OpenRaiser/Intern-Atlas huggingface dataset:
UNIDEAL: Curriculum Knowledge Distillation Federated Learning
Venue: arXiv cs.LG 2023
Year: 2023

Federated Learning (FL) has emerged as a promising approach to enable collaborative learning among multiple clients while preserving data privacy. However, cross-domain FL tasks, where clients possess data from different domains or distributions, remain a challenging problem due to the inherent heterogeneity. In this paper, we present UNIDEAL, a novel FL algorithm specifically designed to tackle the challenges of cross-domain scenarios and heterogeneous model architectures. The proposed method introduces Adjustable Teacher-Student Mutual Evaluation Curriculum Learning, which significantly enhances the effectiveness of knowledge distillation in FL settings. We conduct extensive experiments on various datasets, comparing UNIDEAL with state-of-the-art baselines. Our results demonstrate that UNIDEAL achieves superior performance in terms of both model accuracy and communication efficiency. Additionally, we provide a convergence analysis of the algorithm, showing a convergence rate of O(1/T) under non-convex conditions.
Differential Modulation for Short Packet Transmission in URLLC
Venue: arXiv cs.IT 2023
Year: 2023

One key feature of ultra-reliable low-latency communications (URLLC) in 5G is to support short packet transmission (SPT). However, the pilot overhead in SPT for channel estimation is relatively high, especially in high Doppler environments. In this paper, we advocate the adoption of differential modulation to support ultra-low latency services, which can ease the channel estimation burden and reduce the power and bandwidth overhead incurred in traditional coherent modulation schemes. Specifically, we consider a multi-connectivity (MC) scheme employing differential modulation to enable URLLC services. The popular selection combining and maximal ratio combining schemes are respectively applied to explore the diversity gain in the MC scheme. A first-order autoregressive model is further utilized to characterize the time-varying nature of the channel. Theoretically, the maximum achievable rate and minimum achievable block error rate under ergodic fading channels with PSK inputs and perfect CSI are first derived by using the non-asymptotic information-theoretic bounds. The performance of SPT with differential modulation and MC schemes is then analysed by characterizing the effect of differential modulation and time-varying channels as a reduction in the effective SNR. Simulation results show that differential modulation does offer a significant advantage over the pilot-assisted coherent scheme for SPT, especially in high Doppler environments.
Invertible Mosaic Image Hiding Network for Very Large Capacity Image Steganography
Venue: arXiv cs.MM 2023
Year: 2023

The existing image steganography methods either sequentially conceal secret images or conceal a concatenation of multiple images. In such ways, the interference of information among multiple images will become increasingly severe when the number of secret images becomes larger, thus restrict the development of very large capacity image steganography. In this paper, we propose an Invertible Mosaic Image Hiding Network (InvMIHNet) which realizes very large capacity image steganography with high quality by concealing a single mosaic secret image. InvMIHNet consists of an Invertible Image Rescaling (IIR) module and an Invertible Image Hiding (IIH) module. The IIR module works for downscaling the single mosaic secret image form by spatially splicing the multiple secret images, and the IIH module then conceal this mosaic image under the cover image. The proposed InvMIHNet successfully conceal and reveal up to 16 secret images with a small number of parameters and memory consumption. Extensive experiments on ImageNet-1K, COCO and DIV2K show InvMIHNet outperforms state-of-the-art methods in terms of both the imperceptibility of stego image and recover accuracy of secret image.
Efficient Object Rearrangement via Multi-view Fusion
Venue: arXiv cs.RO 2023
Year: 2023

The prospect of assistive robots aiding in object organization has always been compelling. In an image-goal setting, the robot rearranges the current scene to match the single image captured from the goal scene. The key to an image-goal rearrangement system is estimating the desired placement pose of each object based on the single goal image and observations from the current scene. In order to establish sufficient associations for accurate estimation, the system should observe an object from a viewpoint similar to that in the goal image. Existing image-goal rearrangement systems, due to their reliance on a fixed viewpoint for perception, often require redundant manipulations to randomly adjust an object's pose for a better perspective. Addressing this inefficiency, we introduce a novel object rearrangement system that employs multi-view fusion. By observing the current scene from multiple viewpoints before manipulating objects, our approach can estimate a more accurate pose without redundant manipulation times. A standard visual localization pipeline at the object level is developed to capitalize on the advantages of multi-view observations. Simulation results demonstrate that the efficiency of our system outperforms existing single-view systems. The effectiveness of our system is further validated in a physical experiment.
Bridging Dense and Sparse Maximum Inner Product Search
Venue: arXiv cs.IR 2023
Year: 2023

