The following input profile is derived from election data of the 2009 mayoral election in Burlington, Vermont, which is known to exhibit interesting voting theoretic properties. One of them, James Simpson for the Green Party, gathered almost no votes (35 first-place votes compared to 1,306 first-place votes for the next-lowest candidate), so we ignore this candidate for convenience. The other four candidates are K = Bob Kiss M = Andy Montroll H = Dan Smith W = Kurt Wright. The resulting profile consists of the following 3,352 votes. For instance, the plurality winner (W) is different from the winner under Instant Runoff Voting rule (K) which Burlington used, and both are different from the Condorcet winner (M).

Democrat Vermont Sen. Bernie Sanders said the U.S. has become an "oligarchic society" while responding to President Donald Trump's address to a joint Congress Tuesday evening. "The Trump administration is not hiding it," Sanders said in a streamed response to Trump's address Tuesday. "The Trump administration is a government of the billionaire class by the billionaire class, and for the billionaire class. Notwithstanding some of their rhetoric, this is a government that could care less about ordinary Americans and the working families of our country. My friends, we are no longer moving toward oligarchy. We are living in an oligarchic society."

The generative capabilities of LLM models present opportunities in accelerating tasks and concerns with the authenticity of the knowledge it produces. To address the concerns, we present a computational approach that systematically evaluates the factual accuracy of biomedical knowledge that an LLM model has been prompted to generate. Our approach encompasses two processes: the generation of disease-centric associations and the verification of them using the semantic knowledge of the biomedical ontologies. Using ChatGPT as the select LLM model, we designed a set of prompt-engineering processes to generate linkages between diseases, drugs, symptoms, and genes to establish grounds for assessments. Experimental results demonstrate high accuracy in identifying disease terms (88%-97%), drug names (90%-91%), and genetic information (88%-98%). The symptom term identification accuracy was notably lower (49%-61%), as verified against the DOID, ChEBI, SYMPTOM, and GO ontologies accordingly. The verification of associations reveals literature coverage rates of (89%-91%) among disease-drug and disease-gene associations. The low identification accuracy for symptom terms also contributed to the verification of symptom-related associations (49%-62%).

The following input profile is derived from election data of the 2009 mayoral election in Burlington, Vermont, which is known to exhibit interesting voting theoretic properties. One of them, James Simpson for the Green Party, gathered almost no votes (35 first-place votes compared to 1,306 first-place votes for the next-lowest candidate), so we ignore this candidate for convenience. The other four candidates are K = Bob Kiss M = Andy Montroll H = Dan Smith W = Kurt Wright. The resulting profile consists of the following 3,352 votes. For instance, the plurality winner (W) is different from the winner under Instant Runoff Voting rule (K) which Burlington used, and both are different from the Condorcet winner (M).

Automatic decision and prediction systems are increasingly deployed in applications where they significantly impact the livelihood of people, such as for predicting the creditworthiness of loan applicants or the recidivism risk of defendants. These applications have given rise to a new class of algorithmic-fairness specifications that require the systems to decide and predict without bias against social groups. Verifying these specifications statically is often out of reach for realistic systems, since the systems may, e.g., employ complex learning components, and reason over a large input space. In this paper, we therefore propose stream-based monitoring as a solution for verifying the algorithmic fairness of decision and prediction systems at runtime. Concretely, we present a principled way to formalize algorithmic fairness over temporal data streams in the specification language RTLola and demonstrate the efficacy of this approach on a number of benchmarks. Besides synthetic scenarios that particularly highlight its efficiency on streams with a scaling amount of data, we notably evaluate the monitor on real-world data from the recidivism prediction tool COMPAS.

A German national suspect on a legal visa allegedly killed a United States Border Agent during a traffic stop in Vermont near the Northern border, Fox News Digital has confirmed. "Our partners at the Department of Homeland Security confirmed the deceased subject is a German national in the U.S. on a current Visa," a spokesperson with FBI Albany said. Officials said on Monday, Jan. 20, 44-year-old U.S. Border Patrol Agent David "Chris" Maland was struck by gunfire during a traffic stop on Interstate 91 between Newport and Orleans, Vermont. In a statement, FBI Albany said that Maland was a U.S. Air Force veteran, saying: "We are heartbroken for our partners and share in their grief as they mourn the loss of their colleague." A Border Patrol Agent moves a robotic device next to Border Patrol vehicle on southbound Route 91 near Newport Vermont, where a U.S. Border Patrol Agent was shot dead, on Monday, January 20, 2025.

