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The ongoing discrepancy in the Hubble constant ($H_0$) estimates obtained through local distance ladder methods and early universe observations poses a significant challenge to the $\Lambda$CDM model, suggesting potential new physics. Type II supernovae (SNe II) offer a promising technique for determining $H_0$ in the local universe independently of the traditional distance ladder approach, opening up a complimentary path for testing this discrepancy. We aim to provide the first $H_0$ estimate using the tailored expanding photosphere method (EPM) applied to SNe II, made possible by recent advancements in spectral modelling that enhance its precision and efficiency. Our tailored EPM measurement utilizes a spectral emulator to interpolate between radiative transfer models calculated with TARDIS, allowing us to fit supernova spectra efficiently and derive self-consistent values for luminosity-related parameters. We apply the method on public data for ten SNe II at redshifts between 0.01 and 0.04. Our analysis demonstrates that the tailored EPM allows for $H_0$ measurements with precision comparable to the most competitive established techniques, even when applied to literature data not designed for cosmological applications. We find an independent $H_0$ value of $74.9\pm1.9$ (stat) km/s/Mpc, which is consistent with most current local measurements. Considering dominant sources of systematic effects, we conclude that our systematic uncertainty is comparable to or less than the current statistical uncertainty. This proof-of-principle study highlights the potential of the tailored EPM as a robust and precise tool for investigating the Hubble tension independently of the local distance ladder. Observations of SNe II tailored to $H_0$ estimation can make this an even more powerful tool by improving the precision and by allowing us to better understand and control systematic uncertainties., Comment: 40 pages, 57 figures, 4 tables; submitted to A&A

The time until black hole formation in a binary neutron-star (NS) merger contains invaluable information about the nuclear equation of state (EoS) but has thus far been difficult to measure. We propose a new way to constrain the merger remnant's NS lifetime, which is based on the tendency of the NS remnant neutrino-driven winds to enrich the ejected material with helium. Based on the He I $\lambda 1083.3$ nm line, we show that the feature around 800-1200 nm in AT2017gfo at 4.4 days seems inconsistent with a helium mass fraction of $X_{\mathrm{He}} \gtrsim 0.05$ in the polar ejecta. Recent neutrino-hydrodynamic simulations of merger remnants are only compatible with this limit if the NS remnant collapses within 20-30 ms. Such a short lifetime implies that the total binary mass of GW170817, $M_\mathrm{\rm tot}$, lay close to the threshold binary mass for direct gravitational collapse, $M_\mathrm{thres}$, for which we estimate $M_{\mathrm{thres}}\lesssim 2.93 M_\odot$. This upper bound on $M_\mathrm{thres}$ yields upper limits on the radii and maximum mass of cold, non-rotating NSs, which rule out simultaneously large values for both quantities. In combination with causality arguments, this result implies a maximum NS mass of $M_\mathrm{max}\lesssim2.3 M_\odot$. The combination of all limits constrains the radii of 1.6 M$_\odot$ NSs to about 12$\pm$1 km for $M_\mathrm{max}$ = 2.0 M$_\odot$ and 11.5$\pm$1 km for $M_\mathrm{max}$ = 2.15 M$_\odot$. This $\sim2$ km allowable range then tightens significantly for $M_\mathrm{max}$ above $\approx2.15$ M$_\odot$. This rules out a significant number of current EoS models. The short NS lifetime also implies that a black-hole torus, not a highly magnetized NS, was the central engine powering the relativistic jet of GRB170817A. Our work motivates future developments... [abridged], Comment: 30 pages, 16 figures, 2 tables, submitted to PRX

When a local impurity spin interacts with conduction electrons whose density of states (DOS) has a (pseudo)gap or diverges at the Fermi energy, a local moment (LM) phase can be favored over a Kondo phase. Theoretically studying quantum entanglement between the impurity and conduction electrons, we demonstrate that conduction electrons form an ''LM spin cloud'' in general LM phases, which corresponds to, but has fundamental difference from, the Kondo cloud screening the impurity spin in the Kondo phase. The LM cloud algebraically decays over the distance from the impurity when the DOS has a pseudogap or divergence, and exponentially when it has a hard gap. We find an ''LM cloud length'', a single length scale characterizing a universal form of the LM cloud. The findings are supported by both of analytic theories and numerical computations., Comment: 21 pages, 4 figures for the manuscript; Supplementary material included

Current face anonymization techniques often depend on identity loss calculated by face recognition models, which can be inaccurate and unreliable. Additionally, many methods require supplementary data such as facial landmarks and masks to guide the synthesis process. In contrast, our approach uses diffusion models with only a reconstruction loss, eliminating the need for facial landmarks or masks while still producing images with intricate, fine-grained details. We validated our results on two public benchmarks through both quantitative and qualitative evaluations. Our model achieves state-of-the-art performance in three key areas: identity anonymization, facial attribute preservation, and image quality. Beyond its primary function of anonymization, our model can also perform face swapping tasks by incorporating an additional facial image as input, demonstrating its versatility and potential for diverse applications. Our code and models are available at https://github.com/hanweikung/face_anon_simple .

