Latest papers
Body-based emotion recognition is important for real-time affective systems, but graph-based skeleton models can be computationally expensive. This paper studies whether lightweight temporal convolutional networks (TCNs) can provide an…
Stress is a dynamic process characterized by significant individual variability in facial expression. Traditional architectures, such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), often overlook…
Continuously recorded high-resolution waveform measurements provide rich information about fast power system dynamics. However, they require automated methods to identify events. This problem is addressed by developing a spectrogram-based…
The electrocardiogram (ECG) is a cornerstone of cardiac as- sessment, yet clinical deployment of deep learning models remains con- strained by limited interpretability and the hallucination risk of large language models (LLMs). Existing…
Pixel-aligned Gaussian splatting enables efficient and generalizable novel-view synthesis. However, high-resolution rendering faces a critical trade-off where increasing input resolution improves detail at the expense of quadratically…
In spite of the fundamental role of neural networks in contemporary machine learning research, our understanding of the computational complexity of optimally training neural networks remains incomplete even when dealing with the simplest…
Lightweight large language models (LLMs) are increasingly being deployed locally on personal computers and are expected to play a growing role in resource-constrained edge and mobile environments. In such settings, energy consumption,…
Self-orthogonal codes have attracted considerable attention owing to their applications in quantum error-correcting codes, linear complementary dual codes, and a variety of other fields. In this paper, we construct new families of…
Activation steering enables control and interpretation of LLMs, yet existing work primarily models personality through static trait frameworks such as the Big Five. We investigate whether personality can instead be represented and…
Practical molecular communication (MC) testbeds are essential for translating theoretical concepts toward future applications while retaining physical realism, accessibility, and experimental repeatability. We present a flowbased MC…
Imprecise probability generalizes standard probability theory by replacing a single distribution with a convex set of possible distributions. We show that this generalization requires no change to the standard BDD compilation and weighted…
A relying party validating a hybrid X.509 certificate --- carrying both a classical and a post-quantum credential --- must distinguish whether its accepting judgment rests on the post-quantum evidence or only on the classical path. To…
This paper investigates the problem of optimal piecewise linear approximation of a smooth curve, in which the area of the region enclosed between the graph of the original function and the constructed interpolating polyline is minimized.…
Starting from a variational principle for mass transport, we present a broadly applicable statistical framework describing thermal diffusion in complex solids. We show that microscopic thermodynamic and kinetic fluctuations govern…
We investigate how modified electron dispersion relations affect the structure of cold white dwarfs (WDs). The deformation is introduced only in the degenerate electron equation of state, through the energy of a single particle and the…
Satellite-derived Land Surface Temperature (LST) provides spatially comprehensive data that ground stations cannot match. However, its utility is frequently limited by severe data gaps due to the presence of clouds. As LST is essential for…
Closing an angular moment hierarchy at the stress level omits a definite back-action from higher Fermi-surface harmonics. For a circular two-dimensional Fermi surface, streaming changes angular momentum by one, so the shortest omitted…
Diffusion policies generate multimodal robot action sequences from demonstrations, but steering them toward deployment-time constraints typically relies on differentiable guidance costs. This excludes many practical safety constraints, such…
Artificial intelligence (AI) is accelerating materials prediction and design by enabling efficient exploration of chemical and structural spaces, with particular promise for novel materials discovery. However, novelty in materials discovery…
AI has begun to write systems code: agents now synthesize, verify, and deploy system components. Despite this shift, "AI-native" remains a marketing term with no precise technical definition. This paper gives it one. We define AI-nativeness…