Reviewerly/exHarmony huggingface dataset:
SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities

Multi-modal large language models are regarded as a crucial step towards Artificial General Intelligence (AGI) and have garnered significant interest with the emergence of ChatGPT. However, current speech-language models typically adopt the cascade paradigm, preventing inter-modal knowledge transfer. In this paper, we propose SpeechGPT, a large language model with intrinsic cross-modal conversational abilities, capable of perceiving and generating multi-modal content. With discrete speech representations, we construct SpeechInstruct, the first large-scale cross-modal speech instruction dataset. Additionally, we employ a three-stage training strategy that includes modality-adaptation pre-training, cross-modal instruction fine-tuning, and chain-of-modality instruction fine-tuning. The experimental results demonstrate that SpeechGPT has an impressive capacity to follow cross-modal human instructions and highlight the potential of handling multiple modalities with one model. Code and models are available in https://github.com/0nutation/SpeechGPT. Demos are shown in https://0nutation.github.io/SpeechGPT.github.io/.
Statistical Phase Estimation and Error Mitigation on a Superconducting Quantum Processor

Quantum phase estimation (QPE) is a key quantum algorithm, which has been widely studied as a method to perform chemistry and solid-state calculations on future fault-tolerant quantum computers. Recently, several authors have proposed statistical alternatives to QPE that have benefits on early fault-tolerant devices, including shorter circuits and better suitability for error-mitigation techniques. However, experimental investigations of the algorithm on real quantum processors are lacking. Here, we implement statistical phase estimation on Rigetti’s superconducting processors. Specifically, we use a modification of the Lin and Tong [PRX Quantum 3, 010318 (2022)] algorithm with the improved Fourier approximation of Wan et al. [Phys. Rev. Lett. 129, 030503 (2022)] and apply a variational-compilation technique to reduce the circuit depth. We then incorporate error-mitigation strategies including zero-noise extrapolation and readout-error mitigation with bit-flip averaging. We propose a new method to estimate energies from the statistical phase estimation data, which is found to improve the accuracy in the final energy estimates by 1–2 orders of magnitude with respect to prior theoretical bounds, reducing the cost of performing accurate phase-estimation calculations. We apply these methods to chemistry problems for active spaces up to four electrons in four orbitals, including the application of a quantum embedding method, and use them to correctly estimate energies within chemical precision. Our work demonstrates that statistical phase estimation has a natural resilience to noise, particularly after mitigating coherent errors, and can achieve far higher accuracy than suggested by previous analysis, demonstrating its potential as a valuable quantum algorithm for early fault-tolerant devices.6 MoreReceived 13 April 2023Revised 14 June 2023Accepted 23 October 2023DOI:https://doi.org/10.1103/PRXQuantum.4.040341Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum algorithmsQuantum computationQuantum simulationPhysical SystemsMoleculesStrongly correlated systemsTechniquesElectron-correlation calculationsQuantum Information, Science & TechnologyCondensed Matter, Materials & Applied Physics
Chaotic Multiple-Image Encryption Algorithm Based on Block Scrambling and Dynamic DNA Coding

In this paper, a chaotic multiple-image encryption algorithm using block scrambling and dynamic DNA coding is designed. The algorithm can achieve simultaneous encryption of many different types of images. Firstly, a new block scrambling algorithm is proposed, which is divided into two scrambling modes according to the size of the initial block. And it combines with the cyclic shift operation to achieve scrambling. Secondly, dynamic DNA coding is utilized to diffuse images, which enhances the complexity and security of the proposed multiple-image encryption algorithm. Through the analysis of key space, correlation, information entropy, histogram, differential attacks and robustness, it is verified that the algorithm is safe and effective. Experimental results show that the new multiple-image encryption algorithm is suitable for multiple color and gray images encryption. The algorithm has excellent encryption performance, and can be applied in secure communication.
Diffusion Model as Representation Learner

Diffusion Probabilistic Models (DPMs) have recently demonstrated impressive results on various generative tasks. Despite its promises, the learned representations of pre-trained DPMs, however, have not been fully understood. In this paper, we conduct an in-depth investigation of the representation power of DPMs, and propose a novel knowledge transfer method that leverages the knowledge acquired by generative DPMs for recognition tasks. Our study begins by examining the feature space of DPMs, revealing that DPMs are inherently denoising autoencoders that balance the representation learning with regularizing model capacity. To this end, we introduce a novel knowledge transfer paradigm named RepFusion. Our paradigm extracts representations at different time steps from off-the-shelf DPMs and dynamically employs them as supervision for student networks, in which the optimal time is determined through reinforcement learning. We evaluate our approach on several image classification, semantic segmentation, and landmark detection benchmarks, and demonstrate that it outperforms state-of-the-art methods. Our results uncover the potential of DPMs as a powerful tool for representation learning and provide insights into the usefulness of generative models beyond sample generation. The code is available at https://github.com/Adamdad/Repfusion.
SRFormer: Permuted Self-Attention for Single Image Super-Resolution