We present synthetic light curves and spectra from three-dimensional (3D) Monte Carlo radiative transfer simulations based on a 3D core-collapse supernova explosion model of an ultra-stripped $3.5\,\mathrm{M}_{\odot}$ progenitor. Our calculations predict a fast and faint transient with $\Delta m_{15} \sim 1\texttt{-} 2\,\mathrm{mag}$ and peak bolometric luminosity between $-15.3\,\mathrm{mag}$ and $-16.4\,\mathrm{mag}$. Due to a large-scale unipolar asymmetry in the distribution of $^{56}\mathrm{Ni}$, there is a pronounced viewing-angle dependence with about $1\,\mathrm{mag}$ difference between the directions of highest and lowest luminosity. The predicted spectra for this rare class of explosions do not yet match any observed counterpart. They are dominated by prominent Mg~II lines, but features from O, C, Si, and Ca are also found. In particular, the O~I line at \wl{7}{774} appears as a blended feature together with Mg~II emission. Our model is not only faster and fainter than the observed Ib/c supernova population, but also shows a correlation between higher peak luminosity and larger $\Delta m_{15}$ that is not present in observational samples. A possible explanation is that the unusually small ejecta mass of our model accentuates the viewing-angle dependence of the photometry. We suggest that the viewing-angle dependence of the photometry may be used to constrain asymmetries in explosion models of more typical stripped-envelope supernova progenitors in future., Comment: 13 pages, 18 figures, submitted to MNRAS

Outflows are critical components of many astrophysical systems, including accreting compact binaries and active galactic nuclei (AGN). These outflows can significantly affect a system's evolution and alter its observational appearance by reprocessing the radiation produced by the central engine. Sirocco (Simulating Ionization and Radiation in Outflows Created by Compact Objects - or "the code formerly known as Python") is a Sobolev-based Monte Carlo ionization and radiative transfer code. It is designed to simulate the spectra produced by any system with an azimuthally-symmetric outflow, from spherical stellar winds to rotating, biconical accretion disc winds. Wind models can either be parametrized or imported, e.g. from hydrodynamical simulations. The radiation sources include an optically thick accretion disc and various central sources with flexible spectra and geometries. The code tracks the "photon packets" produced by the sources in any given simulation as they traverse and interact with the wind. The code assumes radiative near-equilibrium, so the thermal and ionization state can be determined iteratively from these interactions. Once the physical properties in the wind have converged, Sirocco can be used to generate synthetic spectra at a series of observer sightlines. Here, we describe the physical assumptions, operation, performance and limitations of the code. We validate it against tardis, cmfgen and cloudy, finding good agreement, and present illustrative synthetic spectra from disc winds in cataclysmic variables, tidal disruption events, AGN and X-ray binaries. Sirocco is publicly available on GitHub, alongside its associated data, documentation and sample input files covering a wide range of astrophysical applications., Comment: 26 pages, 16 figures. Submitted to MNRAS. This is the release paper for the SIROCCO code, which can be found at https://github.com/sirocco-rt/sirocco with links to documentation. Underlying data and figure scripts available at https://github.com/sirocco-rt/release-models -- comments on the code, paper or documentation are welcomed

Efficiently managing papillary thyroid microcarcinoma (PTMC) while minimizing patient discomfort poses a significant clinical challenge. Radiofrequency ablation (RFA) offers a less invasive alternative to surgery and radiation therapy for PTMC treatment, characterized by shorter recovery times and reduced pain. As an image-guided procedure, RFA generates localized heat by delivering high-frequency electrical currents through electrodes to the targeted area under ultrasound imaging guidance. However, the precision and skill required by operators for accurate guidance using current ultrasound B-mode imaging technologies remain significant challenges. To address these challenges, we develop a novel AI segmentation model, E2E-Swin-Unet++. This model enhances ultrasound B-mode imaging by enabling real-time identification and segmentation of PTMC tumors and monitoring of the region of interest for precise targeting during treatment. E2E-Swin- Unet++ is an advanced end-to-end extension of the Swin-Unet architecture, incorporating thyroid region information to minimize the risk of false PTMC segmentation while providing fast inference capabilities. Experimental results on a real clinical RFA dataset demonstrate the superior performance of E2E-Swin-Unet++ compared to related models. Our proposed solution significantly improves the precision and control of RFA ablation treatment by enabling real-time identification and segmentation of PTMC margins during the procedure.

