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We study fair allocation of indivisible chores to agents under budget constraints, where each chore has an objective size and disutility. This model captures scenarios where a set of chores need to be divided among agents with limited time, and each chore has a specific time needed for completion. We propose a budget-constrained model for allocating indivisible chores, and systematically explore the differences between goods and chores in this setting. We establish the existence of an EFX allocation. We then show that EF2 allocations are polynomial-time computable in general; for many restricted settings, we strengthen this result to EF1. For divisible chores, we develop an efficient algorithm for computing an EF allocation., Comment: Appears in the 17th International Symposium on Algorithmic Game Theory (SAGT), 2024

Humans use multiple communication channels to interact with each other. For instance, body gestures or facial expressions are commonly used to convey an intent. The use of such non-verbal cues has motivated the development of prediction models. One such approach is predicting arousal and valence (AV) from facial expressions. However, making these models accurate for human-robot interaction (HRI) settings is challenging as it requires handling multiple subjects, challenging conditions, and a wide range of facial expressions. In this paper, we propose a data augmentation (DA) technique to improve the performance of AV predictors using 3D morphable models (3DMM). We then utilize this approach in an HRI setting with a mediator robot and a group of three humans. Our augmentation method creates synthetic sequences for underrepresented values in the AV space of the SEWA dataset, which is the most comprehensive dataset with continuous AV labels. Results show that using our DA method improves the accuracy and robustness of AV prediction in real-time applications. The accuracy of our models on the SEWA dataset is 0.793 for arousal and valence.

This study is the first to explore the interplay between haptic interaction and avatar representation in Shared Virtual Environments (SVEs). We focus on their combined effect on social presence and task-related scores in dyadic collaborations. In a series of experiments, participants performed the plate control task with haptic interaction under four avatar representation conditions: avatars of both participant and partner were displayed, only the participant's avatar was displayed, only the partner's avatar was displayed, and no avatars were displayed. The study finds that avatar representation, especially of the partner, significantly enhances the perception of social presence, which haptic interaction alone does not fully achieve. In contrast, neither the presence nor the type of avatar representation impacts the task performance or participants' force effort of the task, suggesting that haptic interaction provides sufficient interaction cues for the execution of the task. These results underscore the significance of integrating both visual and haptic modalities to optimize remote collaboration experiences in virtual environments, ensuring effective communication and a strong sense of social presence.

A college-level benchmark dataset for large language models (LLMs) in the materials science field, MaterialBENCH, is constructed. This dataset consists of problem-answer pairs, based on university textbooks. There are two types of problems: one is the free-response answer type, and the other is the multiple-choice type. Multiple-choice problems are constructed by adding three incorrect answers as choices to a correct answer, so that LLMs can choose one of the four as a response. Most of the problems for free-response answer and multiple-choice types overlap except for the format of the answers. We also conduct experiments using the MaterialBENCH on LLMs, including ChatGPT-3.5, ChatGPT-4, Bard (at the time of the experiments), and GPT-3.5 and GPT-4 with the OpenAI API. The differences and similarities in the performance of LLMs measured by the MaterialBENCH are analyzed and discussed. Performance differences between the free-response type and multiple-choice type in the same models and the influence of using system massages on multiple-choice problems are also studied. We anticipate that MaterialBENCH will encourage further developments of LLMs in reasoning abilities to solve more complicated problems and eventually contribute to materials research and discovery.

All-optical switching (AOS) involves manipulating magnetization using only pulsed laser, presenting a promising approach for next-generation magnetic recording devices. NiCo2O4 (NCO) thin films, a rare-earth-free ferrimagnetic oxide, exhibit a high Curie temperature and strong perpendicular magnetic anisotropy. This study demonstrates AOS in NCO thin films at room temperature using long-duration laser pulses and high repetition rates. Unlike previous findings, the AOS phenomena we report here are helicity-dependent and observable with an ultrashort pulsed laser. Consequently, two distinct types of AOS can be observed in a single NCO thin film, contingent on the characteristics of the laser pulses and temperature., Comment: 5 pages, 4 figures