Maximum inner product search (MIPS) over dense and sparse vectors have progressed independently in a bifurcated literature for decades; the latter is better known as top-$k$ retrieval in Information Retrieval. This duality exists because sparse and dense vectors serve different end goals. That is despite the fact that they are manifestations of the same mathematical problem. In this work, we ask if algorithms for dense vectors could be applied effectively to sparse vectors, particularly those that violate the assumptions underlying top-$k$ retrieval methods. We study IVF-based retrieval where vectors are partitioned into clusters and only a fraction of clusters are searched during retrieval. We conduct a comprehensive analysis of dimensionality reduction for sparse vectors, and examine standard and spherical KMeans for partitioning. Our experiments demonstrate that IVF serves as an efficient solution for sparse MIPS. As byproducts, we identify two research opportunities and demonstrate their potential. First, we cast the IVF paradigm as a dynamic pruning technique and turn that insight into a novel organization of the inverted index for approximate MIPS for general sparse vectors. Second, we offer a unified regime for MIPS over vectors that have dense and sparse subspaces, and show its robustness to query distributions.
A store-and-forward cloud-based telemonitoring system for automatic assessing dysarthria evolution in neurological diseases from video-recording analysis
Venue: arXiv cs.AI 2023
Year: 2023

Background and objectives: Patients suffering from neurological diseases may develop dysarthria, a motor speech disorder affecting the execution of speech. Close and quantitative monitoring of dysarthria evolution is crucial for enabling clinicians to promptly implement patient management strategies and maximizing effectiveness and efficiency of communication functions in term of restoring, compensating or adjusting. In the clinical assessment of orofacial structures and functions, at rest condition or during speech and non-speech movements, a qualitative evaluation is usually performed, throughout visual observation. Methods: To overcome limitations posed by qualitative assessments, this work presents a store-and-forward self-service telemonitoring system that integrates, within its cloud architecture, a convolutional neural network (CNN) for analyzing video recordings acquired by individuals with dysarthria. This architecture, called facial landmark Mask RCNN, aims at locating facial landmarks as a prior for assessing the orofacial functions related to speech and examining dysarthria evolution in neurological diseases. Results: When tested on the Toronto NeuroFace dataset, a publicly available annotated dataset of video recordings from patients with amyotrophic lateral sclerosis (ALS) and stroke, the proposed CNN achieved a normalized mean error equal to 1.79 on localizing the facial landmarks. We also tested our system in a real-life scenario on 11 bulbar-onset ALS subjects, obtaining promising outcomes in terms of facial landmark position estimation. Discussion and conclusions: This preliminary study represents a relevant step towards the use of remote tools to support clinicians in monitoring the evolution of dysarthria.
Fast Triangle Counting
Venue: arXiv cs.DS 2023
Year: 2023

Listing and counting triangles in graphs is a key algorithmic kernel for network analyses including community detection, clustering coefficients, k-trusses, and triangle centrality. We design and implement a new serial algorithm for triangle counting that performs competitively with the fastest previous approaches on both real and synthetic graphs, such as those from the Graph500 Benchmark and the MIT/Amazon/IEEE Graph Challenge. The experimental results use the recently-launched Intel Xeon Platinum 8480+ and CPU Max 9480 processors.
Recovering Missing Node Features with Local Structure-based Embeddings
Venue: arXiv cs.LG 2023
Year: 2023

Node features bolster graph-based learning when exploited jointly with network structure. However, a lack of nodal attributes is prevalent in graph data. We present a framework to recover completely missing node features for a set of graphs, where we only know the signals of a subset of graphs. Our approach incorporates prior information from both graph topology and existing nodal values. We demonstrate an example implementation of our framework where we assume that node features depend on local graph structure. Missing nodal values are estimated by aggregating known features from the most similar nodes. Similarity is measured through a node embedding space that preserves local topological features, which we train using a Graph AutoEncoder. We empirically show not only the accuracy of our feature estimation approach but also its value for downstream graph classification. Our success embarks on and implies the need to emphasize the relationship between node features and graph structure in graph-based learning.
Comparative evaluation of bandwidth-bound applications on the Intel Xeon CPU MAX Series
Venue: arXiv cs.PF 2023
Year: 2023

In this paper we explore the performance of Intel Xeon MAX CPU Series, representing the most significant new variation upon the classical CPU architecture since the Intel Xeon Phi Processor. Given the availability of a large on-package high-bandwidth memory, the bandwidth-to-compute ratio has significantly shifted compared to other CPUs on the market. Since a large fraction of HPC workloads are sensitive to the available bandwidth, we explore how this architecture performs on a selection of HPC proxies and applications that are mostly sensitive to bandwidth, and how it compares to the previous 3rd generation Intel Xeon Scalable processors (codenamed Ice Lake) and an AMD EPYC 7003 Series Processor with 3D V-Cache Technology (codenamed Milan-X). We explore performance with different parallel implementations (MPI, MPI+OpenMP, MPI+SYCL), compiled with different compilers and flags, and executed with or without hyperthreading. We show how performance bottlenecks are shifted from bandwidth to communication latencies for some applications, and demonstrate speedups compared to the previous generation between 2.0x-4.3x.
Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions
Venue: arXiv cs.NI 2023
Year: 2023

