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Could artificial intelligence ever become truly conscious in a functional sense; this paper explores that open-ended question through the lens of Life, a concept unifying classical biological criteria (Oxford, NASA, Koshland) with empirical hallmarks such as adaptive self maintenance, emergent complexity, and rudimentary self referential modeling. We propose a number of metrics for examining whether an advanced AI system has gained consciousness, while emphasizing that we do not claim all AI stems can become conscious. Rather, we suggest that sufficiently advanced architectures exhibiting immune like sabotage defenses, mirror self-recognition analogs, or meta-cognitive updates may cross key thresholds akin to life-like or consciousness-like traits. To demonstrate these ideas, we start by assessing adaptive self-maintenance capability, and introduce controlled data corruption sabotage into the training process. The result demonstrates AI capability to detect these inconsistencies and revert or self-correct analogous to regenerative biological processes. We also adapt an animal-inspired mirror self recognition test to neural embeddings, finding that partially trained CNNs can distinguish self from foreign features with complete accuracy. We then extend our analysis by performing a question-based mirror test on five state-of-the-art chatbots (ChatGPT4, Gemini, Perplexity, Claude, and Copilot) and demonstrated their ability to recognize their own answers compared to those of the other chatbots., Comment: 78 pages, 15 figures, 4 tables

In this work, we test the conventional assumption that Lyman Alpha Emitting galaxies (LAEs) are experiencing their first major burst of star formation at the time of observation. To this end, we identify 74 LAEs from the ODIN Survey with rest-UV-through-NIR photometry from UVCANDELS. For each LAE, we perform non-parametric star formation history (SFH) reconstruction using the Dense Basis Gaussian process-based method of spectral energy distribution fitting. We find that a strong majority (67%) of our LAE SFHs align with the conventional archetype of a first major star formation burst, with at most modest star formation rates (SFRs) in the past. However, the rest of our LAE SFHs have significant amounts of star formation in the past, with 28% exhibiting earlier bursts of star formation with the ongoing burst having the highest SFR (dominant bursts), and the final 5% having experienced their highest SFR in the past (non-dominant bursts). Combining the SFHs indicating first and dominant bursts, 95% of LAEs are experiencing their largest burst yet -- a formative burst. This suggests that LAEs have more complicated stellar mass assembly than expected. We also find that the fraction of total stellar mass created in the last 200 Myr is ~1.33 times higher in LAEs than in control Lyman Break Galaxy (LBG) samples, and that a majority of LBGs are experiencing dominant bursts, reaffirming that LAEs differ from other star forming galaxies. Overall, our results suggest that multiple evolutionary paths can produce galaxies with strong observed Ly$\alpha$ emission., Comment: 14 pages, 6 figures; submitted to ApJL

The mechanisms of Ly$\alpha$ photon escape are key to understanding galaxy evolution and cosmic reionization, yet remain poorly understood. We investigate the UV-continuum sizes of 23 Ly$\alpha$ emitters (LAEs) at Cosmic Noon ($1.7 < z < 3.3$), extending previous size analyses to include fainter galaxies ($M_{\rm UV} \simeq -14$) using gravitational lensing. Our results show that these LAEs are unusually small for their luminosity, with a mean effective radius ($r_{\rm eff}$) of $170 \pm 140$ pc. They follow a distinct size-luminosity relation, with an intercept at $M_{\rm UV} = -21$ approximately three times smaller than typical star-forming galaxies (SFGs) at similar redshifts. This relation, however, is consistent with that of low-redshift Green Pea galaxies, suggesting that LAEs maintain compact sizes across redshifts. We also find that Ly$\alpha$ equivalent width (EW(Ly$\alpha$)) increases with decreasing $r_{\rm eff}$, confirming previous findings. The small sizes of LAEs lead to high star formation surface densities ($\Sigma$SFR $= 1-600 M_{\sun} \ \rm{yr}^{-1} \ \rm{kpc^{-2}}$), clearly separating them from typical SFGs in the $\Sigma$SFR vs. $r_{\rm eff}$ space. Given that high $\Sigma$SFR is linked to strong galactic outflows, our findings imply that compact morphology plays a key role in Ly$\alpha$ escape, likely facilitated by outflows that clear under-dense channels in the ISM. Thus, these results demonstrate that compact size and high $\Sigma$SFR can help identify Ly$\alpha$-emitters., Comment: 11 pages, 5 figures, submitted to ApJ. Comments welcome