Fault-tolerant quantum computation using lattice surgery can be abstracted as operations on graphs, wherein each logical qubit corresponds to a vertex of the graph, and multi-qubit measurements are accomplished by connecting the vertices with paths between them. Operations attempting to connect vertices without a valid path will result in abnormal termination. As the permissible paths may evolve during execution, it is necessary to statically verify that the execution of a quantum program can be completed. This paper introduces a type-based method to statically verify that well-typed programs can be executed without encountering halts induced by surgery operations. Alongside, we present $\mathcal{Q}_{LS}$, a first-order quantum programming language to formalize the execution model of surgery operations. Furthermore, we provide a type checking algorithm by reducing the type checking problem to the offline dynamic connectivity problem., Comment: 29 pages, the extended version of the paper accepted by APLAS 2024

Coalition formation over graphs is a well studied class of games whose players are vertices and feasible coalitions must be connected subgraphs. In this setting, the existence and computation of equilibria, under various notions of stability, has attracted a lot of attention. However, the natural process by which players, starting from any feasible state, strive to reach an equilibrium after a series of unilateral improving deviations, has been less studied. We investigate the convergence of dynamics towards individually stable outcomes under the following perspective: what are the most general classes of preferences and graph topologies guaranteeing convergence? To this aim, on the one hand, we cover a hierarchy of preferences, ranging from the most general to a subcase of additively separable preferences, including individually rational and monotone cases. On the other hand, given that convergence may fail in graphs admitting a cycle even in our most restrictive preference class, we analyze acyclic graph topologies such as trees, paths, and stars.

The Fukushima-Daiichi release of radioactivity is a relevant event to study the atmospheric dispersion modelling of radionuclides. Actually, the atmospheric deposition onto the ground may be studied through the map of measured Cs-137 established consecutively to the accident. The limits of detection were low enough to make the measurements possible as far as 250km from the nuclear power plant. This large scale deposition has been modelled with the Eulerian model ldX. However, several weeks of emissions in multiple weather conditions make it a real challenge. Besides, these measurements are accumulated deposition of Cs-137 over the whole period and do not inform of deposition mechanisms involved: in-cloud, below-cloud, dry deposition. In a previous study (Qu{\'e}rel et al., 2016), a comprehensive sensitivity analysis was performed in order to understand wet deposition mechanisms. It has been shown that the choice of the wet deposition scheme has a strong impact on assessment of deposition patterns. Nevertheless, a ``best'' scheme could not be highlighted as it depends on the selected criteria: the ranking differs according to the statistical indicators considered (correlation, figure of merit in space and factor 2). A possibility to explain the difficulty to discriminate between several schemes was the uncertainties in the modelling, resulting from the meteorological data for instance. Since the move of the plume is not properly modelled, the deposition processes are applied with an inaccurate activity concentration in the air. In the framework of the SAKURA project, an MRI-IRSN collaboration, new meteorological fields at higher resolution (Sekiyama et al., 2013) were provided and allow to reconsider the previous study. An update including these new meteorology data is presented. In addition, the focus is put on the deposition schemes commonly used in nuclear emergency context.

Radiofrequency harvesting using ambient wireless energy could be used to reduce the carbon footprint of electronic devices. However, ambient radiofrequency energy is weak (less than -20 dBm), and thermodynamic limits and high-frequency parasitic impedance restrict the performance of state-of-the-art radiofrequency rectifiers. Nanoscale spin rectifiers based on magnetic tunnel junctions have recently demonstrated high sensitivity, but suffer from a low a.c.-to-d.c. conversion efficiency (less than 1%). Here, we report a sensitive spin rectifier rectenna that can harvest ambient radiofrequency signals between -62 and -20 dBm. We also develop an on-chip co-planar waveguide-based spin rectifier array with a large zero-bias sensitivity (around 34,500 mV/mW) and high efficiency (7.81%). Self-parametric excitation driven by voltage-controlled magnetic anisotropy is a key mechanism that contributes to the performance of the spin-rectifier array. We show that these spin rectifiers can wirelessly power a sensor at a radiofrequency power of -27 dBm.

