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Weather forecasting is a long-standing computational challenge with direct societal and economic impacts. This task involves a large amount of continuous data collection and exhibits rich spatiotemporal dependencies over long periods, making it highly suitable for deep learning models. In this paper, we apply pre-training techniques to weather forecasting and propose W-MAE, a Weather model with Masked AutoEncoder pre-training for weather forecasting. W-MAE is pre-trained in a self-supervised manner to reconstruct spatial correlations within meteorological variables. On the temporal scale, we fine-tune the pre-trained W-MAE to predict the future states of meteorological variables, thereby modeling the temporal dependencies present in weather data. We conduct our experiments using the fifth-generation ECMWF Reanalysis (ERA5) data, with samples selected every six hours. Experimental results show that our W-MAE framework offers three key benefits: 1) when predicting the future state of meteorological variables, the utilization of our pre-trained W-MAE can effectively alleviate the problem of cumulative errors in prediction, maintaining stable performance in the short-to-medium term; 2) when predicting diagnostic variables (e.g., total precipitation), our model exhibits significant performance advantages over FourCastNet; 3) Our task-agnostic pre-training schema can be easily integrated with various task-specific models. When our pre-training framework is applied to FourCastNet, it yields an average 20% performance improvement in Anomaly Correlation Coefficient (ACC).

Cold-start issues have been more and more challenging for providing accurate recommendations with the fast increase of users and items. Most existing approaches attempt to solve the intractable problems via content-aware recommendations based on auxiliary information and/or cross-domain recommendations with transfer learning. Their performances are often constrained by the extremely sparse user-item interactions, unavailable side information, or very limited domain-shared users. Recently, meta-learners with meta-augmentation by adding noises to labels have been proven to be effective to avoid overfitting and shown good performance on new tasks. Motivated by the idea of meta-augmentation, in this paper, by treating a user's preference over items as a task, we propose a so-called Diverse Preference Augmentation framework with multiple source domains based on meta-learning (referred to as MetaDPA) to i) generate diverse ratings in a new domain of interest (known as target domain) to handle overfitting on the case of sparse interactions, and to ii) learn a preference model in the target domain via a meta-learning scheme to alleviate cold-start issues. Specifically, we first conduct multi-source domain adaptation by dual conditional variational autoencoders and impose a Multi-domain InfoMax (MDI) constraint on the latent representations to learn domain-shared and domain-specific preference properties. To avoid overfitting, we add a Mutually-Exclusive (ME) constraint on the output of decoders to generate diverse ratings given content data. Finally, these generated diverse ratings and the original ratings are introduced into the meta-training procedure to learn a preference meta-learner, which produces good generalization ability on cold-start recommendation tasks. Experiments on real-world datasets show our proposed MetaDPA clearly outperforms the current state-of-the-art baselines.

Gated wires are widely used in Time Projection Chamber (TPC) to avoid ion back-flow (IBF) in the drift volume. The anode wires can provide stable gain at high voltage with a long lifetime. However, switching on and off the gated grid (GG) leads to a dead time and also limit the readout efficiency of the TPC. Gas Electron Multiplier (GEM) foil provides a possibility of continuous readout for TPC, which can suppress IBF efficiently while keeping stable gain. A prototype chamber including two layers of GEM foils and anode wires has been built to combine both advantages from GEM and anode wire. Using Garfield++ and the finite element analysis (FEA) method, simulations of the transmission processes of electrons and ions are performed and results on absorption ratio of ions, gain and IBF ratio are obtained. The optimized parameters from simulation are then applied to the prototype chamber to test the IBF and other performances. Both GEM foils are run at low voltage (255V), while most of the gain is provided by the anode wire. The measurement shows that the IBF ratio can be suppressed to ~0.58% with double-layer GEM foils (staggered) at an effective gain about 2500 with an energy resolution about 10%., Comment: 9 pages, 11 figures, version to appear in NIMA

Following the Higgs particle discovery, the Large Hadron Collider complex will be upgraded in several phases allowing the luminosity to increase to $7 \times 10^{34}cm^{-2}s^{-1}$. In order to adapt the ATLAS detector to the higher luminosity environment after the upgrade, part of the ATLAS muon end-cap system, the Small Wheel, will be replaced by the New Small Wheel. The New Small Wheel includes two kinds of detectors: small-strip Thin Gap Chambers and Micromegas. Shandong University, part of the ATLAS collaboration, participates in the construction of the ATLAS New Small Wheel by developing, producing and testing the performance of part of the small-strip Thin Gap Chambers. This paper describes the construction and cosmic-ray testing of small-strip Thin Gap Chambers in Shandong University.

A new prototype of STAR inner Time Projection Chamber (iTPC) MWPC sector has been fabricated and tested in an X-ray test system. The wire chamber built at Shandong University has a wire tension precision better than 6$\%$ and wire pitch precision better than 10 $\mu$m. The gas gain uniformity and energy resolution are measured to be better than 1$\%$ (RMS) and 20$\%$ (FWHM), respectively, using an $^{55}$Fe X-ray source. The iTPC upgrade project is to replace all 24 STAR TPC inner sectors as a crucial detector upgrade for the RHIC beam energy scan phase II program. The test results show that the constructed iTPC prototype meets all project requirements., Comment: 8 pages, 12 figures

The STAR experiment at RHIC is planning to upgrade the Time Projection Chamber which lies at the heart of the detector. We have designed an instrument to measure the tension of the wires in the multi-wire proportional chambers (MWPCs) which will be used in the TPC upgrade. The wire tension measurement system causes the wires to vibrate and then it measures the fundamental frequency of the oscillation via a laser based optical platform. The platform can scan the entire wire plane, automatically, in a single run and obtain the wire tension on each wire with high precision. In this paper, the details about the measurement method and the system setup will be described. In addition, the test results for a prototype MWPC to be used in the STAR-iTPC upgrade will be presented., Comment: 6 pages, 10 figues, to appear in NIMA