Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure Unbiased Adversarial Model for Medical Image Segmentation

Generative models have been widely proposed in image recognition to generate more images where the distribution is similar to that of the real ones. It often introduces a discriminator network to differentiate the real data from the generated ones. Such models utilise a discriminator network tasked with differentiating style transferred data from data contained in the target dataset. However in doing so the network focuses on discrepancies in the intensity distribution and may overlook structural differences between the datasets. In this paper we formulate a new image-to-image translation problem to ensure that the structure of the generated images is similar to that in the target dataset. We propose a simple, yet powerful Structure-Unbiased Adversarial (SUA) network which accounts for both intensity and structural differences between the training and test sets when performing image segmentation. It consists of a spatial transformation block followed by an intensity distribution rendering module. The spatial transformation block is proposed to reduce the structure gap between the two images, and also produce an inverse deformation field to warp the final segmented image back. The intensity distribution rendering module then renders the deformed structure to an image with the target intensity distribution. Experimental results show that the proposed SUA method has the capability to transfer both intensity distribution and structural content between multiple datasets.
Deep Dense Local Feature Matching and Vehicle Removal for Indoor Visual Localization

—Visual localization is an essential component of intelligent transportation systems, enabling broad applications that require understanding one’s self-location when other sensors are not available. It is mostly tackled by image retrieval such that the location of a query image is determined by its closest match in the pre-collected images. Existing approaches focus on large-scale localization where landmarks are helpful in ﬁnding the location. However, visual localization becomes challenging in small-scale environments where objects are hardly recognizable. In this paper, we propose a visual localization framework that robustly ﬁnds the match for a query among the images collected from indoor parking lots. It is a challenging problem when the vehicles in the images share similar appearances and are frequently replaced, e.g., parking lots. We propose to 1) employ a deep dense local feature matching that resembles human perception to ﬁnd correspondences and 2) eliminate matches from vehicles automatically with a vehicle detector. The proposed solution is robust to the scenes with low textures and invariant to false matches caused by vehicles. We compare our framework with alternatives to validate our superiority on a benchmark dataset containing 267 pre-collected images and 99 query images taken from 34 sections of a parking lot. Our method achieves 86.9% accuracy, outperforming the alternatives.
Gradient-based explanations for Gaussian Process regression and classification models

Gaussian Processes (GPs) have proven themselves as a reliable and effective method in probabilistic Machine Learning. Thanks to recent and current advances, modeling complex data with GPs is becoming more and more feasible. Thus, these types of models are, nowadays, an interesting alternative to Neural and Deep Learning methods, which are arguably the current state-of-the-art in Machine Learning. For the latter, we see an increasing interest in so-called explainable approaches - in essence methods that aim to make a Machine Learning model's decision process transparent to humans. Such methods are particularly needed when illogical or biased reasoning can lead to actual disadvantageous consequences for humans. Ideally, explainable Machine Learning should help detect such flaws in a model and aid a subsequent debugging process. One active line of research in Machine Learning explainability are gradient-based methods, which have been successfully applied to complex neural networks. Given that GPs are closed under differentiation, gradient-based explainability for GPs appears as a promising field of research. This paper is primarily focused on explaining GP classifiers via gradients where, contrary to GP regression, derivative GPs are not straightforward to obtain.
Constant Curvature Curve Tube Codes for Low-Latency Analog Error Correction

Recent research in ultra-reliable and low latency communications (URLLC) for future wireless systems has spurred interest in short block-length codes. In this context, we analyze arbitrary harmonic bandwidth (BW) expansions for a class of high-dimension constant curvature curve codes for analog error correction of independent continuous-alphabet uniform sources. In particular, we employ the circumradius function from knot theory to prescribe insulating tubes about the centerline of constant curvature curves. We then use tube packing density within a hypersphere to optimize the curve parameters. The resulting constant curvature curve tube (C3T) codes possess the smallest possible latency, i.e., block-length is unity under BW expansion mapping. Further, the codes perform within 5 dB signal-to-distortion ratio of the optimal performance theoretically achievable at a signal-to-noise ratio (SNR) <inline-formula> <tex-math notation="LaTeX">$ < -5$ </tex-math></inline-formula> dB for BW expansion factor <inline-formula> <tex-math notation="LaTeX">$n \leq 10$ </tex-math></inline-formula>. Furthermore, we propose a neural-network-based method to decode C3T codes. We show that, at low SNR, the neural-network-based C3T decoder outperforms the maximum likelihood and minimum mean-squared error decoders for all <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>. The best possible digital codes require two to three orders of magnitude higher latency compared to C3T codes, thereby demonstrating the latter’s utility for URLLC.
Towards More Realistic Generation of Information-Seeking Conversations

