OpenRaiser/Intern-Atlas huggingface dataset:
The Exact Bipartite Matching Polytope Has Exponential Extension Complexity
Venue: arXiv cs.CC 2022
Year: 2022

Given a graph with edges colored red or blue and an integer $k$, the exact perfect matching problem asks if there exists a perfect matching with exactly $k$ red edges. There exists a randomized polylogarithmic-time parallel algorithm to solve this problem, dating back to the eighties, but no deterministic polynomial-time algorithm is known, even for bipartite graphs. In this paper we show that there is no sub-exponential sized linear program that can describe the convex hull of exact matchings in bipartite graphs. In fact, we prove something stronger, that there is no sub-exponential sized linear program to describe the convex hull of perfect matchings with an odd number of red edges.
Performance Comparison of DAOS and Lustre for Object Data Storage Approaches
Venue: arXiv cs.DC 2022
Year: 2022

High-performance object stores are an emerging technology which offers an alternative solution in the field of HPC storage, with potential to address long-standing scalability issues in traditional distributed POSIX file systems due to excessive consistency assurance and metadata prescriptiveness. In this paper we assess the performance of storing object-like data within a standard file system, where the configuration and access mechanisms have not been optimised for object access behaviour, and compare with and investigate the benefits of using an object storage system. Whilst this approach is not exploiting the file system in a standard way, this work allows us to investigate whether the underlying storage technology performance is more or less important than the software interface and infrastructure a file system or object store provides.
Efficient URLLC with a Reconfigurable Intelligent Surface and Imperfect Device Tracking
Venue: arXiv cs.IT 2022
Year: 2022

The use of Reconfigurable Intelligent Surfaces (RIS) technology to extend coverage and allow for better control of the wireless environment has been proposed in several use cases, including Ultra-Reliable Low-Latency Communications (URLLC), communications. However, the extremely challenging latency constraint makes explicit channel estimation difficult, so positioning information is often used to configure the RIS and illuminate the receiver device. In this work, we analyze the effect of imperfections in the positioning information on the reliability, deriving an upper bound to the outage probability. We then use this bound to perform power control, efficiently finding the minimum power that respects the URLLC constraints under positioning uncertainty. The optimization is conservative, so that all points respect the URLLC constraints, and the bound is relatively tight, with an optimality gap between 1.5 and 4.5~dB.
Improved Monotonicity Testers via Hypercube Embeddings
Venue: arXiv cs.CC 2022
Year: 2022

We show improved monotonicity testers for the Boolean hypercube under the $p$-biased measure, as well as over the hypergrid $[m]^n$. Our results are: 1. For any $p\in (0,1)$, for the $p$-biased hypercube we show a non-adaptive tester that makes $\tilde{O}(\sqrt{n}/\varepsilon^2)$ queries, accepts monotone functions with probability $1$ and rejects functions that are $\varepsilon$-far from monotone with probability at least $2/3$. 2. For all $m\in\mathbb{N}$, we show an $\tilde{O}(\sqrt{n}m^3/\varepsilon^2)$ query monotonicity tester over $[m]^n$. We also establish corresponding directed isoperimetric inequalities in these domains. Previously, the best known tester due to Black, Chakrabarty and Seshadhri had $Ω(n^{5/6})$ query complexity. Our results are optimal up to poly-logarithmic factors and the dependency on $m$. Our proof uses a notion of monotone embeddings of measures into the Boolean hypercube that can be used to reduce the problem of monotonicity testing over an arbitrary product domains to the Boolean cube. The embedding maps a function over a product domain of dimension $n$ into a function over a Boolean cube of a larger dimension $n'$, while preserving its distance from being monotone; an embedding is considered efficient if $n'$ is not much larger than $n$, and we show how to construct efficient embeddings in the above mentioned settings.
Prompt Tuning for Parameter-efficient Medical Image Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Neural networks pre-trained on a self-supervision scheme have become the standard when operating in data rich environments with scarce annotations. As such, fine-tuning a model to a downstream task in a parameter-efficient but effective way, e.g. for a new set of classes in the case of semantic segmentation, is of increasing importance. In this work, we propose and investigate several contributions to achieve a parameter-efficient but effective adaptation for semantic segmentation on two medical imaging datasets. Relying on the recently popularized prompt tuning approach, we provide a prompt-able UNet (PUNet) architecture, that is frozen after pre-training, but adaptable throughout the network by class-dependent learnable prompt tokens. We pre-train this architecture with a dedicated dense self-supervision scheme based on assignments to online generated prototypes (contrastive prototype assignment, CPA) of a student teacher combination alongside a concurrent segmentation loss on a subset of classes. We demonstrate that the resulting neural network model is able to attenuate the gap between fully fine-tuned and parameter-efficiently adapted models on CT imaging datasets. As such, the difference between fully fine-tuned and prompt-tuned variants amounts to only 3.83 pp for the TCIA/BTCV dataset and 2.67 pp for the CT-ORG dataset in the mean Dice Similarity Coefficient (DSC, in %) while only prompt tokens, corresponding to 0.85% of the pre-trained backbone model with 6.8M frozen parameters, are adjusted. The code for this work is available on https://github.com/marcdcfischer/PUNet .
Targeted Attention for Generalized- and Zero-Shot Learning
Venue: arXiv cs.CV 2022
Year: 2022

