287 research outputs found
Hierarchical Network with Label Embedding for Contextual Emotion Recognition
Emotion recognition has been used widely in various applications such as mental health monitoring and emotional management. Usually, emotion recognition is regarded as a text classification task. Emotion recognition is a more complex problem, and the relations of emotions expressed in a text are nonnegligible. In this paper, a hierarchical model with label embedding is proposed for contextual emotion recognition. Especially, a hierarchical model is utilized to learn the emotional representation of a given sentence based on its contextual information. To give emotion correlation-based recognition, a label embedding matrix is trained by joint learning, which contributes to the final prediction. Comparison experiments are conducted on Chinese emotional corpus RenCECps, and the experimental results indicate that our approach has a satisfying performance in textual emotion recognition task
Background Knowledge Based Multi-Stream Neural Network for Text Classification
As a foundation and typical task in natural language processing, text classification has been widely applied in many fields. However, as the basis of text classification, most existing corpus are imbalanced and often result in the classifier tending its performance to those categories with more texts. In this paper, we propose a background knowledge based multi-stream neural network to make up for the imbalance or insufficient information caused by the limitations of training corpus. The multi-stream network mainly consists of the basal stream, which retained original sequence information, and background knowledge based streams. Background knowledge is composed of keywords and co-occurred words which are extracted from external corpus. Background knowledge based streams are devoted to realizing supplemental information and reinforce basal stream. To better fuse the features extracted from different streams, early-fusion and two after-fusion strategies are employed. According to the results obtained from both Chinese corpus and English corpus, it is demonstrated that the proposed background knowledge based multi-stream neural network performs well in classification tasks
Burst Denoising with Kernel Prediction Networks
We present a technique for jointly denoising bursts of images taken from a
handheld camera. In particular, we propose a convolutional neural network
architecture for predicting spatially varying kernels that can both align and
denoise frames, a synthetic data generation approach based on a realistic noise
formation model, and an optimization guided by an annealed loss function to
avoid undesirable local minima. Our model matches or outperforms the
state-of-the-art across a wide range of noise levels on both real and synthetic
data.Comment: To appear in CVPR 2018 (spotlight). Project page:
http://people.eecs.berkeley.edu/~bmild/kpn
SSP: A Simple and Safe automatic Prompt engineering method towards realistic image synthesis on LVM
Recently, text-to-image (T2I) synthesis has undergone significant
advancements, particularly with the emergence of Large Language Models (LLM)
and their enhancement in Large Vision Models (LVM), greatly enhancing the
instruction-following capabilities of traditional T2I models. Nevertheless,
previous methods focus on improving generation quality but introduce unsafe
factors into prompts. We explore that appending specific camera descriptions to
prompts can enhance safety performance. Consequently, we propose a simple and
safe prompt engineering method (SSP) to improve image generation quality by
providing optimal camera descriptions. Specifically, we create a dataset from
multi-datasets as original prompts. To select the optimal camera, we design an
optimal camera matching approach and implement a classifier for original
prompts capable of automatically matching. Appending camera descriptions to
original prompts generates optimized prompts for further LVM image generation.
Experiments demonstrate that SSP improves semantic consistency by an average of
16% compared to others and safety metrics by 48.9%.Comment: 10 pages, 8 figure
The Weddell Sea Region: An Important Precipitation Channel to the Interior of the Antarctic Ice Sheet as Revealed by Glaciochemical Investigation of Surface Snow Along the Longest Trans-Antarctic Route
Glaciochemical analysis of surface snow samples, collected along a profile crossing the Antarctic ice sheet from the Larsen Ice Shelf, Antarctic Peninsula, via the Antarctic Plateau through South Pole, Vostok and Komsomolskaya to Mirny station (at the east margin of East Antarctica), shows that the Weddell Sea region is an important channel for air masses to the high plateau of the Antarctic ice sheet (\u3e2000 m a.s.l.). This opinion is supported by the following. (1) The fluxes of sea-salt ions such as Na+, Mg2+ and Cl− display a decreasing trend from the west to the east of interior Antarctica. In general, as sea-salt aerosols are injected into the atmosphere over the Antarctic ice sheet from the Weddell Sea, large aerosols tend to decrease. For the inland plateau, few large particles of sea-salt aerosol reach the area, and the sea-salt concentration levels are low. (2) The high altitude of the East Antarctic plateau, as well as the polar cold high-pressure system, obstruct the intrusive air masses mainly from the South Indian Ocean sector. (3) For the coastal regions of the East Antarctic ice sheet, the elevation rises to 2000 m over a distance from several to several tens of km. High concentrations of sea salt exist in snow in East Antarctica but are limited to a narrow coastal zone. (4) Fluxes of calcium and non-sea-salt sulfate in snow from the interior plateau do not display an eastward-decreasing trend. Since calcium is mainly derived from crustal sources, and nssSO42− is a secondary aerosol, this again confirms that the eastward-declining tendency of sea-salt ions indicates the transfer direction of precipitation vapor
