This paper describes the performance pitfalls when using tiered memory for a networked key-value store and our approach to avoiding them. We observe that when receiving data over the network, writing data to tiered memory results in multiple data stagings and repetitive user-kernel crossings. We also observe sudden bursts of I/O operations when allocating memory if the slower memory tier is backed by a DAX file system. We address these challenges through (1) PPF (packet peek and forward) that peeks at the packets in the kernel layer with eBPF and streamlines data placement decisions on tiered memory, and (2) OMA (opportune memory allocator) that moves zeroing off the critical path. Performance evaluation with the prototype shows that the adoption of our design improves IOPS by up to 128%.

Controllable Dialogue Generation (CDG) enables chatbots to generate responses with desired attributes, and weighted decoding methods have achieved significant success in the CDG task.However, using a fixed constant value to manage the bias of attribute probabilities makes it challenging to find an ideal control strength that satisfies both controllability and fluency.To address this issue, we propose ECO decoding (Entropy-based COntrol), which dynamically adjusts the control strength at each generation step according to the model's entropy in both the language model and attribute classifier probability distributions.Experiments on the DailyDialog and MultiWOZ datasets demonstrate that ECO decoding consistently improves controllability while maintaining fluency and grammaticality, outperforming prior decoding methods across various models and settings.Furthermore, ECO decoding alleviates probability interpolation issues in multiattribute generation and consequently demonstrates strong performance in both single-and multi-attribute scenarios.

Mesh-based 3D static analysis methods have recently emerged as efficient alternatives to traditional computational numerical solvers, significantly reducing computational costs and runtime for various physics-based analyses. However, these methods primarily focus on surface topology and geometry, often overlooking the inherent thickness of real-world 3D objects, which exhibits high correlations and similar behavior between opposing surfaces. This limitation arises from the disconnected nature of these surfaces and the absence of internal edge connections within the mesh. In this work, we propose a novel framework, the Thickness-aware E(3)-Equivariant 3D Mesh Neural Network (T-EMNN), that effectively integrates the thickness of 3D objects while maintaining the computational efficiency of surface meshes. Additionally, we introduce data-driven coordinates that encode spatial information while preserving E(3)-equivariance or invariance properties, ensuring consistent and robust analysis. Evaluations on a real-world industrial dataset demonstrate the superior performance of T-EMNN in accurately predicting node-level 3D deformations, effectively capturing thickness effects while maintaining computational efficiency.

This study investigates the implication of utilizing the psychoacoustic model (PAM) within the neural audio coder (NAC), specifically focusing on the masking of quantization noise. We introduce a novel training strategy to incorporate the PAM into the NAC more accurately. This method involves a discriminator that directly or indirectly measures the PAM loss. For the indirect measurement, a multi-scale STFT discriminator (MS-STFTD) is incorporated to introduce an auxiliary loss term in addition to the existing PAM loss. Conversely, for the direct measurement, we have designed a multi-scale PAM discriminator (MS-PAMD) that quantifies PAM-specific parameters. Experimental results show that adding the discriminator masks the quantization noise better than the previous NAC, and it obtains audio quality comparable to the commercial AAC in both objective and subjective scores.

This paper proposes an end-to-end neural audio coder based on a mean-scale hyperprior model together with a perceptual optimization using a psychoacoustic model (PAM)-based loss function. The proposed coder estimates the mean and scale hyperpriors using a sub-network after assuming that the probability distribution of latent samples is Gaussian. The main network is an autoencoder based on Resnet-type gated linear units (ResGLUs), each comprising a generalized divisive normalization (GDN) layer. We train both networks to optimize perceptual attributes estimated using a multi-timescale scheme to obtain high perceptual quality. Experimental results show that the proposed model accurately predicts the mean and scale hyperpriors. Also, it obtains consistently higher audio quality than the commercial MP3 audio coder at all bitrates.

We present IOTAP, a tool that analyzes and profiles block I/O traces. IOTAP computes the (dis)similarities among a set of workloads and sets a guideline for selecting a subset of traces for benchmarking. By doing so, we avoid experimentally running all workloads or, even worse, arbitrarily selecting a subset that skews the results. We demonstrate the usefulness of IOTAP by comparing its results with experiments on real SSDs, achieving a high correlation of 0.92 for an NVMe SSD.

A new end-to-end audio coder based on a deep neural network (DNN) is proposed. To compensate for the perceptual distortion that occurred by quantization, the proposed coder is optimized to minimize distortions in both signal and perceptual domains. The distortion in the perceptual domain is measured using the psychoacoustic model (PAM), and a loss function is obtained through the two-stage compensation approach. Also, the scalar uniform quantization was approximated using a uniform stochastic noise, together with a compression-decompression scheme, which provides simpler but more stable learning without an additional penalty than the softmax quantizer. Test results showed that the proposed coder achieves more accurate noise-masking than the previous PAM-based method and better perceptual quality then the MP3 audio coder.