Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Control of Soft Robotic Arm: An Experimental Study

The objective of this letter is to investigate the dynamic control of a soft robotic arm. First, a modular soft robotic hardware and an affordable actuator-space encoder were presented. We then discussed the soft robot modeling, and adaptive passivity control strategy with stability proof. The proposed controller was tested in different operation scenarios, and compared to the standard PD feedback linearization control and passivity control. In all experimental settings, the adaptive passivity control scheme was able to achieve superior performance, even with significant modeling uncertainties and disturbances. The theoretical analysis and experimental validations of the proposed controller will pave the way toward the practical implementations of the soft robotic system in the dynamic scenarios.
Modular Degradation Simulation and Restoration for Under-Display Camera

Under-display camera (UDC) provides an elegant solution for full-screen smartphones. However, UDC captured images suffer from severe degradation since sensors lie under the display. Although this issue can be tackled by image restoration networks, these networks require large-scale image pairs for training. To this end, we propose a modular network dubbed MPGNet trained using the generative adversarial network (GAN) framework for simulating UDC imaging. Specifically, we note that the UDC imaging degradation process contains brightness attenuation, blurring, and noise corruption. Thus we model each degradation with a characteristic-related modular network, and all modular networks are cascaded to form the generator. Together with a pixel-wise discriminator and supervised loss, we can train the generator to simulate the UDC imaging degradation process. Furthermore, we present a Transformer-style network named DWFormer for UDC image restoration. For practical purposes, we use depth-wise convolution instead of the multi-head self-attention to aggregate local spatial information. Moreover, we propose a novel channel attention module to aggregate global information, which is critical for brightness recovery. We conduct evaluations on the UDC benchmark, and our method surpasses the previous state-of-the-art models by 1.23 dB on the P-OLED track and 0.71 dB on the T-OLED track, respectively.
MAGIC: Mask-Guided Image Synthesis by Inverting a Quasi-Robust Classifier

We offer a method for one-shot mask-guided image synthesis that allows controlling manipulations of a single image by inverting a quasi-robust classifier equipped with strong regularizers. Our proposed method, entitled MAGIC, leverages structured gradients from a pre-trained quasi-robust classifier to better preserve the input semantics while preserving its classification accuracy, thereby guaranteeing credibility in the synthesis. Unlike current methods that use complex primitives to supervise the process or use attention maps as a weak supervisory signal, MAGIC aggregates gradients over the input, driven by a guide binary mask that enforces a strong, spatial prior. MAGIC implements a series of manipulations with a single framework achieving shape and location control, intense non-rigid shape deformations, and copy/move operations in the presence of repeating objects and gives users firm control over the synthesis by requiring to simply specify binary guide masks. Our study and findings are supported by various qualitative comparisons with the state-of-the-art on the same images sampled from ImageNet and quantitative analysis using machine perception along with a user survey of 100+ participants that endorse our synthesis quality.
Absolutely Periodic Billiard Orbits of Arbitrarily High Order

We show that for all n ∈ N, the set of domains containing absolutely periodic orbits of order n are dense in the set of bounded strictly convex domains with C∞ boundary. The proof that such an orbit exists is an extension to billiard maps of the results of [15], where it is proved that such maps are dense in Newhouse domains in regions of real-analytic area-preserving two-dimensional maps. Our result is a step toward disproving a conjecture that no absolutely periodic billiard orbits of infinite order exist in Euclidean billiards, and is also an indication that Ivrii’s Conjecture about the measure set of periodic orbits may not be true.
Equidistribution theorem over an adelic curve

In this article, we introduce the notion of global adelic space of an arithmetic variety over an adelic curve and prove an equidistribution theorem for a generic sequence of subvarieties. As an application, we prove a Bogomolov type theorem for Abelian varieties over an adelic curve of characteristic 0.
Impact of random alloy fluctuations on the carrier distribution in multi-color (In,Ga)N/GaN quantum well systems