The U.S. Border Patrol agent killed in a shootout with armed suspects Monday has been identified as 44-year-old David Maland, a Customs and Border Protection source told Fox News. The veteran agent died Monday after a traffic stop on Interstate 91 between Newport and Orleans, Vermont, around 3:15 p.m. Monday, about 20 miles south of the U.S.-Canada border, according to the Department of Homeland Security. "A Border Patrol agent assigned to the US Border Patrol's Swanton Sector was fatally shot in the line of duty," acting DHS Secretary Benjamine Huffman said in a statement. "Every single day, our Border Patrol agents put themselves in harm's way so that Americans and our homeland are safe and secure." Wide shot of the scene on southbound Route 91 near Newport, Vermont, where a U.S. Border Patrol Agent was shot dead, Monday, January 20, 2025.

Analysis of single-cell RNA sequencing data is often conducted through network projections such as coexpression networks, primarily due to the abundant availability of network analysis tools for downstream tasks. However, this approach has several limitations: loss of higher-order information, inefficient data representation caused by converting a sparse dataset to a fully connected network, and overestimation of coexpression due to zero-inflation. To address these limitations, we propose conceptualizing scRNA-seq expression data as hypergraphs, which are generalized graphs in which the hyperedges can connect more than two vertices. In the context of scRNA-seq data, the hypergraph nodes represent cells and the edges represent genes. Each hyperedge connects all cells where its corresponding gene is actively expressed and records the expression of the gene across different cells. This hypergraph conceptualization enables us to explore multi-way relationships beyond the pairwise interactions in coexpression networks without loss of information. We propose two novel clustering methods: (1) the Dual-Importance Preference Hypergraph Walk (DIPHW) and (2) the Coexpression and Memory-Integrated Dual-Importance Preference Hypergraph Walk (CoMem-DIPHW). They outperform established methods on both simulated and real scRNA-seq datasets. The improvement brought by our proposed methods is especially significant when data modularity is weak. Furthermore, CoMem-DIPHW incorporates the gene coexpression network, cell coexpression network, and the cell-gene expression hypergraph from the single-cell abundance counts data altogether for embedding computation. This approach accounts for both the local level information from single-cell level gene expression and the global level information from the pairwise similarity in the two coexpression networks.

Classifying genome sequences based on metadata has been an active area of research in comparative genomics for decades with many important applications across the life sciences. Established methods for classifying genomes can be broadly grouped into sequence alignment-based and alignment-free models. Conventional alignment-based models rely on genome similarity measures calculated based on local sequence alignments or consistent ordering among sequences. However, such methods are computationally expensive when dealing with large ensembles of even moderately sized genomes. In contrast, alignment-free (AF) approaches measure genome similarity based on summary statistics in an unsupervised setting and are efficient enough to analyze large datasets. However, both alignment-based and AF methods typically assume fixed scoring rubrics that lack the flexibility to assign varying importance to different parts of the sequences based on prior knowledge. In this study, we integrate AI and network science approaches to develop a comparative genomic analysis framework that addresses these limitations. Our approach, termed the Genome Misclassification Network Analysis (GMNA), simultaneously leverages misclassified instances, a learned scoring rubric, and label information to classify genomes based on associated metadata and better understand potential drivers of misclassification. We evaluate the utility of the GMNA using Naive Bayes and convolutional neural network models, supplemented by additional experiments with transformer-based models, to construct SARS-CoV-2 sampling location classifiers using over 500,000 viral genome sequences and study the resulting network of misclassifications. We demonstrate the global health potential of the GMNA by leveraging the SARS-CoV-2 genome misclassification networks to investigate the role human mobility played in structuring geographic clustering of SARS-CoV-2.

Tokenization is a necessary component within the current architecture of many language models, including the transformer-based large language models (LLMs) of Generative AI, yet its impact on the model's cognition is often overlooked. We argue that LLMs demonstrate that the Distributional Hypothesis (DH) is sufficient for reasonably human-like language performance, and that the emergence of human-meaningful linguistic units among tokens motivates linguistically-informed interventions in existing, linguistically-agnostic tokenization techniques, particularly with respect to their roles as (1) semantic primitives and as (2) vehicles for conveying salient distributional patterns from human language to the model. We explore tokenizations from a BPE tokenizer; extant model vocabularies obtained from Hugging Face and tiktoken; and the information in exemplar token vectors as they move through the layers of a RoBERTa (large) model. Besides creating sub-optimal semantic building blocks and obscuring the model's access to the necessary distributional patterns, we describe how tokenization pretraining can be a backdoor for bias and other unwanted content, which current alignment practices may not remediate. Additionally, we relay evidence that the tokenization algorithm's objective function impacts the LLM's cognition, despite being meaningfully insulated from the main system intelligence.