Sixth-generation (6G) networks anticipate intelligently supporting a wide range of smart services and innovative applications. Such a context urges a heavy usage of Machine Learning (ML) techniques, particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent network functions/operations, which are able to fulfill the various requirements of the envisioned 6G services. Specifically, collaborative ML/DL consists of deploying a set of distributed agents that collaboratively train learning models without sharing their data, thus improving data privacy and reducing the time/communication overhead. This work provides a comprehensive study on how collaborative learning can be effectively deployed over 6G wireless networks. In particular, our study focuses on Split Federated Learning (SFL), a technique recently emerged promising better performance compared with existing collaborative learning approaches. We first provide an overview of three emerging collaborative learning paradigms, including federated learning, split learning, and split federated learning, as well as of 6G networks along with their main vision and timeline of key developments. We then highlight the need for split federated learning towards the upcoming 6G networks in every aspect, including 6G technologies (e.g., intelligent physical layer, intelligent edge computing, zero-touch network management, intelligent resource management) and 6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous systems). Furthermore, we review existing datasets along with frameworks that can help in implementing SFL for 6G networks. We finally identify key technical challenges, open issues, and future research directions related to SFL-enabled 6G networks.
Btor2MLIR: A Format and Toolchain for Hardware Verification
Venue: arXiv cs.LO 2023
Year: 2023

Formats for representing and manipulating verification problems are extremely important for supporting the ecosystem of tools, developers, and practitioners. A good format allows representing many different types of problems, has a strong toolchain for manipulating and translating problems, and can grow with the community. In the world of hardware verification, and, specifically, the Hardware Model Checking Competition (HWMCC), the Btor2 format has emerged as the dominating format. It is supported by Btor2Tools, verification tools, and Verilog design tools like Yosys. In this paper, we present an alternative format and toolchain, called Btor2MLIR, based on the recent MLIR framework. The advantage of Btor2MLIR is in reusing existing components from a mature compiler infrastructure, including parsers, text and binary formats, converters to a variety of intermediate representations, and executable semantics of LLVM. We hope that the format and our tooling will lead to rapid prototyping of verification and related tools for hardware verification.
FDCNet: Feature Drift Compensation Network for Class-Incremental Weakly Supervised Object Localization
Venue: arXiv cs.CV 2023
Year: 2023

This work addresses the task of class-incremental weakly supervised object localization (CI-WSOL). The goal is to incrementally learn object localization for novel classes using only image-level annotations while retaining the ability to localize previously learned classes. This task is important because annotating bounding boxes for every new incoming data is expensive, although object localization is crucial in various applications. To the best of our knowledge, we are the first to address this task. Thus, we first present a strong baseline method for CI-WSOL by adapting the strategies of class-incremental classifiers to mitigate catastrophic forgetting. These strategies include applying knowledge distillation, maintaining a small data set from previous tasks, and using cosine normalization. We then propose the feature drift compensation network to compensate for the effects of feature drifts on class scores and localization maps. Since updating network parameters to learn new tasks causes feature drifts, compensating for the final outputs is necessary. Finally, we evaluate our proposed method by conducting experiments on two publicly available datasets (ImageNet-100 and CUB-200). The experimental results demonstrate that the proposed method outperforms other baseline methods.
Rely-guarantee Reasoning about Concurrent Reactive Systems: The PiCore Framework, Languages Integration and Applications
Venue: arXiv cs.SE 2023
Year: 2023

The rely-guarantee approach is a promising way for compositional verification of concurrent reactive systems (CRSs), e.g. concurrent operating systems, interrupt-driven control systems and business process systems. However, specifications using heterogeneous reaction patterns, different abstraction levels, and the complexity of real-world CRSs are still challenging the rely-guarantee approach. This article proposes PiCore, a rely-guarantee reasoning framework for formal specification and verification of CRSs. We design an event specification language supporting complex reaction structures and its rely-guarantee proof system to detach the specification and logic of reactive aspects of CRSs from event behaviours. PiCore parametrizes the language and its rely-guarantee system for event behaviour using a rely-guarantee interface and allows to easily integrate 3rd-party languages via rely-guarantee adapters. By this design, we have successfully integrated two existing languages and their rely-guarantee proof systems without any change of their specification and proofs. PiCore has been applied to two real-world case studies, i.e. formal verification of concurrent memory management in Zephyr RTOS and a verified translation for a standardized Business Process Execution Language (BPEL) to PiCore.
CLIPUNetr: Assisting Human-robot Interface for Uncalibrated Visual Servoing Control with CLIP-driven Referring Expression Segmentation
Venue: arXiv cs.RO 2023
Year: 2023