Previous works have shown that increasing the window size for Transformer-based image super-resolution models (e.g., SwinIR) can significantly improve the model performance but the computation overhead is also considerable. In this paper, we present SRFormer, a simple but novel method that can enjoy the benefit of large window self-attention but introduces even less computational burden. The core of our SRFormer is the permuted self-attention (PSA), which strikes an appropriate balance between the channel and spatial information for self-attention. Our PSA is simple and can be easily applied to existing super-resolution networks based on window self-attention. Without any bells and whistles, we show that our SRFormer achieves a 33.86dB PSNR score on the Urban100 dataset, which is 0.46dB higher than that of SwinIR but uses fewer parameters and computations. We hope our simple and effective approach can serve as a useful tool for future research in super-resolution model design. Our code is available at https://github.com/HVision-NKU/SRFormer.
GlueStick: Robust Image Matching by Sticking Points and Lines Together

Line segments are powerful features complementary to points. They offer structural cues, robust to drastic viewpoint and illumination changes, and can be present even in texture-less areas. However, describing and matching them is more challenging compared to points due to partial occlusions, lack of texture, or repetitiveness. This paper introduces a new matching paradigm, where points, lines, and their descriptors are unified into a single wireframe structure. We propose GlueStick, a deep matching Graph Neural Network (GNN) that takes two wireframes from different images and leverages the connectivity information between nodes to better glue them together. In addition to the increased efficiency brought by the joint matching, we also demonstrate a large boost of performance when leveraging the complementary nature of these two features in a single architecture. We show that our matching strategy outperforms the state-of-the-art approaches independently matching line segments and points for a wide variety of datasets and tasks. The code is available at https://github.com/cvg/GlueStick.
Structure and Content-Guided Video Synthesis with Diffusion Models

Text-guided generative diffusion models unlock powerful image creation and editing tools. Recent approaches that edit the content of footage while retaining structure require expensive re-training for every input or rely on error-prone propagation of image edits across frames.In this work, we present a structure and content-guided video diffusion model that edits videos based on descriptions of the desired output. Conflicts between user-provided content edits and structure representations occur due to insufficient disentanglement between the two aspects. As a solution, we show that training on monocular depth estimates with varying levels of detail provides control over structure and content fidelity. A novel guidance method, enabled by joint video and image training, exposes explicit control over temporal consistency. Our experiments demonstrate a wide variety of successes; fine-grained control over output characteristics, customization based on a few reference images, and a strong user preference towards results by our model.
Tracking Anything with Decoupled Video Segmentation

Training data for video segmentation are expensive to annotate. This impedes extensions of end-to-end algorithms to new video segmentation tasks, especially in large-vocabulary settings. To ‘track anything’ without training on video data for every individual task, we develop a decoupled video segmentation approach (DEVA), composed of task-specific image-level segmentation and class/task-agnostic bi-directional temporal propagation. Due to this design, we only need an image-level model for the target task (which is cheaper to train) and a universal temporal propagation model which is trained once and generalizes across tasks. To effectively combine these two modules, we use bi-directional propagation for (semi-)online fusion of segmentation hypotheses from different frames to generate a coherent segmentation. We show that this decoupled formulation compares favorably to end-to-end approaches in several data-scarce tasks including large-vocabulary video panoptic segmentation, open-world video segmentation, referring video segmentation, and unsupervised video object segmentation. Code is available at: hkchengrex.github.io/Tracking-Anything-with-DEVA.
IIP-Transformer: Intra-Inter-Part Transformer for Skeleton-Based Action Recognition

