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Recent measurements of Baryon Acoustic Oscillations (BAO) and distance moduli from Type Ia supernovae suggest a preference for Dynamical Dark Energy (DDE) scenarios characterized by a time-varying equation of state (EoS). This focused review assesses its robustness across independent measurements and surveys. Using the Chevallier-Polarski-Linder (CPL) parameterization to describe the evolution of the DE EoS, we analyze over 35 dataset combinations, incorporating Planck Cosmic Microwave Background (CMB) anisotropies, three independent Type Ia supernova (SN) catalogs (PantheonPlus, Union3, DESY5), BAO measurements from DESI and SDSS, and expansion rate measurements $H(z)$ inferred from the relative ages of massive, passively evolving galaxies at early cosmic times known as Cosmic Chronometers (CC). This review has two main objectives: first, to evaluate the statistical significance of the DDE preference across different dataset combinations, which incorporate varying sources of information. Specifically, we consider cases where only low-redshift probes are used in different combinations, others where individual low-redshift probes are analyzed together with CMB data, and finally, scenarios where high- and low-redshift probes are included in all possible independent combinations. Second, we provide a reader-friendly synthesis of what the latest cosmological and astrophysical probes can (and cannot yet) reveal about DDE. Overall, our findings highlight that combinations that \textit{simultaneously} include PantheonPlus SN and SDSS BAO significantly weaken the preference for DDE. However, intriguing hints supporting DDE emerge in combinations that do not include DESI-BAO measurements: SDSS-BAO combined with SN from Union3 and DESY5 (with and without CMB) support the preference for DDE., Comment: A short review; 20 pages including references; 2 tables, 5 figures; comments are welcome!

Nb3O2(OH) has emerged as a highly attractive photocatalyst based on its chemical stability, energetic band positions, and large active lattice sites. Compared to other various photocatalytic semiconductors, it can be synthesized easily. This study presents a systematic analysis of pristine and doped Nb3O7(OH) based on recent developments in related research. The current study summarizes the modeling approach and computationally used techniques for doped Nb3O7(OH) based photocatalysts, focusing on their structural properties, defects engineering, and band structure engineering. This study demonstrates that the Trans-Blaha modified Becke Johnson approximation (TB-mBJ) is an effective approach for optoelectronic properties of pristine and Ta/Sb-doped Nb3O7(OH). The generalized gradient approximation is used for structure optimization of all systems studied. Spin-orbit (SO) coupling is also applied to deal with the Ta f orbital and Sb d orbital in the Ta/Sb-doped systems. Doping shifts the energetic band positions and relocates the Fermi level i.e. both the valence band maximum and the conduction band minimum are relocated, decreasing the band gap from 1.7 eV (pristine), to 1.266 eV (Ta-doped)/1.203 eV (Sb-doped). Moreover, doped systems shift the optical threshold to the visible region. Transport properties like effective mass and electrical conductivity are calculated, reflecting that the mobility of charge carriers increases with the doping of Ta/Sb atoms.The reduction in the band gap and red-shift in the optical properties of the Ta/Sb-doped Nb3O7(OH) to the visible region suggest their promising potential for photocatalytic activity and photoelectrochemical solar cells., Comment: Final submited version, postprint

Trajectory prediction and planning are fundamental components for autonomous vehicles to navigate safely and efficiently in dynamic environments. Traditionally, these components have often been treated as separate modules, limiting the ability to perform interactive planning and leading to computational inefficiency in multi-agent scenarios. In this paper, we present a novel unified and data-driven framework that integrates prediction and planning with a single consistency model. Trained on real-world human driving datasets, our consistency model generates samples from high-dimensional, multimodal joint trajectory distributions of the ego and multiple surrounding agents, enabling end-to-end predictive planning. It effectively produces interactive behaviors, such as proactive nudging and yielding to ensure both safe and efficient interactions with other road users. To incorporate additional planning constraints on the ego vehicle, we propose an alternating direction method for multi-objective guidance in online guided sampling. Compared to diffusion models, our consistency model achieves better performance with fewer sampling steps, making it more suitable for real-time deployment. Experimental results on Waymo Open Motion Dataset (WOMD) demonstrate our method's superiority in trajectory quality, constraint satisfaction, and interactive behavior compared to various existing approaches.

The solid-state reaction was used to synthesize CoO2 nanostructured material. Cobalt nitrate tetrahydrate and sodium oxide (NaOH) were combined to produce CoO2 nanostructured material. The three synthesized working electrodes were each tested individually, using 3 M KOH as the electrolyte. The CV analysis of a three-electrode system revealed redox peaks, indicating Faradaic processes. The estimated specific capacitances of CoO2, CoO2 (250oC), CoO2 (300oC) nanostructured material at scan rates of (10) mVs-1 is (223, 348, and 473) Fg-1. The diffraction peaks at 2{\theta} = 26.264o, 33.527o, 37.579, 51.264 and 54.367o correspond respectively to the diffraction planes of 111, 112, 200, 211, and 311 of CoO2 nanostructured material. The annealing temperature, which affects the bandgap, can influence the size, shape, and crystallinity of nanostructures. For the unannealed material, the energy bandgap of CoO2 is 2.00 eV, whereas for the annealed material it ranges from 1.77 to 1.86 eV., Comment: 16 pages, 6 figures

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20\% increase in the efficiency of sub-1\,cm vertex reconstruction across all neutrino flavours., Comment: 32 pages, 18 figures

Konrad Burnik suggests a structure-preserving QR factorization for centrosymmetric matrices, known as QX factorization. In this article, we obtain the explicit expressions for rigorous perturbation bounds of the QX factorization when the original matrix is perturbed, either norm-wise or component-wise. First, using the matrix-equation approach, weak rigorous perturbation bounds are derived. Then, strong rigorous perturbation bounds are obtained by combining the modified matrix-vector equation approach with the strategy for the Lyapunov majorant function and the Banach fixed-point theorem. The mixed and component-wise condition numbers and their upper bounds are also explicitly expressed. Numerical tests illustrate the validity of the obtained results., Comment: 21

Seismic data often face challenges in their utilization due to noise contamination, incomplete acquisition, and limited low-frequency information, which hinder accurate subsurface imaging and interpretation. Traditional processing methods rely heavily on task-specific designs to address these challenges and fail to account for the variability of data. To address these limitations, we present a generative seismic foundation model (GSFM), a unified framework based on generative diffusion models (GDMs), designed to tackle multi-task seismic processing challenges, including denoising, backscattered noise attenuation, interpolation, and low-frequency extrapolation. GSFM leverages a pre-training stage on synthetic data to capture the features of clean, complete, and broadband seismic data distributions and applies an iterative fine-tuning strategy to adapt the model to field data. By adopting a target-oriented diffusion process prediction, GSFM improves computational efficiency without compromising accuracy. Synthetic data tests demonstrate GSFM surpasses benchmarks with equivalent architectures in all tasks and achieves performance comparable to traditional pre-training strategies, even after their fine-tuning. Also, field data tests suggest that our iterative fine-tuning approach addresses the generalization limitations of conventional pre-training and fine-tuning paradigms, delivering significantly enhanced performance across diverse tasks. Furthermore, GSFM's inherent probabilistic nature enables effective uncertainty quantification, offering valuable insights into the reliability of processing results.

