Constructing a Non-Negative Low Rank and Sparse Graph with Data-Adaptive Features

This paper aims at constructing a good graph for discovering intrinsic data structures in a semi-supervised learning setting. Firstly, we propose to build a non-negative low-rank and sparse (referred to as NNLRS) graph for the given data representation. Specifically, the weights of edges in the graph are obtained by seeking a nonnegative low-rank and sparse matrix that represents each data sample as a linear combination of others. The so-obtained NNLRS-graph can capture both the global mixture of subspaces structure (by the low rankness) and the locally linear structure (by the sparseness) of the data, hence is both generative and discriminative. Secondly, as good features are extremely important for constructing a good graph, we propose to learn the data embedding matrix and construct the graph jointly within one framework, which is termed as NNLRS with embedded features (referred to as NNLRS-EF). Extensive experiments on three publicly available datasets demonstrate that the proposed method outperforms the state-of-the-art graph construction method by a large margin for both semi-supervised classification and discriminative analysis, which verifies the effectiveness of our proposed method

‘In the interest of your bank and our country’: two encounters between China and the International Chamber of Commerce

This article examines China’s path to joining the International Chamber of Commerce (ICC), a private international organization founded in Paris in 1920, of which China was a member from 1931–1949 and from 1994 onwards. The article charts the actors and debates behind two meaningful encounters. The first took place while the Nanjing government was raising funds for economic reconstruction, and the ICC aimed to mediate China’s fundraising efforts through private multilateral channels. The second was in the 1980s, when the People’s Republic was seeking to enter the world trade system. ICC members acted as educators and facilitators of world trade practicalities for the People’s Republic, which eventually rejoined the ICC in 1994. The article draws on Chinese, European, and American source material collected from governments, chambers of commerce, and private businessmen to make a twofold contribution. First, it adds nuance to the narrative of China’s economic internationalization by identifying an important non-governmental diplomatic channel. Second, it questions the ICC’s self-proclaimed identity as a non-political economic organization by showing how the political was indissociable from the economic when it came to China’s membership

Hashimoto’s encephalopathy cases: Chinese experience

BACKGROUND: Hashimoto’s encephalopathy is a poorly understood syndrome consisting of heterogeneous neurological symptoms and high serum antithyroid antibody titers, typically responding to steroids. More clinical series studies are required to characterize the clinical, laboratory and imaging features, and outcomes, especially in the Chinese population. METHODS: We analyzed the clinical, laboratory, and imaging features and outcomes of thirteen consecutive patients with Hashimoto’s encephalopathy diagnosed in Xuan Wu Hospital, Beijing from 2005 to 2010 retrospectively. RESULTS: Cognitive impairment (84.6%) and psychiatric symptoms (38.5%) were the most frequent symptoms. Seizures (30.8%) and myoclonus (7.7%) were less common than previously described. Three (23.1%) patients showed abnormal signals in hippocampus or temporal lobe, which were believed related to their memory disorders or seizures. MRI changes showed resolution paralleling clinical improvement in one patient. Among eight patients who received steroid therapy, five patients recovered, one patient improved with residual deficits, and two patients relapsed or had no effect. Among five non-steroid treated patients, three patients experienced stable remission with antiepileptic drugs or general neurotrophic therapy, and two patients experienced continuous deterioration. CONCLUSIONS: Most patients with Hashimoto’s encephalopathy showed good response to steroids. Some patients improved without steroid therapy. Considering its reversible course, we recommend that Hashimoto’s encephalopathy should always be in the differential diagnosis while evaluating disorders of the central nervous system

Diagnosis and surgical treatment of multiple endocrine neoplasia type 2A

BACKGROUND: This study aims to introduce the diagnosis and surgical treatment of the rare disease multiple endocrine neoplasia type 2A (MEN 2A). METHODS: Thirteen cases of MEN 2A were diagnosed as medullary thyroid carcinoma (MTC) and pheochromocytoma by biochemical tests and imaging examination. They were treated by bilateral adrenal tumor excision or laparoscopic surgery. RESULTS: Nine patients were treated by bilateral adrenal tumor excision and the remaining four were treated by laparoscopic surgery for pheochromocytoma. Ten patients were treated by total thyroidectomy and bilateral lymph nodes dissection and the remaining three were treated by unilateral thyroidectomy for MTC. Up to now, three patients have died of MTC distant metastasis. CONCLUSIONS: We confirmed that MEN 2A can be diagnosed by biochemical tests and imaging examination when genetic testing is not available. Surgical excision is the predominant way to treat MEN 2A; pheochromocytoma should be excised at first when pheochromocytoma and MTC occur simultaneously