In this paper, we introduce a novel framework SimSeek ( sim ulating information-seek ing conversation from unlabeled documents) and compare two variants of it to provide a deeper perspective into the information-seeking behavior. We ﬁrst introduce a strong simulator for information-sym metric conversation, SimSeek-sym, where questioner and answerer share all knowledge when conversing with one another. Although it simulates reasonable conversations, we take a further step toward more realistic information-seeking conversation. Hence, we propose SimSeek-asym that assumes information asym metry between two agents, which encourages the questioner to seek new information from an inaccessible document. In our experiments, we demonstrate that SimSeek-asym successfully generates information-seeking conversations for two downstream tasks, CQA and conversational search. In particular, SimSeek-asym improves baseline models by 1.1-1.9 F1 score in QuAC (Choi et al., 2018), and by 1.1 of MRR in OR-QuAC (Qu et al., 2020). Moreover, we thoroughly analyze our synthetic datasets to identify crucial factors for realistic information-seeking conversation.
Real-time Visualization of Stream-based Monitoring Data

Stream-based runtime monitors are used in safety-critical applications such as Unmanned Aerial Systems (UAS) to compute comprehensive statistics and logical assessments of system health that provide the human operator with critical information in hand-over situations. In such applications, a visual display of the monitoring data can be much more helpful than the textual alerts provided by a more traditional user interface. This visualization requires extensive real-time data processing, which includes the synchronization of data from diﬀerent streams, ﬁltering and aggregation, and priorization and management of user attention. We present a visualization approach for the RTLola monitoring framework. Our approach is based on the principle that the necessary data processing is the responsibility of the monitor itself, rather than the responsibility of some external visualization tool. We show how the various aspects of the data transformation can be described as RTLola stream equations and linked to the visualization component through a bidirec-tional synchronous interface. In our experience, this approach leads to highly informative visualizations as well as to understandable and easily maintainable monitoring code.
Toward Discovering Options that Achieve Faster Planning

We propose a new objective for option discovery that emphasizes the computational advantage of using options in planning. In a sequential machine, the speed of planning is proportional to the number of elementary operations used to achieve a good policy. For episodic tasks, the number of elementary operations depends on the number of options composed by the policy in an episode and the number of options being considered at each decision point. To reduce the amount of computation in planning, for a given set of episodic tasks and a given number of options, our objective prefers options with which it is possible to achieve a high return by composing few options, and also prefers a smaller set of options to choose from at each decision point. We develop an algorithm that optimizes the proposed objective. In a variant of the classic four-room domain, we show that 1) a higher objective value is typically associated with fewer number of elementary planning operations used by the option-value iteration algorithm to obtain a near-optimal value function, 2) our algorithm achieves an objective value that matches it achieved by two human-designed options 3) the amount of computation used by option-value iteration with options discovered by our algorithm matches it with the human-designed options, 4) the options produced by our algorithm also make intuitive sense--they seem to move to and terminate at the entrances of rooms.
A Low Memory Footprint Quantized Neural Network for Depth Completion of Very Sparse Time-of-Flight Depth Maps

Sparse active illumination enables precise time-of-flight depth sensing as it maximizes signal-to-noise ratio for low power budgets. However, depth completion is required to produce dense depth maps for 3D perception. We address this task with realistic illumination and sensor resolution constraints by simulating ToF datasets for indoor 3D perception with challenging sparsity levels. We propose a quantized convolutional encoder-decoder network for this task. Our model achieves optimal depth map quality by means of input pre-processing and carefully tuned training with a geometry-preserving loss function. We also achieve low memory footprint for weights and activations by means of mixed precision quantization-at-training techniques. The resulting quantized models are comparable to the state of the art in terms of quality, but they require very low GPU times and achieve up to 14-fold memory size reduction for the weights w.r. t. their floating point counterpart with minimal impact on quality metrics.
A CNN with Noise Inclined Module and Denoise Framework for Hyperspectral Image Classification

