Link Scheduling Algorithms For In-Band Full-Duplex Wireless Networks

In the last two decades, wireless networks and their corresponding data traffic have grown significantly. This is because wireless networks have become an indispens- able and critical communication infrastructure in a modern society. An on-going challenge in communication systems is meeting the continuous increase in traffic de- mands. This is driven by the proliferation of electronic devices such as smartphones with a WiFi interface along with their bandwidth intensive applications. Moreover, in the near future, sensor devices that form the Internet of Things (IoTs) ecosystem will also add to future traffic growth. One promising approach to meet growing traffic demands is to equip nodes with an In-band-Full-Duplex (IBFD) radio. This radio thus allows nodes to transmit and receive data concurrently over the same frequency band. Another approach to in- crease network or link capacity is to exploit the benefits of Multiple-Input-Multiple- Output (MIMO) technologies; namely, (i) spatial diversity gain, which improves Signal-to-Noise Ratio (SNR) and thus has a direct impact on the data rate used by nodes, and (ii) spatial multiplexing gain, whereby nodes are able to form concurrent links to neighbors

Roles of PLODs in Collagen Synthesis and Cancer Progression

Collagen is the major component of extracellular matrix. Collagen cross-link and deposition depend on lysyl hydroxylation, which is catalyzed by procollagen-lysine, 2-oxoglutarate 5-dioxygenase (PLOD). Aberrant lysyl hydroxylation and collagen cross-link contributes to the progression of many collagen-related diseases, such as fibrosis and cancer. Three lysyl hydroxylases (LH1, LH2, and LH3) are identified, encoded by PLOD1, PLOD2, and PLOD3 genes. Expression of PLODs is regulated by multiple cytokines, transcription factors and microRNAs. Dysregulation of PLODs promotes cancer progression and metastasis, suggesting that targeting PLODs is potential strategy for cancer treatment. Here, we summarize the recent progress in the investigation of function and regulation of PLODs in normal tissue development and disease progression, especially in cancer

Learning Robust Kernel Ensembles with Kernel Average Pooling

Model ensembles have long been used in machine learning to reduce the variance in individual model predictions, making them more robust to input perturbations. Pseudo-ensemble methods like dropout have also been commonly used in deep learning models to improve generalization. However, the application of these techniques to improve neural networks' robustness against input perturbations remains underexplored. We introduce Kernel Average Pooling (KAP), a neural network building block that applies the mean filter along the kernel dimension of the layer activation tensor. We show that ensembles of kernels with similar functionality naturally emerge in convolutional neural networks equipped with KAP and trained with backpropagation. Moreover, we show that when trained on inputs perturbed with additive Gaussian noise, KAP models are remarkably robust against various forms of adversarial attacks. Empirical evaluations on CIFAR10, CIFAR100, TinyImagenet, and Imagenet datasets show substantial improvements in robustness against strong adversarial attacks such as AutoAttack without training on any adversarial examples

Roles of PLODs in Collagen Synthesis and Cancer Progression

Collagen is the major component of extracellular matrix. Collagen cross-link and deposition depend on lysyl hydroxylation, which is catalyzed by procollagen-lysine, 2-oxoglutarate 5-dioxygenase (PLOD). Aberrant lysyl hydroxylation and collagen cross-link contributes to the progression of many collagen-related diseases, such as fibrosis and cancer. Three lysyl hydroxylases (LH1, LH2, and LH3) are identified, encoded by PLOD1, PLOD2, and PLOD3 genes. Expression of PLODs is regulated by multiple cytokines, transcription factors and microRNAs. Dysregulation of PLODs promotes cancer progression and metastasis, suggesting that targeting PLODs is potential strategy for cancer treatment. Here, we summarize the recent progress in the investigation of function and regulation of PLODs in normal tissue development and disease progression, especially in cancer

A Two-phase Simulation of Wave Impact on a Horizontal Deck Based on SPH Method

AbstractIn this paper, a two-phase numerical model is developed to study the effect of the gas phase in problems of wave attacking deck. The model is based on weakly compressible smoothed particles hydrodynamics (WCSPH) method, and is implemented in C++ and CUDA language to running on GPUs starting from DualSPHysics. Surface tension is considered by incorporating the surface tension force into the momentum equations. Therefore, the continuum surface force (CSF) model is proposed to eliminating the interphase particle penetrations. To calculate surface tension force at interfaces, a color function is assigned to each particle from different phases. Furthermore, to enhance the robustness of this method, the assigned color function is smoothed by calculating the weighted averaging value of initial color function on its support domain. The simulation results are compared with experiment results. The point pressure from numerical results shows well agreement with experimental ones, and is more accurate than one phase simulation. The reason is lying on its satisfying on the condition of air pressure

Acceleration compensation of a novel piezoelectric balance for the short duration impulse measurement: a time series analysis approach