MelHuBERT: A simplified HuBERT on Mel spectrograms

Self-supervised models have had great success in learning speech representations that can generalize to various downstream tasks. However, most self-supervised models require a large amount of compute and multiple GPUs to train, significantly hampering the development of self-supervised learning. In an attempt to reduce the computation of training, we revisit the training of HuBERT, a highly successful self-supervised model. We improve and simplify several key components, including the loss function, input representation, and training in multiple stages. Our model, MelHuBERT, is able to achieve favorable performance on phone recognition, speaker identification, and automatic speech recognition against HuBERT, while saving 31.2% of the pre-training time, or equivalently 33.5% MACs per one second speech. The code and pre-trained models are available in https://github.com/nervjack2/MelHuBERT.Comment: ASRU 202

A tunable plasmonic refractive index sensor with nanoring-strip graphene arrays

In this paper, a tunable plasmonic refractive index sensor with nanoring-strip graphene arrays is numerically investigated by the finite difference time domain (FDTD) method. The simulation results exhibit that by changing the sensing medium refractive index nmed of the structure, the sensing range of the system is large. By changing the doping level ng, we noticed that the transmission characteristics can be adjusted flexibly. The resonance wavelength remains entirely the same and the transmission dip enhancement over a big range of incidence angles [0,45] for both TM and TE polarizations, which indicates that the resonance of the graphene nanoring-strip arrays is insensitive to angle polarization. The above results are undoubtedly a new way to realize various tunable plasmon devices, and may have a great application prospect in biosensing, detection and imaging

A Local Weighted Nearest Neighbor Algorithm and a Weighted and Constrained Least-Squared Method for Mixed Odor Analysis by Electronic Nose Systems

A great deal of work has been done to develop techniques for odor analysis by electronic nose systems. These analyses mostly focus on identifying a particular odor by comparing with a known odor dataset. However, in many situations, it would be more practical if each individual odorant could be determined directly. This paper proposes two methods for such odor components analysis for electronic nose systems. First, a K-nearest neighbor (KNN)-based local weighted nearest neighbor (LWNN) algorithm is proposed to determine the components of an odor. According to the component analysis, the odor training data is firstly categorized into several groups, each of which is represented by its centroid. The examined odor is then classified as the class of the nearest centroid. The distance between the examined odor and the centroid is calculated based on a weighting scheme, which captures the local structure of each predefined group. To further determine the concentration of each component, odor models are built by regressions. Then, a weighted and constrained least-squares (WCLS) method is proposed to estimate the component concentrations. Experiments were carried out to assess the effectiveness of the proposed methods. The LWNN algorithm is able to classify mixed odors with different mixing ratios, while the WCLS method can provide good estimates on component concentrations

Separation-Free Spectral Super-Resolution via Convex Optimization

Atomic norm methods have recently been proposed for spectral super-resolution with flexibility in dealing with missing data and miscellaneous noises. A notorious drawback of these convex optimization methods however is their lower resolution in the high signal-to-noise (SNR) regime as compared to conventional methods such as ESPRIT. In this paper, we devise a simple weighting scheme in existing atomic norm methods and show that the resolution of the resulting convex optimization method can be made arbitrarily high in the absence of noise, achieving the so-called separation-free super-resolution. This is proved by a novel, kernel-free construction of the dual certificate whose existence guarantees exact super-resolution using the proposed method. Numerical results corroborating our analysis are provided.Comment: 19 pages, 6 figure

A VLC Smartphone Camera based Indoor Positioning System

We present a real-time indoor visible light positioning system based on the optical camera communication, where the coordinate data in the ON–OFF keying format is transmitted via light-emitting diode-based lights and captured using a smartphone camera. The position of the camera is estimated using a novel perspective- n -point problem algorithm, which determines the position of a calibrated camera from n 3D -to-2D point correspondences. The experimental results show that the proposed system offers mean position errors of 4.81 and 6.58 cm for the heights of 50 and 80 cm, respectively