Systematic Biases in LLM Simulations of Debates

Recent advancements in natural language processing, especially the emergence of Large Language Models (LLMs), have opened exciting possibilities for constructing computational simulations designed to replicate human behavior accurately. However, LLMs are complex statistical learners without straightforward deductive rules, making them prone to unexpected behaviors. In this study, we highlight the limitations of LLMs in simulating human interactions, particularly focusing on LLMs' ability to simulate political debates. Our findings indicate a tendency for LLM agents to conform to the model's inherent social biases despite being directed to debate from certain political perspectives. This tendency results in behavioral patterns that seem to deviate from well-established social dynamics among humans. We reinforce these observations using an automatic self-fine-tuning method, which enables us to manipulate the biases within the LLM and demonstrate that agents subsequently align with the altered biases. These results underscore the need for further research to develop methods that help agents overcome these biases, a critical step toward creating more realistic simulations

Derandomized Novelty Detection with FDR Control via Conformal E-values

Conformal prediction and other randomized model-free inference techniques are gaining increasing attention as general solutions to rigorously calibrate the output of any machine learning algorithm for novelty detection. This paper contributes to the field by developing a novel method for mitigating their algorithmic randomness, leading to an even more interpretable and reliable framework for powerful novelty detection under false discovery rate control. The idea is to leverage suitable conformal e-values instead of p-values to quantify the significance of each finding, which allows the evidence gathered from multiple mutually dependent analyses of the same data to be seamlessly aggregated. Further, the proposed method can reduce randomness without much loss of power, partly thanks to an innovative way of weighting conformal e-values based on additional side information carefully extracted from the same data. Simulations with synthetic and real data confirm this solution can be effective at eliminating random noise in the inferences obtained with state-of-the-art alternative techniques, sometimes also leading to higher power.Comment: 19 pages, 11 figure

Universal computing by DNA origami robots in a living animal

Biological systems are collections of discrete molecular objects that move around and collide with each other. Cells carry out elaborate processes by precisely controlling these collisions, but developing artificial machines that can interface with and control such interactions remains a significant challenge. DNA is a natural substrate for computing and has been used to implement a diverse set of mathematical problems1-3, logic circuits4-6 and robotics7-9. The molecule also naturally interfaces with living systems, and different forms of DNA-based biocomputing have previously been demonstrated10-13. Here we show that DNA origami14-16 can be used to fabricate nanoscale robots that are capable of dynamically interacting with each other17-18 in a living animal. The interactions generate logical outputs, which are relayed to switch molecular payloads on or off. As a proof-of-principle, we use the system to create architectures that emulate various logic gates (AND, OR, XOR, NAND, NOT, CNOT, and a half adder). Following an ex vivo prototyping phase, we successfully employed the DNA origami robots in living cockroaches (Blaberus discoidalis) to control a molecule that targets the cells of the animal

Drosophila HUWE1 Ubiquitin Ligase Regulates Endoreplication and Antagonizes JNK Signaling During Salivary Gland Development

The HECT-type ubiquitin ligase HECT, UBA and WWE Domain Containing 1, (HUWE1) regulates key cancer-related pathways, including the Myc oncogene. It affects cell proliferation, stress and immune signaling, mitochondria homeostasis, and cell death. HUWE1 is evolutionarily conserved from Caenorhabditis elegance to Drosophila melanogaster and Humans. Here, we report that the Drosophila ortholog, dHUWE1 (CG8184), is an essential gene whose loss results in embryonic lethality and whose tissue-specific disruption establishes its regulatory role in larval salivary gland development. dHUWE1 is essential for endoreplication of salivary gland cells and its knockdown results in the inability of these cells to replicate DNA. Remarkably, dHUWE1 is a survival factor that prevents premature activation of JNK signaling, thus preventing the disintegration of the salivary gland, which occurs physiologically during pupal stages. This function of dHUWE1 is general, as its inhibitory effect is observed also during eye development and at the organismal level. Epistatic studies revealed that the loss of dHUWE1 is compensated by dMyc proeitn expression or the loss of dmP53. dHUWE1 is therefore a conserved survival factor that regulates organ formation during Drosophila development.Peer reviewe

The Proto-Oncogene Int6 Is Essential for Neddylation of Cul1 and Cul3 in Drosophila

Int6 is a proto-oncogene implicated in various types of cancer, but the mechanisms underlying its activity are not clear. Int6 encodes a subunit of the eukaryotic translation initiation factor 3, and interacts with two related complexes, the proteasome, whose activity is regulated by Int6 in S. pombe, and the COP9 signalosome. The COP9 signalosome regulates the activity of Cullin-Ring Ubiquitin Ligases via deneddylation of their cullin subunit. We report here the generation and analysis of two Drosophila mutants in Int6. The mutants are lethal demonstrating that Int6 is an essential gene. The mutant larvae accumulate high levels of non-neddylated Cul1, suggesting that Int6 is a positive regulator of cullin neddylation. Overexpression in Int6 in cell culture leads to accumulation of neddylated cullins, further supporting a positive role for Int6 in regulating neddylation. Thus Int6 and the COP9 signalosome play opposing roles in regulation of cullin neddylation

Explainable automated recognition of emotional states from canine facial expressions: the case of positive anticipation and frustration.