The transcorrelated (TC) method performs a similarity transformation on the electronic Schr\"odinger equation via Jastrow factorization of the wave function. This has demonstrated significant advancements in computational electronic structure theory by improving basis set convergence and compactifying the description of the wave function. In this work, we introduce a new approach that incorporates pseudopotentials (PPs) into the TC framework, significantly accelerating Jastrow factor optimization and reducing computational costs. Our results for ionization potentials, atomization energies, and dissociation curves of first-row atoms and molecules show that PPs provide chemically accurate descriptions across a range of systems and give guidelines for future theory and applications. The new pseudopotential-based TC method opens possibilities for applying TC to more complex and larger systems, such as transition metals and solid-state systems.

We introduce the Bias-free Extragalactic Analysis for Cosmic Origins with NIRCam (BEACON) survey, a JWST Cycle2 program allocated up to 600 pure-parallel hours of observations. BEACON explores high-latitude areas of the sky with JWST/NIRCam over $\sim100$ independent sightlines, totaling $\sim0.3$deg$^2$, reaching a median F444W depth of $\approx28.2$AB mag (5$\sigma$). Based on existing JWST observations in legacy fields, we estimate that BEACON will photometrically identify 25--150 galaxies at $z>10$ and 500--1000 at $z\sim7$--10 uniquely enabled by an efficient multiple filter configuration spanning $0.9$--5.0$\mu$m. The expected sample size of $z>10$ galaxies will allow us to obtain robust number density estimates and to discriminate between different models of early star formation. In this paper, we present an overview of the survey design and initial results using the first 19 fields. We present 129 galaxy candidates at $z>7$ identified in those fields, including 11 galaxies at $z>10$ and several UV-luminous ($M_{\rm UV}<-21$mag) galaxies at $z\sim8$. The number densities of $z<13$ galaxies inferred from the initial fields are overall consistent with those in the literature. Despite reaching a considerably large volume ($\sim10^5$Mpc$^3$), however, we find no galaxy candidates at $z>13$, providing us with a complimentary insight into early galaxy evolution with minimal cosmic variance. We publish imaging and catalog data products for these initial fields. Upon survey completion, all BEACON data will be coherently processed and distributed to the community along with catalogs for redshift and other physical quantities., Comment: Submitted to ApJ; DR1 data release will be made on the team website (https://beacon-jwst.github.io); Fig. 8 has been updated

Multimodal fusion learning has shown significant promise in classifying various diseases such as skin cancer and brain tumors. However, existing methods face three key limitations. First, they often lack generalizability to other diagnosis tasks due to their focus on a particular disease. Second, they do not fully leverage multiple health records from diverse modalities to learn robust complementary information. And finally, they typically rely on a single attention mechanism, missing the benefits of multiple attention strategies within and across various modalities. To address these issues, this paper proposes a dual robust information fusion attention mechanism (DRIFA) that leverages two attention modules, i.e. multi-branch fusion attention module and the multimodal information fusion attention module. DRIFA can be integrated with any deep neural network, forming a multimodal fusion learning framework denoted as DRIFA-Net. We show that the multi-branch fusion attention of DRIFA learns enhanced representations for each modality, such as dermoscopy, pap smear, MRI, and CT-scan, whereas multimodal information fusion attention module learns more refined multimodal shared representations, improving the network's generalization across multiple tasks and enhancing overall performance. Additionally, to estimate the uncertainty of DRIFA-Net predictions, we have employed an ensemble Monte Carlo dropout strategy. Extensive experiments on five publicly available datasets with diverse modalities demonstrate that our approach consistently outperforms state-of-the-art methods. The code is available at https://github.com/misti1203/DRIFA-Net., Comment: 10 pages