Tellurium is a primary candidate for the identification of the 2.1 $\mu$m emission line in kilonovae (KNe) spectra AT2017gfo and GRB230307A. Despite this, there is currently an insufficient amount of atomic data available for this species. We calculate the required atomic structure and collisional data, particularly the data required for accurate Non-Local-Thermodynamic-Equilibrium (NLTE) modelling of the low temperatures and densities in KNe. We use a Multi-Configurational-Dirac-Hartree-Fock method to produce optimised one-electron orbitals for Te {\sc i}-{\sc iii}. As a result energy levels and Einstein A-coefficients for Te {\sc i}-{\sc iii} have been calculated. These orbitals are then employed within Dirac $R$-matrix collision calculations to provide electron-impact-excitation collision strengths that were subsequently averaged according to a thermal Maxwellian distribution. Subsequent \textsc{tardis} simulations using this new atomic data reveal no significant changes to the synthetic spectra due to the very minor contribution of Te at early epochs. NLTE simulations with the ColRadPy package reveal optically thin spectra consistent with the increasing prominence of the Te {\sc iii} 2.1 $\mu$m line as the KNe ejecta cools. This is reinforced by the estimation of luminosities at nebular KNe conditions. New line ratios for both observation and laboratory benchmarks of the atomic data are proposed., Comment: Accepted Manuscript

Earth rotation is one of astronomical phenomena without which it is impossible to think of human life. That is why the investigation on the Earth rotation is very important and it has a long history of study. Invention of quartz clocks in the 1930s and atomic time 1950s and introduction of modern technology into astronomic observation in recent years resulted in rapid development of the study in Earth's rotation. The theory of the Earth rotation, however, has not been up to the high level of astronomic observation due to limitation of the time such as impossibility of quantitative calculation of moment of external force for Euler's dynamical equation based on Newtoniam mechanics. As a typical example, we can take the problems that cover the instabilities of the Earth's rotation proved completely by the astronomic observations as well as polar motion, the precession and nutation of the Earth rotation axis which have not been described in a single equation in a quantitative way from the unique law of the Earth rotation. In particular, at present the problem of what the main factor causing the instabilities of the Earth rotation is has not been solved clearly in quantitative ways yet. Therefore, this paper addresses a quantitative proof that the main factor which causes the instabilities of the Earth rotation is the moment of external force rather than variations in the relative atmospheric angular momentum and in moment of inertia of the Earth's body due to the time limitation and under some assumptions. Then the future direction of research is proposed., Comment: 30 pages, 1 Figure. Some grammatical errors and typos were deleted

While fine-tuning pretrained models has become common practice, these models often underperform outside their specific domains. Recently developed model merging techniques enable the direct integration of multiple models, each fine-tuned for distinct tasks, into a single model. This strategy promotes multitasking capabilities without requiring retraining on the original datasets. However, existing methods fall short in addressing potential conflicts and complex correlations between tasks, especially in parameter-level adjustments, posing a challenge in effectively balancing parameter competition across various tasks. This paper introduces an innovative technique named PCB-Merging (Parameter Competition Balancing), a lightweight and training-free technique that adjusts the coefficients of each parameter for effective model merging. PCB-Merging employs intra-balancing to gauge parameter significance within individual tasks and inter-balancing to assess parameter similarities across different tasks. Parameters with low importance scores are dropped, and the remaining ones are rescaled to form the final merged model. We assessed our approach in diverse merging scenarios, including cross-task, cross-domain, and cross-training configurations, as well as out-of-domain generalization. The experimental results reveal that our approach achieves substantial performance enhancements across multiple modalities, domains, model sizes, number of tasks, fine-tuning forms, and large language models, outperforming existing model merging methods. The code is publicly available at: \url{https://github.com/duguodong7/pcb-merging}., Comment: Accepted by NeurIPS2024