Physics-informed neural networks (PINNs) face significant challenges in modeling multi-frequency wavefields in complex velocity models due to their slow convergence, difficulty in representing high-frequency details, and lack of generalization to varying frequencies and velocity scenarios. To address these issues, we propose Meta-LRPINN, a novel framework that combines low-rank parameterization using singular value decomposition (SVD) with meta-learning and frequency embedding. Specifically, we decompose the weights of PINN's hidden layers using SVD and introduce an innovative frequency embedding hypernetwork (FEH) that links input frequencies with the singular values, enabling efficient and frequency-adaptive wavefield representation. Meta-learning is employed to provide robust initialization, improving optimization stability and reducing training time. Additionally, we implement adaptive rank reduction and FEH pruning during the meta-testing phase to further enhance efficiency. Numerical experiments, which are presented on multi-frequency scattered wavefields for different velocity models, demonstrate that Meta-LRPINN achieves much fast convergence speed and much high accuracy compared to baseline methods such as Meta-PINN and vanilla PINN. Also, the proposed framework shows strong generalization to out-of-distribution frequencies while maintaining computational efficiency. These results highlight the potential of our Meta-LRPINN for scalable and adaptable seismic wavefield modeling.

The true vision for real-time virtual and augmented reality is reproducing our visual reality in its entirety on immersive displays. To this end, foveated rendering leverages the limitations of spatial acuity in human peripheral vision to allocate computational resources to the fovea while reducing quality in the periphery. Such methods are often derived from studies on the spatial resolution of the human visual system and its ability to perceive blur in the periphery, enabling the potential for high spatial quality in real-time. However, the effects of blur on other visual cues that depend on luminance contrast, such as depth, remain largely unexplored. It is critical to understand this interplay, as accurate depth representation is a fundamental aspect of visual realism. In this paper, we present the first evaluation exploring the effects of foveated rendering on stereoscopic depth perception. We design a psychovisual experiment to quantitatively study the effects of peripheral blur on depth perception. Our analysis demonstrates that stereoscopic acuity remains unaffected (or even improves) by high levels of peripheral blur. Based on our studies, we derive a simple perceptual model that determines the amount of foveation that does not affect stereoacuity. Furthermore, we analyze the model in the context of common foveation practices reported in literature. The findings indicate that foveated rendering does not impact stereoscopic depth perception, and stereoacuity remains unaffected up to 2x stronger foveation than commonly used. Finally, we conduct a validation experiment and show that our findings hold for complex natural stimuli., Comment: 8 pages including references

Mobile robot path planning in complex environments remains a significant challenge, especially in achieving efficient, safe and robust paths. The traditional path planning techniques like DRL models typically trained for a given configuration of the starting point and target positions, these models only perform well when these conditions are satisfied. In this paper, we proposed a novel path planning framework that embeds Large Language Models to empower mobile robots with the capability of dynamically interpreting natural language commands and autonomously generating efficient, collision-free navigation paths. The proposed framework uses LLMs to translate high-level user inputs into actionable waypoints while dynamically adjusting paths in response to obstacles. We experimentally evaluated our proposed LLM-based approach across three different environments of progressive complexity, showing the robustness of our approach with llama3.1 model that outperformed other LLM models in path planning time, waypoint generation success rate, and collision avoidance. This underlines the promising contribution of LLMs for enhancing the capability of mobile robots, especially when their operation involves complex decisions in large and complex environments. Our framework has provided safer, more reliable navigation systems and opened a new direction for the future research. The source code of this work is publicly available on GitHub., Comment: 18 pages, 6 figures, submitted in Journal Expert Systems with Applications

Low-frequency data are essential to constrain the low-wavenumber model components in seismic full-waveform inversion (FWI). However, due to acquisition limitations and ambient noise it is often unavailable. Deep learning (DL) can learn to map from high frequency model updates of elastic FWI to a low-wavenumber model update, producing an initial model estimation as if it was available from low-frequency data. We train a FusionNET-based convolutional neural network (CNN) on a synthetic dataset to produce an initial low-wavenumber model from a set of model updates produced by FWI on the data with missing low frequencies. We validate this DL-fused approach using a synthetic benchmark with data generated in an unrelated model to the training dataset. Finally, applying our trained network to estimate an initial low-wavenumber model based on field data, we see that elastic FWI starting from such a 'DL-fused' model update shows improved convergence on real-world marine streamer data., Comment: 14 pages, 13 figures

Counterfactual explanation methods have recently received significant attention for explaining CNN-based image classifiers due to their ability to provide easily understandable explanations that align more closely with human reasoning. However, limited attention has been given to utilizing explainability methods to improve model performance. In this paper, we propose to leverage counterfactual concepts aiming to enhance the performance of CNN models in image classification tasks. Our proposed approach utilizes counterfactual reasoning to identify crucial filters used in the decision-making process. Following this, we perform model retraining through the design of a novel methodology and loss functions that encourage the activation of class-relevant important filters and discourage the activation of irrelevant filters for each class. This process effectively minimizes the deviation of activation patterns of local predictions and the global activation patterns of their respective inferred classes. By incorporating counterfactual explanations, we validate unseen model predictions and identify misclassifications. The proposed methodology provides insights into potential weaknesses and biases in the model's learning process, enabling targeted improvements and enhanced performance. Experimental results on publicly available datasets have demonstrated an improvement of 1-2\%, validating the effectiveness of the approach., Comment: This manuscript is currently under consideration for publication in Pattern Recognition Letters