The Zero-Shot Learning (ZSL) task attempts to learn concepts without any labeled data. Unlike traditional classification/detection tasks, the evaluation environment is provided unseen classes never encountered during training. As such, it remains both challenging, and promising on a variety of fronts, including unsupervised concept learning, domain adaptation, and dataset drift detection. Recently, there have been a variety of approaches towards solving ZSL, including improved metric learning methods, transfer learning, combinations of semantic and image domains using, e.g. word vectors, and generative models to model the latent space of known classes to classify unseen classes. We find many approaches require intensive training augmentation with attributes or features that may be commonly unavailable (attribute-based learning) or susceptible to adversarial attacks (generative learning). We propose combining approaches from the related person re-identification task for ZSL, with key modifications to ensure sufficiently improved performance in the ZSL setting without the need for feature or training dataset augmentation. We are able to achieve state-of-the-art performance on the CUB200 and Cars196 datasets in the ZSL setting compared to recent works, with NMI (normalized mutual inference) of 63.27 and top-1 of 61.04 for CUB200, and NMI 66.03 with top-1 82.75% in Cars196. We also show state-of-the-art results in the Generalized Zero-Shot Learning (GZSL) setting, with Harmonic Mean R-1 of 66.14% on the CUB200 dataset.
Optimal Beamforming and Outage Analysis for Max Mean SNR under RIS-aided Communication
Venue: arXiv cs.IT 2022
Year: 2022

This paper considers beamforming for a reconfigurable intelligent surface (RIS)-aided multiple input single output (MISO) communication system in the presence of Rician multipath fading. Our aim is to jointly optimize the transmit beamformer and RIS phase shift matrix for maximizing the mean signal-to-noise (SNR) of the combined signal received over direct and indirect links. While numerical solutions are known for such optimization problems, this is the first paper to derive closed-form expressions for the optimal beamformer and the phase shifter for a closely related problem. In particular, we maximize a carefully constructed lower bound of the mean SNR, which is more conducive to analytical treatment. Further, we show that effective channel gain under optimal beamforming follows Rice distribution. Next, we use these results to characterize a closed-form expression for the outage probability under the proposed beamforming scheme, which is subsequently employed to derive an analytical expression for the ergodic capacity. Finally, we numerically demonstrate the efficacy of the proposed beamformer solution in comparison with the existing algorithmically obtained optimal solution for the exact mean SNR maximization.
Learning Domain and Pose Invariance for Thermal-to-Visible Face Recognition
Venue: arXiv cs.CV 2022
Year: 2022

