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Lithium-ion batteries (LiBs) have transformed electrochemical energy storage technologies and made a substantial contribution to grid-scale energy storage and the e-mobility revolution. Notwithstanding their many benefits, safety issues specifically, thermal runaway incidents have drawn attention from all around the world. In addition to discussing safety concerns, cooling techniques, and the history of battery materials, this study offers a thorough analysis of the growth, difficulties, and developments in Li-ion battery technology. Quantum Espresso software has been used to compute the magnetic characteristics of several potential Li-ion battery materials, which can enhance Li-ion battery performance.

As gravitational wave detections increase the number of observed compact binaries (consisting of neutron stars or blacks), we begin to probe the different conditions producing these binaries. Most studies of compact remnant formation focus either on stellar collapse from the evolution of field binary stars in gas-free environments or the formation of stars in clusters where dynamical interactions capture the compact objects, forming binaries. But a third scenario exists. In this paper, we study the fate of massive stars formed, accrete gas, and evolve in the dense disks surrounding supermassive black holes. We calculate the explosions produced and compact objects formed by the collapse of these massive stars. Nucleosynthetic yields may provide an ideal, directly observable, diagnostic of the formation and fate of these stars in active galactic nuclei. We present a first study of the explosive yields from these stars, comparing these yields with the observed nucleosynthetic signatures in the disks around supermassive stars with quasars. We show that, even though these stars tend to form black holes, their rapid rotation leads to disks that can eject a considerable amount of iron during the collapse of the star. The nucleosynthetic yields from these stars can produce constraints on the number of systems formed in this manner, but further work is needed to exploit variations from the initial models presented in this paper., Comment: 15 pages, 17 figures

The integration of DNA methylation data with a Whole Slide Image (WSI) offers significant potential for enhancing the diagnostic precision of central nervous system (CNS) tumor classification in neuropathology. While existing approaches typically integrate encoded omic data with histology at either an early or late fusion stage, the potential of reintroducing omic data through dual fusion remains unexplored. In this paper, we propose the use of omic embeddings during early and late fusion to capture complementary information from local (patch-level) to global (slide-level) interactions, boosting performance through multimodal integration. In the early fusion stage, omic embeddings are projected onto WSI patches in latent-space, which generates embeddings that encapsulate per-patch molecular and morphological insights. This effectively incorporates omic information into the spatial representation of the WSI. These embeddings are then refined with a Multiple Instance Learning gated attention mechanism which attends to diagnostic patches. In the late fusion stage, we reintroduce the omic data by fusing it with slide-level omic-WSI embeddings using a Multimodal Outer Arithmetic Block (MOAB), which richly intermingles features from both modalities, capturing their correlations and complementarity. We demonstrate accurate CNS tumor subtyping across 20 fine-grained subtypes and validate our approach on benchmark datasets, achieving improved survival prediction on TCGA-BLCA and competitive performance on TCGA-BRCA compared to state-of-the-art methods. This dual fusion strategy enhances interpretability and classification performance, highlighting its potential for clinical diagnostics., Comment: Revised to 10 pages, with corrected typos, updated references (some added, others removed), improved figure quality, modified text for better method validation, added one more co-author, and identified the IEEE member

While WISE is the largest, best quality infrared all-sky survey to date, a smaller coverage mission, Spitzer, was designed to have better sensitivity and spatial resolution at similar wavelengths. Confusion and contamination in WISE data result in discrepancies between them. We present a novel approach to work with WISE measurements with the goal of maintaining both its high coverage and vast amount of data while taking full advantage of the higher sensitivity and spatial resolution of Spitzer. We have applied machine learning (ML) techniques to a complete WISE data sample of open cluster members, using a training set of paired data from high-quality Spitzer Enhanced Imaging Products (SEIP), MIPS and IRAC, and allWISE catalogs, W1 (3.4 {\mu}m) to W4 (22 {\mu}m) bands. We have tested several ML regression models with the aim of predicting mid-infrared fluxes at MIPS1 (24 {\mu}m) and IRAC4 (8 {\mu}m) bands from WISE fluxes and quality flags. In addition, to improve the prediction quality, we have implemented feature selection techniques to remove irrelevant WISE variables. We have notably enhanced WISE detection capabilities, mostly at lowest magnitudes, which previously showed the largest discrepancies with Spitzer. In our particular case, extremely randomized trees was found to be the best algorithm to predict mid-infrared fluxes from WISE variables. We have tested our results in the SED of members of IC 348. We show discrepancies in the measurements of Spitzer and WISE and demonstrate the good concordance of our predicted fluxes with the real ones. ML is a fast and powerful tool that can be used to find hidden relationships between datasets, as the ones that exist between WISE and Spitzer fluxes. We believe this approach could be employed for other samples from the allWISE catalog with SEIP positional counterparts, and in other astrophysical studies with analogous discrepancies., Comment: 13 pages, 10 figures

