Quantum computing is fundamentally limited by the Planck constant ( \(h = 6.63\ \times {{10}^{ - 34}}\ J \cdot s\) ) through the Heisenberg Limit. The energy consumption over a given time, or the speed of processing information with a specific energy budget, is a core research focus in quantum computing. To date, the smallest action (the energy-time cost) achieved is approximately \({{10}^{ - 29}}J \cdot s\) , using a giant spin qubit composed of 20 spins. In our study, we achieved an action of \(1.66\ \times {{10}^{ - 34}}\ J \cdot s\) to reversibly manipulate a single spin qubit through a spin-spin magnetic interaction experiment. By adhering to the principle of least action, our theoretical and experimental results establish the minimal action required. Our findings highlight the potential of spin-qubit quantum computers as accelerators for computation-intensive applications, such as AI and Post-Quantum Cryptography, since they exhibit several unique advantages: 1. High energy efficiency (by approaching the Heisenberg limit as well as the Landauer bound); 2. High-density integration (with just an atom/ion per qubit); 3. Long coherence times (tens of seconds); 4. High-fidelity (98%); and 5. Fault tolerance (through decoherence-free subspaces).

Audio-visual learning has been a major pillar of multi-modal machine learning, where the community mostly focused on its $\textit{modality-aligned}$ setting, $\textit{i.e.}$, the audio and visual modality are $\textit{both}$ assumed to signal the prediction target. With the Look, Listen, and Parse dataset (LLP), we investigate the under-explored $\textit{unaligned}$ setting, where the goal is to recognize audio and visual events in a video with only weak labels observed. Such weak video-level labels only tell what events happen without knowing the modality they are perceived (audio, visual, or both). To enhance learning in this challenging setting, we incorporate large-scale contrastively pre-trained models as the modality teachers. A simple, effective, and generic method, termed $\textbf{V}$isual-$\textbf{A}$udio $\textbf{L}$abel Elab$\textbf{or}$ation (VALOR), is innovated to harvest modality labels for the training events. Empirical studies show that the harvested labels significantly improve an attentional baseline by $\textbf{8.0}$ in average F-score (Type@AV). Surprisingly, we found that modality-independent teachers outperform their modality-fused counterparts since they are noise-proof from the other potentially unaligned modality. Moreover, our best model achieves the new state-of-the-art on all metrics of LLP by a substantial margin ($\textbf{+5.4}$ F-score for Type@AV). VALOR is further generalized to Audio-Visual Event Localization and achieves the new state-of-the-art as well.

Novel object captioning (NOC) aims to describe images containing objects without observing their ground truth captions during training. Due to the absence of caption annotation, captioning models cannot be directly optimized via sequence-to-sequence training or CIDEr optimization. As a result, we present Paraphrasing-to-Captioning (P2C), a two-stage learning framework for NOC, which would heuristically optimize the output captions via paraphrasing. With P2C, the captioning model first learns paraphrasing from a language model pre-trained on text-only corpus, allowing expansion of the word bank for improving linguistic fluency. To further enforce the output caption sufficiently describing the visual content of the input image, we perform self-paraphrasing for the captioning model with fidelity and adequacy objectives introduced. Since no ground truth captions are available for novel object images during training, our P2C leverages cross-modality (image-text) association modules to ensure the above caption characteristics can be properly preserved. In the experiments, we not only show that our P2C achieves state-of-the-art performances on nocaps and COCO Caption datasets, we also verify the effectiveness and flexibility of our learning framework by replacing language and cross-modality association models for NOC. Implementation details and code are available in the supplementary materials.

We introduce four new real-world distribution shift datasets consisting of changes in image style, image blurriness, geographic location, camera operation, and more. With our new datasets, we take stock of previously proposed methods for improving out-of-distribution robustness and put them to the test. We find that using larger models and artificial data augmentations can improve robustness on real-world distribution shifts, contrary to claims in prior work. We find improvements in artificial robustness benchmarks can transfer to real-world distribution shifts, contrary to claims in prior work. Motivated by our observation that data augmentations can help with real-world distribution shifts, we also introduce a new data augmentation method which advances the state-of-the-art and outperforms models pre-trained with 1000× more labeled data. Overall we find that some methods consistently help with distribution shifts in texture and local image statistics, but these methods do not help with some other distribution shifts like geographic changes. Our results show that future research must study multiple distribution shifts simultaneously, as we demonstrate that no evaluated method consistently improves robustness.