Deep Neural Networks have been successfully applied in hyperspectral image classification. However, most of prior works adopt general deep architectures while ignore the intrinsic structure of the hyperspectral image, such as the physical noise generation. This would make these deep models unable to generate discriminative features and provide impressive classification performance. To leverage such intrinsic information, this work develops a novel deep learning framework with the noise inclined module and denoise framework for hyperspectral image classification. First, we model the spectral signature of hyperspectral image with the physical noise model to describe the high intraclass variance of each class and great overlapping between different classes in the image. Then, a noise inclined module is developed to capture the physical noise within each object and a denoise framework is then followed to remove such noise from the object. Finally, the CNN with noise inclined module and the denoise framework is developed to obtain discriminative features and provides good classification performance of hyperspectral image. Experiments are conducted over two commonly used real-world datasets and the experimental results show the effectiveness of the proposed method. The implementation of the proposed method and other compared methods could be accessed at https://github.com/shendu-sw/noise-physical-framework.
Searching for Quasi-periodic Oscillations in Astrophysical Transients Using Gaussian Processes

Analyses of quasi-periodic oscillations (QPOs) are important to understanding the dynamic behavior in many astrophysical objects during transient events like gamma-ray bursts, solar flares, magnetar flares, and fast radio bursts. Astrophysicists often search for QPOs with frequency-domain methods such as (Lomb–Scargle) periodograms, which generally assume power-law models plus some excess around the QPO frequency. Time-series data can alternatively be investigated directly in the time domain using Gaussian process (GP) regression. While GP regression is computationally expensive in the general case, the properties of astrophysical data and models allow fast likelihood strategies. Heteroscedasticity and nonstationarity in data have been shown to cause bias in periodogram-based analyses. GPs can take account of these properties. Using GPs, we model QPOs as a stochastic process on top of a deterministic flare shape. Using Bayesian inference, we demonstrate how to infer GP hyperparameters and assign them physical meaning, such as the QPO frequency. We also perform model selection between QPOs and alternative models such as red noise and show that this can be used to reliably find QPOs. This method is easily applicable to a variety of different astrophysical data sets. We demonstrate the use of this method on a range of short transients: a gamma-ray burst, a magnetar flare, a magnetar giant flare, and simulated solar flare data.
Recipe for a General, Powerful, Scalable Graph Transformer

We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks. Graph Transformers (GTs) have gained popularity in the field of graph representation learning with a variety of recent publications but they lack a common foundation about what constitutes a good positional or structural encoding, and what differentiates them. In this paper, we summarize the different types of encodings with a clearer definition and categorize them as being $\textit{local}$, $\textit{global}$ or $\textit{relative}$. The prior GTs are constrained to small graphs with a few hundred nodes, here we propose the first architecture with a complexity linear in the number of nodes and edges $O(N+E)$ by decoupling the local real-edge aggregation from the fully-connected Transformer. We argue that this decoupling does not negatively affect the expressivity, with our architecture being a universal function approximator on graphs. Our GPS recipe consists of choosing 3 main ingredients: (i) positional/structural encoding, (ii) local message-passing mechanism, and (iii) global attention mechanism. We provide a modular framework $\textit{GraphGPS}$ that supports multiple types of encodings and that provides efficiency and scalability both in small and large graphs. We test our architecture on 16 benchmarks and show highly competitive results in all of them, show-casing the empirical benefits gained by the modularity and the combination of different strategies.
Deniable Steganography

Steganography conceals the secret message into the cover media, generating a stego media which can be transmitted on public channels without drawing suspicion. As its countermeasure, steganalysis mainly aims to detect whether the secret message is hidden in a given media. Although the steganography techniques are improving constantly, the sophisticated steganalysis can always break a known steganographic method to some extent. With a stego media discovered, the adversary could find out the sender or receiver and coerce them to disclose the secret message, which we name as coercive attack in this paper. Inspired by the idea of deniable encryption, we build up the concepts of deniable steganography for the first time and discuss the feasible constructions for it. As an example, we propose a receiver-deniable steganographic scheme to deal with the receiver-side coercive attack using deep neural networks (DNN). Specifically, besides the real secret message, a piece of fake message is also embedded into the cover. On the receiver side, the real message can be extracted with an extraction module; while once the receiver has to surrender a piece of secret message under coercive attack, he can extract the fake message to deceive the adversary with another extraction module. Experiments demonstrate the scalability and sensitivity of the DNN-based receiver-deniable steganographic scheme.
Sparse*BERT: Sparse Models are Robust