We introduce a prototyping testbed, GenSC-6G, developed to generate a comprehensive dataset that supports the integration of generative artificial intelligence (AI), quantum computing, and semantic communication for emerging sixth-generation (6G) applications. The GenSC-6G dataset is designed with noise-augmented synthetic data optimized for semantic decoding, classification, and localization tasks, significantly enhancing flexibility for diverse AI-driven communication applications. This adaptable prototype supports seamless modifications across baseline models, communication modules, and goal-oriented decoders. Case studies demonstrate its application in lightweight classification, semantic upsampling, and edge-based language inference under noise conditions. The GenSC-6G dataset serves as a scalable and robust resource for developing goal-oriented communication systems tailored to the growing demands of 6G networks., Comment: SUBMITTED FOR PUBLICATION IN IEEE COMMUNICATIONS MAGAZINE

In reading tasks drift can move fixations from one word to another or even another line, invalidating the eye tracking recording. Manual correction is time-consuming and subjective, while automated correction is fast yet limited in accuracy. In this paper we present Fix8 (Fixate), an open-source GUI tool that offers a novel semi-automated correction approach for eye tracking data in reading tasks. The proposed approach allows the user to collaborate with an algorithm to produce accurate corrections faster without sacrificing accuracy. Through a usability study (N=14) we assess the time benefits of the proposed technique, and measure the correction accuracy in comparison to manual correction. In addition, we assess subjective workload through NASA Task Load Index, and user opinions through Likert-scale questions. Our results show that on average the proposed technique was 44% faster than manual correction without any sacrifice in accuracy. In addition, users reported a preference for the proposed technique, lower workload, and higher perceived performance compared to manual correction. Fix8 is a valuable tool that offers useful features for generating synthetic eye tracking data, visualization, filters, data converters, and eye movement analysis in addition to the main contribution in data correction.

Deep learning models in computer vision have made remarkable progress, but their lack of transparency and interpretability remains a challenge. The development of explainable AI can enhance the understanding and performance of these models. However, existing techniques often struggle to provide convincing explanations that non-experts easily understand, and they cannot accurately identify models' intrinsic decision-making processes. To address these challenges, we propose to develop a counterfactual explanation (CE) model that balances plausibility and faithfulness. This model generates easy-to-understand visual explanations by making minimum changes necessary in images without altering the pixel data. Instead, the proposed method identifies internal concepts and filters learned by models and leverages them to produce plausible counterfactual explanations. The provided explanations reflect the internal decision-making process of the model, thus ensuring faithfulness to the model.

Explainability of deep convolutional neural networks (DCNNs) is an important research topic that tries to uncover the reasons behind a DCNN model's decisions and improve their understanding and reliability in high-risk environments. In this regard, we propose a novel method for generating interpretable counterfactual and contrastive explanations for DCNN models. The proposed method is model intrusive that probes the internal workings of a DCNN instead of altering the input image to generate explanations. Given an input image, we provide contrastive explanations by identifying the most important filters in the DCNN representing features and concepts that separate the model's decision between classifying the image to the original inferred class or some other specified alter class. On the other hand, we provide counterfactual explanations by specifying the minimal changes necessary in such filters so that a contrastive output is obtained. Using these identified filters and concepts, our method can provide contrastive and counterfactual reasons behind a model's decisions and makes the model more transparent. One of the interesting applications of this method is misclassification analysis, where we compare the identified concepts from a particular input image and compare them with class-specific concepts to establish the validity of the model's decisions. The proposed method is compared with state-of-the-art and evaluated on the Caltech-UCSD Birds (CUB) 2011 dataset to show the usefulness of the explanations provided.

An ADC is used to convert analog signals into binary signals. Compared with many other types of ADCs, flash converters are incredibly quick. A typical Flash ADC consists of 2n resistors, 2n-1 op-amp comparators, and an encoder which requires more area. The resistors and comparators can be eliminated by using threshold inverter quantization (TIQ) comparators. As a voltage comparator, TIQ technique uses two cascaded CMOS inverters. So that there will be no variation in the fabrication process, and temperature. A 6-bit flash ADC based on threshold inverter quantization (TIQ) comparator was designed and software implementation was performed employing a fat tree encoder with 0.25 micrometer CMOS technology. The design consists of 2n-1 TIQ comparator arrays, a gain booster, a 1-out-of-n code generator, and a fat tree encoder. This TIQ flash ADC is simulated with the Tanner EDA Tool. Here the supply voltage is 2.5 V, input frequency of 10 kHz and 10 MHz. The power consumption of the ADC is 6.25 mW, and the propagation delay is 1.07 microseconds for 10 kHz input frequency. For 10 MHz input frequency, power consumption is 12.12 mW and propagation delay is 947.14 ms.

The increasing sophistication of modern cyber threats, particularly file-less malware relying on living-off-the-land techniques, poses significant challenges to traditional detection mechanisms. Memory forensics has emerged as a crucial method for uncovering such threats by analysing dynamic changes in memory. This research introduces SPECTRE (Snapshot Processing, Emulation, Comparison, and Threat Reporting Engine), a modular Cyber Incident Response System designed to enhance threat detection, investigation, and visualization. By adopting Volatility JSON format as an intermediate output, SPECTRE ensures compatibility with widely used DFIR tools, minimizing manual data transformations and enabling seamless integration into established workflows. Its emulation capabilities safely replicate realistic attack scenarios, such as credential dumping and malicious process injections, for controlled experimentation and validation. The anomaly detection module addresses critical attack vectors, including RunDLL32 abuse and malicious IP detection, while the IP forensics module enhances threat intelligence by integrating tools like Virus Total and geolocation APIs. SPECTRE advanced visualization techniques transform raw memory data into actionable insights, aiding Red, Blue and Purple teams in refining strategies and responding effectively to threats. Bridging gaps between memory and network forensics, SPECTRE offers a scalable, robust platform for advancing threat detection, team training, and forensic research in combating sophisticated cyber threats., Comment: 23 Version 2.1

Enhancing the reasoning capabilities of Large Language Models remains a critical challenge in artificial intelligence. We introduce RDoLT, Recursive Decomposition of Logical Thought prompting, a novel framework that significantly boosts LLM reasoning performance. RDoLT is built on three key innovations: (1) recursively breaking down complex reasoning tasks into sub-tasks of progressive complexity; (2) employing an advanced selection and scoring mechanism to identify the most promising reasoning thoughts; and (3) integrating a knowledge propagation module that mimics human learning by keeping track of strong and weak thoughts for information propagation. Our approach was evaluated across multiple benchmarks, including GSM8K, SVAMP, MultiArith, LastLetterConcatenation, and Gaokao2023 Math. The results demonstrate that RDoLT consistently outperforms existing state-of-the-art techniques, achieving a 90.98 percent accuracy on GSM8K with ChatGPT-4, surpassing state-of-the-art techniques by 6.28 percent. Similar improvements were observed on other benchmarks, with accuracy gains ranging from 5.5 percent to 6.75 percent. These findings highlight RDoLT's potential to advance prompt engineering, offering a more effective and generalizable approach to complex reasoning tasks.