Vision-language models (VLMs) are essential for contextual understanding of both visual and textual information. However, their vulnerability to adversarially manipulated inputs presents significant risks, leading to compromised outputs and raising concerns about the reliability in VLM-integrated applications. Detecting these malicious prompts is thus crucial for maintaining trust in VLM generations. A major challenge in developing a safeguarding prompt classifier is the lack of a large amount of labeled benign and malicious data. To address the issue, we introduce VLMGuard, a novel learning framework that leverages the unlabeled user prompts in the wild for malicious prompt detection. These unlabeled prompts, which naturally arise when VLMs are deployed in the open world, consist of both benign and malicious information. To harness the unlabeled data, we present an automated maliciousness estimation score for distinguishing between benign and malicious samples within this unlabeled mixture, thereby enabling the training of a binary prompt classifier on top. Notably, our framework does not require extra human annotations, offering strong flexibility and practicality for real-world applications. Extensive experiment shows VLMGuard achieves superior detection results, significantly outperforming state-of-the-art methods. Disclaimer: This paper may contain offensive examples; reader discretion is advised., Comment: arXiv admin note: text overlap with arXiv:2409.17504

Spins in solid systems can inherently serve as qubits for quantum simulation or quantum information processing. Spin qubits are usually prone to environmental magnetic field fluctuations; however, a spin qubit encoded in a decoherence-free-subspace (DFS) can be protected from certain degrees of environmental noise depending on the specific structure of the DFS. Here, we derive the "true" DFS from an antiferromagnetic Heisenberg spin-1/2 trimer, which protects the qubit states against both short- and long-wavelength magnetic field fluctuations. We define the spin trimer with three electrons confined in a gate-defined GaAs double quantum dot (DQD) where we exploit Wigner-molecularization in one of the quantum dots. We first utilize the trimer for dynamic nuclear polarization (DNP), which generates a sizable magnetic field difference, $\Delta B_\mathrm{z}$, within the DQD. We show that large $\Delta B_\mathrm{z}$ significantly alters the eigenspectrum of the trimer and results in the "true" DFS in the DQD. Real-time Bayesian estimation of the DFS energy gap explicitly demonstrates protection of the DFS against short-wavelength magnetic field fluctuations in addition to long-wavelength ones. Our findings pave the way toward compact DFS structures for exchange-coupled quantum dot spin chains, the internal structure of which can be coherently controlled completely decoupled from environmental magnetic fields.

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the Clinical and Translational Science Award (CTSA) Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. With the rapid advancement of GenAI technologies, including large language models (LLMs), healthcare institutions face unprecedented opportunities and challenges. This research explores the current status of GenAI integration, focusing on stakeholder roles, governance structures, and ethical considerations by administering a survey among leaders of health institutions (i.e., representing academic medical centers and health systems) to assess the institutional readiness and approach towards GenAI adoption. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The study highlights significant variations in governance models, with a strong preference for centralized decision-making but notable gaps in workforce training and ethical oversight. Moreover, the results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis also reveals concerns regarding GenAI bias, data security, and stakeholder trust, which must be addressed to ensure the ethical and effective implementation of GenAI technologies. This study offers valuable insights into the challenges and opportunities of GenAI integration in healthcare, providing a roadmap for institutions aiming to leverage GenAI for improved quality of care and operational efficiency.

LLMs are an integral component of retrieval-augmented generation (RAG) systems. While many studies focus on evaluating the overall quality of end-to-end RAG systems, there is a gap in understanding the appropriateness of LLMs for the RAG task. To address this, we introduce Trust-Score, a holistic metric that evaluates the trustworthiness of LLMs within the RAG framework. Our results show that various prompting methods, such as in-context learning, fail to effectively adapt LLMs to the RAG task as measured by Trust-Score. Consequently, we propose Trust-Align, a method to align LLMs for improved Trust-Score performance. The LLaMA-3 family, aligned using our method, significantly outperforms open-source LLMs of similar sizes on ASQA (up 14.0), QAMPARI (up 28.9), and ELI5 (up 13.7). We also demonstrate the effectiveness of Trust-Align across different open-weight models, including the LLaMA series (1b to 8b), Qwen-2.5 series (0.5b to 7b), and Phi3.5 (3.8b). We release our code at \url{https://anonymous.4open.science/r/trust-align}

In the field of machine learning, domain-specific annotated data is an invaluable resource for training effective models. However, in the medical domain, this data often includes Personal Health Information (PHI), raising significant privacy concerns. The stringent regulations surrounding PHI limit the availability and sharing of medical datasets, which poses a substantial challenge for researchers and practitioners aiming to develop advanced machine learning models. In this paper, we introduce a novel method to "clone" datasets containing PHI. Our approach ensures that the cloned datasets retain the essential characteristics and utility of the original data without compromising patient privacy. By leveraging differential-privacy techniques and a novel fine-tuning task, our method produces datasets that are free from identifiable information while preserving the statistical properties necessary for model training. We conduct utility testing to evaluate the performance of machine learning models trained on the cloned datasets. The results demonstrate that our cloned datasets not only uphold privacy standards but also enhance model performance compared to those trained on traditional anonymized datasets. This work offers a viable solution for the ethical and effective utilization of sensitive medical data in machine learning, facilitating progress in medical research and the development of robust predictive models.

