with channel sparsity in the angular domain. Recent progress has shown that sparsity-learning-based blind signal detection is able to retrieve the channel and data by using massage-passing-based sparse matrix factorization methods. Short pilot sequences are inserted into user packets to eliminate the so-called phase and permutation ambiguities inherent in sparse matrix factorization. In this paper, to exploit the knowledge of these short pilot sequences more efficiently, we propose a semi-blind channel-and-signal estimation (SCSE) scheme in which the knowledge of the pilot sequences are integrated into the message passing algorithm for sparse matrix factorization. The SCSE algorithm involves enumeration over all possible user permutations, and so is time-consuming when the number of users is relatively large. To reduce complexity, we further develop the simplified SCSE (S-SCSE) to accommodate systems with a large number of users. The numerical results show that our semi-blind signal detection scheme substantially outperforms the state-of-the-art blind detection and training-based schemes in the short-pilot regime.
Individual cognitive behavioral therapy and combined family/individual therapy for young adults with Anorexia nervosa: A randomized controlled trial

Abstract Objective: In this study, we evaluate the efficacy of outpatient individual cognitive behavioral therapy for young adults (CBT-YA) and combined family/individual therapy for young adults (FT-YA) for anorexia nervosa (AN). Method: Participants (aged 17–24 years) with AN in Sweden were recruited and assigned to 18 months of CBT-YA or FT-YA. Treatment efficacy was assessed primarily using BMI, presence of diagnosis, and degree of eating-related psychopathology at post-treatment and follow-up. Secondary outcomes included depression and general psychological psychopathology. The trial was registered at http://www.isrctn. com/, ISRCTN (25181390). Results: Seventy-eight participants were randomized, and seventy-four of them received allocated treatment and provided complete data. Clinical outcomes from within groups resulted in significant improvements for both groups. BMI increased from baseline (CBT-YA 16.49; FT-YA 16.54) to post-treatment (CBT-YA 19.61; FT-YA 19.33) with high effect sizes. The rate of weight restoration was 64.9% in the CBT-YA group and 83.8% in the FT-YA group. The rate of recovery was 76% in both groups at post-treatment, and at follow-up, 89% and 81% had recovered in the CBT-YA and FT-YA groups respectively. Conclusions: Outpatient CBT-YA and FT-YA appear to be of benefit to young adults with AN in terms of weight restoration and reduced eating disorder and general psychopathology.
Manifold Preserving Adversarial Learning

How can we generate semantically meaningful adversarial examples? We propose to answer this question by restricting the search for adversarial examples in the low dimensional manifold of the data using variational autoencoders. First, we introduce a stochastic variational inference method to learn the manifold, in the presence of continuous latent variables, with minimal assumptions about the parametric form of the encoder network. Then, we apply Gram-Schmidt orthogonalization to partition the span of representative local points of the manifold, and use the basis vectors as directions along which we perturb the points with learned noise. In doing so, we encourage the perturbed points to remain in the manifold. Finally, we map these points onto the input space to generate adversarial examples. Experiments on a number of image and text classification tasks and a pilot study show the effectiveness of our approach in producing coherent adversarial examples capable of evading defenses known traditionally to be resilient to adversarial attacks.
A Hybrid Controller for Obstacle Avoidance in an $n$-dimensional Euclidean Space

For a vehicle moving in an n-dimensional Euclidean space, we present a construction of a hybrid feedback that guarantees both global asymptotic stabilization of a reference position and avoidance of an obstacle corresponding to a bounded spherical region. The proposed hybrid control algorithm switches between two modes of operation: stabilization (motion-to-goal) and avoidance (boundary-following). The geometric construction of the flow and jump sets of the hybrid controller, exploiting a hysteresis region, guarantees robust switching (chattering-free) between stabilization and avoidance. Simulation results illustrate the performance of the proposed hybrid control approach for a 3-dimensional scenario.
POSTER: GPU Based Near Data Processing for Image Processing with Pattern Aware Data Allocation and Prefetching

This paper proposes a GPU-based Near-data-processing (NDP) architecture as well as a well-matched programming model considering both the characteristics of image applications and NDP constraints. First, data allocation to the processing unit is handled to keep the data locality considering the memory access pattern. Second, this predictable allocation enables to design a compact but efficient NDP architecture. By applying a prefetcher that leverages the pattern aware data allocation, the number of active warps and on-chip SRAM size of NDP are significantly reduced. This allows to satisfy the NDP constraints and increases the opportunity to integrate more processing units on a memory logic die. The evaluation results for various image processing benchmarks show that the proposed NDP GPU improves the performance compared to the baseline GPU.