In this work, we study the impact that random alloy fluctuations have on the distribution of electrons and holes across the active region of a (In,Ga)N/GaN multi-quantum well based light emitting diode (LED). To do so, an atomistic tight-binding model is employed to account for alloy fluctuations on a microscopic level and the resulting tight-binding energy landscape forms input to a drift-diffusion model. Here, quantum corrections are introduced via localization landscape theory and we show that when neglecting alloy disorder our theoretical framework yields results similar to commercial software packages that employ a self-consistent Schroedinger-Poisson-drift-diffusion solver. Similar to experimental studies in the literature, we have focused on a multi-quantum well system where two of the three wells have the same In content while the third well differs in In content. By changing the order of wells in this multicolor quantum well structure and looking at the relative radiative recombination rates of the different emitted wavelengths, we (i) gain insight into the distribution of carriers in such a system and (ii) can compare our findings to trends observed in experiment. Our results indicate that the distribution of carriers depends significantly on the treatment of the quantum well microstructure. When including random alloy fluctuations and quantum corrections in the simulations, the calculated trends in the relative radiative recombination rates as a function of the well ordering are consistent with previous experimental studies. The results from the widely employed virtual crystal approximation contradict the experimental data. Overall, our work highlights the importance of a careful and detailed theoretical description of the carrier transport in an (In,Ga)N/GaN multi-quantum well system to ultimately guide the design of the active region of III-N-based LED structures.
Pose-Aided Video-Based Person Re-Identification via Recurrent Graph Convolutional Network

Existing methods for video-based person re- identification (ReID) mainly learn the appearance feature of a given pedestrian via a feature extractor and a feature aggregator. However, the appearance models would fail to learn a large inter-class variance when different pedestrians have similar appearances. Considering that different pedestrians have different walking postures and body proportions, we propose to learn the discriminative pose feature beyond the appearance feature for video retrieval. Specifically, we implement a two-branch architecture to separately learn the appearance feature and pose feature, and then concatenate them together for inference. To learn the pose feature, we first detect the pedestrian pose in each frame through an off-the-shelf pose detector, and construct a temporal graph using the pose sequence. We then exploit a recurrent graph convolutional network (RGCN) to learn the node embeddings of the temporal pose graph, which devises a global information propagation mechanism to simultaneously achieve the neighborhood aggregation of intra-frame nodes and message passing among inter-frame graphs. Finally, we propose a dual-attention method (DAM) consisting of node-attention and time-attention to obtain the temporal graph representation from the node embeddings, where the self-attention mechanism is employed to learn the importance of each node and each frame. We verify the proposed method on three video-based ReID datasets, i.e., Mars, DukeMTMC and iLIDS-VID, whose experimental results demonstrate that the learned pose feature can effectively improve the performance of existing appearance models.
Semantic scene descriptions as an objective of human vision

Interpreting the meaning of a visual scene requires not only identification of its constituent objects, but also a rich semantic characterization of object interrelations. Here, we study the neural mechanisms underlying visuo-semantic transformations by applying modern computational techniques to a large-scale 7T fMRI dataset of human brain responses elicited by complex natural scenes. Using semantic embeddings obtained by applying linguistic deep learning models to human-generated scene descriptions, we identify a widely distributed network of brain regions that encode semantic scene descriptions. Importantly, these semantic embeddings better explain activity in these regions than traditional object category labels. In addition, they are effective predictors of activity despite the fact that the participants did not actively engage in a semantic task, suggesting that visuo-semantic transformations are a default mode of vision. In support of this view, we then show that highly accurate reconstructions of scene captions can be directly linearly decoded from patterns of brain activity. Finally, a recurrent convolutional neural network trained on semantic embeddings further outperforms semantic embeddings in predicting brain activity, providing a mechanistic model of the brain’s visuo-semantic transformations. Together, these experimental and computational results suggest that transforming visual input into rich semantic scene descriptions may be a central objective of the visual system, and that focusing efforts on this new objective may lead to improved models of visual information processing in the human brain.
The Stellar Mass Function in CANDELS and Frontier Fields: The Buildup of Low-mass Passive Galaxies since z ∼ 3

