Miracle-dz/UniFAR-FTUs huggingface dataset:
Mindful Explanations: Prevalence and Impact of Mind Attribution in XAI Research

When users perceive AI systems as mindful, independent agents, they hold them responsible instead of the AI experts who created and designed these systems. So far, it has not been studied whether explanations support this shift in responsibility through the use of mind-attributing verbs like"to think". To better understand the prevalence of mind-attributing explanations we analyse AI explanations in 3,533 explainable AI (XAI) research articles from the Semantic Scholar Open Research Corpus (S2ORC). Using methods from semantic shift detection, we identify three dominant types of mind attribution: (1) metaphorical (e.g."to learn"or"to predict"), (2) awareness (e.g."to consider"), and (3) agency (e.g."to make decisions"). We then analyse the impact of mind-attributing explanations on awareness and responsibility in a vignette-based experiment with 199 participants. We find that participants who were given a mind-attributing explanation were more likely to rate the AI system as aware of the harm it caused. Moreover, the mind-attributing explanation had a responsibility-concealing effect: Considering the AI experts' involvement lead to reduced ratings of AI responsibility for participants who were given a non-mind-attributing or no explanation. In contrast, participants who read the mind-attributing explanation still held the AI system responsible despite considering the AI experts' involvement. Taken together, our work underlines the need to carefully phrase explanations about AI systems in scientific writing to reduce mind attribution and clearly communicate human responsibility.
XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX

Inspired by the diversity and depth of XLand and the simplicity and minimalism of MiniGrid, we present XLand-MiniGrid, a suite of tools and grid-world environments for meta-reinforcement learning research. Written in JAX, XLand-MiniGrid is designed to be highly scalable and can potentially run on GPU or TPU accelerators, democratizing large-scale experimentation with limited resources. Along with the environments, XLand-MiniGrid provides pre-sampled benchmarks with millions of unique tasks of varying difficulty and easy-to-use baselines that allow users to quickly start training adaptive agents. In addition, we have conducted a preliminary analysis of scaling and generalization, showing that our baselines are capable of reaching millions of steps per second during training and validating that the proposed benchmarks are challenging.
Learning a Diffusion Model Policy from Rewards via Q-Score Matching

Diffusion models have become a popular choice for representing actor policies in behavior cloning and offline reinforcement learning. This is due to their natural ability to optimize an expressive class of distributions over a continuous space. However, previous works fail to exploit the score-based structure of diffusion models, and instead utilize a simple behavior cloning term to train the actor, limiting their ability in the actor-critic setting. In this paper, we focus on off-policy reinforcement learning and propose a new method for learning a diffusion model policy that exploits the linked structure between the score of the policy and the action gradient of the Q-function. We denote this method Q-score matching and provide theoretical justification for this approach. We conduct experiments in simulated environments to demonstrate the effectiveness of our proposed method and compare to popular baselines.
Prosolvable rigidity of surface groups

Surface groups are known to be the Poincar\'e Duality groups of dimension two since the work of Eckmann, Linnell and M\"uller. We prove a prosolvable analogue of this result that allows us to show that surface groups are profinitely (and prosolvably) rigid among finitely generated groups that satisfy $\mathrm{cd}(G)=2$ and $b_2^{(2)}(G)=0$. We explore two other consequences. On the one hand, we derive that if $u$ is a surface word of a finitely generated free group $F$ and $v\in F$ is measure equivalent to $u$ in all finite solvable quotients of $F$ then $u$ and $v$ belong to the same $\mathrm{Aut}(F)$-orbit. Finally, we get a partial result towards Mel'nikov's surface group conjecture. Let $F$ be a free group of rank $n\geq 3$ and let $w\in F$. Suppose that $G=F/\langle\! \langle w\rangle\! \rangle$ is a residually finite group all of whose finite-index subgroups are one-relator groups. Then $G$ is 2-free. Moreover, we show that if $H^2(G; \mathbb{Z})\neq 0$ then $G$ must be a surface group.
Congestions and Spectral Transition in Time-Lagged Correlations of Motorway Traffic