Interest in thermal to visible face recognition has grown significantly over the last decade due to advancements in thermal infrared cameras and analytics beyond the visible spectrum. Despite large discrepancies between thermal and visible spectra, existing approaches bridge domain gaps by either synthesizing visible faces from thermal faces or by learning the cross-spectrum image representations. These approaches typically work well with frontal facial imagery collected at varying ranges and expressions, but exhibit significantly reduced performance when matching thermal faces with varying poses to frontal visible faces. We propose a novel Domain and Pose Invariant Framework that simultaneously learns domain and pose invariant representations. Our proposed framework is composed of modified networks for extracting the most correlated intermediate representations from off-pose thermal and frontal visible face imagery, a sub-network to jointly bridge domain and pose gaps, and a joint-loss function comprised of cross-spectrum and pose-correction losses. We demonstrate efficacy and advantages of the proposed method by evaluating on three thermal-visible datasets: ARL Visible-to-Thermal Face, ARL Multimodal Face, and Tufts Face. Although DPIF focuses on learning to match off-pose thermal to frontal visible faces, we also show that DPIF enhances performance when matching frontal thermal face images to frontal visible face images.
MDS and $I$-Perfect Codes in Pomset block Metric
Venue: arXiv cs.IT 2022
Year: 2022

In this paper, we establish the Singleton bound for pomset block codes ($(Pm,π)$-codes) of length $N$ over the ring $\mathbb{Z}_m$. We give a necessary condition for a code to be MDS in the pomset (block) metric and prove that every MDS $(Pm,π)$-code is an MDS $(P,π)$-code. Then we proceed on to find $I$-perfect and $r$-perfect codes. Further, given an ideal with partial and full counts, we look into how MDS and $I$-perfect codes relate to one another. For chain pomset, we obtain the duality theorem for pomset block codes of length $N$ over $\mathbb{Z}_m$; and, the weight distribution of MDS pomset block codes is then determined.
BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object Detection
Venue: arXiv cs.CV 2022
Year: 2022

3D object detection from multiple image views is a fundamental and challenging task for visual scene understanding. Owing to its low cost and high efficiency, multi-view 3D object detection has demonstrated promising application prospects. However, accurately detecting objects through perspective views is extremely difficult due to the lack of depth information. Current approaches tend to adopt heavy backbones for image encoders, making them inapplicable for real-world deployment. Different from the images, LiDAR points are superior in providing spatial cues, resulting in highly precise localization. In this paper, we explore the incorporation of LiDAR-based detectors for multi-view 3D object detection. Instead of directly training a depth prediction network, we unify the image and LiDAR features in the Bird-Eye-View (BEV) space and adaptively transfer knowledge across non-homogenous representations in a teacher-student paradigm. To this end, we propose \textbf{BEVDistill}, a cross-modal BEV knowledge distillation (KD) framework for multi-view 3D object detection. Extensive experiments demonstrate that the proposed method outperforms current KD approaches on a highly-competitive baseline, BEVFormer, without introducing any extra cost in the inference phase. Notably, our best model achieves 59.4 NDS on the nuScenes test leaderboard, achieving new state-of-the-art in comparison with various image-based detectors. Code will be available at https://github.com/zehuichen123/BEVDistill.
DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation
Venue: arXiv cs.RO 2022
Year: 2022

We propose a sim-to-real framework for dexterous manipulation which can generalize to new objects of the same category in the real world. The key of our framework is to train the manipulation policy with point cloud inputs and dexterous hands. We propose two new techniques to enable joint learning on multiple objects and sim-to-real generalization: (i) using imagined hand point clouds as augmented inputs; and (ii) designing novel contact-based rewards. We empirically evaluate our method using an Allegro Hand to grasp novel objects in both simulation and real world. To the best of our knowledge, this is the first policy learning-based framework that achieves such generalization results with dexterous hands. Our project page is available at https://yzqin.github.io/dexpoint
Machine Learning for Software Engineering: A Tertiary Study
Venue: arXiv cs.SE 2022
Year: 2022

