PESCO: Prompt-enhanced Self Contrastive Learning for Zero-shot Text Classification

We present PESCO, a novel contrastive learning framework that substantially improves the performance of zero-shot text classification. We formulate text classification as a neural text matching problem where each document is treated as a query, and the system learns the mapping from each query to the relevant class labels by (1) adding prompts to enhance label matching, and (2) using retrieved labels to enrich the training set in a self-training loop of contrastive learning. PESCO achieves state-of-the-art performance on four benchmark text classification datasets. On DBpedia, we achieve 98.5\% accuracy without any labeled data, which is close to the fully-supervised result. Extensive experiments and analyses show all the components of PESCO are necessary for improving the performance of zero-shot text classification.Comment: accepted by ACL 202

Single-shot compressed ultrafast photography: a review

Compressed ultrafast photography (CUP) is a burgeoning single-shot computational imaging technique that provides an imaging speed as high as 10 trillion frames per second and a sequence depth of up to a few hundred frames. This technique synergizes compressed sensing and the streak camera technique to capture nonrepeatable ultrafast transient events with a single shot. With recent unprecedented technical developments and extensions of this methodology, it has been widely used in ultrafast optical imaging and metrology, ultrafast electron diffraction and microscopy, and information security protection. We review the basic principles of CUP, its recent advances in data acquisition and image reconstruction, its fusions with other modalities, and its unique applications in multiple research fields

Equal status in ultimatum games promotes rational sharing

Experiments on the Ultimatum Game (UG) repeatedly show that people's behaviour is far from rational. In UG experiments, a subject proposes how to divide a pot and the other can accept or reject the proposal, in which case both lose everything. While rational people would offer and accept the minimum possible amount, in experiments low offers are often rejected and offers are typically larger than the minimum, and even fair. Several theoretical works have proposed that these results may arise evolutionarily when subjects act in both roles and there is a fixed interaction structure in the population specifying who plays with whom. We report the first experiments on structured UG with subjects playing simultaneously both roles. We observe that acceptance levels of responders approach rationality and proposers accommodate their offers to their environment. More precisely, subjects keep low acceptance levels all the time, but as proposers they follow a best-response-like approach to choose their offers. We thus find that status equality promotes rational sharing while the influence of structure leads to fairer offers compared to well-mixed populations. Our results are far from what is observed in single-role UG experiments and largely different from available predictions based on evolutionary game theory.We thank Long Ma, Xiao-Yan Sun, Zhao-Long Hu for assistance with carrying out the experiments and Xin-Di Wang, Ke-Qiang Li for helpful discussions. This work was partially supported by the National Science Foundation of China (Grant No. 71771026, BZ; Grant No. 71401037, CS; Grant No. 71631002, WW, BZ), by EU through FET-Proactive Project DOLFINS (contract no. 640772, AS) and FET-Open Project IBSEN (contract no. 662725, AS), and by grant FIS2015-64349-P (MINECO/FEDER, UE, AS)

The X-ray Properties of the Energetic Pulsar PSR J1838-0655

We present and interpret several new X-ray features of the X-ray pulsar PSR J1838-0655. The X-ray data are obtained from the archival data of CHANDRA, RXTE}, and SUZAKU. We combine all these X-ray data and fit the spectra with different models. We find that the joint spectra are difficult to fit with a single power law; a broken power-law model with a break at around 6.5 keV can improve the fit significantly. The photon index changes from $\Gamma$ = 1.0 (below 6.5 keV) to $\Gamma$ = 1.5 (above 6.5 keV); this indicates a softer spectral behaviour at hard X-rays. The X-ray flux at 2-20 keV is found to be 1.6x10^{-11} ergs cm^{-2} s^{-1}. The conversion efficiency from the spin-down luminosity is ~ 0.9% at 0.8-10 keV, which is much higher than that (~ 10^{-3}% - 10^{-4}%) of the pulsars that show similar timing properties. We discuss non-thermal radiation mechanisms for the observed high X-ray conversion efficiency and find that emission from the magnetosphere of a greatly inclined rotator is the most favorable interpretation for the conversion rate and the pulse profiles at X-ray bands. A line feature close to 6.65 keV is also detected in the spectra of SUZAKU/XIS; it might be the K$_\alpha$ emission of highly ionised Fe surrounding the pulsar.Comment: 8 pages, 6 figures and 1 tabl

A flexible and accurate total variation and cascaded denoisers-based image reconstruction algorithm for hyperspectrally compressed ultrafast photography

Hyperspectrally compressed ultrafast photography (HCUP) based on compressed sensing and the time- and spectrum-to-space mappings can simultaneously realize the temporal and spectral imaging of non-repeatable or difficult-to-repeat transient events passively in a single exposure. It possesses an incredibly high frame rate of tens of trillions of frames per second and a sequence depth of several hundred, and plays a revolutionary role in single-shot ultrafast optical imaging. However, due to the ultra-high data compression ratio induced by the extremely large sequence depth as well as the limited fidelities of traditional reconstruction algorithms over the reconstruction process, HCUP suffers from a poor image reconstruction quality and fails to capture fine structures in complex transient scenes. To overcome these restrictions, we propose a flexible image reconstruction algorithm based on the total variation (TV) and cascaded denoisers (CD) for HCUP, named the TV-CD algorithm. It applies the TV denoising model cascaded with several advanced deep learning-based denoising models in the iterative plug-and-play alternating direction method of multipliers framework, which can preserve the image smoothness while utilizing the deep denoising networks to obtain more priori, and thus solving the common sparsity representation problem in local similarity and motion compensation. Both simulation and experimental results show that the proposed TV-CD algorithm can effectively improve the image reconstruction accuracy and quality of HCUP, and further promote the practical applications of HCUP in capturing high-dimensional complex physical, chemical and biological ultrafast optical scenes.Comment: 25 pages, 5 figures and 1 tabl

Single-shot compressed ultrafast photography: a review

Compressed ultrafast photography (CUP) is a burgeoning single-shot computational imaging technique that provides an imaging speed as high as 10 trillion frames per second and a sequence depth of up to a few hundred frames. This technique synergizes compressed sensing and the streak camera technique to capture nonrepeatable ultrafast transient events with a single shot. With recent unprecedented technical developments and extensions of this methodology, it has been widely used in ultrafast optical imaging and metrology, ultrafast electron diffraction and microscopy, and information security protection. We review the basic principles of CUP, its recent advances in data acquisition and image reconstruction, its fusions with other modalities, and its unique applications in multiple research fields