We systematically investigate the variability of polarized X-rays on a timescale of a few seconds in the low/hard state of the black hole binary Cygnus X-1. The correlation between polarization degrees and angles with X-Ray intensity was analyzed using data collected by the Imaging X-ray Polarimetry Explorer (IXPE) in June 2022. Given that X-Ray variability in the low/hard state of Cygnus X-1 is non-periodic, flux peaks were aggregated to suppress statistical fluctuations. We divided the temporal profiles of these aggregated flux peaks into seven time segments and evaluated the polarization for each segment. The results reveal that the polarization degree was 4.6\%$\pm$1.2 and 5.3\%$\pm$1.2 before and after the peak, respectively, but decreased to 3.4\%$\pm$1.1 and 2.7\%$\pm$1.1 in the segments including and immediately following the peak. Furthermore, the polarization angle exhibited a slight shift from approximately 30$^{\circ}$ to $\sim$40$^{\circ}$ before and after the peak. These findings suggest that the accretion disk contracts with increasing X-Ray luminosity, and the closer proximity of the X-Ray emitting gas to the black hole may lead to reduced polarization.

Object pose estimation plays a vital role in mixed-reality interactions when users manipulate tangible objects as controllers. Traditional vision-based object pose estimation methods leverage 3D reconstruction to synthesize training data. However, these methods are designed for static objects with diffuse colors and do not work well for objects that change their appearance during manipulation, such as deformable objects like plush toys, transparent objects like chemical flasks, reflective objects like metal pitchers, and articulated objects like scissors. To address this limitation, we propose Rocap, a robotic pipeline that emulates human manipulation of target objects while generating data labeled with ground truth pose information. The user first gives the target object to a robotic arm, and the system captures many pictures of the object in various 6D configurations. The system trains a model by using captured images and their ground truth pose information automatically calculated from the joint angles of the robotic arm. We showcase pose estimation for appearance-changing objects by training simple deep-learning models using the collected data and comparing the results with a model trained with synthetic data based on 3D reconstruction via quantitative and qualitative evaluation. The findings underscore the promising capabilities of Rocap.

Strong soft X-ray emission called soft X-ray excess is often observed in luminous active galactic nuclei (AGN). It has been suggested that the soft X-rays are emitted from a warm ($T=10^6\sim10^7\ \rm{K}$) region that is optically thick for the Thomson scattering (warm Comptonization region). Motivated by the recent observations that soft X-ray excess appears in changing look AGN (CLAGN) during the state transition from a dim state without broad emission lines to a bright state with broad emission lines, we performed global three-dimensional radiation magnetohydrodynamic simulations assuming that the mass accretion rate increases and becomes around $10$\% of the Eddington accretion rate. The simulation successfully reproduces a warm, Thomson-thick region outside the hot radiatively inefficient accretion flow near the black hole. The warm region is formed by efficient radiative cooling due to inverse Compton scattering. The calculated luminosity $0.01L_{{\rm Edd}}-0.08L_{{\rm Edd}}$ is consistent with the luminosity of CLAGN. We also found that the warm Comptonization region is well described by the steady model of magnetized disks supported by azimuthal magnetic fields. When the anti-parallel azimuthal magnetic fields supporting the radiatively cooled region reconnect around the equatorial plane of the disk, the temperature of the region becomes higher by releasing the magnetic energy transported to the region., Comment: Published in ApJ

Single pulse all-optical switching of magnetization (AOS) in Co/Gd based synthetic ferrimagnets carries promises for hybrid spintronic-photonic integration. A crucial next step progressing towards this vision is to gain insight into AOS and multi-domain state (MDS) behavior using longer pulses, which is compatible with state-of-the-art integrated photonics. In this work, we present our studies on the AOS and MDS of [Co/Gd]n (n = 1, 2) using ps optical pulses across a large composition range. We theoretically and experimentally show that a large Gd layer thickness can enhance the AOS energy efficiency and maximum pulse duration. We have identified two augmenting roles of Gd in extending the maximum pulse duration. On the inter-atomic level, we found that more Gd offers a prolonged angular momentum supply to Co. On the micromagnetic level, a higher Gd content brings the system to be closer to magnetic compensation in the equilibrized hot state, thereby reducing the driving force for thermally assisted nucleation of domain walls, combating the formation of a MDS. Our study presents a composition overview of AOS in [Co/Gd]n and offers useful physical insights regarding AOS fundamentals as well as the projected photonic integration.

