530 research outputs found
Joint Subcarrier Pairing and Power Allocation for OFDM Transmission with Decode-and-Forward Relaying
In this paper, a point-to-point Orthogonal Frequency Division Multiplexing
(OFDM) system with a decode-and-forward (DF) relay is considered. The
transmission consists of two hops. The source transmits in the first hop, and
the relay transmits in the second hop. Each hop occupies one time slot. The
relay is half-duplex, and capable of decoding the message on a particular
subcarrier in one time slot, and re-encoding and forwarding it on a different
subcarrier in the next time slot. Thus each message is transmitted on a pair of
subcarriers in two hops. It is assumed that the destination is capable of
combining the signals from the source and the relay pertaining to the same
message. The goal is to maximize the weighted sum rate of the system by jointly
optimizing subcarrier pairing and power allocation on each subcarrier in each
hop. The weighting of the rates is to take into account the fact that different
subcarriers may carry signals for different services. Both total and individual
power constraints for the source and the relay are investigated. For the
situations where the relay does not transmit on some subcarriers because doing
so does not improve the weighted sum rate, we further allow the source to
transmit new messages on these idle subcarriers. To the best of our knowledge,
such a joint optimization inclusive of the destination combining has not been
discussed in the literature. The problem is first formulated as a mixed integer
programming problem. It is then transformed to a convex optimization problem by
continuous relaxation, and solved in the dual domain. Based on the optimization
results, algorithms to achieve feasible solutions are also proposed. Simulation
results show that the proposed algorithms almost achieve the optimal weighted
sum rate, and outperform the existing methods in various channel conditions.Comment: 33 pages, 11 figure
Direct Large-Area Growth of Graphene on Silicon for Potential Ultra-Low-Friction Applications and Silicon-Based Technologies
Deposition of layers of graphene on silicon has the potential for a wide range of optoelectronic and mechanical applications. However, direct growth of graphene on silicon has been difficult due to the inert, oxidized silicon surfaces. Transferring graphene from metallic growth substrates to silicon is not a good solution either, because most transfer methods involve multiple steps that often lead to polymer residues or degradation of sample quality. Here we report a single-step method for large-area direct growth of continuous horizontal graphene sheets and vertical graphene nano-walls on silicon substrates by plasma-enhanced chemical vapor deposition (PECVD) without active heating. Comprehensive studies utilizing Raman spectroscopy, x-ray/ultraviolet photoelectron spectroscopy (XPS/UPS), atomic force microscopy (AFM), scanning electron microscopy (SEM) and optical transmission are carried out to characterize the quality and properties of these samples. Data gathered by the residual gas analyzer (RGA) during the growth process further provide information about the synthesis mechanism. Additionally, ultra-low friction (with a frictional coefficient ~0.015) on multilayer graphene-covered silicon surface is achieved, which is approaching the superlubricity limit (for frictional coefficients <0.01). Our growth method therefore opens up a new pathway towards scalable and direct integration of graphene into silicon technology for potential applications ranging from structural superlubricity to nanoelectronics, optoelectronics, and even the next-generation lithium-ion batteries
Google trends as an early indicator of African swine fever outbreaks in Southeast Asia
African Swine Fever (ASF) is a reportable disease of swine that causes far-reaching losses to affected countries and regions. Early detection is critically important to contain and mitigate the impact of ASF outbreaks, for which timely available data is essential. This research examines the potential use of Google Trends data as an early indicator of ASF outbreaks in Southeast Asia, focusing on the three largest swine producing countries, namely, Vietnam, the Philippines, and Thailand. Cross-correlation and KullbackâLeibler (KL) divergence indicators were used to evaluate the association between Google search trends and the number of ASF outbreaks reported. Our analysis indicate strong and moderate correlations between Google search trends and number of ASF outbreaks reported in Vietnam and the Philippines, respectively. In contrast, Thailand, the country of this group in which outbreaks were reported last, exhibits the weakest correlation (KLâ=â2.64), highlighting variations in public awareness and disease dynamics. These findings suggest that Google search trends are valuable for early detection of ASF. As the disease becomes endemic, integrating trends with other epidemiological data may support the design and implementation of surveillance strategies for transboundary animal diseases in Southeast Asia