The unpredictability of random numbers is fundamental to both digital security and applications that fairly distribute resources. However, existing random number generators have limitations-the generation processes cannot be fully traced, audited, and certified to be unpredictable. The algorithmic steps used in pseudorandom number generators are auditable, but they cannot guarantee that their outputs were a priori unpredictable given knowledge of the initial seed. Device-independent quantum random number generators can ensure that the source of randomness was unknown beforehand, but the steps used to extract the randomness are vulnerable to tampering. Here, for the first time, we demonstrate a fully traceable random number generation protocol based on device-independent techniques. Our protocol extracts randomness from unpredictable non-local quantum correlations, and uses distributed intertwined hash chains to cryptographically trace and verify the extraction process. This protocol is at the heart of a public traceable and certifiable quantum randomness beacon that we have launched. Over the first 40 days of operation, we completed the protocol 7434 out of 7454 attempts -- a success rate of 99.7%. Each time the protocol succeeded, the beacon emitted a pulse of 512 bits of traceable randomness. The bits are certified to be uniform with error times actual success probability bounded by $2^{-64}$. The generation of certifiable and traceable randomness represents one of the first public services that operates with an entanglement-derived advantage over comparable classical approaches., Comment: 40 pages, 4 main figures, 10 supplementary figures

Memetic algorithms are techniques that orchestrate the interplay between population-based and trajectory-based algorithmic components. In particular, some memetic models can be regarded under this broad interpretation as a group of autonomous basic optimization algorithms that interact among them in a cooperative way in order to deal with a specific optimization problem, aiming to obtain better results than the algorithms that constitute it separately. Going one step beyond this traditional view of cooperative optimization algorithms, this work tackles deep meta-cooperation, namely the use of cooperative optimization algorithms in which some components can in turn be cooperative methods themselves, thus exhibiting a deep algorithmic architecture. The objective of this paper is to demonstrate that such models can be considered as an efficient alternative to other traditional forms of cooperative algorithms. To validate this claim, different structural parameters, such as the communication topology between the agents, or the parameter that influences the depth of the cooperative effort (the depth of meta-cooperation), have been analyzed. To do this, a comparison with the state-of-the-art cooperative methods to solve a specific combinatorial problem, the Tool Switching Problem, has been performed. Results show that deep models are effective to solve this problem, outperforming metaheuristics proposed in the literature., Comment: 32 pages, 5 figures

This study evaluates the generalisation capabilities of state-of-the-art histopathology foundation models on out-of-distribution multi-stain autoimmune Immunohistochemistry datasets. We compare 13 feature extractor models, including ImageNet-pretrained networks, and histopathology foundation models trained on both public and proprietary data, on Rheumatoid Arthritis subtyping and Sjogren's Disease detection tasks. Using a simple Attention-Based Multiple Instance Learning classifier, we assess the transferability of learned representations from cancer H&E images to autoimmune IHC images. Contrary to expectations, histopathology-pretrained models did not significantly outperform ImageNet-pretrained models. Furthermore, there was evidence of both autoimmune feature misinterpretation and biased feature importance. Our findings highlight the challenges in transferring knowledge from cancer to autoimmune histopathology and emphasise the need for careful evaluation of AI models across diverse histopathological tasks. The code to run this benchmark is available at https://github.com/AmayaGS/ImmunoHistoBench., Comment: Accepted at Workshop on Advancements In Medical Foundation Models (NeurIPS 2024)