The complementary second Zagreb index of a graph $G$ is defined as $cM_2(G)=\sum_{uv\in E(G)}|(d_u(G))^2-(d_v(G))^2|$, where $d_u(G)$ denotes the degree of a vertex $u$ in $G$ and $E(G)$ represents the edge set of $G$. Let $G^*$ be a graph having the maximum value of $cM_2$ among all connected graphs of order $n$. Furtula and Oz [MATCH Commun. Math. Comput. Chem. 93 (2025) 247--263] conjectured that $G^*$ is the join $K_k+\overline{K}_{n-k}$ of the complete graph $K_k$ of order $k$ and the complement $\overline{K}_{n-k}$ of the complete graph $K_{n-k}$ such that the inequality $k<\lceil n/2 \rceil$ holds. We prove that (i) the maximum degree of $G^*$ is $n-1$ and (ii) no two vertices of minimum degree in $G^*$ are adjacent; both of these results support the aforementioned conjecture. We also prove that the number of vertices of maximum degree in $G^*$, say $k$, is at most $-\frac{2}{3}n+\frac{3}{2}+\frac{1}{6}\sqrt{52n^2-132n+81}$, which implies that $k<5352n/10000$. Furthermore, we establish results that support the conjecture under consideration for certain bidegreed and tridegreed graphs. In the aforesaid paper, it was also mentioned that determining the $k$ as a function of the $n$ is far from being an easy task; we obtain the values of $k$ for $5\le n\le 149$ in the case of certain bidegreed graphs by using computer software and found that the resulting sequence of the values of $k$ does not exist in "The On-Line Encyclopedia of Integer Sequences" (an online database of integer sequences)., Comment: 13 pages, 1 figure

As the use of text-to-image generative models increases, so does the adoption of automatic benchmarking methods used in their evaluation. However, while metrics and datasets abound, there are few unified benchmarking libraries that provide a framework for performing evaluations across many datasets and metrics. Furthermore, the rapid introduction of increasingly robust benchmarking methods requires that evaluation libraries remain flexible to new datasets and metrics. Finally, there remains a gap in synthesizing evaluations in order to deliver actionable takeaways about model performance. To enable unified, flexible, and actionable evaluations, we introduce EvalGIM (pronounced ''EvalGym''), a library for evaluating generative image models. EvalGIM contains broad support for datasets and metrics used to measure quality, diversity, and consistency of text-to-image generative models. In addition, EvalGIM is designed with flexibility for user customization as a top priority and contains a structure that allows plug-and-play additions of new datasets and metrics. To enable actionable evaluation insights, we introduce ''Evaluation Exercises'' that highlight takeaways for specific evaluation questions. The Evaluation Exercises contain easy-to-use and reproducible implementations of two state-of-the-art evaluation methods of text-to-image generative models: consistency-diversity-realism Pareto Fronts and disaggregated measurements of performance disparities across groups. EvalGIM also contains Evaluation Exercises that introduce two new analysis methods for text-to-image generative models: robustness analyses of model rankings and balanced evaluations across different prompt styles. We encourage text-to-image model exploration with EvalGIM and invite contributions at https://github.com/facebookresearch/EvalGIM/., Comment: For code, see https://github.com/facebookresearch/EvalGIM/tree/main

The popularity of the CLEAN algorithm in radio interferometric imaging stems from its maturity, speed, and robustness. While many alternatives have been proposed in the literature, none have achieved mainstream adoption by astronomers working with data from interferometric arrays operating in the big data regime. This lack of adoption is largely due to increased computational complexity, absence of mature implementations, and the need for astronomers to tune obscure algorithmic parameters. This work introduces pfb-imaging: a flexible library that implements the scaffolding required to develop and accelerate general radio interferometric imaging algorithms. We demonstrate how the framework can be used to implement a sparsity-based image reconstruction technique known as (unconstrained) SARA in a way that scales with image size rather than data volume and features interpretable algorithmic parameters. The implementation is validated on terabyte-sized data from the MeerKAT telescope, using both a single compute node and Amazon Web Services computing instances., Comment: 29 pages, submitted to Astronomy & Computing

Full waveform inversion (FWI) often faces challenges due to inadequate seismic observations, resulting in band-limited and geologically inaccurate inversion results. Incorporating prior information from potential velocity distributions, well-log information, and our geological knowledge and expectations can significantly improve FWI convergence to a realistic model. While diffusion-regularized FWI has shown improved performance compared to conventional FWI by incorporating the velocity distribution prior, it can benefit even more by incorporating well-log information and other geological knowledge priors. To leverage this fact, we propose a geological class and well-information prior-assisted FWI using conditional diffusion models. This method seamlessly integrates multi-modal information into FWI, simultaneously achieving data fitting and universal geologic and geophysics prior matching, which is often not achieved with traditional regularization methods. Specifically, we propose to combine conditional diffusion models with FWI, where we integrate well-log data and geological class conditions into these conditional diffusion models using classifier-free guidance for multi-modal prior matching beyond the original velocity distribution prior. Numerical experiments on the OpenFWI datasets and field marine data demonstrate the effectiveness of our method compared to conventional FWI and the unconditional diffusion-regularized FWI.