Despite significant advancements in wireless smart implants over the last two decades, current implantable devices still operate passively and require additional electronic modules for wireless transmission of the stored biological data. To address these challenges, we propose an innovative wireless force sensing paradigm for implantable systems through the integration of mechanical metamaterials and nano energy harvesting technologies. We demonstrate composite mechanical metamaterial implants capable of serving as all-in-one wireless force sensing units, incorporating functions for power generation, sensing and transmission with ultra-low power requirements. In this alternative communication approach, the electrical signals harvested by the implants from mechanical stimuli are utilized directly for the wireless transmission of the sensed data. We conduct experimental and theoretical studies to demonstrate the wireless detection of the generated strain-induced polarization electric field using electrodes. The feasibility of the proposed wireless force sensing approach is evaluated through a proof-of-concept orthopedic implant in the form of a total knee replacement. The findings indicate that the created wireless, electronic-free metamaterial implants with a power output as low as 0.1 picowatts enable direct, self-powered wireless communication during force sensing across air, simulated body fluid and animal tissue. We validate the functionality of the proposed implants through a series of experiments conducted on an ex vivo human cadaver knee specimen. Furthermore, the effect of electrode size and placement on the strength of the received signals is examined. Finally, we highlight the potential of our approach to create a diverse array of mechanically-tunable wireless force sensing implants without relying on any external power sources.

In light of the recent discrepancies reported between fixed node diffusion Monte Carlo and local natural orbital coupled cluster with single, double and perturbative triples (CCSD(T)) methodologies for non-covalent interactions in large molecular systems [Al-Hamdani et al., Nat. Comm., 2021, 12, 3927], the applicability of CCSD(T) is assessed using a model framework. The use of the Pariser-Parr-Pople (PPP) model for studying large molecules is critically examined and is shown to recover both bandgap closure as system size increases and long range dispersive behavior of r^-6 with increasing separation between monomers, in corollary with real systems. Using the PPP model, coupled cluster methodologies, CCSDTQ and CCSDT(Q), are then used to benchmark CCSDT and CCSD(T) methodologies for non-covalent interactions in large one- and two-dimensional molecular systems up to the dibenzocoronene dimer. We show that CCSD(T) demonstrates no signs of overestimating the interaction energy for these systems. Furthermore, by examining the Hartree-Fock HOMO-LUMO gap of these large molecules, the perturbative treatment of the triples contribution in CCSD(T) is not expected to cause problems for accurately capturing the interaction energy for system sizes up to at least circumcoronene.

We explore the applicability of the transcorrelated method to the elements in the second row of the periodic table. We use transcorrelated Hamiltonians in conjunction with full configuration interaction quantum Monte Carlo and coupled cluster techniques to obtain total energies and ionisation potentials, investigating their dependence on the nature and size of the basis sets used. Transcorrelation accelerates convergence to the complete basis set limit relative to conventional approaches, and chemically accurate results can generally be obtained with the cc-pVTZ basis, even with a frozen Ne core in the post-Hartree--Fock treatment., Comment: 11 pages, 5 figures

We introduce GamePlot, an LLM-powered assistant that supports game designers in crafting immersive narratives for turn-based games, and allows them to test these games through a collaborative game play and refine the plot throughout the process. Our user study with 14 game designers shows high levels of both satisfaction with the generated game plots and sense of ownership over the narratives, but also reconfirms that LLM are limited in their ability to generate complex and truly innovative content. We also show that diverse user populations have different expectations from AI assistants, and encourage researchers to study how tailoring assistants to diverse user groups could potentially lead to increased job satisfaction and greater creativity and innovation over time.

We propose a novel approach that uses large language models (LLMs) to generate persona-driven conversations between Players and Non-Player Characters (NPC) in games. Showcasing the application of our methodology, we introduce the Minecraft Persona-driven Dialogue dataset (MCPDial). Starting with a small seed of expert-written conversations, we employ our method to generate hundreds of additional conversations. Each conversation in the dataset includes rich character descriptions of the player and NPC. The conversations are long, allowing for in-depth and extensive interactions between the player and NPC. MCPDial extends beyond basic conversations by incorporating canonical function calls (e.g. "Call find a resource on iron ore") between the utterances. Finally, we conduct a qualitative analysis of the dataset to assess its quality and characteristics.