We present the properties of a new organic $S=1$ antiferromagnetic chain system $m$-NO$_2$PhBNO (abbreviated BoNO). In this biradical system two unpaired electrons from aminoxyl groups are strongly ferromagnetically coupled ($|J_\text{FM}| /k_B \gtrsim 500$ K) which leads to the formation of an effective $S=1$ state for each molecule. The chains of BoNO biradicals propagate along the crystallographic $a$ axis. Temperature dependence of the $g$ factor and electron paramagnetic resonance (EPR) linewidth are consistent with a low-dimensional system with antiferromagnetic interactions. The EPR data further suggest that BoNO is the first known Haldane system with an almost isotropic $g$ factor ($2.0023 \pm 2 \unicode{x2030}$). The magnetization measurements in magnetic fields up to $40$ T and low-field susceptibility, together with $^1$H nuclear magnetic resonance (NMR) spectra, reveal a dominant intrachain antiferromagnetic exchange coupling of $J_\text{1D}/k_B = (11.3\pm0.1)$ K, and attainable critical magnetic fields of $\mu_0 H_\text{c1} \approx 2$ T and $\mu_0 H_\text{c2} \approx 33$ T. These measurements therefore suggest that BoNO is a unique Haldane system with extremely small magnetic anisotropy. Present results are crucial for a future in-depth NMR study of the low-temperature Tomonaga-Luttinger liquid (TLL) and magnetic field-induced phases, which can be performed in the entire phase space., Comment: 13 pages, 12 figures

In this paper, we explore how performers' embodied interactions with a Neural Audio Synthesis model allow the exploration of the latent space of such a model, mediated through movements sensed by e-textiles. We provide background and context for the performance, highlighting the potential of embodied practices to contribute to developing explainable AI systems. By integrating various artistic domains with explainable AI principles, our interdisciplinary exploration contributes to the discourse on art, embodiment, and AI, offering insights into intuitive approaches found through bodily expression., Comment: In Proceedings of Explainable AI for the Arts Workshop 2024 (XAIxArts 2024) arXiv:2406.14485

The developed system using a mobile electronic device for monitoring and warnings of heart problems, when the heart rate is outside the nominal range, which ranges from 60 to 100 beats per minute. Also, a system has been developed to save and monitor in real time changes of the cardiac pulsations, through a sensor connected to a control system. The connection of the communication module for Arduino GSM/GPRS/GPS, using the GPS network to locate the user. In addition, this device connects with GSM / GPRS technology that allows text messages to be sent to the contact number configured in the device, when warnings of heart problems are issued, moreover connects to the internet to store data in the cloud., Comment: 5 pages, 13 figures

The monoclinic (m-LBO) and tetragonal (t-LBO) polymorphs of LiBO2 have significant potential for applications such as solid electrolytes and electrode coatings of lithium-ion batteries. While comparative experimental studies of electron and lithium transport in these polymorphs exist, the role of lattice vacancies on lithium transport remains unclear. In this study, we employed density functional theory (DFT) to investigate the impact of boron and oxygen vacancies on the lattice structure, electronic properties, and lithium migration energy barrier (Em) in m-LBO and t-LBO. Our DFT results reveal that boron and oxygen vacancies affect lithium transport in both the polymorphs, but in different ways. While oxygen vacancies lower Em in m-LBO, they increases Em in t-LBO. In contrast, boron vacancies significantly reduce Em in both m-LBO and t-LBO, leading to enhanced diffusivity and ionic conductivity in both polymorphs. This improvement suggests a potential strategy for improving ionic conductivity in LiBO2 through boron vacancy generation., Comment: 34 pages, 9 figures, 6 tables, submitted for publication in Journal of Materials Chemistry A

Stars in an open cluster are assumed to have formed from a broadly homogeneous distribution of gas, implying that they should be chemically homogeneous. Quantifying the level to which open clusters are chemically homogeneous can therefore tell us about ISM pollution and gas-mixing in progenitor molecular clouds. Using SDSS-V Milky Way Mapper and SDSS-IV APOGEE DR17 abundances, we test this assumption by quantifying intrinsic chemical scatter in up to 20 different chemical abundances across 26 Milky Way open clusters. We find that we can place 3${\sigma}$ upper limits on open cluster homogeneity within 0.02 dex or less in the majority of elements, while for neutron capture elements, as well as those elements having weak lines, we place limits on their homogeneity within 0.2 dex. Finally, we find that giant stars in open clusters are ~0.01 dex more homogeneous than a matched sample of field stars., Comment: 26 pages, 14 figures