In animal research, automation of affective states recognition has so far mainly addressed pain in a few species. Emotional states remain uncharted territories, especially in dogs, due to the complexity of their facial morphology and expressions. This study contributes to fill this gap in two aspects. First, it is the first to address dog emotional states using a dataset obtained in a controlled experimental setting, including videos from (n = 29) Labrador Retrievers assumed to be in two experimentally induced emotional states: negative (frustration) and positive (anticipation). The dogs' facial expressions were measured using the Dogs Facial Action Coding System (DogFACS). Two different approaches are compared in relation to our aim: (1) a DogFACS-based approach with a two-step pipeline consisting of (i) a DogFACS variable detector and (ii) a positive/negative state Decision Tree classifier; (2) An approach using deep learning techniques with no intermediate representation. The approaches reach accuracy of above 71% and 89%, respectively, with the deep learning approach performing better. Secondly, this study is also the first to study explainability of AI models in the context of emotion in animals. The DogFACS-based approach provides decision trees, that is a mathematical representation which reflects previous findings by human experts in relation to certain facial expressions (DogFACS variables) being correlates of specific emotional states. The deep learning approach offers a different, visual form of explainability in the form of heatmaps reflecting regions of focus of the network's attention, which in some cases show focus clearly related to the nature of particular DogFACS variables. These heatmaps may hold the key to novel insights on the sensitivity of the network to nuanced pixel patterns reflecting information invisible to the human eye

West Nile Virus: Seroprevalence in Animals in Palestine and Israel

West Nile virus (WNV) epidemiological situation in Israel and Palestine, due to their unique location, draws attention following to the global spread of West Nile fever (WNF). Although much information is available from Israel on clinical cases and prevalence of WNV, clinical cases are rarely reported in Palestine, and prevalence is not known. The objectives of this study were to determine WNV seroprevalence in various domestic animals in Palestine and to reevaluate current seroprevalence, force of infection, and risk factors for WNV exposure in horses in Israel. Sera samples were collected from 717 animals from Palestine and Israel (460 horses, 124 donkeys, 3 mules, 50 goats, 45 sheep, and 35 camels). Two hundred and ten horses were sampled twice. The level of WNV antibodies was determined using commercial Enzyme-linked Immunosorbent Assay (ELISA) Kit. Seroprevalence in equids was 73%. Seroprevalence in Israel (84.6%) was significantly higher than in Palestine (48.6%). Seroprevalence in horses (82.6%) was significantly higher than in donkeys and mules (39.3%). Multivariable statistical analysis showed that geographical area, landscape features (altitude), environmental factors (land surface temperature during the day [LSTD]), species, and age significantly influenced WNV seroprevalence. Fourteen of 95 (14.7%) sheep and goats and 14/35 camels (40%) sampled in Palestine were seropositive for WNV. Of the horses that were sampled twice, 82.8% were seropositive for WNV at the first sampling, and all remained seropositive. Three of the seronegative horses, all from Palestine, converted to positive when resampled (8.5%). The results indicate that domestic animals in Palestine were infected with WNV in the past, and the seroconversion indicates that WNV was circulating in Palestine in the summer of 2014. Control measures to prevent human infection should be implemented in Palestine. Anti WNV antibodies in domestic animals suggest that those species can be used as sentinels for WNV activity in areas where most horses are either seropositive or vaccinated.This research was supported financially by grant 2014.52146 funded by the Netherlands Ministry of Foreign Affairs (The Hague, Netherlands)

Explainable automated recognition of emotional states from canine facial expressions: the case of positive anticipation and frustration

In animal research, automation of affective states recognition has so far mainly addressed pain in a few species. Emotional states remain uncharted territories, especially in dogs, due to the complexity of their facial morphology and expressions. This study contributes to fill this gap in two aspects. First, it is the first to address dog emotional states using a dataset obtained in a controlled experimental setting, including videos from (n = 29) Labrador Retrievers assumed to be in two experimentally induced emotional states: negative (frustration) and positive (anticipation). The dogs’ facial expressions were measured using the Dogs Facial Action Coding System (DogFACS). Two different approaches are compared in relation to our aim: (1) a DogFACS-based approach with a two-step pipeline consisting of (i) a DogFACS variable detector and (ii) a positive/negative state Decision Tree classifier; (2) An approach using deep learning techniques with no intermediate representation. The approaches reach accuracy of above 71% and 89%, respectively, with the deep learning approach performing better. Secondly, this study is also the first to study explainability of AI models in the context of emotion in animals. The DogFACS-based approach provides decision trees, that is a mathematical representation which reflects previous findings by human experts in relation to certain facial expressions (DogFACS variables) being correlates of specific emotional states. The deep learning approach offers a different, visual form of explainability in the form of heatmaps reflecting regions of focus of the network’s attention, which in some cases show focus clearly related to the nature of particular DogFACS variables. These heatmaps may hold the key to novel insights on the sensitivity of the network to nuanced pixel patterns reflecting information invisible to the human eye

Tax evasion in a Cournot oligopoly with endogenous entry

If an additional competitor reduces output per firm in a homogenous Cournot-oligopoly, market entry will be excessive. Taxes can correct the so-called business stealing externality. We investigate how evading a tax on operating profits affects the excessive entry prediction. Tax evasion raises the number of firms in market equilibrium and can alter their welfare-maximizing number. In consequence, evasion can aggravate or mitigate excessive entry. Which of these outcomes prevails is determined by the direct welfare consequences of tax evasion and the relationship between evasion and the tax base. We also determine conditions which imply that overall welfare declines with tax evasion