Machine learning (ML) techniques increase the effectiveness of software engineering (SE) lifecycle activities. We systematically collected, quality-assessed, summarized, and categorized 83 reviews in ML for SE published between 2009-2022, covering 6,117 primary studies. The SE areas most tackled with ML are software quality and testing, while human-centered areas appear more challenging for ML. We propose a number of ML for SE research challenges and actions including: conducting further empirical validation and industrial studies on ML; reconsidering deficient SE methods; documenting and automating data collection and pipeline processes; reexamining how industrial practitioners distribute their proprietary data; and implementing incremental ML approaches.
Learning to Control Rapidly Changing Synaptic Connections: An Alternative Type of Memory in Sequence Processing Artificial Neural Networks
Venue: arXiv cs.NE 2022
Year: 2022

Short-term memory in standard, general-purpose, sequence-processing recurrent neural networks (RNNs) is stored as activations of nodes or "neurons." Generalising feedforward NNs to such RNNs is mathematically straightforward and natural, and even historical: already in 1943, McCulloch and Pitts proposed this as a surrogate to "synaptic modifications" (in effect, generalising the Lenz-Ising model, the first non-sequence processing RNN architecture of the 1920s). A lesser known alternative approach to storing short-term memory in "synaptic connections" -- by parameterising and controlling the dynamics of a context-sensitive time-varying weight matrix through another NN -- yields another "natural" type of short-term memory in sequence processing NNs: the Fast Weight Programmers (FWPs) of the early 1990s. FWPs have seen a recent revival as generic sequence processors, achieving competitive performance across various tasks. They are formally closely related to the now popular Transformers. Here we present them in the context of artificial NNs as an abstraction of biological NNs -- a perspective that has not been stressed enough in previous FWP work. We first review aspects of FWPs for pedagogical purposes, then discuss connections to related works motivated by insights from neuroscience.
Detecting Arbitrary Keypoints on Limbs and Skis with Sparse Partly Correct Segmentation Masks
Venue: arXiv cs.CV 2022
Year: 2022

Analyses based on the body posture are crucial for top-class athletes in many sports disciplines. If at all, coaches label only the most important keypoints, since manual annotations are very costly. This paper proposes a method to detect arbitrary keypoints on the limbs and skis of professional ski jumpers that requires a few, only partly correct segmentation masks during training. Our model is based on the Vision Transformer architecture with a special design for the input tokens to query for the desired keypoints. Since we use segmentation masks only to generate ground truth labels for the freely selectable keypoints, partly correct segmentation masks are sufficient for our training procedure. Hence, there is no need for costly hand-annotated segmentation masks. We analyze different training techniques for freely selected and standard keypoints, including pseudo labels, and show in our experiments that only a few partly correct segmentation masks are sufficient for learning to detect arbitrary keypoints on limbs and skis.
Towards Trace-based Deductive Verification (Tech Report)
Venue: arXiv cs.SE 2022
Year: 2022

Contracts specifying a procedure's behavior in terms of pre- and postconditions are essential for scalable software verification, but cannot express any constraints on the events occurring during execution of the procedure. This necessitates to annotate code with intermediate assertions, preventing full specification abstraction. We propose a logic over symbolic traces able to specify recursive procedures in a modular manner that refers to specified programs only in terms of events. We also provide a deduction system based on symbolic execution and induction that we prove to be sound relative to a trace semantics. Our work generalizes contract-based to trace-based deductive verification.
Attacking Digital Twins of Robotic Systems to Compromise Security and Safety
Venue: arXiv cs.RO 2022
Year: 2022