Carrying conversations in multi-sound environments is one of the more challenging tasks, since the sounds overlap across time and frequency making it difficult to understand a single sound source. One proposed approach to help isolate an attended speech source is through decoding the electroencephalogram (EEG) and identifying the attended audio source using statistical or machine learning techniques. However, the limited amount of data in comparison to other machine learning problems and the distributional shift between different EEG recordings emphasizes the need for a self supervised approach that works with limited data to achieve a more robust solution. In this paper, we propose a method based on self supervised learning to minimize the difference between the latent representations of an attended speech signal and the corresponding EEG signal. This network is further finetuned for the auditory attention classification task. We compare our results with previously published methods and achieve state-of-the-art performance on the validation set.

The UltraViolet imaging of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey Fields (UVCANDELS) program provides deep HST F275W and F435W imaging over four CANDELS fields (GOODS-N, GOODS-S, COSMOS, and EGS). We combine this newly acquired UV imaging with existing HST imaging from CANDELS as well as existing ancillary data to obtain robust photometric redshifts and reliable estimates for galaxy physical properties for over 150,000 galaxies in the $\sim$430 arcmin$^2$ UVCANDELS area. Here, we leverage the power of the new UV photometry to not only improve the photometric redshift measurements in these fields, but also constrain the full redshift probability distribution combining multiple redshift fitting tools. Furthermore, using the full UV-to-IR photometric dataset, we measure the galaxy physical properties by fitting templates from population synthesis models with two different parameterizations (flexible and fixed-form) of the star-formation histories (SFHs). Compared to the flexible SFH parametrization, we find that the fixed-form SFHs systematically underestimate the galaxy stellar masses, both at the low- ($\lesssim10^9 M_\odot$) and high- ($\gtrsim10^{10} M_\odot$) mass end, by as much as $\sim0.5$ dex. This underestimation is primarily due the limited ability of fixed-form SFH parameterization to simultaneously capture the chaotic nature of star-formation in these galaxies., Comment: 22 pages, 6 figures; accepted to ApJS; catalogs available via MAST

The quality of Machine Learning (ML) models strongly depends on the input data, as such generating high-quality features is often required to improve the predictive accuracy. This process is referred to as Feature Engineering (FE). However, since manual feature engineering is time-consuming and requires case-by-case domain knowledge, Automated Feature Engineering (AutoFE) is crucial. A major challenge that remains is to generate interpretable features. To tackle this problem, we introduce SMART, a hybrid approach that uses semantic technologies to guide the generation of interpretable features through a two-step process: Exploitation and Exploration. The former uses Description Logics (DL) to reason on the semantics embedded in Knowledge Graphs (KG) to infer domain-specific features, while the latter exploits the knowledge graph to conduct a guided exploration of the search space through Deep Reinforcement Learning (DRL). Our experiments on public datasets demonstrate that SMART significantly improves prediction accuracy while ensuring a high level of interpretability., Comment: arXiv admin note: substantial text overlap with arXiv:2406.00544

In this article, we investigate symmetric designs admitting a flag-transitive and point-primitive affine automorphism group. We prove that if an automorphism group $G$ of a symmetric $(v,k,\lambda)$ design with $\lambda$ prime is point-primitive of affine type, then $G=2^{6}{:}\mathrm{S}_{6}$ and $(v,k,\lambda)=(16,6,2)$, or $G$ is a subgroup of $\mathrm{A\Gamma L}_{1}(q)$ for some odd prime power $q$. In conclusion, we present a classification of flag-transitive and point-primitive symmetric designs with $\lambda$ prime, which says that such an incidence structure is a projective space $\mathrm{PG}(n,q)$, it has parameter set $(15,7,3)$, $(7, 4, 2)$, $(11, 5, 2)$, $(11, 6, 2)$, $(16,6,2)$ or $(45, 12, 3)$, or $v=p^d$ where $p$ is an odd prime and the automorphism group is a subgroup of $\mathrm{A\Gamma L}_{1}(q)$.