Active metamaterials are a type of metamaterial with tunable properties enabled by structural reconfigurations. Existing active metamaterials often achieve only a limited number of structural reconfigurations upon the application of an external load across the entire structure. Here, we propose a selective actuation strategy for inhomogeneous deformations of magneto-mechanical metamaterials, which allows for the integration of multiple functionalities into a single metamaterial design. Central to this actuation strategy is that a magnetic field is applied to specific unit cells instead of the entire metamaterial, and the unit cell can transform between two geometrically distinct shapes, which exhibit very different mechanical responses to elastic wave excitations. Our numerical simulations and experiments demonstrate that the tunable response of the unit cell, coupled with inhomogeneous deformation achieved through selective actuation, unlocks multifunctional capabilities of magneto-mechanical metamaterials such as tunable elastic wave transmittance, elastic waveguide, and vibration isolation. The proposed selective actuation strategy offers a simple but effective way to control the tunable properties and thus enhance the programmability of magneto-mechanical metamaterials, which also expands the application space of magneto-mechanical metamaterials in elastic wave manipulation.

WebAssembly (Wasm for short) brings a new, powerful capability to the web as well as Edge, IoT, and embedded systems. Wasm is a portable, compact binary code format with high performance and robust sandboxing properties. As Wasm applications grow in size and importance, the complex performance characteristics of diverse Wasm engines demand robust, representative benchmarks for proper tuning. Stopgap benchmark suites, such as PolyBenchC and libsodium, continue to be used in the literature, though they are known to be unrepresentative. Porting of more complex suites remains difficult because Wasm lacks many system APIs and extracting real-world Wasm benchmarks from the web is difficult due to complex host interactions. To address this challenge, we introduce Wasm-R3, the first record and replay technique for Wasm. Wasm-R3 transparently injects instrumentation into Wasm modules to record an execution trace from inside the module, then reduces the execution trace via several optimizations, and finally produces a replay module that is executable sandalone without any host environment - on any engine. The benchmarks created by our approach are (i) realistic, because the approach records real-world web applications, (ii) faithful to the original execution, because the replay benchmark includes the unmodified original code, only adding emulation of host interactions, and (iii) standalone, because the replay benchmarks run on any engine. Applying Wasm-R3 to web-based Wasm applications in the wild demonstrates the correctness of our approach as well as the effectiveness of our optimizations, which reduce the recorded traces by 99.53 percent and the size of the replay benchmark by 9.98 percent. We release the resulting benchmark suite of 27 applications, called Wasm-R3-Bench, to the community, to inspire a new generation of realistic and standalone Wasm benchmarks., Comment: Accepted at OOPSLA 2024

The increase in global trade, the impact of COVID-19, and the tightening of environmental and safety regulations have brought significant changes to the maritime transportation market. To address these challenges, the port logistics sector is rapidly adopting advanced technologies such as big data, Internet of Things, and AI. However, despite these efforts, solving several issues related to productivity, environment, and safety in the port logistics sector requires collaboration among various stakeholders. In this study, we introduce an AI-based port logistics metaverse framework (PLMF) that facilitates communication, data sharing, and decision-making among diverse stakeholders in port logistics. The developed PLMF includes 11 AI-based metaverse content modules related to productivity, environment, and safety, enabling the monitoring, simulation, and decision making of real port logistics processes. Examples of these modules include the prediction of expected time of arrival, dynamic port operation planning, monitoring and prediction of ship fuel consumption and port equipment emissions, and detection and monitoring of hazardous ship routes and accidents between workers and port equipment. We conducted a case study using historical data from Busan Port to analyze the effectiveness of the PLMF. By predicting the expected arrival time of ships within the PLMF and optimizing port operations accordingly, we observed that the framework could generate additional direct revenue of approximately 7.3 million dollars annually, along with a 79% improvement in ship punctuality, resulting in certain environmental benefits for the port. These findings indicate that PLMF not only provides a platform for various stakeholders in port logistics to participate and collaborate but also significantly enhances the accuracy and sustainability of decision-making in port logistics through AI-based simulations.

