147 research outputs found
CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction
Quotation extraction aims to extract quotations from written text. There are
three components in a quotation: source refers to the holder of the quotation,
cue is the trigger word(s), and content is the main body. Existing solutions
for quotation extraction mainly utilize rule-based approaches and sequence
labeling models. While rule-based approaches often lead to low recalls,
sequence labeling models cannot well handle quotations with complicated
structures. In this paper, we propose the Context and Former-Label Enhanced Net
(CofeNet) for quotation extraction. CofeNet is able to extract complicated
quotations with components of variable lengths and complicated structures. On
two public datasets (i.e., PolNeAR and Riqua) and one proprietary dataset
(i.e., PoliticsZH), we show that our CofeNet achieves state-of-the-art
performance on complicated quotation extraction.Comment: Accepted by COLING 202
The role of gamma oscillations in central nervous system diseases: Mechanism and treatment
Gamma oscillation is the synchronization with a frequency of 30–90 Hz of neural oscillations, which are rhythmic electric processes of neuron groups in the brain. The inhibitory interneuron network is necessary for the production of gamma oscillations, but certain disruptions such as brain inflammation, oxidative stress, and metabolic imbalances can cause this network to malfunction. Gamma oscillations specifically control the connectivity between different brain regions, which is crucial for perception, movement, memory, and emotion. Studies have linked abnormal gamma oscillations to conditions of the central nervous system, including Alzheimer’s disease, Parkinson’s disease, and schizophrenia. Evidence suggests that gamma entrainment using sensory stimuli (GENUS) provides significant neuroprotection. This review discusses the function of gamma oscillations in advanced brain activities from both a physiological and pathological standpoint, and it emphasizes gamma entrainment as a potential therapeutic approach for a range of neuropsychiatric diseases
FLM-101B: An Open LLM and How to Train It with $100K Budget
Large language models (LLMs) have achieved remarkable success in NLP and
multimodal tasks, among others. Despite these successes, two main challenges
remain in developing LLMs: (i) high computational cost, and (ii) fair and
objective evaluations. In this paper, we report a solution to significantly
reduce LLM training cost through a growth strategy. We demonstrate that a
101B-parameter LLM with 0.31T tokens can be trained with a budget of 100K US
dollars. Inspired by IQ tests, we also consolidate an additional range of
evaluations on top of existing evaluations that focus on knowledge-oriented
abilities. These IQ evaluations include symbolic mapping, rule understanding,
pattern mining, and anti-interference. Such evaluations minimize the potential
impact of memorization. Experimental results show that our model, named
FLM-101B, trained with a budget of 100K US dollars, achieves performance
comparable to powerful and well-known models, e.g., GPT-3 and GLM-130B,
especially on the additional range of IQ evaluations. The checkpoint of
FLM-101B is released at https://huggingface.co/CofeAI/FLM-101B
Index System Research on Environmental Impact Assessment of Ecological Project in Xishui River
The problems were analyzed about the environmental impact in the construction projects of water conservancy in China. Some relevant data and relevant guidelines were combined with the actual work which were referred to several environmental impact assessment reports. An index system was proposed about environmental impact assessment of ecological improvement project in Xishui River
Intramuscular vitamin A injection in newborn lambs enhances antioxidant capacity and improves meat quality
IntroductionVitamin A (VA) and its metabolite, retinoic acid (RA) possess several biological functions. This report investigated whether neonatal intramuscular VA injection affected antioxidative activity and meat quality in longissimus dorsi (LD) muscle of lambs.MethodsLambs were injected with 0 (control) or 7,500 IU VA palmitate into the biceps femoris muscle on day 2 after birth. At 3, 12, and 32 weeks of age, blood samples were collected in the jugular vein for serum levels of RA and muscle samples were collected in the biceps femoris for analysis of relative mRNA expression of enzyme contributors to retinoid metabolism. All animals were harvested at 32 weeks of age and muscle samples were collected to explore the role of VA on the meat quality and antioxidant capacity of lambs.Results and discussionOur results indicated that VA increased the redness, crude protein, and crude fat (p < 0.05), without affecting moisture, ash, and amino acid composition in LD muscle (p > 0.05). In addition, VA increased catalase (CAT) activity and decreased malondialdehyde (MDA) levels in LD muscle (p < 0.05). Meanwhile, greater levels of CAT and NRF2 mRNA and protein contents with VA treatment were observed in LD muscle (p < 0.05), partly explained by the increased level of RA (p < 0.05). Collectively, our findings indicated that VA injection at birth could improve lamb meat quality by elevating the redness, crude protein, crude fat, and antioxidative capacity in LD muscle of lambs
Distributed UAV Swarm Augmented Wideband Spectrum Sensing Using Nyquist Folding Receiver
Distributed unmanned aerial vehicle (UAV) swarms are formed by multiple UAVs
with increased portability, higher levels of sensing capabilities, and more
powerful autonomy. These features make them attractive for many recent
applica-tions, potentially increasing the shortage of spectrum resources. In
this paper, wideband spectrum sensing augmented technology is discussed for
distributed UAV swarms to improve the utilization of spectrum. However, the
sub-Nyquist sampling applied in existing schemes has high hardware complexity,
power consumption, and low recovery efficiency for non-strictly sparse
conditions. Thus, the Nyquist folding receiver (NYFR) is considered for the
distributed UAV swarms, which can theoretically achieve full-band spectrum
detection and reception using a single analog-to-digital converter (ADC) at low
speed for all circuit components. There is a focus on the sensing model of two
multichannel scenarios for the distributed UAV swarms, one with a complete
functional receiver for the UAV swarm with RIS, and another with a
decentralized UAV swarm equipped with a complete functional receiver for each
UAV element. The key issue is to consider whether the application of RIS
technology will bring advantages to spectrum sensing and the data fusion
problem of decentralized UAV swarms based on the NYFR architecture. Therefore,
the property for multiple pulse reconstruction is analyzed through the
Gershgorin circle theorem, especially for very short pulses. Further, the block
sparse recovery property is analyzed for wide bandwidth signals. The proposed
technology can improve the processing capability for multiple signals and wide
bandwidth signals while reducing interference from folded noise and subsampled
harmonics. Experiment results show augmented spectrum sensing efficiency under
non-strictly sparse conditions
Wideband Power Spectrum Sensing: a Fast Practical Solution for Nyquist Folding Receiver
The limited availability of spectrum resources has been growing into a
critical problem in wireless communications, remote sensing, and electronic
surveillance, etc. To address the high-speed sampling bottleneck of wideband
spectrum sensing, a fast and practical solution of power spectrum estimation
for Nyquist folding receiver (NYFR) is proposed in this paper. The NYFR
architectures is can theoretically achieve the full-band signal sensing with a
hundred percent of probability of intercept. But the existing algorithm is
difficult to realize in real-time due to its high complexity and complicated
calculations. By exploring the sub-sampling principle inherent in NYFR, a
computationally efficient method is introduced with compressive covariance
sensing. That can be efficient implemented via only the non-uniform fast
Fourier transform, fast Fourier transform, and some simple multiplication
operations. Meanwhile, the state-of-the-art power spectrum reconstruction model
for NYFR of time-domain and frequency-domain is constructed in this paper as a
comparison. Furthermore, the computational complexity of the proposed method
scales linearly with the Nyquist-rate sampled number of samples and the
sparsity of spectrum occupancy. Simulation results and discussion demonstrate
that the low complexity in sampling and computation is a more practical
solution to meet the real-time wideband spectrum sensing applications
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