Recently, body part as an intuitive movement unit has received increasing attention in skeleton-based action. However, the part-level embedding is hard to be fully exploited, especially for fine-grained actions, as the body joints are aggregated into parts. To address this problem, we propose a novel transformer-based network (IIP-Transformer). Different from previous models that rely on specially designed partition method, our proposed IIPA mechanism which incorporates joint-level (intra-part) and part-level (inter-part) interactions simultaneously is the keypoint for IIP-Transformer to fully exploit part-level data, making considerable improvements in both coarse-grained and fine-grained action recognition. Ablation studies on three typical partition methods show that IIP-Transformer is a relatively general solution for part-level data and thus we could choose the simplest hand-craft partition embedding to significantly reduce computational complexity and model size. Besides, The proposed IIP-Transformer exceeds the state-of-the-art methods with much less computational complexity on NTU RGB+D, NTU RGB+D120 and NW-UCLA datasets.
Multiple Processes in Graph-based Reasoning

Current models of graph understanding typically address the encoding and interpretive processes involved during the course of comprehension and largely focus on the visual properties of the graph. An experiment comparing reasoning with two types of graph is presented. On the basis and scope of existing models, performance with the two graphs would not be predicted to differ substantially. There are substantial computational differences between the graphs, however. It is suggested, therefore, that an adequate model of graph use must incorporate different combinations of visual properties of the graphs, levels of graph complexity, interpretive schemas and task requirements.
The Importance of Play in Digital Placemaking

In this position paper, I argue for the importance of play when designing systems for digital placemaking. I offer examples of existing placemaking systems that embody the openness prescribed by ludic design and provide an idea that could be further developed during the workshop: digital graffiti.
Topology Adaptive Graph Estimation in High Dimensions

We introduce Graphical TREX (GTREX), a novel method for graph estimation in high-dimensional Gaussian graphical models. By conducting neighborhood selection with TREX, GTREX avoids tuning parameters and is adaptive to the graph topology. We compared GTREX with standard methods on a new simulation setup that was designed to assess accurately the strengths and shortcomings of different methods. These simulations showed that a neighborhood selection scheme based on Lasso and an optimal (in practice unknown) tuning parameter outperformed other standard methods over a large spectrum of scenarios. Moreover, we show that GTREX can rival this scheme and, therefore, can provide competitive graph estimation without the need for tuning parameter calibration.
Automatic Radiocarbon Calibration: Illustrative Examples

A PC-based computer program for automatic calibration of radiocarbon dates has been developed. It uses a calibration curve, generated along the calibration data points as published in the Trondheim (12th Radiocarbon Conference) proceedings. The program produces a calendar age probability distribution. Well-chosen example calibrations are discussed to aid the non-specialist in interpretation of the obtained probability distributions.
The Center for Research Libraries and Cooperative Collection Development: Partnerships in Progress

The environment for cooperative collection efforts has significantly altered in the last five years due to online public access catalogs, technology changes, and economic realities. One of the most effective mechanisms for establishing a cooperative collection agenda including providing access to a much richer bank of resources, making the most economical use of funds, and setting a shared political agenda is the establishment of library consortia. The changes that have made this a viable model are considered in light of the role of the Center for Research Libraries (CRL) as a partner in the cooperative collection development agenda.
Corrections to “An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System” [Apr 12 224-234]

In the above-named work (IEEE Trans. Fuzzy Syst., vol. 20, no. 2, pp. 224-234, Apr. 2012), the branch name of Islamic Azad University in the affiliation of R. Hosseini and M. Mazinani appeared incorrectly. It should have read as the "Shahr-e-Qods Branch."
Look in, Turn on, Drop out

The Attention-Responsive Technology (ART) research project is developing a system to enable mobility-impaired individuals to access technology efficiently. Users of the ART system are targeted as being anyone with mobility problems or a mobile person in an environment where, for some reason, they are restricted in their physical movements. The ART eye movement system must be capable of accurately identifying where the user is looking so that the appropriate object is selected. Clearly it is essential to know which ICT objects are important to the specific user as well as knowing which ICT objects potential users currently use and which they would like to additionally use through implementing the ART system. The ART system is just in its second year of a three year development process and progress to date has concentrated upon finding state of the art technical solutions to the overall research problem.
INTRODUCTION TO CLOUD COMPUTING

This paper describes cloud computing, its main characteristics and the models that are currently used for both deployment and delivery. It examines the benefits and business issues with using the cloud, and how they can be addressed. It describes some of the early adapters of cloud computing, together with their experiences.
Information Visualization Techniques for Exploring Oil Well Trajectories in Reservoir Models

We present a set of interactive 3D visualizations, designed to explore oil/gas reservoir simulation post-processing models. With these visualizations we aim to provide reservoir engineers with better access to the data within their 3D models. We provide techniques for exploring existing oil well trajectories, and for planning future wells, to assist in decision making. Our approach focuses on designing visualization techniques that present the necessary details using concepts from information visualization. We created three new visualization variations - lollipop-up, information circles and path indicator, which present well trajectory specific information in different visual formats. Our paper describes these visualizations and discusses them in context of our exploratory evaluation.
A Model Curriculum For Service Systems Engineering