Generally, the components constituting a metaverse are classified into hardware, software, and content categories. As a content component, text design is known to positively affect user immersion and usability. Unlike English, where designing texts involves only 26 letters, designing texts in Korean and Chinese requires creating 11,172 and over 60,000 individual glyphs, respectively, owing to the nature of the languages. Consequently, applying new text designs to enhance user immersion within the metaverse can be tedious and expensive, particularly for certain languages. Recently, efforts have been devoted toward addressing this issue using generative artificial intelligence (AI). However, challenges remain in creating new text designs for the metaverse owing to inaccurate character structures. This study proposes a new AI learning method known as Legacy Learning, which enables high-quality text design at a lower cost. Legacy Learning involves recombining existing text designs and intentionally introducing variations to produce fonts that are distinct from the originals while maintaining high quality. To demonstrate the effectiveness of the proposed method in generating text designs for the metaverse, we performed evaluations from the following three aspects: 1) Quantitative performance evaluation 2) Qualitative evaluationand 3) User usability evaluation. The quantitative and qualitative performance results indicated that the generated text designs differed from the existing ones by an average of over 30% while still maintaining high visual quality. Additionally, the SUS test performed with metaverse content designers achieved a score of 95.8, indicating high usability.

Most real-world variables are multivariate time series influenced by past values and explanatory factors. Consequently, predicting these time series data using artificial intelligence is ongoing. In particular, in fields such as healthcare and finance, where reliability is crucial, having understandable explanations for predictions is essential. However, achieving a balance between high prediction accuracy and intuitive explainability has proven challenging. Although attention-based models have limitations in representing the individual influences of each variable, these models can influence the temporal dependencies in time series prediction and the magnitude of the influence of individual variables. To address this issue, this study introduced DLFormer, an attention-based architecture integrated with distributed lag embedding, to temporally embed individual variables and capture their temporal influence. Through validation against various real-world datasets, DLFormer showcased superior performance improvements compared to existing attention-based high-performance models. Furthermore, comparing the relationships between variables enhanced the reliability of explainability.

This paper demonstrates that model-based reinforcement learning (model-based RL) is a suitable approach for the task of analogical reasoning. We hypothesize that model-based RL can solve analogical reasoning tasks more efficiently through the creation of internal models. To test this, we compared DreamerV3, a model-based RL method, with Proximal Policy Optimization, a model-free RL method, on the Abstraction and Reasoning Corpus (ARC) tasks. Our results indicate that model-based RL not only outperforms model-free RL in learning and generalizing from single tasks but also shows significant advantages in reasoning across similar tasks., Comment: Accepted to IJCAI 2024 IARML Workshop

Out-of-distribution (OOD) detection, which determines whether a given sample is part of the in-distribution (ID), has recently shown promising results through training with synthetic OOD datasets. Nonetheless, existing methods often produce outliers that are considerably distant from the ID, showing limited efficacy for capturing subtle distinctions between ID and OOD. To address these issues, we propose a novel framework, Semantic Outlier generation via Nuisance Awareness (SONA), which notably produces challenging outliers by directly leveraging pixel-space ID samples through diffusion models. Our approach incorporates SONA guidance, providing separate control over semantic and nuisance regions of ID samples. Thereby, the generated outliers achieve two crucial properties: (i) they present explicit semantic-discrepant information, while (ii) maintaining various levels of nuisance resemblance with ID. Furthermore, the improved OOD detector training with SONA outliers facilitates learning with a focus on semantic distinctions. Extensive experiments demonstrate the effectiveness of our framework, achieving an impressive AUROC of 88% on near-OOD datasets, which surpasses the performance of baseline methods by a significant margin of approximately 6%.

We revisit the standard ``telescoping sum'' argument ubiquitous in the final steps of analyzing evaluation complexity of algorithms for smooth nonconvex optimization, and obtain a refined formulation of the resulting bound as a function of the requested accuracy $\epsilon$. While bounds obtained using the standard argument typically are of the form $\mathcal{O}(\epsilon^{-\alpha})$ for some positive $\alpha$, the refined results are of the form $o(\epsilon^{-\alpha})$. We then explore to which known algorithms our refined bounds are applicable and finally describe an example showing how close the standard and refined bounds can be., Comment: 10 ages, 1 figure

We present the first systematic evaluation examining format bias in performance of large language models (LLMs). Our approach distinguishes between two categories of an evaluation metric under format constraints to reliably and accurately assess performance: one measures performance when format constraints are adhered to, while the other evaluates performance regardless of constraint adherence. We then define a metric for measuring the format bias of LLMs and establish effective strategies to reduce it. Subsequently, we present our empirical format bias evaluation spanning four commonly used categories -- multiple-choice question-answer, wrapping, list, and mapping -- covering 15 widely-used formats. Our evaluation on eight generation tasks uncovers significant format bias across state-of-the-art LLMs. We further discover that improving the format-instruction following capabilities of LLMs across formats potentially reduces format bias. Based on our evaluation findings, we study prompting and fine-tuning with synthesized format data techniques to mitigate format bias. Our methods successfully reduce the variance in ChatGPT's performance among wrapping formats from 235.33 to 0.71 (%$^2$).