The classical human-robot interface in uncalibrated image-based visual servoing (UIBVS) relies on either human annotations or semantic segmentation with categorical labels. Both methods fail to match natural human communication and convey rich semantics in manipulation tasks as effectively as natural language expressions. In this paper, we tackle this problem by using referring expression segmentation, which is a prompt-based approach, to provide more in-depth information for robot perception. To generate high-quality segmentation predictions from referring expressions, we propose CLIPUNetr - a new CLIP-driven referring expression segmentation network. CLIPUNetr leverages CLIP's strong vision-language representations to segment regions from referring expressions, while utilizing its ``U-shaped'' encoder-decoder architecture to generate predictions with sharper boundaries and finer structures. Furthermore, we propose a new pipeline to integrate CLIPUNetr into UIBVS and apply it to control robots in real-world environments. In experiments, our method improves boundary and structure measurements by an average of 120% and can successfully assist real-world UIBVS control in an unstructured manipulation environment.
Shuffling posets on trajectories (technical report)
Venue: arXiv cs.FL 2023
Year: 2023

Choreographies describe possible sequences of interactions among a set of agents. We aim to join two lines of research on choreographies: the use of the shuffle on trajectories operator to design more expressive choreographic languages, and the use of models featuring partial orders, to compactly represent concurrency between agents. Specifically, in this paper, we explore the application of the shuffle on trajectories operator to individual posets, and we give a characterisation of shuffles of posets which again yield an individual poset.
End-to-End Optimized Pipeline for Prediction of Protein Folding Kinetics
Venue: arXiv cs.LG 2023
Year: 2023

Protein folding is the intricate process by which a linear sequence of amino acids self-assembles into a unique three-dimensional structure. Protein folding kinetics is the study of pathways and time-dependent mechanisms a protein undergoes when it folds. Understanding protein kinetics is essential as a protein needs to fold correctly for it to perform its biological functions optimally, and a misfolded protein can sometimes be contorted into shapes that are not ideal for a cellular environment giving rise to many degenerative, neuro-degenerative disorders and amyloid diseases. Monitoring at-risk individuals and detecting protein discrepancies in a protein's folding kinetics at the early stages could majorly result in public health benefits, as preventive measures can be taken. This research proposes an efficient pipeline for predicting protein folding kinetics with high accuracy and low memory footprint. The deployed machine learning (ML) model outperformed the state-of-the-art ML models by 4.8% in terms of accuracy while consuming 327x lesser memory and being 7.3% faster.
MFRL-BI: Design of a Model-free Reinforcement Learning Process Control Scheme by Using Bayesian Inference
Venue: arXiv cs.LG 2023
Year: 2023

Design of process control scheme is critical for quality assurance to reduce variations in manufacturing systems. Taking semiconductor manufacturing as an example, extensive literature focuses on control optimization based on certain process models (usually linear models), which are obtained by experiments before a manufacturing process starts. However, in real applications, pre-defined models may not be accurate, especially for a complex manufacturing system. To tackle model inaccuracy, we propose a model-free reinforcement learning (MFRL) approach to conduct experiments and optimize control simultaneously according to real-time data. Specifically, we design a novel MFRL control scheme by updating the distribution of disturbances using Bayesian inference to reduce their large variations during manufacturing processes. As a result, the proposed MFRL controller is demonstrated to perform well in a nonlinear chemical mechanical planarization (CMP) process when the process model is unknown. Theoretical properties are also guaranteed when disturbances are additive. The numerical studies also demonstrate the effectiveness and efficiency of our methodology.
A Zigbee Based Cost-Effective Home Monitoring System Using WSN
Venue: arXiv cs.NI 2023
Year: 2023

WSNs are vital in a variety of applications, including environmental monitoring, industrial process control, and healthcare. WSNs are a network of spatially scattered and dedicated sensors that monitor and record the physical conditions of the environment.Significant obstacles to WSN efficiency include the restricted power and processing capabilities of individual sensor nodes and the issues with remote and inaccessible deployment sites. By maximising power utilisation, enhancing network effectiveness, and ensuring adaptability and durability through dispersed and decentralised operation, this study suggests a comprehensive approach to dealing with these challenges. The suggested methodology involves data compression, aggregation, and energy-efficient protocol. Using these techniques, WSN lifetimes can be increased and overall performance can be improved. In this study we also provide methods to collect data generated by several nodes in the WSN and store it in a remote cloud such that it can be processed and analyzed whenever it is required.