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract A Model Curriculum for Service Systems Engineering Abstract Over the past 100 years, the US economy has evolved from one based primarily in the goods- producing sector (agriculture, manufacturing, and mining) to the service sector. Today the service sector accounts for more than 80% of US Gross Domestic Product and more than 85% of the workforce. In fact, today many engineering graduates go on to work in service sector industries instead of more traditional manufacturing industries. In part, the service sector may be such a large segment of our economy because its processes are highly inefficient. Engineering problem-solving and talent, if properly applied to processes in the service sector, could serve to significantly increase efficiency and reduce costs, similar to advances made in the goods- producing sector over the past century. In 2003 Michigan Tech received a planning grant from the National Science Foundation to define curricular characteristics for Service Sector Engineering through a Delphi Study. Armed with these characteristics, a workshop was convened at Michigan Tech in August 2006 to define a Service Systems Engineering curriculum. Workshop participants consisted of faculty from several universities as well as industry leaders interested in engineering for the service sector. An additional grant from NSF’s Course Curriculum and Laboratory Improvement (CCLI) program was recently awarded for the implementation of this curriculum. Introduction We are living in a service economy and our educational system has failed to keep pace to provide employers with graduates meeting the demands of the service sector. Over 80% of the economic activity in the United States is within the service sector1 with similar high rates in other developed countries.2 This sector is also growing; between 2000 and 2005 employment in this sector grew by 2.7 million while employment in the other sectors shrank by 2.1 million.3 A recent report by the National Academy of Engineering4 described how innovation and productivity gains in service industries positively affect the whole economy, but the academic community could do more to meet the needs of service businesses. They indicate that the needs of the service industry could be better meet by adapting Industrial Engineering concepts and methodologies to services. The National Science Foundation started the Service Enterprise Engineering program in 2002 to focus on the “design, planning and control of operations and processes in commercial and institutional service enterprises.”5 Universities are beginning to respond by creating educational programs to address the design needs of service enterprises, mostly at the graduate level.6,7 This paper describes the development at Michigan Tech of the first undergraduate degree program to address the unique engineering needs of the service economy.
Review of Thomson &amp; Martinet (198O): A Practical English Grammar

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A Note on Algebraic Semantics for S5 with Propositional Quantifiers

In two of the earliest papers on extending modal logic with propositional quantifiers, R. A. Bull and K. Fine studied a modal logic S5Π extending S5 with axioms and rules for propositional quantification. Surprisingly, there seems to have been no proof in the literature of the completeness of S5Π with respect to its most natural algebraic semantics, with propositional quantifiers interpreted by meets and joins over all elements in a complete Boolean algebra. In this note, we give such a proof. This result raises the question: For which normal modal logics L can one axiomatize the quantified propositional modal logic determined by the complete modal algebras for L?
Pushing the Limits Further: Sub-Atomic AES

The recent trend to connect a plethora of sensors, embedded and ubiquitous systems with low computing power, in short the rise of the Internet of Things, has created a great demand for compact, lightweight and cheap to produce implementations of cryptographic primitives. One approach to meet this demand is the development and standardisation of new tailored primitives, most prominently PRESENT. Yet, the wide proliferation of the Advanced Encryption Standard and the trust it earned through its long history of withstanding cryptanalysis spurred anew the search for small, lightweight implementations of AES. Among the smallest published architectures is the AtomicAES design by Banik et al., who reported a design size of just over 2000 GE. Here we present a new 8-bit serial architecture that has been designed from careful observation of the minimum required connections between storage elements to support all dataflows required for execution of the algorithm. While we reach similar conclusions to previous publications, the new architecture enables us to push the area requirement for a fully featured AES primitive further down by more than 8% from the area requirement of AtomicAES while offering more functionality. Along the way we also answer in the affirmative the open question whether the AES reverse keyschedule can be implemented with negligible hardware overhead based on the forward keyschedule. Our design sets a new record for an 8-bit serial architecture with full functionality for encryption and decryption including the keyschedule, as well as for a sole encryption architecture. Furthermore our design is flexible enough to allow scaling the S-Box architecture from single-cycle to multi-stage pipelined approaches as are required for high operation frequencies or for protection against side-channel attacks. We demonstrate this by instantiating the design with a serial version of the S-Box to reduce the area requirement even further.