The analysis of retinal images for the diagnosis of various diseases is one of the emerging areas of research. Recently, the research direction has been inclined towards investigating several changes in retinal blood vessels in subjects with many neurological disorders, including dementia. This research focuses on detecting diseases early by improving the performance of models for segmentation of retinal vessels with fewer parameters, which reduces computational costs and supports faster processing. This paper presents a novel lightweight encoder-decoder model that segments retinal vessels to improve the efficiency of disease detection. It incorporates multi-scale convolutional blocks in the encoder to accurately identify vessels of various sizes and thicknesses. The bottleneck of the model integrates the Focal Modulation Attention and Spatial Feature Refinement Blocks to refine and enhance essential features for efficient segmentation. The decoder upsamples features and integrates them with the corresponding feature in the encoder using skip connections and the spatial feature refinement block at every upsampling stage to enhance feature representation at various scales. The estimated computation complexity of our proposed model is around 29.60 GFLOP with 0.71 million parameters and 2.74 MB of memory size, and it is evaluated using public datasets, that is, DRIVE, CHASE\_DB, and STARE. It outperforms existing models with dice scores of 86.44\%, 84.22\%, and 87.88\%, respectively.

Brain aging involves structural and functional changes and therefore serves as a key biomarker for brain health. Combining structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) has the potential to improve brain age estimation by leveraging complementary data. However, fMRI data, being noisier than sMRI, complicates multimodal fusion. Traditional fusion methods often introduce more noise than useful information, which can reduce accuracy compared to using sMRI alone. In this paper, we propose a novel multimodal framework for biological brain age estimation, utilizing a sex-aware adversarial variational autoencoder (SA-AVAE). Our framework integrates adversarial and variational learning to effectively disentangle the latent features from both modalities. Specifically, we decompose the latent space into modality-specific codes and shared codes to represent complementary and common information across modalities, respectively. To enhance the disentanglement, we introduce cross-reconstruction and shared-distinct distance ratio loss as regularization terms. Importantly, we incorporate sex information into the learned latent code, enabling the model to capture sex-specific aging patterns for brain age estimation via an integrated regressor module. We evaluate our model using the publicly available OpenBHB dataset, a comprehensive multi-site dataset for brain age estimation. The results from ablation studies and comparisons with state-of-the-art methods demonstrate that our framework outperforms existing approaches and shows significant robustness across various age groups, highlighting its potential for real-time clinical applications in the early detection of neurodegenerative diseases.

The coldest metal-poor population made of T and Y dwarfs are archaeological tracers of our Galaxy because they are very old and have kept the pristine material. The optical properties of these objects are important to characterise their atmospheric properties. We aim at characterising further the optical properties of ultracool metal-poor population with deep far-red optical images and parallax determinations. We solve trigonometric parallaxes of five metal-poor T dwarf candidates using 2-year monitoring with Calar-Alto 3.5-m telescope. We obtain $z'$-band photometry for the other 12 metal-poor T dwarf candidates using the 10.4-m GTC, the 8.2-m VLT, and the DES, increasing the sample of T subdwarfs with optical photometry from 12 to 24. We report a 3-$\sigma$ limit for the Accident in five optical bands using the 10.4-m GTC. We confirm four T subdwarfs and the Accident as a Y subdwarf, and propose two more Y subdwarf candidates. We emphasise that the $z_{PS1}-W1$ colour combining with the $W1-W2$ colour could break the metallicity-temperature degeneracy for T and possibly for Y dwarfs. The $z_{PS1}-W1$ colour shifts redward when metallicity decreases for a certain temperature, which is not predicted by state-of-the-art ultracool models. The Accident has the reddest $z_{PS1}-W1$ colour among our sample. The $z_{PS1}-W1$ colour will be useful to search for other examples of this cold and old population in upcoming and existing deep optical and infrared large-area surveys., Comment: 17 pages, 7 figures, 2 appendices, submitted to A&A, comments are welcome

Incorporating modern computer vision techniques into clinical protocols shows promise in improving skin lesion segmentation. The U-Net architecture has been a key model in this area, iteratively improved to address challenges arising from the heterogeneity of dermatologic images due to varying clinical settings, lighting, patient attributes, and hair density. To further improve skin lesion segmentation, we developed TAFM-Net, an innovative model leveraging self-adaptive transformer attention (TA) coupled with focal modulation (FM). Our model integrates an EfficientNetV2B1 encoder, which employs TA to enhance spatial and channel-related saliency, while a densely connected decoder integrates FM within skip connections, enhancing feature emphasis, segmentation performance, and interpretability crucial for medical image analysis. A novel dynamic loss function amalgamates region and boundary information, guiding effective model training. Our model achieves competitive performance, with Jaccard coefficients of 93.64\%, 86.88\% and 92.88\% in the ISIC2016, ISIC2017 and ISIC2018 datasets, respectively, demonstrating its potential in real-world scenarios.

Modern deep-learning based super-resolution techniques process images and videos independently of the underlying content and viewing conditions. However, the sensitivity of the human visual system to image details changes depending on the underlying content characteristics, such as spatial frequency, luminance, color, contrast, or motion. This observation hints that computational resources spent on up-sampling visual content may be wasted whenever a viewer cannot resolve the results. Motivated by this observation, we propose a perceptually inspired and architecture-agnostic approach for controlling the visual quality and efficiency of super-resolution techniques. The core is a perceptual model that dynamically guides super-resolution methods according to the human's sensitivity to image details. Our technique leverages the limitations of the human visual system to improve the efficiency of super-resolution techniques by focusing computational resources on perceptually important regions; judged on the basis of factors such as adapting luminance, contrast, spatial frequency, motion, and viewing conditions. We demonstrate the application of our proposed model in combination with network branching, and network complexity reduction to improve the computational efficiency of super-resolution methods without visible quality loss. Quantitative and qualitative evaluations, including user studies, demonstrate the effectiveness of our approach in reducing FLOPS by factors of 2$\mathbf{x}$ and greater, without sacrificing perceived quality.

Alloy is a well-known, formal, declarative language for modelling systems early in the software development process. Currently, it uses the Kodkod library as a back-end for finite model finding. Kodkod translates the model to a SAT problem; however, this method can often handle only problems of fairly low-size sets and is inherently finite. We present Portus, a method for translating Alloy into an equivalent many-sorted first-order logic problem (MSFOL). Once in MSFOL, the problem can be evaluated by an SMT-based finite model finding method implemented in the Fortress library, creating an alternative back-end for the Alloy Analyzer. Fortress converts the MSFOL finite model finding problem into the logic of uninterpreted functions with equality (EUF), a decidable fragment of first-order logic that is well-supported in many SMT solvers. We compare the performance of Portus with Kodkod on a corpus of 49 expert Alloy models. Our method is fully integrated into the Alloy Analyzer.