catena-Poly[[tetraaquanickel(II)]-Îź3-benzene-1,3,5-tricarboxylato-3â˛:1:2-Îş4 O 1:O 3,O 3â˛:O 5-[tetraaquanickel(II)]-Îź2-benzene-1,3,5-tricarboxylato-2:3Îş2 O 1:O 3-[tetraaquanickel(II)]]
The microwave solvothermal reaction of nickel nitrate with trimesic acid provided the title compound, [Ni3(BTC)2(H2O)12]n (BTC = benzene-1,3,5-tricarboxylÂate anion, C9H3O6), which is a metal coordination polymer composed of one-dimensional zigzag chains. The crystal under investigation was ramecically twinned with an approximate twin domain ratio of 1:1. In the asymmetric unit, there are two types of Ni atoms. One of the NiO6 groups (2 symmetry) is coordinated to only one carboxylÂate group and thus terminal, the other is bridging, forming the coordination polymer. The extended chains are connected by the organic BTC anions via Îź
2-linkages. OâHâŻO hydrogen bonds and ĎâĎ interÂactions between the chains [centroidâcentroid distance 3.58â
(1)â
Ă
] induce the complex to mimic a three-dimensional structure
QuantTune: Optimizing Model Quantization with Adaptive Outlier-Driven Fine Tuning
Transformer-based models have gained widespread popularity in both the
computer vision (CV) and natural language processing (NLP) fields. However,
significant challenges arise during post-training linear quantization, leading
to noticeable reductions in inference accuracy. Our study focuses on uncovering
the underlying causes of these accuracy drops and proposing a
quantization-friendly fine-tuning method, \textbf{QuantTune}. Firstly, our
analysis revealed that, on average, 65\% of quantization errors result from the
precision loss incurred by the dynamic range amplification effect of outliers
across the target Transformer-based models. Secondly, \textbf{QuantTune}
adjusts weights based on the deviation of outlier activations and effectively
constrains the dynamic ranges of the problematic activations. As a result, it
successfully mitigates the negative impact of outliers on the inference
accuracy of quantized models. Lastly, \textbf{QuantTune} can be seamlessly
integrated into the back-propagation pass in the fine-tuning process without
requiring extra complexity in inference software and hardware design. Our
approach showcases significant improvements in post-training quantization
across a range of Transformer-based models, including ViT, Bert-base, and OPT.
QuantTune reduces accuracy drops by 12.09\% at 8-bit quantization and 33.8\% at
7-bit compared to top calibration methods, outperforming state-of-the-art
solutions by over 18.84\% across ViT models
Cosmology and Accelerator Tests of Strongly Interacting Dark Matter
A natural possibility for dark matter is that it is composed of the stable
pions of a QCD-like hidden sector. Existing literature largely assumes that
pion self-interactions alone control the early universe cosmology. We point out
that processes involving vector mesons typically dominate the physics of dark
matter freeze-out and significantly widen the viable mass range for these
models. The vector mesons also give rise to striking signals at accelerators.
For example, in most of the cosmologically favored parameter space, the vector
mesons are naturally long-lived and produce Standard Model particles in their
decays. Electron and proton beam fixed-target experiments such as HPS,
SeaQuest, and LDMX can exploit these signals to explore much of the viable
parameter space. We also comment on dark matter decay inherent in a large class
of previously considered models and explain how to ensure dark matter
stability.Comment: 20 pages (4 in the Appendix), 6 figures; references adde
Clinical application of tumor volume in advanced nasopharyngeal carcinoma to predict outcome
<p>Abstract</p> <p>Background</p> <p>Current staging systems have limited ability to adjust optimal therapy in advanced nasopharyngeal carcinoma (NPC). This study aimed to delineate the correlation between tumor volume, treatment outcome and chemotherapy cycles in advanced NPC.</p> <p>Methods</p> <p>A retrospective review of 110 patients with stage III-IV NPC was performed. All patients were treated first with neoadjuvant chemotherapy, then concurrent chemoradiation, and followed by adjuvant chemotherapy as being the definitive therapy. Gross tumor volume of primary tumor plus retropharyngeal nodes (GTVprn) was calculated to be an index of treatment outcome.</p> <p>Results</p> <p>GTVprn had a close relationship with survival and recurrence in advanced NPC. Large GTVprn (â§13 ml) was associated with a significantly poorer local control, lower distant metastasis-free rate, and poorer survival. In patients with GTVprn ⧠13 ml, overall survival was better after â§4 cycles of chemotherapy than after less than 4 cycles.</p> <p>Conclusions</p> <p>The incorporation of GTVprn can provide more information to adjust treatment strategy.</p
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