We formulate the problem of fair and efficient completion of indivisible goods, defined as follows: Given a partial allocation of indivisible goods among agents, does there exist an allocation of the remaining goods (i.e., a completion) that satisfies fairness and economic efficiency guarantees of interest? We study the computational complexity of the completion problem for prominent fairness and efficiency notions such as envy-freeness up one good (EF1), proportionality up to one good (Prop1), maximin share (MMS), and Pareto optimality (PO), and focus on the class of additive valuations as well as its subclasses such as binary additive and lexicographic valuations. We find that while the completion problem is significantly harder than the standard fair division problem (wherein the initial partial allocation is empty), the consideration of restricted preferences facilitates positive algorithmic results for threshold-based fairness notions (Prop1 and MMS). On the other hand, the completion problem remains computationally intractable for envy-based notions such as EF1 and EF1+PO even under restricted preferences.

We propose noise-robust voice conversion (VC) which takes into account the recording quality and environment of noisy source speech. Conventional denoising training improves the noise robustness of a VC model by learning noisy-to-clean VC process. However, the naturalness of the converted speech is limited when the noise of the source speech is unseen during the training. To this end, our proposed training conditions a VC model on two latent variables representing the recording quality and environment of the source speech. These latent variables are derived from deep neural networks pre-trained on recording quality assessment and acoustic scene classification and calculated in an utterance-wise or frame-wise manner. As a result, the trained VC model can explicitly learn information about speech degradation during the training. Objective and subjective evaluations show that our training improves the quality of the converted speech compared to the conventional training., Comment: 5 pages, accepted for INTERSPEECH 2024, audio samples: http://y-saito.sakura.ne.jp/sython/Corpus/SRC4VC/IS2024_CDT_supplementary/demo_cdt.html

We present SRC4VC, a new corpus containing 11 hours of speech recorded on smartphones by 100 Japanese speakers. Although high-quality multi-speaker corpora can advance voice conversion (VC) technologies, they are not always suitable for testing VC when low-quality speech recording is given as the input. To this end, we first asked 100 crowdworkers to record their voice samples using smartphones. Then, we annotated the recorded samples with speaker-wise recording-quality scores and utterance-wise perceived emotion labels. We also benchmark SRC4VC on any-to-any VC, in which we trained a multi-speaker VC model on high-quality speech and used the SRC4VC speakers' voice samples as the source in VC. The results show that the recording quality mismatch between the training and evaluation data significantly degrades the VC performance, which can be improved by applying speech enhancement to the low-quality source speech samples., Comment: Accepted for INTERSPEECH 2024, corpus project page: https://y-saito.sakura.ne.jp/sython/Corpus/SRC4VC/index.html

Segmenting dance video into short movements is a popular way to easily understand dance choreography. However, it is currently done manually and requires a significant amount of effort by experts. That is, even if many dance videos are available on social media (e.g., TikTok and YouTube), it remains difficult for people, especially novices, to casually watch short video segments to practice dance choreography. In this paper, we propose a method to automatically segment a dance video into each movement. Given a dance video as input, we first extract visual and audio features: the former is computed from the keypoints of the dancer in the video, and the latter is computed from the Mel spectrogram of the music in the video. Next, these features are passed to a Temporal Convolutional Network (TCN), and segmentation points are estimated by picking peaks of the network output. To build our training dataset, we annotate segmentation points to dance videos in the AIST Dance Video Database, which is a shared database containing original street dance videos with copyright-cleared dance music. The evaluation study shows that the proposed method (i.e., combining the visual and audio features) can estimate segmentation points with high accuracy. In addition, we developed an application to help dancers practice choreography using the proposed method., Comment: 9 pages, 11 figures

