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Abstract Creativity is a key 21st-century skill and a consistent predictor of academic learning outcomes. Despite decades of research on creativity and learning, little is known about the cognitive mechanisms underlying their relationship. In two studies, we examined whether creativity supports associative learning through associative thinking—the ability to generate novel word associations—an ability central to creativity which has not been previously tied to associative learning. In Study 1, we found that students who generated more novel word associations learned more words on a foreign language learning test 24 h later. In Study 2, we replicated and extended the effect to naturalistic creativity tasks (i.e., writing short stories and sketching line drawings), finding associative thinking mediated the relationship between creativity and associative learning. Importantly, both studies controlled for general intelligence. Our findings suggest that creativity’s contribution to learning operates partly through a shared cognitive capacity for making new connections. 

Package confusion attacks such as typosquatting threaten soft- ware supply chains. Attackers make packages with names that syntactically or semantically resemble legitimate ones, trick- ing engineers into installing malware. While prior work has developed defenses against package confusions in some soft- ware package registries, notably NPM, PyPI, and RubyGems, gaps remain: high false-positive rates, generalization to more software package ecosystems, and insights from real-world deployment. In this work, we introduce ConfuGuard, a state-of-art de- tector for package confusion threats. We begin by presenting the first empirical analysis of benign signals derived from prior package confusion data, uncovering their threat patterns, engineering practices, and measurable attributes. Advancing existing detectors, we leverage package metadata to distin- guish benign packages, and extend support from three up to seven software package registries. Our approach significantly reduces false positive rates (from 80% to 28%), at the cost of an additional 14s average latency to filter out benign pack- ages by analyzing the package metadata. ConfuGuard is used in production at our industry partner, whose analysts have already confirmed 630 real attacks detected by ConfuGuard 

Regular Expression Denial of Service (ReDoS) is a vulnerability class that has become prominent in recent years. Attackers can weaponize such weaknesses as part of asymmetric cyberattacks that exploit the slow worst-case matching time of regular expres- sion (regex) engines. In the past, problematic regular expressions have led to outages at Cloudflare and Stack Overflow, showing the severity of the problem. While ReDoS has drawn significant research attention, there has been no systematization of knowledge to delineate the state of the art and identify opportunities for fur- ther research. In this paper, we describe the existing knowledge on ReDoS. We first provide a systematic literature review, discussing approaches for detecting, preventing, and mitigating ReDoS vul- nerabilities. Then, our engineering review surveys the latest regex engines to examine whether and how ReDoS defenses have been re- alized. Combining our findings, we observe that (1) in the literature, almost no studies evaluate whether and how ReDoS vulnerabilities can be weaponized against real systems, making it difficult to assess their real-world impact; and (2) from an engineering view, many mainstream regex engines now have ReDoS defenses, rendering many threat models obsolete. We conclude with an extensive dis- cussion, highlighting avenues for future work. The open challenges in ReDoS research are to evaluate emerging defenses and support engineers in migrating to defended engines. We also highlight the parallel between performance bugs and asymmetric DoS, and we argue that future work should capitalize more on this similarity and adopt a more systematic view on ReDoS-like vulnerabilities. 

ABSTRACT Reproduction is often costly for males, as it may require the growth of structural traits that aid in dispersal to find females, competition over mating opportunities, and ejaculate production. The growth of such traits can be energetically demanding, and these demands often arise concurrently during development. As such, these traits may be especially prone to resource allocation trade‐offs. Yet, such traits are rarely studied in tandem. We designed a study to improve understanding of investment dynamics in flight muscle, a dispersal trait; a sexually selected weapon used in mate competition; and testes used for sperm production. We used the leaf‐footed cactus bug,Narnia femorata(Hemiptera: Coreidae), a species where males use their hindleg as weapons to compete for matings. Males can naturally drop their limbs, and when hindlegs are lost during development, adult males do not grow a weapon. Existing studies have revealed that testes growth increases when investment in weapons ceases. Yet, this work only examined responses to the loss of a single hindleg and limited the scope of traits to testes. Here, we examined weapon loss at two levels and investigated a third trait: dispersal. We found that testes size increased stepwise with limb loss; the loss of one hindleg weapon increased testes mass by around 9%, and two legs increased it by 20%. This intriguing pattern suggests a direct, quantity‐specific trade‐off in tissue development across traits. We also detected only a limited increase in dispersal investment when males did not grow weapons. Yet, dispersal may still be enhanced for those that drop hind legs; those without the substantial weight of hind limbs may have the potential to disperse farther. 

We report transition metal catalysis using novel chiral metal-chelating ligands featuring a silanol coordinating group and peptide-like aminoamide scaffold. The catalytic properties of the silanol ligand are demonstrated through an enantioselective Cu-catalyzed N–H insertion affording unnatural amino acid derivatives in high selectivity. Our investigations into the silanol coordination mode include DFT calculations, ligand structure investigations, and X-ray structure analyses, which support the formation of an H-bond stabilized silanol-chelating copper carbenoid complex. A p–p stacking interaction revealed by DFT calculations is proposed to enable selectivity for aryl diazoacetate substrates, overcoming some of the traditional limitations of using these substrates. 

As AI systems grow increasingly specialized and complex, managing hardware heterogeneity becomes a pressing challenge. How can we efficiently coordinate and synchronize heterogeneous hardware resources to achieve high utilization? How can we minimize the friction of transitioning between diverse computation phases, reducing costly stalls from initialization, pipeline setup, or drain? Our insight is that a network abstraction at the ISA level naturally unifies heterogeneous resource orchestration and phase transitions. This paper presents a Reconfigurable Stream Network Architecture (RSN), a novel ISA abstraction designed for the DNN domain. RSN models the datapath as a circuit-switched network with stateful functional units as nodes and data streaming on the edges. Programming a computation corresponds to triggering a path. Software is explicitly exposed to the compute and communication latency of each functional unit, enabling precise control over data movement for optimizations such as compute-communication overlap and layer fusion. As nodes in a network naturally differ, the RSN abstraction can efficiently virtualize heterogeneous hardware resources by separating control from the data plane, enabling low instruction-level intervention. We build a proof-of-concept design RSN-XNN on VCK190, a heterogeneous platform with FPGA fabric and AI engines. Compared to the SOTA solution on this platform, it reduces latency by 6.1x and improves throughput by 2.4x–3.2x. Compared to the T4 GPU with the same FP32 performance, it matches latency with only 18% of the memory bandwidth. Compared to the A100 GPU at the same 7nm process node, it achieves 2.1x higher energy efficiency in FP32.