Security and safety are of paramount importance to human-robot interaction, either for autonomous robots or human-robot collaborative manufacturing. The intertwined relationship between security and safety has imposed new challenges on the emerging digital twin systems of various types of robots. To be specific, the attack of either the cyber-physical system or the digital-twin system could cause severe consequences to the other. Particularly, the attack of a digital-twin system that is synchronized with a cyber-physical system could cause lateral damage to humans and other surrounding facilities. This paper demonstrates that for Robot Operating System (ROS) driven systems, attacks such as the person-in-the-middle attack of the digital-twin system could eventually lead to a collapse of the cyber-physical system, whether it is an industrial robot or an autonomous mobile robot, causing unexpected consequences. We also discuss potential solutions to alleviate such attacks.
Hey ASR System! Why Aren't You More Inclusive? Automatic Speech Recognition Systems' Bias and Proposed Bias Mitigation Techniques. A Literature Review
Venue: arXiv cs.CL 2022
Year: 2022

Speech is the fundamental means of communication between humans. The advent of AI and sophisticated speech technologies have led to the rapid proliferation of human-to-computer-based interactions, fueled primarily by Automatic Speech Recognition (ASR) systems. ASR systems normally take human speech in the form of audio and convert it into words, but for some users, it cannot decode the speech, and any output text is filled with errors that are incomprehensible to the human reader. These systems do not work equally for everyone and actually hinder the productivity of some users. In this paper, we present research that addresses ASR biases against gender, race, and the sick and disabled, while exploring studies that propose ASR debiasing techniques for mitigating these discriminations. We also discuss techniques for designing a more accessible and inclusive ASR technology. For each approach surveyed, we also provide a summary of the investigation and methods applied, the ASR systems and corpora used, and the research findings, and highlight their strengths and/or weaknesses. Finally, we propose future opportunities for Natural Language Processing researchers to explore in the next level creation of ASR technologies.
Robust Downlink Multi-Antenna Beamforming with Heterogenous CSI: Enabling eMBB and URLLC Coexistence
Venue: arXiv cs.NI 2022
Year: 2022

Two of the main problems to achieve ultra-reliable low-latency communications (URLLC) are related to instantaneous channel state information (I-CSI) acquisition and the coexistence with other service modes such as enhanced mobile broadband (eMBB). The former comes from the non-negligible time required for accurate I-CSI acquisition, while the latter, from the heterogeneous and conflicting requirements of different nodes sharing the same network resources. In this paper, we leverage the I-CSI of multiple eMBB links and the channel measurement's history of a URLLC user for multi-antenna beamforming design. Specifically, we propose a precoding design that minimizes the transmit power of a base station (BS) providing eMBB and URLLC services with signal-to-interference-plus-noise ratio (SINR) and outage constraints, respectively, by modifying existing I-CSI-based precoding schemes to account for URLLC channel history information. Moreover, we illustrate and validate the proposed method by adopting zero-forcing (ZF) and the transmit power minimization (TPM) precoding with SINR constraints. We show that the ZF implementation outperforms TPM in adverse channel conditions as in Rayleigh fading, while the situation is rapidly reversed as the channel experiences some line-of-sight (LOS). Finally, we determine the confidence levels at which the target outage probabilities are reached. For instance, we show that outage probabilities below $10^{-3}$ are achievable with more than 99$\%$ confidence for both precoding schemes under favorable LOS conditions with 16 transmit antennas and 500 samples of URLLC channel history.
Spatial Graph Convolution Neural Networks for Water Distribution Systems
Venue: arXiv cs.LG 2022
Year: 2022

We investigate the task of missing value estimation in graphs as given by water distribution systems (WDS) based on sparse signals as a representative machine learning challenge in the domain of critical infrastructure. The underlying graphs have a comparably low node degree and high diameter, while information in the graph is globally relevant, hence graph neural networks face the challenge of long-term dependencies. We propose a specific architecture based on message passing which displays excellent results for a number of benchmark tasks in the WDS domain. Further, we investigate a multi-hop variation, which requires considerably less resources and opens an avenue towards big WDS graphs.