Climatological Significance of δ\u3csup\u3e18\u3c/sup\u3eO in Precipitation and Ice Cores: a Case Study at the Head of the Ürütnqi River, Tien Shan, China
Stable-oxygen-isotope ratios (δ18O) collected from the headwaters of the Ürümqi river, Tien Shan, China, were used to test the relationship between δ18O temporal relationship is found between δ18O monthly averages which remove synoptic-scale influences such as changes in condensation level, condensation temperature and moisture sources (Yao and others, 1996). Linear fits as high as 0.95‰°C-1 for precipitation events and 1.23‰°C-1 for monthly averages are found. Although the δ18O (∼2 km from the precipitation sampling site) decreased dramatically compared to the precipitation samples , the ice-core records of annually averaged δ18O with contemporaneous air temperature, especially summer air temperature, at the nearby Daxigou meteorological station. Nevertheless, the relationship between the ice core δ18O records and contemporaneous air temperature is less significant than that [or the precipitation samples due to depositional and post-depositional modification processes, which are highlighted by the successive snow-pit δ18O No. 1. Our results might extend the application of high-altitude and subtropical ice-core δ18
Generative AI-enabled Quantum Computing Networks and Intelligent Resource Allocation
Quantum computing networks enable scalable collaboration and secure
information exchange among multiple classical and quantum computing nodes while
executing large-scale generative AI computation tasks and advanced quantum
algorithms. Quantum computing networks overcome limitations such as the number
of qubits and coherence time of entangled pairs and offer advantages for
generative AI infrastructure, including enhanced noise reduction through
distributed processing and improved scalability by connecting multiple quantum
devices. However, efficient resource allocation in quantum computing networks
is a critical challenge due to factors including qubit variability and network
complexity. In this article, we propose an intelligent resource allocation
framework for quantum computing networks to improve network scalability with
minimized resource costs. To achieve scalability in quantum computing networks,
we formulate the resource allocation problem as stochastic programming,
accounting for the uncertain fidelities of qubits and entangled pairs.
Furthermore, we introduce state-of-the-art reinforcement learning (RL)
algorithms, from generative learning to quantum machine learning for optimal
quantum resource allocation to resolve the proposed stochastic resource
allocation problem efficiently. Finally, we optimize the resource allocation in
heterogeneous quantum computing networks supporting quantum generative learning
applications and propose a multi-agent RL-based algorithm to learn the optimal
resource allocation policies without prior knowledge
Surface mass balance and ice flow of the glaciers Austre Lovénbreen and Pedersenbreen, Svalbard, Arctic
The glaciers Austre Lovénbreen and Pedersenbreen are located at Ny-Ålesund, Svalbard. The surface mass balance and ice flow velocity of both glaciers have been determined from the first year of observations(2005/2006), while the front edge of Austre Lovénbreen was also surveyed. The results are as follows: (1)The net mass balances of Austre Lovénbreen and Pedersenbreen are -0.44 and -0.20 m w. e., the annual ablation is -0.99 and -0.94m w. e.,
and the corresponding equilibrium line altitudes are 478.10 and 494.87 m, respectively
(2)Austre Lovénbreen and Pedersenbreen are characterized as ice flow models of surge-type glaciers in Svalbard. The horizontal vectors of the ice flow velocities are parallel or converge to the central lines of both glaciers, with lower velocities in the lower ablation areas and higher velocities in the middle and upper reaches of the glaciers. The vertical vectors of ice flow velocities show that there is a mass loss in the ablation areas, which reduces with increasing altitude, while there is a mass gain near the equilibrium line of Austre Lovénbreen. (3)The front edge of Austre Lovénbreen receded at an average rate of 21.83 m·a-1, with remarkable variability-a maximum rate of 77.30m·a-1 and a minimum rate of 2.76m·a-1
Evidence for Recent Climate Change from Ice Cores in the Central Himalaya
Comparison of the terminus locations of Rongbuk Glacier, Mount Everest, measured in 1966 and 1997 shows that in the past 30 years the glacier has retreated 170-270 m, equivalent to a retreat speed of 5.5-8.7 m a−1. During summer 1997, a 15 m firn core was recovered from Dasuopu glacier (28°23′N, 85°44′E; 7000 m a.s.l.) on the northwest margin of Xixabangma Feng, Xizang (Tibet). The seasonal variations of δ18O values in the core indicate that monsoon signals are clearly recorded in the glacier. δ18O values are controlled by the amount effect in the monsoon season; more negative δ18O is representative of the monsoon season in snow layers. Analysis of the relationship between ice-core δ18O, sampled from 6500 m a.s.l. on the north side of Mount Everest, and instrumental series representing regional-scale precipitation, atmospheric circulation and temperature suggests a change in the relative influence of these parameters on δ18O since the 1940s. The results of the comparison add to and lengthen the sparse array of instrument data available for the Tibetan (Qinghai-Xizang) Plateau and demonstrate a recent decline in moisture flux for at least the southern part of the plateau. Glacier retreat, associated with a recent increase in temperature in the region, is coincident with this period of decreased moisture flux
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