On-orbit servicing (OOS) activities will power the next big step for sustainable exploration and commercialization of space. Developing robotic capabilities for autonomous OOS operations is a priority for the space industry. Visual Servoing (VS) enables robots to achieve the precise manoeuvres needed for critical OOS missions by utilizing visual information for motion control. This article presents an overview of existing VS approaches for autonomous OOS operations with space manipulator systems (SMS). We divide the approaches according to their contribution to the typical phases of a robotic OOS mission: a) Recognition, b) Approach, and c) Contact. We also present a discussion on the reviewed VS approaches, identifying current trends. Finally, we highlight the challenges and areas for future research on VS techniques for robotic OOS., Comment: Accepted for publication at the 2024 International Conference on Space Robotics (iSpaRo)

It is widely accepted that cosmic rays are a plausible cause of DRAM errors in high-performance computing (HPC) systems, and various studies suggest that they could explain some aspects of the observed DRAM error behavior. However, this phenomenon is insufficiently studied in production environments. We analyze the correlations between cosmic rays and DRAM errors on two HPC clusters: a production supercomputer with server-class DDR3-1600 and a prototype with LPDDR3-1600 and no hardware error correction. Our error logs cover 2000 billion MB-hours for the MareNostrum 3 supercomputer and 135 million MB-hours for the Mont-Blanc prototype. Our analysis combines quantitative analysis, formal statistical methods and machine learning. We detect no indications that cosmic rays have any influence on the DRAM errors. To understand whether the findings are specific to systems under study, located at 100 meters above the sea level, the analysis should be repeated on other HPC clusters, especially the ones located on higher altitudes. Also, analysis can (and should) be applied to revisit and extend numerous previous studies which use cosmic rays as a hypothetical explanation for some aspects of the observed DRAM error behaviors., Comment: Accepted for publication in SBAC-PAD'24

In this work we present a theorem regarding two convex bodies $K_1, K_2\subset \mathbb{R}^{n}$, $n\geq 3$, and two families of sections of them, given by two families of tangent planes of two spheres $S_i\subset \textrm{int}\textrm{ } K_i$, $i=1,2$ such that, for every pair $\Pi_1$, $\Pi_2$ of parallel supporting planes of $S_1$, $S_2$, respectively, which are corresponding (this means, that the outer normal vectors of the supporting half spaces determined by the two planes have the same direction), the sections $\Pi_1\cap K_1$, $\Pi_2\cap K_2$ are translated, the theorem claims that if $S_1$, $S_2$ have the same radius, the bodies are translated, otherwise, the bodies are also spheres.

Modeling observations of the archetypal debris disk around $\beta$ Pic, obtained in 2023 January with the MIRI MRS on board JWST, reveals significant differences compared with that obtained with the IRS on board Spitzer. The bright 5 - 15 $\mu$m continuum excess modeled using a $\sim$600 K black body has disappeared. The previously prominent 18 and 23 $\mu$m crystalline forsterite emission features, arising from cold dust ($\sim$100 K) in the Rayleigh limit, have disappeared and been replaced by very weak features arising from the hotter 500 K dust population. Finally, the shape of the 10 $\mu$m silicate feature has changed, consistent with a shift in the temperature of the warm dust population from $\sim$300 K to $\sim$500 K and an increase in the crystalline fraction of the warm, silicate dust. Stellar radiation pressure may have blown both the hot and the cold crystalline dust particles observed in the Spitzer spectra out of the planetary system during the intervening 20 years between the Spitzer and JWST observations. These results indicate that the $\beta$ Pic system has a dynamic circumstellar environment, and that periods of enhanced collisions can create large clouds of dust that sweep through the planetary system., Comment: 15 pages, 8 figures, ApJ in press

Generalizing a Theorem due to A. Rogers \cite{ro1}, we are going to prove that if for a pair of convex bodies $K_{1},K_{2}\subset \Rn$, $n\geq 3$, there exists a hyperplane $H$ and a pair of different points $p_1$ and $p_2$ in $\Rn \backslash H$ such that for each $(n-2)$-plane $M\subset H$, there exists a \textit{mirror} which maps the hypersection of $K_1$ defined by $\aff\{ p_1,M\}$ onto the hypersection of $K_2$ defined by $\aff\{ p_2,M\}$, then there exists a \textit{mirror} which maps $K_1$ onto $K_2$.