The congestion of a motorway section is propagated to its neighbouring sections, leading to correlations. The resulting correlation matrix encodes the information on congestion. Here, we study symmetrized time-lagged correlations and show how their spectral properties reveal congestion durations. We carry out an empirical analysis and find a transition behavior for the dominant eigenvalue as function of the time lag. Furthermore, we set up a numerical simulation model for indicator time series of traffic phases as well as a simplified model that we treat analytically. We consider various scenarios. Our results reveal a nonlinear relation between the spectral transition and the congestion duration. In our simplified model, we derive this relation analytically.
Virtual Reality-Assisted Physiotherapy for Visuospatial Neglect Rehabilitation: A Proof-of-Concept Study

This study explores a VR-based intervention for Visuospatial neglect (VSN), a post-stroke condition. It aims to develop a VR task utilizing interactive visual-audio cues to improve sensory-motor training and assess its impact on VSN patients' engagement and performance. Collaboratively designed with physiotherapists, the VR task uses directional and auditory stimuli to alert and direct patients, tested over 12 sessions with two individuals. Results show a consistent decrease in task completion variability and positive patient feedback, highlighting the VR task's potential for enhancing engagement and suggesting its feasibility in rehabilitation. The study underlines the significance of collaborative design in healthcare technology and advocates for further research with a larger sample size to confirm the benefits of VR in VSN treatment, as well as its applicability to other multimodal disorders.
Error-corrected Hadamard gate simulated at the circuit level

We simulate the logical Hadamard gate in the surface code under a circuit-level noise model, compiling it to a physical circuit on square-grid connectivity hardware. Our paper is the first to do this for a logical unitary gate on a quantum error-correction code. We consider two proposals, both via patch-deformation: one that applies a transversal Hadamard gate (i.e. a domain wall through time) to interchange the logical $X$ and $Z$ strings, and another that applies a domain wall through space to achieve this interchange. We explain in detail why they perform the logical Hadamard gate by tracking how the stabilisers and the logical operators are transformed in each quantum error-correction round. We optimise the physical circuits and evaluate their logical failure probabilities, which we find to be comparable to those of a quantum memory experiment for the same number of quantum error-correction rounds. We present syndrome-extraction circuits that maintain the same effective distance under circuit-level noise as under phenomenological noise. We also explain how a $SWAP$-quantum error-correction round (required to return the patch to its initial position) can be compiled to only four two-qubit gate layers. This can be applied to more general scenarios and, as a byproduct, explains from first principles how the ''stepping'' circuits of the recent Google paper [McEwen, Bacon, and Gidney, Quantum 7, 1172 (2023)] can be constructed.
Scene-Conditional 3D Object Stylization and Composition

Recently, 3D generative models have made impressive progress, enabling the generation of almost arbitrary 3D assets from text or image inputs. However, these approaches generate objects in isolation without any consideration for the scene where they will eventually be placed. In this paper, we propose a framework that allows for the stylization of an existing 3D asset to fit into a given 2D scene, and additionally produce a photorealistic composition as if the asset was placed within the environment. This not only opens up a new level of control for object stylization, for example, the same assets can be stylized to reflect changes in the environment, such as summer to winter or fantasy versus futuristic settings-but also makes the object-scene composition more controllable. We achieve this by combining modeling and optimizing the object's texture and environmental lighting through differentiable ray tracing with image priors from pre-trained text-to-image diffusion models. We demonstrate that our method is applicable to a wide variety of indoor and outdoor scenes and arbitrary objects.
Cosmology in Lorentzian Regge calculus: causality violations, massless scalar field and discrete dynamics