The silicon-on-insulator pixel (SOIPIX) detector is a unique monolithic-structure imaging device currently being developed by the SOIPIX group led by the High Energy Accelerator Research Organization (KEK). The detector team at KEK Photon Factory (PF) is also developing an X-ray camera using INTPIX4NA with a 14.1 $\times$ 8.7 $\mathsf{mm^2}$ sensitive area and 425,984 (832 column $\times$ 512 row matrix) pixels, with a pixel size of 17 $\times$ 17 $\mathsf{\mu m^2}$. The detector has high resolution and sensitivity for low-intensity X-rays, making it suitable for imaging in optical systems with lower X-ray intensities, such as an X-ray zooming microscope using two Fresnel zone plates (FZPs), which is also under development at PF. To enable imaging under such conditions, we developed a detector cooling system using a Peltier element to support longer exposure time (~0.5 seconds per frame). Additionally, we developed a new readout system using DAQ boards developed by PF, equipped with SiTCP-XG (network controller implemented on field-programmable gate array) that supports 10 Gbps Ethernet for high-frame-rate imaging at several hundred hertz. The new X-ray camera was tested at the PF BL-14A, BL-14B, and AR-NE1A experimental stations, and the resolution and sensitivity characteristics were confirmed. Given these confirmed characteristics, this X-ray camera is suitable for X-ray imaging using 5--20 keV X-rays under low-intensity, low-contrast conditions. These conditions are ideal for capturing soft tissues with poor contrast, objects with fine structures, and specimens vulnerable to radiation damage., Comment: Presented at 13th International "Hiroshima" Symposium on the Development and Application of Semiconductor Tracking Detectors (HSTD13), Vancouver, Canada

This paper proposes a method to estimate the locations of grid handles in free-form deformation (FFD) while preserving the local shape characteristics of the 2D/3D input model embedded into the grid, named locality-preserving FFD (lp-FFD). Users first specify some vertex locations in the input model and grid handle locations. The system then optimizes all locations of grid handles by minimizing the distortion of the input model's mesh elements. The proposed method is fast and stable, allowing the user to directly and indirectly make the deformed shape of mesh model and grid. This paper shows some examples of deformation results to demonstrate the robustness of our lp-FFD. In addition, we conducted a user study and confirm our lp-FFD's efficiency and effectiveness in shape deformation is higher than those of existing methods used in commercial software.

Many effect systems for algebraic effect handlers are designed to guarantee that all invoked effects are handled adequately. However, respective researchers have developed their own effect systems that differ in how to represent the collections of effects that may happen. This situation results in blurring what is required for the representation and manipulation of effect collections in a safe effect system. In this work, we present a language ${\lambda_{\mathrm{EA}}}$ equipped with an effect system that abstracts the existing effect systems for algebraic effect handlers. The effect system of ${\lambda_{\mathrm{EA}}}$ is parameterized over effect algebras, which abstract the representation and manipulation of effect collections in safe effect systems. We prove the type-and-effect safety of ${\lambda_{\mathrm{EA}}}$ by assuming that a given effect algebra meets certain properties called safety conditions. As a result, we can obtain the safety properties of a concrete effect system by proving that an effect algebra corresponding to the concrete system meets the safety conditions. We also show that effect algebras meeting the safety conditions are expressive enough to accommodate some existing effect systems, each of which represents effect collections in a different style. Our framework can also differentiate the safety aspects of the effect collections of the existing effect systems. To this end, we extend ${\lambda_{\mathrm{EA}}}$ and the safety conditions to lift coercions and type-erasure semantics, propose other effect algebras including ones for which no effect system has been studied in the literature, and compare which effect algebra is safe and which is not for the extensions.