We introduce an interpretable-by-design method, optimized model-analog, that integrates deep learning with model-analog forecasting which generates forecasts from similar initial climate states in a repository of model simulations. This hybrid framework employs a convolutional neural network to estimate state-dependent weights to identify initial analog states that lead to shadowing target trajectories. The advantage of our method lies in its inherent interpretability, offering insights into initial-error-sensitive regions through estimated weights and the ability to trace the physically-based evolution of the system through analog forecasting. We evaluate our approach using the Community Earth System Model Version 2 Large Ensemble to forecast the El Ni\~no-Southern Oscillation (ENSO) on a seasonal-to-annual time scale. Results show a 10% improvement in forecasting equatorial Pacific sea surface temperature anomalies at 9-12 months leads compared to the unweighted model-analog technique. Furthermore, our model demonstrates improvements in boreal winter and spring initialization when evaluated against a reanalysis dataset. Our approach reveals state-dependent regional sensitivity linked to various seasonally varying physical processes, including the Pacific Meridional Modes, equatorial recharge oscillator, and stochastic wind forcing. Additionally, forecasts of El Ni\~no and La Ni\~na are sensitive to different initial states: El Ni\~no forecasts are more sensitive to initial error in tropical Pacific sea surface temperature in boreal winter, while La Ni\~na forecasts are more sensitive to initial error in tropical Pacific zonal wind stress in boreal summer. This approach has broad implications for forecasting diverse climate phenomena, including regional temperature and precipitation, which are challenging for the model-analog approach alone.

Large-scale vision 2D vision language models, such as CLIP can be aligned with a 3D encoder to learn generalizable (open-vocabulary) 3D vision models. However, current methods require supervised pre-training for such alignment, and the performance of such 3D zero-shot models remains sub-optimal for real-world adaptation. In this work, we propose an optimization framework: Cross-MoST: Cross-Modal Self-Training, to improve the label-free classification performance of a zero-shot 3D vision model by simply leveraging unlabeled 3D data and their accompanying 2D views. We propose a student-teacher framework to simultaneously process 2D views and 3D point clouds and generate joint pseudo labels to train a classifier and guide cross-model feature alignment. Thereby we demonstrate that 2D vision language models such as CLIP can be used to complement 3D representation learning to improve classification performance without the need for expensive class annotations. Using synthetic and real-world 3D datasets, we further demonstrate that Cross-MoST enables efficient cross-modal knowledge exchange resulting in both image and point cloud modalities learning from each other's rich representations., Comment: To be published in Workshop for Learning 3D with Multi-View Supervision (3DMV) at CVPR 2024

GaSe is an important member of the post-transition metal chalcogenide family and is an emerging two-dimensional (2D) semiconductor material. Because it is a van der Waals material, it can be fabricated into atomic-scale ultrathin films, making it suitable for the preparation of compact, heterostructure devices. In addition, GaSe possesses unusual optical and electronic properties, such as a shift from an indirect-bandgap single-layer film to a direct-bandgap bulk material, rare intrinsic p-type conduction, and nonlinear optical behaviors. These properties make GaSe an appealing candidate for the fabrication of field-effect transistors, photodetectors, and photovoltaics. However, the wafer-scale production of pure GaSe single crystal thin films remains challenging. This study develops an approach for the direct growth of nanometer-thick GaSe films on GaAs substrates using molecular beam epitaxy. It yields smooth thin GaSe films with the rare {\gamma}'-polymorph. We analyze the formation mechanism of {\gamma}'-GaSe using density functional theory and speculate that it is stabilized by Ga vacancies since the formation enthalpy of {\gamma}'-GaSe tends to become lower than that of other polymorphs when the Ga vacancy concentration increases. Finally, we investigate the growth conditions of GaSe, providing valuable insights for exploring 2D/3D quasi-van der Waals epitaxial growth.

As robots become smarter and more ubiquitous, optimizing the power consumption of intelligent compute becomes imperative towards ensuring the sustainability of technological advancements. Neuromorphic computing hardware makes use of biologically inspired neural architectures to achieve energy and latency improvements compared to conventional von Neumann computing architecture. Applying these benefits to robots has been demonstrated in several works in the field of neurorobotics, typically on relatively simple control tasks. Here, we introduce an example of neuromorphic computing applied to the real-world industrial task of object insertion. We trained a spiking neural network (SNN) to perform force-torque feedback control using a reinforcement learning approach in simulation. We then ported the SNN to the Intel neuromorphic research chip Loihi interfaced with a KUKA robotic arm. At inference time we show latency competitive with current CPU/GPU architectures, and one order of magnitude less energy usage in comparison to state-of-the-art low-energy edge-hardware. We offer this example as a proof of concept implementation of a neuromoprhic controller in real-world robotic setting, highlighting the benefits of neuromorphic hardware for the development of intelligent controllers for robots., Comment: Accepted at IROS 2024