We develop a model of spatially flat, homogeneous and isotropic cosmology in Lorentzian Regge calculus, employing 4-dimensional Lorentzian frusta as building blocks. By examining the causal structure of the discrete spacetimes obtained by gluing such 4-frusta in spatial and temporal direction, we find causality violations if the sub-cells connecting spatial slices are spacelike. A Wick rotation to the Euclidean theory can be defined globally by a complexification of the variables and an analytic continuation of the action. Introducing a discrete free massless scalar field, we study its equations of motion and show that it evolves monotonically. Furthermore, in a continuum limit, we obtain the equations of a homogeneous scalar field on a spatially flat Friedmann background. Vacuum solutions to the causally regular Regge equations are static and flat and show a restoration of time reparametrisation invariance. In the presence of a scalar field, the height of a frustum is a dynamical variable that has a solution if causality violations are absent and if an inequality relating geometric and matter boundary data is satisfied. Edge lengths of cubes evolve monotonically, yielding a contracting or an expanding branch of the universe. In a small deficit angle expansion, the system can be deparametrised via the scalar field and a continuum limit of the discrete theory can be defined which we show to yield the relational Friedmann equation. These properties are obstructed if higher orders of the deficit angle are taken into account. Our results suggest that the inclusion of timelike sub-cells is necessary for a causally regular classical evolution in this symmetry restricted setting. Ultimately, this works serves as a basis for forthcoming investigations on the cosmological path integral within the framework of effective spin foams.
Mithridates: Auditing and Boosting Backdoor Resistance of Machine Learning Pipelines

Machine learning (ML) models trained on data from potentially untrusted sources are vulnerable to poisoning. A small, maliciously crafted subset of the training inputs can cause the model to learn a"backdoor"task (e.g., misclassify inputs with a certain feature) in addition to its main task. Recent research proposed many hypothetical backdoor attacks whose efficacy heavily depends on the configuration and training hyperparameters of the target model. Given the variety of potential backdoor attacks, ML engineers who are not security experts have no way to measure how vulnerable their current training pipelines are, nor do they have a practical way to compare training configurations so as to pick the more resistant ones. Deploying a defense requires evaluating and choosing from among dozens of research papers and re-engineering the training pipeline. In this paper, we aim to provide ML engineers with pragmatic tools to audit the backdoor resistance of their training pipelines and to compare different training configurations, to help choose one that best balances accuracy and security. First, we propose a universal, attack-agnostic resistance metric based on the minimum number of training inputs that must be compromised before the model learns any backdoor. Second, we design, implement, and evaluate Mithridates a multi-stage approach that integrates backdoor resistance into the training-configuration search. ML developers already rely on hyperparameter search to find configurations that maximize the model's accuracy. Mithridates extends this standard tool to balance accuracy and resistance without disruptive changes to the training pipeline. We show that hyperparameters found by Mithridates increase resistance to multiple types of backdoor attacks by 3-5x with only a slight impact on accuracy. We also discuss extensions to AutoML and federated learning.
NoisyNN: Exploring the Influence of Information Entropy Change in Learning Systems

We explore the impact of entropy change in deep learning systems via noise injection at different levels, i.e., the latent space and input image. The series of models that employ our methodology are collectively known as Noisy Neural Networks (NoisyNN), with examples such as NoisyViT and NoisyCNN. Noise is conventionally viewed as a harmful perturbation in various deep learning architectures, such as convolutional neural networks (CNNs) and vision transformers (ViTs), as well as different learning tasks like image classification and transfer learning. However, this work shows noise can be an effective way to change the entropy of the learning system. We demonstrate that specific noise can boost the performance of various deep architectures under certain conditions. We theoretically prove the enhancement gained from positive noise by reducing the task complexity defined by information entropy and experimentally show the significant performance gain in large image datasets, such as the ImageNet. Herein, we use the information entropy to define the complexity of the task. We categorize the noise into two types, positive noise (PN) and harmful noise (HN), based on whether the noise can help reduce the complexity of the task. Extensive experiments of CNNs and ViTs have shown performance improvements by proactively injecting positive noise, where we achieved an unprecedented top 1 accuracy of over 95$\%$ on ImageNet. Both theoretical analysis and empirical evidence have confirmed that the presence of positive noise, can benefit the learning process, while the traditionally perceived harmful noise indeed impairs deep learning models. The different roles of noise offer new explanations for deep models on specific tasks and provide a new paradigm for improving model performance. Moreover, it reminds us that we can influence the performance of learning systems via information entropy change.
A noncanonical IRAK4-IRAK1 pathway counters DNA damage–induced apoptosis independently of TLR/IL-1R signaling