A novel piezoelectric balance was developed to measure the six-component forces for the complex aircraft scaled model in the impulse combustion wind tunnel at a short duration airloads Mach number of 5. The piezoelectric balance using four triaxial piezoelectric load cells yields the high stiffness, sensitive and good dynamic response characteristics. The dynamic model-balance system was built to analyze the vibration characteristic. The time series analysis approach was developed on the basis of the system transfer function and the natural frequency, and the accelerated forces which induce the airloads overshooting oscillations had been obtained by the second order derivatives function. The experimental results have shown that the problem of overshooting oscillations effect of the impulse can be effectively solved by the acceleration compensation technology for the complex test model with the novel piezoelectric balance

A High-Performance and Low-Complexity 5G LDPC Decoder: Algorithm and Implementation

5G New Radio (NR) has stringent demands on both performance and complexity for the design of low-density parity-check (LDPC) decoding algorithms and corresponding VLSI implementations. Furthermore, decoders must fully support the wide range of all 5G NR blocklengths and code rates, which is a significant challenge. In this paper, we present a high-performance and low-complexity LDPC decoder, tailor-made to fulfill the 5G requirements. First, to close the gap between belief propagation (BP) decoding and its approximations in hardware, we propose an extension of adjusted min-sum decoding, called generalized adjusted min-sum (GA-MS) decoding. This decoding algorithm flexibly truncates the incoming messages at the check node level and carefully approximates the non-linear functions of BP decoding to balance the error-rate and hardware complexity. Numerical results demonstrate that the proposed fixed-point GAMS has only a minor gap of 0.1 dB compared to floating-point BP under various scenarios of 5G standard specifications. Secondly, we present a fully reconfigurable 5G NR LDPC decoder implementation based on GA-MS decoding. Given that memory occupies a substantial portion of the decoder area, we adopt multiple data compression and approximation techniques to reduce 42.2% of the memory overhead. The corresponding 28nm FD-SOI ASIC decoder has a core area of 1.823 mm2 and operates at 895 MHz. It is compatible with all 5G NR LDPC codes and achieves a peak throughput of 24.42 Gbps and a maximum area efficiency of 13.40 Gbps/mm2 at 4 decoding iterations.Comment: 14 pages, 14 figure

Test-Time Distribution Normalization for Contrastively Learned Vision-language Models

Advances in the field of vision-language contrastive learning have made it possible for many downstream applications to be carried out efficiently and accurately by simply taking the dot product between image and text representations. One of the most representative approaches proposed recently known as CLIP has garnered widespread adoption due to its effectiveness. CLIP is trained with an InfoNCE loss that takes into account both positive and negative samples to help learn a much more robust representation space. This paper reveals that the common downstream practice of taking a dot product is only a zeroth-order approximation of the optimization goal, resulting in a loss of information during test-time. Intuitively, since the model has been optimized based on the InfoNCE loss, test-time procedures should also be in alignment. The question lies in how one can retrieve any semblance of negative samples information during inference in a computationally efficient way. To this end, we propose Distribution Normalization (DN), where we approximate the mean representation of a batch of test samples and use such a mean to represent what would be analogous to negative samples in the InfoNCE loss. DN requires no retraining or fine-tuning and can be effortlessly applied during inference. Extensive experiments on a wide variety of downstream tasks exhibit a clear advantage of DN over the dot product on top of other existing test-time augmentation methods.Comment: Accepted to NeurIPS 2023, project webpage: https://fengyuli-dev.github.io/dn-website

User Response Learning for Directly Optimizing Campaign Performance in Display Advertising

Learning and predicting user responses, such as clicks and conversions, are crucial for many Internet-based businesses including web search, e-commerce, and online advertising. Typically, a user response model is established by optimizing the prediction accuracy, e.g., minimizing the error between the prediction and the ground truth user response. However, in many practical cases, predicting user responses is only part of a rather larger predictive or optimization task, where on one hand, the accuracy of a user response prediction determines the final (expected) utility to be optimized, but on the other hand, its learning may also be influenced from the follow-up stochastic process. It is, thus, of great interest to optimize the entire process as a whole rather than treat them independently or sequentially. In this paper, we take real-time display advertising as an example, where the predicted user's ad click-through rate (CTR) is employed to calculate a bid for an ad impression in the second price auction. We reformulate a common logistic regression CTR model by putting it back into its subsequent bidding context: rather than minimizing the prediction error, the model parameters are learned directly by optimizing campaign profit. The gradient update resulted from our formulations naturally fine-tunes the cases where the market competition is high, leading to a more cost-effective bidding. Our experiments demonstrate that, while maintaining comparable CTR prediction accuracy, our proposed user response learning leads to campaign profit gains as much as 78.2% for offline test and 25.5% for online A/B test over strong baselines