Bounds On (t,r)(t,r) Broadcast Domination of nn-Dimensional Grids

In this paper, we study at a variant of graph domination known as $(t, r)$ broadcast domination, first defined by Blessing, Insko, Johnson, and Mauretour in 2015. In this variant, each broadcast provides $t-d$ reception to each vertex a distance $d < t$ from the broadcast. A vertex is considered dominated if it receives $r$ total reception from all broadcasts. Our main results provide some upper and lower bounds on the density of a $(t, r)$ dominating pattern of an infinite grid, as well as methods of computing them. Also, when $r \ge 2$ we describe a family of counterexamples to a generalization of Vizing's Conjecture to $(t,r)$ broadcast domination.Comment: 15 pages, 4 figure

Learning the Wrong Lessons: Inserting Trojans During Knowledge Distillation

In recent years, knowledge distillation has become a cornerstone of efficiently deployed machine learning, with labs and industries using knowledge distillation to train models that are inexpensive and resource-optimized. Trojan attacks have contemporaneously gained significant prominence, revealing fundamental vulnerabilities in deep learning models. Given the widespread use of knowledge distillation, in this work we seek to exploit the unlabelled data knowledge distillation process to embed Trojans in a student model without introducing conspicuous behavior in the teacher. We ultimately devise a Trojan attack that effectively reduces student accuracy, does not alter teacher performance, and is efficiently constructible in practice.Comment: ICLR 2023 Workshop on Backdoor Attacks and Defenses in Machine Learnin

Baselines for Identifying Watermarked Large Language Models

We consider the emerging problem of identifying the presence and use of watermarking schemes in widely used, publicly hosted, closed source large language models (LLMs). We introduce a suite of baseline algorithms for identifying watermarks in LLMs that rely on analyzing distributions of output tokens and logits generated by watermarked and unmarked LLMs. Notably, watermarked LLMs tend to produce distributions that diverge qualitatively and identifiably from standard models. Furthermore, we investigate the identifiability of watermarks at varying strengths and consider the tradeoffs of each of our identification mechanisms with respect to watermarking scenario. Along the way, we formalize the specific problem of identifying watermarks in LLMs, as well as LLM watermarks and watermark detection in general, providing a framework and foundations for studying them

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Altered adenosine-to-inosine RNA editing in human cancer

Adenosine-to-inosine (A-to-I) RNA editing was recently shown to be abundant in the human transcriptome, affecting thousands of genes. Employing a bioinformatic approach, we identified significant global hypoediting of Alu repetitive elements in brain, prostate, lung, kidney, and testis tumors. Experimental validation confirmed this finding, showing significantly reduced editing in Alu sequences within MED13 transcripts in brain tissues. Looking at editing of specific recoding and noncoding sites, including in cancer-related genes, a more complex picture emerged, with a gene-specific editing pattern in tumors vs. normal tissues. Additionally, we found reduced RNA levels of all three editing mediating enzymes, ADAR, ADARB1, and ADARB2, in brain tumors. The reduction of ADARB2 correlated with the grade of malignancy of glioblastoma multiforme, the most aggressive of brain tumors, displaying a 99% decrease in ADARB2 RNA levels. Consistently, overexpression of ADAR and ADARB1 in the U87 glioblastoma multiforme cell line resulted in decreased proliferation rate, suggesting that reduced A-to-I editing in brain tumors is involved in the pathogenesis of cancer. Altered epigenetic control was recently shown to play a central role in oncogenesis. We suggest that A-to-I RNA editing may serve as an additional epigenetic mechanism relevant to cancer development and progression