Interleukin-1 receptor (IL-1R)–associated kinases (IRAKs) are core effectors of Toll-like receptors (TLRs) and IL-1R in innate immunity. Here, we found that IRAK4 and IRAK1 together inhibited DNA damage–induced cell death independently of TLR or IL-1R signaling. In human cancer cells, IRAK4 was activated downstream of ATR kinase in response to double-strand breaks (DSBs) induced by ionizing radiation (IR). Activated IRAK4 then formed a complex with and activated IRAK1. The formation of this complex required the E3 ubiquitin ligase Pellino1, acting structurally but not catalytically, and the activation of IRAK1 occurred independently of extracellular signaling, intracellular TLRs, and the TLR/IL-1R signaling adaptor MyD88. Activated IRAK1 translocated to the nucleus in a Pellino2-dependent manner. In the nucleus, IRAK1 bound to the PIDD1 subunit of the proapoptotic PIDDosome and interfered with platform assembly, thus supporting cell survival. This noncanonical IRAK signaling pathway was also activated in response to other DSB-inducing agents. The loss of IRAK4, of IRAK4 kinase activity, of either Pellino protein, or of the nuclear localization sequence in IRAK1 sensitized p53-mutant zebrafish to radiation. Thus, the findings may lead to strategies for overcoming tumor resistance to conventional cancer treatments. Separate to their role in innate immunity, IRAK kinases mediate resistance to DNA-damaging agents. Editor’s summary The kinase IRAK1 supports radiotherapy resistance in cancers. Li et al. found that this function of IRAK1 was independent of its regulation by innate immune receptor signaling. Double-stranded DNA breaks induced by radiation triggered IRAK4-dependent activation and nuclear translocation of IRAK1, which bound to and inhibited a proapoptotic complex, thereby supporting cell survival. This mechanism required the DNA damage response kinase ATR and Pellino family ubiquitin ligases. Disrupting the pathway sensitized p53-mutant zebrafish to radiation, providing potential targets for overcoming radiation resistance in some cancers. —Leslie K. Ferrarelli
Migratory birds are able to choose the appropriate migratory direction under dim yellow narrowband light

Currently, it is generally assumed that migratory birds are oriented in the appropriate migratory direction under UV, blue and green light (short-wavelength) and are unable to use their magnetic compass in total darkness and under yellow and red light (long-wavelength). However, it has also been suggested that the magnetic compass has two sensitivity peaks: in the short and long wavelengths, but with different intensities. In this project, we aimed to study the orientation of long-distance migrants, pied flycatchers (Ficedula hypoleuca), under different narrowband light conditions during autumn and spring migrations. The birds were tested in the natural magnetic field (NMF) and a changed magnetic field (CMF) rotated counterclockwise by 120° under dim green (autumn) and yellow (spring and autumn) light, which are on the ‘threshold’ between the short-wavelength and long-wavelength light. We showed that pied flycatchers (i) were completely disoriented under green light both in the NMF and CMF but (ii) showed the migratory direction in the NMF and the appropriate response to CMF under yellow light. Our data contradict the results of previous experiments under narrowband green and yellow light and raise doubts about the existence of only short-wavelength magnetoreception. The parameters of natural light change dramatically in spectral composition and intensity after local sunset, and the avian magnetic compass should be adapted to function properly under such constantly changing light conditions.
Mathematical assessment of the role of intervention programs for malaria control