The rupture of a liquid film, where a thin liquid layer between two other fluids breaks and forms holes, commonly occurs in both natural phenomena and industrial applications. The post-rupture dynamics, from initial hole formation to the complete collapse of the film, are crucial because they govern droplet formation, which plays a significant role in many applications such as disease transmission, aerosol formation, spray drying nanodrugs, oil spill remediation, inkjet printing, and spray coating. While single-hole rupture has been extensively studied, the dynamics of multiple-hole ruptures, especially the interactions between neighboring holes, are less well understood. Here, this study reveals that when two holes 'meet' on a curved film, the film evolves into a spinning twisted ribbon before breaking into droplets, distinctly different from what occurs on flat films. We explain the formation and evolution of the spinning twisted ribbon, including its geometry, corrugations, ligaments, and orbits, and compare the experimental observations with models. We compare and contrast this phenomena with its counterpart on planar films. While our experiments are based on the multiple-hole ruptures in corona splash, the underlying principles are likely applicable to other systems. This study sheds light on understanding and controlling droplet formation in multiple-hole rupture, improving public health, climate science, and various industrial applications.

Despite advances in deep learning for estimating brain age from structural MRI data, incorporating functional MRI data is challenging due to its complex structure and the noisy nature of functional connectivity measurements. To address this, we present the Multitask Adversarial Variational Autoencoder, a custom deep learning framework designed to improve brain age predictions through multimodal MRI data integration. This model separates latent variables into generic and unique codes, isolating shared and modality-specific features. By integrating multitask learning with sex classification as an additional task, the model captures sex-specific aging patterns. Evaluated on the OpenBHB dataset, a large multisite brain MRI collection, the model achieves a mean absolute error of 2.77 years, outperforming traditional methods. This success positions M-AVAE as a powerful tool for metaverse-based healthcare applications in brain age estimation.

Artificial intelligence (AI) tools are now prevalent in many knowledge work industries. As AI becomes more capable and interactive, there is a growing need for guidance on how to employ AI most effectively. The A2C framework (Tariq, Chhetri, Nepal & Paris, 2024) distinguishes three decision-making modes for engaging AI: automation (AI completes a task, including decision/action), augmentation (AI supports human to decide) and collaboration (iterative interaction between human and AI). However, selecting the appropriate mode for a specific application is not always straightforward. The goal of the present study was to compile and trial a simple set of criteria to support recommendations about appropriate A2C mode for a given application. Drawing on human factors and computer science literature, we identified key criteria related to elements of the task, impacts on worker and support needs. From these criteria we built a scoring rubric with recommendation for A2C mode. As a preliminary test of this approach, we applied the criteria to cognitive task analysis (CTA) outputs from three tasks in the science domain - genome annotation, biological collections curation and protein crystallization - which provided insights into worker decision points, challenges and expert strategies. This paper describes the method for connecting CTA to A2C, reflecting on the challenges and future directions., Comment: 27 pages

Latent diffusion models (LDMs) power state-of-the-art high-resolution generative image models. LDMs learn the data distribution in the latent space of an autoencoder (AE) and produce images by mapping the generated latents into RGB image space using the AE decoder. While this approach allows for efficient model training and sampling, it induces a disconnect between the training of the diffusion model and the decoder, resulting in a loss of detail in the generated images. To remediate this disconnect, we propose to leverage the internal features of the decoder to define a latent perceptual loss (LPL). This loss encourages the models to create sharper and more realistic images. Our loss can be seamlessly integrated with common autoencoders used in latent diffusion models, and can be applied to different generative modeling paradigms such as DDPM with epsilon and velocity prediction, as well as flow matching. Extensive experiments with models trained on three datasets at 256 and 512 resolution show improved quantitative -- with boosts between 6% and 20% in FID -- and qualitative results when using our perceptual loss., Comment: Pre-print

Large-scale training of latent diffusion models (LDMs) has enabled unprecedented quality in image generation. However, the key components of the best performing LDM training recipes are oftentimes not available to the research community, preventing apple-to-apple comparisons and hindering the validation of progress in the field. In this work, we perform an in-depth study of LDM training recipes focusing on the performance of models and their training efficiency. To ensure apple-to-apple comparisons, we re-implement five previously published models with their corresponding recipes. Through our study, we explore the effects of (i)~the mechanisms used to condition the generative model on semantic information (e.g., text prompt) and control metadata (e.g., crop size, random flip flag, etc.) on the model performance, and (ii)~the transfer of the representations learned on smaller and lower-resolution datasets to larger ones on the training efficiency and model performance. We then propose a novel conditioning mechanism that disentangles semantic and control metadata conditionings and sets a new state-of-the-art in class-conditional generation on the ImageNet-1k dataset -- with FID improvements of 7% on 256 and 8% on 512 resolutions -- as well as text-to-image generation on the CC12M dataset -- with FID improvements of 8% on 256 and 23% on 512 resolution., Comment: Accepted as a conference paper (poster) for NeurIPS 2024

The rare three body decay of a Higgs boson to a lepton-anti lepton pair and a photon has begun to attract attention, after the first evidence for the $H\to Z\gamma$ at CMS and ATLAS, which is a sub process of $H \to \ell^+ \ell^- \gamma$ . To investigate some important features of this process, we suggest that the polarized forward-backward and the photon polarization asymmetries could be useful to probe its important properties, such as the behavior of Yukawa coupling, resonance, and non-resonance contributions. Our analysis introduces a comprehensive framework to evaluate the aforementioned polarization-dependent observables. By analyzing the polarization effects of the final-state photon and lepton separately on forward-backward asymmetries, we demonstrate that loop-induced contributions play a significant role to investigate these asymmetries. Unlike the unpolarized case, where the interference effects of resonance and non-resonance effects are minimal, we show that polarization dependent observables offer a powerful tool to analyze these features of this decay mode. Furthermore, these observables can provide a handy tool for probing possible signatures of physics beyond the SM.