Spatially-resolved images of debris disks are necessary to determine disk morphological properties and the scattering phase function (SPF) which quantifies the brightness of scattered light as a function of phase angle. Current high-contrast imaging instruments have successfully resolved several dozens of debris disks around other stars, but few studies have investigated trends in the scattered-light, resolved population of debris disks in a uniform and consistent manner. We have combined Karhunen-Loeve Image Projection (KLIP) with radiative-transfer disk forward modeling in order to obtain the highest quality image reductions and constrain disk morphological properties of eight debris disks imaged by the Gemini Planet Imager at H-band with a consistent and uniformly-applied approach. In describing the scattering properties of our models, we assume a common SPF informed from solar system dust scattering measurements and apply it to all systems. We identify a diverse range of dust density properties among the sample, including critical radius, radial width, and vertical width. We also identify radially narrow and vertically extended disks that may have resulted from substellar companion perturbations, along with a tentative positive trend in disk eccentricity with relative disk width. We also find that using a common SPF can achieve reasonable model fits for disks that are axisymmetric and asymmetric when fitting models to each side of the disk independently, suggesting that scattering behavior from debris disks may be similar to Solar System dust., Comment: 23+5 pages, 12+6 figures, 15 pages of Online Supplemental Material included; Accepted for publication in MNRAS

We strongly believe that in order to prove two important geometrical pro\-blems in convexity, namely, the G. Bianchi and P. Gruber's Conjecture \cite{bigru} and the J. A. Barker and D. G. Larman's Conjecture \cite{Barker}, it is necessary obtain new characteristic properties of the ellipsoid, which involves the notions defined in such problems. In this work we present a series of results which intent to be a progress in such direction: Let $L,K\subset \mathbb{R}^n$ be convex bodies, $n\geq 3$, and $L$ be a subset in the interior of $K$. Then each of the following conditions i), ii) and iii) implies that $L$ is an ellipsoid. i) $L$ is $O$-symmetric and, for every $x$ in the boundary of $K$, the support cone $S(L,x)$ is ellipsoidal. ii) there exists a point $p\in \mathbb{R}^n$ such that for every $x$ in the boundary of $K$, there exists a point $y$ in the boundary of $K$ and hyperplane $\Pi$, passing through $p$, such that \[ S(L,x)\cap S(L,y)=\Pi \cap \textrm{bd } K. \] iii) $K$ and $L$ are $O$-symmetric, every $x$ in the boundary of $K$ is a pole of $L$ and $\Omega_x:=S(L,x)\cap S(L,-x)$ is contained in the interior of $K$. In the case ii), $K$ is also an ellipsoid and it is concentric with $L$. On the other hand, let $K\subset \mathbb{R}^n$ be a $O$-symmetric convex body, $n\geq 3$, and let $B$ in $\mathbb{R}^n$ be a ball with centre at $O$. We are going to prove that if $B$ is small enough and all the sections of $K$ given by planes tangent to $B$ are $(n-1)$-ellipsoids, then $K$ is an $n$-ellipsoid.

Generative AI capabilities have grown substantially in recent years, raising renewed concerns about potential malicious use of generated data, or "deep fakes". However, deep fake datasets have not kept up with generative AI advancements sufficiently to enable the development of deep fake detection technology which can meaningfully alert human users in real-world settings. Existing datasets typically use GAN-based models and introduce spurious correlations by always editing similar face regions. To counteract the shortcomings, we introduce DETER, a large-scale dataset for DETEcting edited image Regions and deterring modern advanced generative manipulations. DETER includes 300,000 images manipulated by four state-of-the-art generators with three editing operations: face swapping (a standard coarse image manipulation), inpainting (a novel manipulation for deep fake datasets), and attribute editing (a subtle fine-grained manipulation). While face swapping and attribute editing are performed on similar face regions such as eyes and nose, the inpainting operation can be performed on random image regions, removing the spurious correlations of previous datasets. Careful image post-processing is performed to ensure deep fakes in DETER look realistic, and human studies confirm that human deep fake detection rate on DETER is 20.4% lower than on other fake datasets. Equipped with the dataset, we conduct extensive experiments and break-down analysis using our rich annotations and improved benchmark protocols, revealing future directions and the next set of challenges in developing reliable regional fake detection models., Comment: First two authors contribute equally to this work. Project page at https://deter2024.github.io/deter/