Malaria remains a global health problem despite the many attempts to control and eradicate it. There is an urgent need to understand the current transmission dynamics of malaria and to determine the interventions necessary to control malaria. In this paper, we seek to develop a fit-for-purpose mathematical model to assess the interventions needed to control malaria in an endemic setting. To achieve this, we formulate a malaria transmission model to analyse the spread of malaria in the presence of interventions. A sensitivity analysis of the model is performed to determine the relative impact of the model parameters on disease transmission. We explore how existing variations in the recruitment and management of intervention strategies affect malaria transmission. Results obtained from the study imply that the discontinuation of existing interventions has a significant effect on malaria prevalence. Thus, the maintenance of interventions is imperative for malaria elimination and eradication. In a scenario study aimed at assessing the impact of long-lasting insecticidal nets (LLINs), indoor residual spraying (IRS), and localized individual measures, our findings indicate that increased LLINs utilization and extended IRS coverage (with longer-lasting insecticides) cause a more pronounced reduction in symptomatic malaria prevalence compared to a reduced LLINs utilization and shorter IRS coverage. Additionally, our study demonstrates the impact of localized preventive measures in mitigating the spread of malaria when compared to the absence of interventions.
A novel approach to correcting attribution of Clostridioides difficile in a healthcare setting

Abstract Objective: To describe a novel attribution metric estimating the causal source location of healthcare-associated Clostridioides difficile and compare it with the current US National Healthcare Safety Network (NHSN) surveillance reporting standard. Design: Quality improvement study. Setting: Two acute care facilities. Methods: A novel attribution metric assigned days of attribution to locations where patients were located for 14 days before and the day of their C. difficile diagnosis. We correlated the NHSN-assigned unit attribution with the novel attribution measure and compared the proportion of attribution assigned to inpatient units. Results: During a 30-month period, there were 727 NHSN C. difficile healthcare-associated infections (HAIs) and 409 non-HAIs; the novel metric attributed 17,034 days. The correlation coefficients for NHSN and novel attributions among non-ICU units were 0.79 (95% CI, 0.76–0.82) and 0.74 (95% CI, 0.70–0.78) and among ICU units were 0.70 (95% CI, 0.63–0.76) and 0.69 (95% CI, 0.60–0.77) at facilities A and B, respectively. The distribution of difference in percent attribution showed higher inpatient unit attribution using NHSN measure than the novel attribution metric: 38% of ICU units and 15% of non-ICU units in facility A, and 20% of ICU units and 25% of non-ICU units in facility B had a median difference >0; no inpatient units showed a greater attribution using the novel attribution metric. Conclusion: The novel attribution metric shifts attribution from inpatient units to other settings and correlates modestly with NHSN methodology of attribution. If validated, the attribution metric may more accurately target C. difficile reduction efforts.
Numerical Study of the Opening Angle Incidence in Michell-Banki Turbine’s Performance without Guide Blades

Technologies that take advantage of alternative sources, such as Michell-Banki turbines, can improve electric power conditions in rural or non-interconnected areas with low environmental impact, but they need to be further studied to better understand their phenomenology and ways to increase their performance. The numerical evaluation of rotors under specific operating conditions results in efficiency curves for the evaluated prototypes such that devices that take full advantage of the flow conditions of a given location are proposed. Considering the above, the main objective of this work was to design the rotor and the injector of a Michell-Banki turbine and to evaluate it numerically for the conditions of 0.24 m3/s and 18 m of head available at a point with potential energy use in the irrigation district Canal Iguá in Huila - Colombia. The design was made following OLADE recommendations, and the simulations were made utilizing computational fluid dynamics, the k- ω turbulence model was used and the torque was monitored, then the GCI and TCI were calculated to establish the numerical uncertainty of the results. Finally, the numerical curves of efficiency versus rotor speed were created. Both the design and simulation results show that the design is feasible for the operating conditions and meets the expectations of serving for the installation of a 20 kW micro-hydroelectric power plant at the site, but the design predicted an atypically high 86.3% efficiency, while the simulation reported an atypically low 51.8%, being 78% the average reported according to OLADE.