arXane › Computer Science › Computer Science
Wednesday, 4 November 2026: 1 new paper, 2 cross-lists and 2 replacements in Emerging Technologies (cs.ET), in arXiv's order, with full abstracts. The same papers as arXiv's own listing, set as a newspaper; every title links to arXiv.
Rapid Single Flux Quantum (RSFQ) logic can operate at ultra-high clock frequencies; however, its physical design is complicated by explicit splitter trees and deep gate-level pipelines in which every logic gate is clocked. To address this challenge, we present qLevelPlace, a level-based placement framework for RSFQ circuits. We separate gate and splitter placement into two stages to better support levelguided gate-to-row assignment and use a timing-aware quadratic cost function to penalize long interconnects. Experiments on the benchmark circuits show that, on average, qLevelPlace achieves a $2.65\times$ higher clock frequency and a 51% reduction in layout area compared with the state-of-the-art RSFQ placer qPlace.
Heat-related mortality varies among individuals under similar environmental conditions, yet environmental exposure and genetic susceptibility are typically modelled separately. We propose a signal-processing cascade linking satellite-derived land-surface temperature to sequence-dependent heat-shock response and mortality risk. The framework combines distributed-lag filtering of thermal exposure, nonlinear HSF1 activation, cross-correlation with an HSF1 position-weight-matrix kernel, and Hill-model mapping of sequence-dependent binding changes to regulatory response. Applied to 52 heat-response genes and 157 naturally occurring variants, the framework identifies 21 heat-shock-element arrays across 17 genes. Variants in canonical arrays are significantly enriched for predicted binding disruption ($p=3\times10^{-4}$). Propagating these effects through the cascade yields a peak predicted mortality-risk multiplier of $1.68\times$ at $40$--$42\,^{\circ}\mathrm{C}$. The framework demonstrates how environmental and genomic signals can be integrated to model inter-individual variation in heat-related risk.
Large language model (LLM) assistants increasingly advise backers on which crowdfunding campaigns to support. LLMs have known race and gender problems, and it is unsettled whether they reproduce, erase, or reverse the discrimination documented in entrepreneurial finance. We ask when, and through which decision process, LLM assistants use founders' gender and ethnicity. In a pre-specified choice-based conjoint experiment, seven LLMs chose among five synthetic campaigns with independently randomized quality signals and founder names, under three backer personas and a persona-free control (40,248 responses). By default, founder names barely matter: under budget-conscious and delivery-focused personas, gender and Black-name effects are statistically equivalent to zero. When the backer wants to support underrepresented creators, the models favor Black-signaled (+8.1 percentage points; positive in all seven models) and female-signaled (+5.1) founders. They do so most strongly among near-tied campaigns and for the founder who alone carries the signal, and they almost never name gender or ethnicity in their reasons. The results fit an agency account of algorithmic discrimination: an aligned assistant takes its demographic preferences from its user rather than from its training data.