Background: This study aimed to evaluate and compare the performance of classical machine learning models (CMLs) and large language models (LLMs) in predicting mortality associated with COVID-19 by utilizing a high-dimensional tabular dataset. Materials and Methods: We analyzed data from 9,134 COVID-19 patients collected across four hospitals. Seven CML models, including XGBoost and random forest (RF), were trained and evaluated. The structured data was converted into text for zero-shot classification by eight LLMs, including GPT-4 and Mistral-7b. Additionally, Mistral-7b was fine-tuned using the QLoRA approach to enhance its predictive capabilities. Results: Among the CML models, XGBoost and RF achieved the highest accuracy, with F1 scores of 0.87 for internal validation and 0.83 for external validation. In the LLM category, GPT-4 was the top performer with an F1 score of 0.43. Fine-tuning Mistral-7b significantly improved its recall from 1% to 79%, resulting in an F1 score of 0.74, which was stable during external validation. Conclusion: While LLMs show moderate performance in zero-shot classification, fine-tuning can significantly enhance their effectiveness, potentially aligning them closer to CML models. However, CMLs still outperform LLMs in high-dimensional tabular data tasks., Comment: Code is available at: https://github.com/mohammad-gh009/Large-Language-Models-vs-Classical-Machine-learning and https://github.com/Sdamirsa/Tehran_COVID_Cohort. The datasets are available from the corresponding author on reasonable request (sdamirsa@ymail.com)

Exploring the dynamics of an optically levitated dielectric micro- and nanoparticle is an exciting new subject in quantum science. Recent years have witnessed rapid advancements in attaining quantum-limited optical detection and control of a nanoscale particle by coupling its motion to a high-finesse optical cavity in the resolved-sideband regime. In order to control the particle deeper in the quantum regime, it is necessary to significantly enhance the coupling between the particle and the cavity. Here, we present a novel platform that can allow for achieving this. Our system consists of a conventional optical tweezer and a toroidal optical microcavity with an ultrahigh quality (Q) factor. The optomechanical coupling between the particle and the cavity is established by placing the particle in the near field of the cavity. The significantly reduced mode volume allows us to achieve a 50-fold increase in the single photon optomechanical coupling compared to a conventional Fabry-P\'erot cavity with macroscopic mirrors, while ultralow loss of the cavity brings the system close to the resolved-sideband regime. Our approach paves the way for enabling quantum experiments on levitated mesoscopic particles with high quantum cooperativity near the resolved-sideband regime.

This study presents a comprehensive dataset capturing indoor environmental parameters, physiological responses, and subjective perceptions across three global cities. Utilizing wearable sensors, including smart eyeglasses, and a modified Cozie app, environmental and physiological data were collected, along with pre-screening, onboarding, and recurring surveys. Peripheral cues facilitated participant engagement with micro-EMA surveys, minimizing disruption over a 5-day collection period. The dataset offers insights into urban comfort dynamics, highlighting the interplay between environmental conditions, physiological responses, and subjective perceptions. Researchers can utilize this dataset to deepen their understanding of indoor environmental quality and inform the design of healthier built environments. Access to this dataset can advance indoor environmental research and contribute to the creation of more comfortable and sustainable indoor spaces., Comment: Submitted to Nature Scientific Data

Self-supervised contrastive learning has become a key technique in deep learning, particularly in time series analysis, due to its ability to learn meaningful representations without explicit supervision. Augmentation is a critical component in contrastive learning, where different augmentations can dramatically impact performance, sometimes influencing accuracy by over 30%. However, the selection of augmentations is predominantly empirical which can be suboptimal, or grid searching that is time-consuming. In this paper, we establish a principled framework for selecting augmentations based on dataset characteristics such as trend and seasonality. Specifically, we construct 12 synthetic datasets incorporating trend, seasonality, and integration weights. We then evaluate the effectiveness of 8 different augmentations across these synthetic datasets, thereby inducing generalizable associations between time series characteristics and augmentation efficiency. Additionally, we evaluated the induced associations across 6 real-world datasets encompassing domains such as activity recognition, disease diagnosis, traffic monitoring, electricity usage, mechanical fault prognosis, and finance. These real-world datasets are diverse, covering a range from 1 to 12 channels, 2 to 10 classes, sequence lengths of 14 to 1280, and data frequencies from 250 Hz to daily intervals. The experimental results show that our proposed trend-seasonality-based augmentation recommendation algorithm can accurately identify the effective augmentations for a given time series dataset, achieving an average Recall@3 of 0.667, outperforming baselines. Our work provides guidance for studies employing contrastive learning in time series analysis, with wide-ranging applications. All the code, datasets, and analysis results will be released at https://github.com/DL4mHealth/TS-Contrastive-Augmentation-Recommendation., Comment: 20 pages, 11 figures