We often encounter limited FOV situations due to various factors such as sensor fusion or sensor mount in real-world robot navigation. However, the limited FOV interrupts the generation of descriptions and impacts place recognition adversely. Therefore, we suffer from correcting accumulated drift errors in a consistent map using LiDAR-based place recognition with limited FOV. Thus, in this paper, we propose a robust LiDAR-based place recognition method for handling narrow FOV scenarios. The proposed method establishes spatial organization based on the range-elevation bin and azimuth-elevation bin to represent places. In addition, we achieve a robust place description through reweighting based on vertical direction information. Based on these representations, our method enables addressing rotational changes and determining the initial heading. Additionally, we designed a lightweight and fast approach for the robot's onboard autonomy. For rigorous validation, the proposed method was tested across various LiDAR place recognition scenarios (i.e., single-session, multi-session, and multi-robot scenarios). To the best of our knowledge, we report the first method to cope with the restricted FOV. Our place description and SLAM codes will be released. Also, the supplementary materials of our descriptor are available at \texttt{\url{https://sites.google.com/view/lidar-solid}}., Comment: Accepted in IEEE Robotics and Automation Letters (2024)

Drawing inspiration from the philosophy of Yi Jing, the Yin-Yang pair optimization (YYPO) algorithm has been shown to achieve competitive performance in single objective optimizations, in addition to the advantage of low time complexity when compared to other population-based meta-heuristics. Building upon a reversal concept in Yi Jing, we propose the novel Yi optimization (YI) algorithm. Specifically, we enhance the Yin-Yang pair in YYPO with a proposed Yi-point, in which we use Cauchy flight to update the solution, by implementing both the harmony and reversal concept of Yi Jing. The proposed Yi-point balances both the effort of exploration and exploitation in the optimization process. To examine YI, we use the IEEE CEC 2017 benchmarks and compare YI against the dynamical YYPO, CV1.0 optimizer, and four classical optimizers, i.e., the differential evolution, the genetic algorithm, the particle swarm optimization, and the simulated annealing. According to the experimental results, YI shows highly competitive performance while keeping the low time complexity. The results of this work have implications for enhancing a meta-heuristic optimizer using the philosophy of Yi Jing. While this work implements only certain aspects of Yi Jing, we envisage enhanced performance by incorporating other aspects., Comment: This work has been submitted to the IEEE for possible publication. arXiv admin note: substantial text overlap with arXiv:2104.08564

Darwinian evolution of the biological brain is documented through multiple lines of evidence, although the modes of evolutionary changes remain unclear. Drawing inspiration from the evolved neural systems (e.g., visual cortex), deep learning models have demonstrated superior performance in visual tasks, among others. While the success of training deep neural networks has been relying on back-propagation (BP) and its variants to learn representations from data, BP does not incorporate the evolutionary processes that govern biological neural systems. This work proposes a neural network optimization framework based on evolutionary theory. Specifically, BP-trained deep neural networks for visual recognition tasks obtained from the ending epochs are considered the primordial ancestors (initial population). Subsequently, the population evolved with differential evolution. Extensive experiments are carried out to examine the relationships between Darwinian evolution and neural network optimization, including the correspondence between datasets, environment, models, and living species. The empirical results show that the proposed framework has positive impacts on the network, with reduced over-fitting and an order of magnitude lower time complexity compared to BP. Moreover, the experiments show that the proposed framework performs well on deep neural networks and big datasets., Comment: This work has been submitted to the IEEE for possible publication