Given positive integers $v,k,t$ and $\lambda$ with $v \geq k \geq t$, a {packing design} PD$_{\lambda}(t,k,v)$ is a pair $(V,\mathcal{B})$, where $V$ is a $v$-set and $\mathcal{B}$ is a collection of $k$-subsets of $V$ such that each $t$-subset of $V$ appears in at most $\lambda$ elements of $\mathcal{B}$. The maximum size of a PD$_{\lambda}(t,k,v)$ is called the {packing number}, denoted PDN$_{\lambda}(t,k,v)$. In this paper we consider packing designs with $t=2$ and $k$ large (linear) relative to $v$. We provide a new bound on the packing number: if $n \geq \lambda+2$ and $nk-\binom{n}{\lambda+1} \leq \lambda v < (n+1)k-\binom{n+1}{\lambda+1}$, then PDN$_{\lambda}(2,k,v) \leq n$. When $\lambda=1$ this is equivalent to the second Johnson bound. We show that this bound is met when $\lambda=1$ and $n$ is a positive integer, and when $\lambda=2$ and $2 \leq n \leq 6$. We also consider a directed variant and show that for the cases we consider, this has the same packing number as the undirected case with $\lambda=2$., Comment: 20 pages, 1 table

We report here the results obtained from a systematic optical photometric survey aimed at finding new compact binary millisecond pulsars (also known as "spiders"): the COmpact BInary PULsar SEarch (COBIPULSE). We acquired multi-band optical images over one year around $33$ unidentified Fermi-LAT sources, selected as pulsar candidates based on their curved GeV spectra and steady $\gamma$-ray emission. We present the discovery of four optical variables coinciding with the Fermi sources 3FGL J0737.2$-$3233, 3FGL J2117.6$+$3725 (two systems in this field) and 3FGL J2221.6$+$6507, which we propose as new candidate spider systems. Indeed, they all show optical flux modulation consistent with orbital periods of $0.3548(5) \ \mathrm{d}$, $0.25328(6) \ \mathrm{d}$, $0.441961(2) \ \mathrm{d}$, and $0.165(4) \ \mathrm{d}$, respectively, with amplitudes $\gtrsim 0.3 \ \mathrm{mag}$ and colors compatible with companion star temperatures of $5000$--$6000 \ \mathrm{K}$. These properties are consistent with the "redback" sub-class of spider pulsars. If confirmed as a millisecond pulsar, 3FGL J0737.2$-$3233 will be the closest known spider to Earth ($D=659_{-20}^{+16} \ \mathrm{pc}$, from Gaia-DR3 parallax). We searched and did not find any X-ray sources matching our four candidates, placing $3\sigma$ upper limits of $\sim10^{31}$--$10^{32} \ \mathrm{erg} \ \mathrm{s}^{-1}$ ($0.3$--$10 \ \mathrm{keV}$) on their soft X-ray luminosities. We also present and discuss other multi-wavelength information on our spider candidates, from infrared to X-rays., Comment: 38 pages, 93 figures, 4 tables. This paper has been accepted for publication in ApJ

Recent advancements in speech-language models have yielded significant improvements in speech tokenization and synthesis. However, effectively mapping the complex, multidimensional attributes of speech into discrete tokens remains challenging. This process demands acoustic, semantic, and contextual information for precise speech representations. Existing speech representations generally fall into two categories: acoustic tokens from audio codecs and semantic tokens from speech self-supervised learning models. Although recent efforts have unified acoustic and semantic tokens for improved performance, they overlook the crucial role of contextual representation in comprehensive speech modeling. Our empirical investigations reveal that the absence of contextual representations results in elevated Word Error Rate (WER) and Word Information Lost (WIL) scores in speech transcriptions. To address these limitations, we propose two novel distillation approaches: (1) a language model (LM)-guided distillation method that incorporates contextual information, and (2) a combined LM and self-supervised speech model (SM)-guided distillation technique that effectively distills multimodal representations (acoustic, semantic, and contextual) into a comprehensive speech tokenizer, termed DM-Codec. The DM-Codec architecture adopts a streamlined encoder-decoder framework with a Residual Vector Quantizer (RVQ) and incorporates the LM and SM during the training process. Experiments show DM-Codec significantly outperforms state-of-the-art speech tokenization models, reducing WER by up to 13.46%, WIL by 9.82%, and improving speech quality by 5.84% and intelligibility by 1.85% on the LibriSpeech benchmark dataset. The code, samples, and model checkpoints are available at https://github.com/mubtasimahasan/DM-Codec.

Recent advancements in robot capabilities have enabled them to interact with people in various human-social environments (HSEs). In many of these environments, the perception of the robot often depends on its capabilities, e.g., task competency, language fluency, etc. To enable fluent human-robot interaction (HRI) in HSEs, it is crucial to understand the impact of these capabilities on the perception of the robot. Although many works have investigated the effects of various robot capabilities on the robot's perception separately, in this paper, we present a large-scale HRI study (n = 60) to investigate the combined impact of both language fluency and task competency on the perception of a robot. The results suggest that while language fluency may play a more significant role than task competency in the perception of the verbal competency of a robot, both language fluency and task competency contribute to the perception of the intelligence and reliability of the robot. The results also indicate that task competency may play a more significant role than language fluency in the perception of meeting expectations and being a good teammate. The findings of this study highlight the relationship between language fluency and task competency in the context of social HRI and will enable the development of more intelligent robots in the future., Comment: Accepted at the IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2024

Collaborative robots are increasingly deployed alongside humans in factories, hospitals, schools, and other domains to enhance teamwork and efficiency. Systems that seamlessly integrate humans and robots into cohesive teams for coordinated and efficient task execution are needed, enabling studies on how robot collaboration policies affect team performance and teammates' perceived fairness, trust, and safety. Such a system can also be utilized to study the impact of a robot's normative behavior on team collaboration. Additionally, it allows for investigation into how the legibility and predictability of robot actions affect human-robot teamwork and perceived safety and trust. Existing systems are limited, typically involving one human and one robot, and thus require more insight into broader team dynamics. Many rely on games or virtual simulations, neglecting the impact of a robot's physical presence. Most tasks are turn-based, hindering simultaneous execution and affecting efficiency. This paper introduces CoHRT (Collaboration System for Human-Robot Teamwork), which facilitates multi-human-robot teamwork through seamless collaboration, coordination, and communication. CoHRT utilizes a server-client-based architecture, a vision-based system to track task environments, and a simple interface for team action coordination. It allows for the design of tasks considering the human teammates' physical and mental workload and varied skill labels across the team members. We used CoHRT to design a collaborative block manipulation and jigsaw puzzle-solving task in a team of one Franka Emika Panda robot and two humans. The system enables recording multi-modal collaboration data to develop adaptive collaboration policies for robots. To further utilize CoHRT, we outline potential research directions in diverse human-robot collaborative tasks., Comment: 8 Pages, Robotics Science and Systems (RSS), Safety and Normative Behaviors in Human-Robot Interaction Workshop 2024 (accepted), https://sites.google.com/view/safe-hri/accepted-papers