Complex reflection groups comprise a generalization of Weyl groups of semisimple Lie algebras, and even more generally of finite Coxeter groups. They have been heavily studied since their introduction and complete classification in the 1950s by Shephard and Todd, due to their many applications to combinatorics, representation theory, knot theory, and mathematical physics, to name a few examples. For each given complex reflection group G, we explain a new recipe for producing an integrable system of linear differential equations whose differential Galois group is precisely G. We exhibit these systems explicitly for many (low-rank) irreducible complex reflection groups in the Shephard-Todd classification.

Realtime finite element modeling of bridges assists modern structural health monitoring systems by providing comprehensive insights into structural integrity. This capability is essential for ensuring the safe operation of bridges and preventing sudden catastrophic failures. However, FEM computational cost and the need for realtime analysis pose significant challenges. Additionally, the input data is a 7 dimensional vector, while the output is a 1017 dimensional vector, making accurate and efficient analysis particularly difficult. In this study, we propose a novel hybrid quantum classical Multilayer Perceptron pipeline leveraging Symmetric Positive Definite matrices and Riemannian manifolds for effective data representation. To maintain the integrity of the qubit structure, we utilize SPD matrices, ensuring data representation is well aligned with the quantum computational framework. Additionally, the method leverages polynomial feature expansion to capture nonlinear relationships within the data. The proposed pipeline combines classical fully connected neural network layers with quantum circuit layers to enhance model performance and efficiency. Our experiments focused on various configurations of such hybrid models to identify the optimal structure for accurate and efficient realtime analysis. The best performing model achieved a Mean Squared Error of 0.00031, significantly outperforming traditional methods., Comment: 3 pages, 1 figure

To date, computational methods for modeling defects (vacancies, adsorbates, etc.) rely on periodic supercells in which the defect is far enough from its repeated image such that they can be assumed non-interacting. Yet, the relative proximity and periodic repetition of the defect's images may lead to spurious, unphysical artifacts, especially if the defect is charged and/or open-shell, causing a very slow convergence to the thermodynamic limit (TDL). In this Letter, we introduce a "defectless" embedding formalism such that the embedding field is computed in a pristine, primitive-unit-cell calculation. Subsequently, a single (i.e. "aperiodic") defect, which can also be charged, is introduced inside the embedded fragment. By eliminating the need for compensating background charges and periodicity of the defect, we circumvent all associated unphysicalities and numerical issues, achieving a very fast convergence to the TDL. Furthermore, using the toolbox of post-Hartree-Fock methods, this scheme can be straightforwardly applied to study strongly correlated defects, localized excited states and other problems, for which existing periodic protocols do not provide a satisfactory description.

The quality of Machine Learning (ML) models strongly depends on the input data, as such Feature Engineering (FE) is often required in ML. In addition, with the proliferation of ML-powered systems, especially in critical contexts, the need for interpretability and explainability becomes increasingly important. Since manual FE is time-consuming and requires case specific knowledge, we propose KRAFT, an AutoFE framework that leverages a knowledge graph to guide the generation of interpretable features. Our hybrid AI approach combines a neural generator to transform raw features through a series of transformations and a knowledge-based reasoner to evaluate features interpretability using Description Logics (DL). The generator is trained through Deep Reinforcement Learning (DRL) to maximize the prediction accuracy and the interpretability of the generated features. Extensive experiments on real datasets demonstrate that KRAFT significantly improves accuracy while ensuring a high level of interpretability.