Variety in Big Data means we have a wide range of data types and sources: e.g. File systems and database systems co-exist for decades as two popular data-accessing interfaces. This work is to unify these two interfaces by presenting a Data Interface All-iN-A-place (DIANA). The first challenge lies in distinguishing structured and un-structured data and diverting them to different underlying platforms. It is demonstrated that a speedup of 5000 in indexing has been achieved at the expense of a slowdown of 100 in extracting attributes. A DIANA-based cloud storage system is constructed for versatile, long distance and large volume big data accessing operations to address "Volume" and "Velocity" in Big Data. It encapsulates a dynamic multi-stream/multi-path engine at the socket level, which conforms to Portable Operating System Interface (POSIX).

Internet censorship by governments is an increasingly common practice worldwide. Internet users and censors are locked in an arms race: as users find ways to evade censorship schemes, the censors develop countermeasures for the evasion tactics. One of the most popular and effective circumvention tools, Tor, must regularly adjust its network traffic signature to remain usable.

A grid-powered Web Geographical Information Science (GIS)/Web Processing Service (WPS) system has been developed for archiving and distributing large volumes of geospatial data. However, users, WPS servers, and data resources are always distributed across different locations, attempting to access and archive geospatial data from a GIS survey via conventional Hypertext Transport Protocol, Network File System Protocol, and File Transfer Protocol, which often encounters long waits and frustration in wide area network (WAN) environments. To provide a “local-like” performance, a WAN/grid-optimized protocol known as “GridJet” developed at our lab was used as the underlying engine between WPS servers and clients, which utilizes a wide range of technologies including the one of paralleling the remote file access. No change in the way of using software is required since the multistreamed GridJet protocol remains fully compatible with the existing IP infrastructures. Our recent progress includes a real-world test that PyWPS and Google Earth over the GridJet protocol beat those over the classic ones by a factor of two to eight, where the distribution/archiving distance is over 10 000 km.

In the above-named work the name of one of the authors is incorrectly given. Included here also is the biography with the missing IEEE membership information.

This paper describes the grid-oriented storage (GOS) architecture and its implementations. A GOS-specific file system (GOS-FS), the single-purpose intent of a GOS OS, and secure interfaces via grid security infrastructure (GSI) motivate and enable this new architecture. As an FTP server, GOS with a slimmed OS, with a total volume of around 150 MB, outperforms the standard GridFTP by 20-40 percent. As a file server, GOS-FS acts as a network/grid interface, enabling a user to perform searches and access resources without downloading them locally. In the real-world tests between Cambridge and Beijing, where the transfer distance is 10,000 km, the multistreamed GOS-FS file opening/saving resulted in a remarkable performance increase of about 2-25 times, compared to the single-streamed network file system (NFSv4). GOS is expected to be a variant of or successor to the well-used network-attached storage (NAS) and/or storage area network (SAN) products in the grid era

The Web Intelligence Consortium (WIC) UK Centre has been founded in 2003 as an affiliated Centre of the WIC international organization. Since its foundation, the WIC UK Centre has devoted itself to developing and disseminating Web Intelligence (WI) technologies with a focus on Web Services powered by Grid Computing. The Centre has become a well organized and active group of strong academics and industrial practitioners, playing an important role in the international WI research. A number of activities have been carried out and planned by the WIC UK Centre.

In this paper we present a new algorithm for computing reduced-order models of interconnect which utilizes the dominant controllable subspace of the system.The dominant controllable modes are computed via a new iterative Lyapunov equation solver, Vector ADI.This new algorithm is as inexpensive as Krylov subspace-based moment matching methods, and often produces a better approximation over a wide frequency range.A spiral inductor and a transmission line example show this new method can be much more accurate than moment matching via Arnoldi.