In this work, we introduce a foundational definition of statistical virtual temperature, derived from the spectrum of the Gibbs Kubo-Martin-Schwinger (KMS) state and formulated using d-1 indices of purity (IP), where d represents the Hilbert space dimension within the C*-algebra framework. We demonstrate that the universal physical bounds between von Neumann entropy and statistical virtual temperature are constrained by these IPs, which may offer broader applications to quantum systems. Additionally, we geometrize classical optical polarization states of an arbitrary electromagnetic field and provide an interpretation of the quantum Mpemba effect, where a quantum system prepared at a higher statistical virtual temperature relaxes to equilibrium faster than one at a lower temperature. This behavior is explained through a novel concept of temperature-resolved entanglement asymmetry. Additionally, we present a geometric interpretation of the third law of thermodynamics using these entropy-temperature diagrams. Nevertheless, the defined statistical virtual temperature inherently exhibits the third law of thermodynamics. We believe that this work has the potential to significantly advance our understanding of classical polarization theory, quantum information theory, and quantum thermodynamics, and it may establish new connections and insights into these fields.

Random numbers are central to various applications such as secure communications, quantum key distribution theory (QKD), statistics, and other tasks. One of today's most popular generators is quantum random numbers (QRNGs). The inherent randomness and true unpredictability in quantum mechanics allowed us to construct QRNGs that are more accurate and useful than traditional random number generators. Based on different quantum mechanical principles, several QRNGs have already been designed. The primary focus of this paper is the generation and analysis of quantum random numbers based on radioactive decay. In the experimental set, two beta-active radioactive sources, cobalt-60 (Co60) and Strontium-90 (Sr 90), and an ST-360 counter with a Geiger-Muller (GM) tube are used to record the counts. The recorded data was then self-tested by entropy and frequency measurement. Moreover, popular testing technique, the National Institute of Science and Technology (NIST) randomness testing is used, to ensure that the guaranteed randomness meets security standards. The research provides the impact of the nature of the radioactive source, the distance between the counter and sources, and the recording time of the counts on generating quantum random numbers of radioactive QRNGs.

This paper introduces a novel track-length extension fitting algorithm for measuring the kinetic energies of inelastically interacting particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe the impact of the dE/dx model on energy measurement performance. The method described in this paper to characterize the energy measurement performance can be repeated in any LArTPC experiment using stopping secondary charged pions.

We prove an effective slope gap distribution result first for the square torus and then for general lattice translation surfaces. As a corollary, we obtain a dynamical proof for an effective gap distribution result for the Farey fractions. As an intermediate step, we prove an effective equidistribution result for the intersection points of long horocycles with a particular transversal of the horocycle flow in $\mathrm{SL}_2 (\mathbb R)/\Gamma$ where $\Gamma$ is a lattice., Comment: 37 pages, 3 figures

Human immune system contains white blood cells (WBC) that are good indicator of many diseases like bacterial infections, AIDS, cancer, spleen, etc. White blood cells have been sub classified into four types: monocytes, lymphocytes, eosinophils and neutrophils on the basis of their nucleus, shape and cytoplasm. Traditionally in laboratories, pathologists and hematologists analyze these blood cells through microscope and then classify them manually. This manual process takes more time and increases the chance of human error. Hence, there is a need to automate this process. In this paper, first we have used different CNN pre-train models such as ResNet-50, InceptionV3, VGG16 and MobileNetV2 to automatically classify the white blood cells. These pre-train models are applied on Kaggle dataset of microscopic images. Although we achieved reasonable accuracy ranging between 92 to 95%, still there is need to enhance the performance. Hence, inspired by these architectures, a framework has been proposed to automatically categorize the four kinds of white blood cells with increased accuracy. The aim is to develop a convolution neural network (CNN) based classification system with decent generalization ability. The proposed CNN model has been tested on white blood cells images from Kaggle and LISC datasets. Accuracy achieved is 99.57% and 98.67% for both datasets respectively. Our proposed convolutional neural network-based model provides competitive performance as compared to previous results reported in literature.

Identification of suspects based on partial and smudged fingerprints, commonly referred to as fingermarks or latent fingerprints, presents a significant challenge in the field of fingerprint recognition. Although fixed-length embeddings have shown effectiveness in recognising rolled and slap fingerprints, the methods for matching latent fingerprints have primarily centred around local minutiae-based embeddings, failing to fully exploit global representations for matching purposes. Consequently, enhancing latent fingerprints becomes critical to ensuring robust identification for forensic investigations. Current approaches often prioritise restoring ridge patterns, overlooking the fine-macroeconomic details crucial for accurate fingerprint recognition. To address this, we propose a novel approach that uses generative adversary networks (GANs) to redefine Latent Fingerprint Enhancement (LFE) through a structured approach to fingerprint generation. By directly optimising the minutiae information during the generation process, the model produces enhanced latent fingerprints that exhibit exceptional fidelity to ground-truth instances. This leads to a significant improvement in identification performance. Our framework integrates minutiae locations and orientation fields, ensuring the preservation of both local and structural fingerprint features. Extensive evaluations conducted on two publicly available datasets demonstrate our method's dominance over existing state-of-the-art techniques, highlighting its potential to significantly enhance latent fingerprint recognition accuracy in forensic applications.

The detection and localization of deepfake content, particularly when small fake segments are seamlessly mixed with real videos, remains a significant challenge in the field of digital media security. Based on the recently released AV-Deepfake1M dataset, which contains more than 1 million manipulated videos across more than 2,000 subjects, we introduce the 1M-Deepfakes Detection Challenge. This challenge is designed to engage the research community in developing advanced methods for detecting and localizing deepfake manipulations within the large-scale high-realistic audio-visual dataset. The participants can access the AV-Deepfake1M dataset and are required to submit their inference results for evaluation across the metrics for detection or localization tasks. The methodologies developed through the challenge will contribute to the development of next-generation deepfake detection and localization systems. Evaluation scripts, baseline models, and accompanying code will be available on https://github.com/ControlNet/AV-Deepfake1M., Comment: ACM MM 2024. Challenge webpage: https://deepfakes1m.github.io/