Large Language Models have become the core architecture upon which most modern natu-ral language processing (NLP) systems build. These models can consistently deliver impressive accuracy and robustness across tasks and domains, but their high computational overhead can make inference difﬁcult and expensive. To make the usage of these models less costly recent work has explored leveraging structured and unstructured pruning , quantization , and distillation as ways to improve inference speed and decrease size. This paper studies how models pruned using Gradual Unstruc-tured Magnitude Pruning can transfer between domains and tasks. Our experimentation shows that models that are pruned during pretraining using general domain masked language models can transfer to novel domains and tasks without extensive hyperparameter exploration or specialized approaches. We demonstrate that our general sparse model Sparse*BERT can become SparseBioBERT simply by pretraining the compressed architecture on unstructured biomedical text. Moreover, we show that SparseBioBERT can match the quality of BioBERT with only 10% of the parameters.
A Survey of Graph-Theoretic Approaches for Analyzing the Resilience of Networked Control Systems

As the scale of networked control systems increases and interactions between different subsystems become more sophisticated, questions of the resilience of such networks increase in importance. The need to redefine classical system and control-theoretic notions using the language of graphs has recently started to gain attention as a fertile and important area of research. This paper presents an overview of graph-theoretic methods for analyzing the resilience of networked control systems. We discuss various distributed algorithms operating on networked systems and investigate their resilience against adversarial actions by looking at the structural properties of their underlying networks. We present graph-theoretic methods to quantify the attack impact, and reinterpret some system-theoretic notions of robustness from a graph-theoretic standpoint to mitigate the impact of the attacks. Moreover, we discuss miscellaneous problems in the security of networked control systems which use graph-theory as a tool in their analyses. We conclude by introducing some avenues for further research in this field.
Know Where You're Going: Meta-Learning for Parameter-Efficient Fine-tuning

A recent family of techniques, dubbed lightweight fine-tuning methods, facilitates parameter-efficient transfer learning by updating only a small set of additional parameters while keeping the parameters of the pretrained language model frozen. While proven to be an effective method, there are no existing studies on if and how such knowledge of the downstream fine-tuning approach should affect the pretraining stage. In this work, we show that taking the ultimate choice of fine-tuning method into consideration boosts the performance of parameter-efficient fine-tuning. By relying on optimization-based meta-learning using MAML with certain modifications for our distinct purpose, we prime the pretrained model specifically for parameter-efficient fine-tuning, resulting in gains of up to 1.7 points on cross-lingual NER fine-tuning. Our ablation settings and analyses further reveal that the tweaks we introduce in MAML are crucial for the attained gains.
On-Demand Redundancy Grouping: Selectable Soft-Error Tolerance for a Multicore Cluster

With the shrinking of technology nodes and the use of parallel processor clusters in hostile and critical environments, such as space, run-time faults caused by radiation are a serious cross-cutting concern, also impacting architectural design. This paper introduces an architectural approach to run-time configurable soft-error tolerance at the core level, augmenting a six-core open-source RISC-V cluster with a novel On-Demand Redundancy Grouping (ODRG) scheme. ODRG allows the cluster to operate either as two fault-tolerant cores, or six individual cores for high-performance, with limited overhead to switch between these modes during run-time. The ODRG unit adds less than 11% of a core's area for a three-core group, or a total of 1% of the cluster area, and shows negligible timing increase, which compares favorably to a commercial state-of-the-art implementation, and is $2.5\times$ faster in fault recovery re-synchronization. Furthermore, when redundancy is not necessary, the ODRG approach allows the redundant cores to be used for independent computation, allowing up to $2.96\times$ increase in performance for selected applications.
Efficient Textured Mesh Recovery from Multiple Views with Differentiable Rendering

Despite of the promising results on shape and color recovery using self-supervision, the multi-layer perceptrons-based methods usually costs hours to train the deep neural network due to the implicit surface representation. Moreover, it is quite computational intensive to render a single image, since a forward network inference is required for each pixel. To tackle these challenges, in this paper, we propose an efficient coarse-to-fine approach to recover the textured mesh from multi-view images. Specifically, we take advantage of a differentiable Poisson Solver to represent the shape, which is able to produce topology-agnostic and watertight surfaces. To account for the depth information, we optimize the shape geometry by minimizing the difference between the rendered mesh with the depth predicted by the learning-based multi-view stereo algorithm. In contrast to the implicit neural representation on shape and color, we introduce a physically based inverse rendering scheme to jointly estimate the lighting and reflectance of the objects, which is able to render the high resolution image at real-time. Additionally, we fine-tune the extracted mesh by inverse rendering to obtain the mesh with fine details and high fidelity image. We have conducted the extensive experiments on several multi-view stereo datasets, whose promising results demonstrate the efficacy of our proposed approach. We will make our full implementation publicly available.