China's Sovereign Wealth Fund : Weakness and Challenges

The establishment of sovereign wealth funds in large developing countries has generated hot debate among participants in the international financial market. When accumulated foreign exchange reserves surpass a sufficient and an appropriate level, the costs, risks and impacts on the macro-economy of countries holding reserves need to be considered. The Chinese Government established China Investment Corporation (CIC) in 2007 to diversify its investment of foreign reserves and to raise investment income. However, because of certain conflicts of interest and institution-design caveats, CIC possesses some internal weakness, including a vague orientation, mixed investment strategies and inefficient bureaucratic style. Although the subprime crisis has softened certain regulations and lessened rejection by the USA of CIC potential investments, the increased volatility and uncertainty of the market means that CIC is facing some new challenges in terms of its investment decisions. Moreover, CIC is competing with other Chinese investment institutions for injections of funds from the Chinese Government.CIC, external challenge, internal weakness, foreign exchange reserve management, sovereign wealth fund

Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance

Natural language understanding(NLU) is challenging for finance due to the lack of annotated data and the specialized language in that domain. As a result, researchers have proposed to use pre-trained language model and multi-task learning to learn robust representations. However, aggressive fine-tuning often causes over-fitting and multi-task learning may favor tasks with significantly larger amounts data, etc. To address these problems, in this paper, we investigate model-agnostic meta-learning algorithm(MAML) in low-resource financial NLU tasks. Our contribution includes: 1. we explore the performance of MAML method with multiple types of tasks: GLUE datasets, SNLI, Sci-Tail and Financial PhraseBank; 2. we study the performance of MAML method with multiple single-type tasks: a real scenario stock price prediction problem with twitter text data. Our models achieve the state-of-the-art performance according to the experimental results, which demonstrate that our method can adapt fast and well to low-resource situations.Comment: 13 pages, 6 figures, 8 table

Design of Wideband Dual-Circularly Polarized Endfire Antenna Array on Gap Waveguide

A wideband dual-circularly polarized (CP) linear antenna array is presented in this paper. Firstly, a dual-CP endfire antenna based on septum polarizer is designed as the element for the array. Secondly, the feeding network is realized by ridge gap waveguide. Then a 1 78 linear antenna array is built up by the elements. The proposed array antenna achieves wide impedance bandwidth of 44.6% with the reflection coefficient below -10 dB, the isolation between ports greater than 15 dB, and a wide 3-dB axial ratio (AR) bandwidth of 46.2%

Confidant: Customizing Transformer-based LLMs via Collaborative Edge Training

Transformer-based large language models (LLMs) have demonstrated impressive capabilities in a variety of natural language processing (NLP) tasks. Nonetheless, it is challenging to deploy and fine-tune LLMs on mobile edge devices with limited computing, memory, and energy budgets. In this paper, we propose Confidant, a multi-backend collaborative training framework for customizing state-of-the-art LLMs on commodity mobile devices like smartphones. Confidant partitions an LLM into several sub-models so that each fits into a mobile device's memory. A pipeline parallel training mechanism is further developed to ensure fast and efficient distributed training. In addition, we propose a novel backend scheduler to allocate different attention heads to heterogeneous compute hardware, including mobile CPU and GPUs, to maximize the compute resource utilization on each edge device. Our preliminary experimental results show that Confidant achieves at most 45.3% memory reduction and 8.03x inference speedup in practical settings.Comment: 6 pages, 7 figures; Submitted to HotMobile 202

Time-varying resonant mass at collider and beam dump experiments

A new particle usually manifests itself as a single resonant peak located at its mass. We propose if the new particle mass is time-varying due to environmental effects, then its mass spectrum typically has a novel double-peak feature. A representative model is the kinetic mixing dark photon interacting with an ultralight complex scalar dark matter charged under U(1)\u27. We reanalyze the existing experiments, showing the constraints on such a model are drastically weakened than those on the traditional single-peak resonance model, due to the reduction of the luminosity exposure in each resonant mass bin. Consequently, for mass around tens of MeV, the muon gμ -2 solution from the kinetic mixing dark photon becomes viable again. The scenario can be further tested by reanalyzing the existing data with timing information included

A Method of EV Detour-to-Recharge Behavior Modeling and Charging Station Deployment

Electric vehicles (EVs) are increasingly used in transportation. Worldwide use of EVs, for their limited battery capacity, calls for effective planning of EVs charging stations to enhance the efficiency of using EVs. This paper provides a methodology of describing EV detouring behavior for recharging, and based on this, we adopt the extra driving length caused by detouring and the length of uncompleted route as the indicators of evaluating an EV charging station deployment plan. In this way, we can simulate EV behavior based on travel data (demand). Then, a genetic algorithm (GA) based EV charging station sitting optimization method is developed to obtain an effective plan. A detailed case study based on a 100-node 203-branch transportation network within a 30 km * 30 km region is included to test the effectiveness of our method. Insights from our method may be applicable for charging station planning in various transportation networks

Neural topic modeling with bidirectional adversarial training

Recent years have witnessed a surge of interests of using neural topic models for automatic topic extraction from text, since they avoid the complicated mathematical derivations for model inference as in traditional topic models such as Latent Dirichlet Allocation (LDA). However, these models either typically assume improper prior (e.g. Gaussian or Logistic Normal) over latent topic space or could not infer topic distribution for a given document. To address these limitations, we propose a neural topic modeling approach, called Bidirectional Adversarial Topic (BAT) model, which represents the first attempt of applying bidirectional adversarial training for neural topic modeling. The proposed BAT builds a two-way projection between the document-topic distribution and the document-word distribution. It uses a generator to capture the semantic patterns from texts and an encoder for topic inference. Furthermore, to incorporate word relatedness information, the Bidirectional Adversarial Topic model with Gaussian (Gaussian-BAT) is extended from BAT. To verify the effectiveness of BAT and Gaussian-BAT, three benchmark corpora are used in our experiments. The experimental results show that BAT and Gaussian-BAT obtain more coherent topics, outperforming several competitive baselines. Moreover, when performing text clustering based on the extracted topics, our models outperform all the baselines, with more significant improvements achieved by Gaussian-BAT where an increase of near 6% is observed in accuracy