Neural architecture search (NAS) automates neural network design by using optimization algorithms to navigate architecture spaces, reducing the burden of manual architecture design. While NAS has achieved success, applying it to emerging domains, such as analyzing unstructured 3D point clouds, remains underexplored due to the data lying in non-Euclidean spaces, unlike images. This paper presents Success-History-based Self-adaptive Differential Evolution with a Joint Point Interaction Dimension Search (SHSADE-PIDS), an evolutionary NAS framework that encodes discrete deep neural network architectures to continuous spaces and performs searches in the continuous spaces for efficient point cloud neural architectures. Comprehensive experiments on challenging 3D segmentation and classification benchmarks demonstrate SHSADE-PIDS's capabilities. It discovered highly efficient architectures with higher accuracy, significantly advancing prior NAS techniques. For segmentation on SemanticKITTI, SHSADE-PIDS attained 64.51% mean IoU using only 0.55M parameters and 4.5GMACs, reducing overhead by over 22-26X versus other top methods. For ModelNet40 classification, it achieved 93.4% accuracy with just 1.31M parameters, surpassing larger models. SHSADE-PIDS provided valuable insights into bridging evolutionary algorithms with neural architecture optimization, particularly for emerging frontiers like point cloud learning., Comment: This work has been submitted to the IEEE for possible publication

In the double detonation scenario the ignition of a surface He detonation on a sub-Chandrasekhar mass white dwarf leads to a secondary core detonation. Double detonation models have shown promise for explaining Type Ia supernovae (SNe Ia) with a variety of luminosities. A key feature of such models is unburnt He in the ejecta, which can show significant variation in both its mass and velocity distribution. Many previous radiative transfer simulations for double detonation models have neglected treatment of non-thermal ionization and excitation, preventing them from robustly assessing whether He spectral features are expected to form. We present a non local thermodynamic equilibrium radiative transfer simulation, including treatment for non-thermal electrons, for a double detonation model with a modest mass of He (${\sim}$0.04 M${\odot}$) ejected at reasonably low velocities (${\sim}$12000$\,\mathrm{km}\,\mathrm{s}^{-1}$). Despite our simulation predicting no clear optical He features, a strong and persistent He I 10830$\,\r{A}$ absorption feature forms that is significantly blended with the spectral contribution of Mg II 10927$\,\r{A}$. For some normal SNe Ia the Mg feature shows an extended blue wing, previously attributed to C I, however the simulated He feature shows its strongest absorption at wavelengths consistent with this wing. We therefore suggest this extended wing is instead a spectral signature of He. The He feature predicted by this particular model is too strong and persistent to be consistent with normal SNe Ia, however, this motivates further work to use this observable signature to test the parameter space for double detonation models., Comment: 11 pages, 5 figures. Submitted to MNRAS

A leading model for Type Ia supernovae involves the double-detonation of a sub-Chandrasekhar mass white dwarf. Double-detonations arise when a surface helium shell detonation generates shockwaves that trigger a core detonation; this mechanism may be triggered via accretion or during the merger of binaries. Most previous double-detonation simulations only included the primary white dwarf; however, the fate of the secondary has significant observational consequences. Recently, hydrodynamic simulations accounted for the companion in double-degenerate double-detonation mergers. In the merger of a 1.05$\text{M}_{\odot}$ primary white dwarf and 0.7$\text{M}_{\odot}$ secondary white dwarf, the primary consistently detonates while the fate of the secondary remains uncertain. We consider two versions of this scenario, one in which the secondary survives and another in which it detonates. We present the first 3D radiative transfer calculations for these models and show that the synthetic observables for both models are similar and match properties of the peculiar 02es-like subclass of Type Ia supernovae. Our calculations show angle dependencies sensitive to the companion's fate, and we can obtain a closer spectroscopic match to normal Type Ia supernovae when the secondary detonates and the effects of helium detonation ash are minimised. The asymmetry in the width-luminosity relationship is comparable to previous double-detonation models, but the overall spread is increased with a secondary detonation. The secondary detonation has a meaningful impact on all synthetic observables; however, multidimensional nebular phase calculations are needed to support or rule out either model as a likely explanation for Type Ia supernovae., Comment: 17 pages, 19 figures, MNRAS accepted

Person search is the task to localize a query person in gallery datasets of scene images. Existing methods have been mainly developed to handle a single target dataset only, however diverse datasets are continuously given in practical applications of person search. In such cases, they suffer from the catastrophic knowledge forgetting in the old datasets when trained on new datasets. In this paper, we first introduce a novel problem of lifelong person search (LPS) where the model is incrementally trained on the new datasets while preserving the knowledge learned in the old datasets. We propose an end-to-end LPS framework that facilitates the knowledge distillation to enforce the consistency learning between the old and new models by utilizing the prototype features of the foreground persons as well as the hard background proposals in the old domains. Moreover, we also devise the rehearsal-based instance matching to further improve the discrimination ability in the old domains by using the unlabeled person instances additionally. Experimental results demonstrate that the proposed method achieves significantly superior performance of both the detection and re-identification to preserve the knowledge learned in the old domains compared with the existing methods., Comment: 10 pages, 6 figure