In real-world usage, existing GAN image generation tools come up short due to their lack of intuitive interfaces and limited flexibility. To overcome these limitations, we developed CanvasPic, an innovative tool for flexible GAN image generation. Our tool introduces a novel 2D layout design that allows users to intuitively control image attributes based on real-world images. By interacting with the distances between images in the spatial layout, users are able to conveniently control the influence of each attribute on the target image and explore a wide range of generated results. Considering practical application scenarios, a user study involving 24 participants was conducted to compare our tool with existing tools in GAN image generation. The results of the study demonstrate that our tool significantly enhances the user experience, enabling more effective achievement of desired generative results.

Attention-based models are proliferating in the space of image analytics, including segmentation. The standard method of feeding images to transformer encoders is to divide the images into patches and then feed the patches to the model as a linear sequence of tokens. For high-resolution images, e.g. microscopic pathology images, the quadratic compute and memory cost prohibits the use of an attention-based model, if we are to use smaller patch sizes that are favorable in segmentation. The solution is to either use custom complex multi-resolution models or approximate attention schemes. We take inspiration from Adapative Mesh Refinement (AMR) methods in HPC by adaptively patching the images, as a pre-processing step, based on the image details to reduce the number of patches being fed to the model, by orders of magnitude. This method has a negligible overhead, and works seamlessly with any attention-based model, i.e. it is a pre-processing step that can be adopted by any attention-based model without friction. We demonstrate superior segmentation quality over SoTA segmentation models for real-world pathology datasets while gaining a geomean speedup of $6.9\times$ for resolutions up to $64K^2$, on up to $2,048$ GPUs.

We explore the integration of metaprogramming in a call-by-value linear lambda-calculus and sketch its extension to a session type system. We build on a model of contextual modal type theory with multi-level contexts, where contextual values, closing arbitrary terms over a series of variables, may then be boxed and transmitted in messages. Once received, one such value may then be unboxed (with a let-box construct) and locally applied before being run. We present a series of examples where servers prepare and ship code on demand via session typed messages., Comment: In Proceedings PLACES 2024, arXiv:2404.03712

Predicting physical properties of materials from their crystal structures is a fundamental problem in materials science. In peripheral areas such as the prediction of molecular properties, fully connected attention networks have been shown to be successful. However, unlike these finite atom arrangements, crystal structures are infinitely repeating, periodic arrangements of atoms, whose fully connected attention results in infinitely connected attention. In this work, we show that this infinitely connected attention can lead to a computationally tractable formulation, interpreted as neural potential summation, that performs infinite interatomic potential summations in a deeply learned feature space. We then propose a simple yet effective Transformer-based encoder architecture for crystal structures called Crystalformer. Compared to an existing Transformer-based model, the proposed model requires only 29.4% of the number of parameters, with minimal modifications to the original Transformer architecture. Despite the architectural simplicity, the proposed method outperforms state-of-the-art methods for various property regression tasks on the Materials Project and JARVIS-DFT datasets., Comment: 13 main pages, 3 figures, 4 tables, 10 appendix pages. Published as a conference paper at ICLR 2024. For more information, see https://omron-sinicx.github.io/crystalformer/

Acquiring the desired font for various design tasks can be challenging and requires professional typographic knowledge. While previous font retrieval or generation works have alleviated some of these difficulties, they often lack support for multiple languages and semantic attributes beyond the training data domains. To solve this problem, we present FontCLIP: a model that connects the semantic understanding of a large vision-language model with typographical knowledge. We integrate typography-specific knowledge into the comprehensive vision-language knowledge of a pretrained CLIP model through a novel finetuning approach. We propose to use a compound descriptive prompt that encapsulates adaptively sampled attributes from a font attribute dataset focusing on Roman alphabet characters. FontCLIP's semantic typographic latent space demonstrates two unprecedented generalization abilities. First, FontCLIP generalizes to different languages including Chinese, Japanese, and Korean (CJK), capturing the typographical features of fonts across different languages, even though it was only finetuned using fonts of Roman characters. Second, FontCLIP can recognize the semantic attributes that are not presented in the training data. FontCLIP's dual-modality and generalization abilities enable multilingual and cross-lingual font retrieval and letter shape optimization, reducing the burden of obtaining desired fonts., Comment: 11 pages. Eurographics 2024. https://yukistavailable.github.io/fontclip.github.io/