Despite significant efforts in recent years, the physical processes responsible for the formation of passive galaxies through cosmic time remain unclear. The shape and evolution of the stellar mass function (SMF) give an insight into these mechanisms. Taking advantage of the CANDELS and the deep Hubble Frontier Fields (HFF) programs, we estimated the SMF of total, star-forming, and passive galaxies from z = 0.25 to z = 2.75 to unprecedented depth and we focus on the latter population. The density of passive galaxies underwent a significant evolution over the last 11 Gyr. They account for 60% of the total mass in the nearby universe against ∼20% observed at z ∼ 2.5. The inclusion of the HFF program allows us to detect, for the first time at z > 1.5, the characteristic upturn in the SMF of passive galaxies at low masses, usually associated with environmental quenching. We observe two separate populations of passive galaxies evolving on different timescales: roughly half of the high-mass systems were already quenched at high redshift, while low-mass passive galaxies are gradually building up over the redshift range probed. In the framework of environmental quenching at low masses, we interpret this finding as evidence of an increasing role of the environment in the buildup of passive galaxies as a function of time. Finally, we compared our findings with a set of theoretical predictions. Despite good agreement in some redshift and mass intervals, none of the models are able to fully reproduce the observations. This calls for further investigation into the involved physical mechanisms, both theoretically and observationally, especially with the brand-new JWST data.
Plasma out of thermodynamical equilibrium : influence of the plasma environment on atomic structure and collisional cross sections

In hot dense plasmas, the free-electron and ion spatial distribution may strongly affect the atomic structure. To account for such effects we have implemented a potential correction based on the uniform electron gas model and on a Thomas-Fermi Approach in the Flexible Atomic Code (FAC). This code has been applied to obtain energies, wave-functions and radiative rates modiﬁed by the plasma environment. In hydrogen-like ions, these numerical results have been successfully compared to an analytical calculation based on ﬁrst-order perturbation theory. In the case of multi-electron ions, we observe level crossings in agreement with another recent model calculation. Various methods for the collision cross-section calculations are reviewed. The inﬂuence of plasma environment on these cross-sections is analyzed in detail. Some analytical expressions are proposed for hydrogen-like ions in the limit where Born or Lotz approximations apply and are compared to the numerical results from the FAC code. Finally, from this work, we study the inﬂuence of the plasma environment on our collisional-radiative model so-called -Foch-. Because of this environment, the mean charge state of the ions increases. The line shift is observed on the bound-bound emission spectra. A good agreement is found between our work and experimental data on a Titanium plasma.
CUTS: A Framework for Multigranular Unsupervised Medical Image Segmentation

Segmenting medical images is critical to facilitating both patient diagnoses and quantitative research. A major limiting factor is the lack of labeled data, as obtaining expert annotations for each new set of imaging data or task can be expensive, labor intensive, and inconsistent among annotators. To address this, we present CUTS (Contrastive and Unsupervised Training for multi-granular medical image Segmentation), a fully unsupervised deep learning framework for medical image segmentation to better utilize the vast majority of imaging data that are not labeled or annotated. CUTS works by leveraging a novel two-stage approach. First, it produces an image-specific embedding map via intra-image contrastive loss and a local patch reconstruction objective. Second, these embeddings are partitioned at dynamic levels of granularity that correspond to the data topology. Ultimately, CUTS yields a series of coarse-to-fine-grained segmentations that highlight image features at various scales. We apply CUTS to retinal fundus images and two types of brain MRI images in order to delineate structures and patterns at different scales, providing distinct information relevant for clinicians. When evaluated against predefined anatomical masks at a given granularity, CUTS demonstrates improvements ranging from 10% to 200% on dice coefficient and Hausdorff distance compared to existing unsupervised methods. Further, CUTS shows performance on par with the latest Segment Anything Model which was pre-trained in a supervised fashion on 11 million images and 1.1 billion masks. In summary, with CUTS we demonstrate that medical image segmentation can be effectively solved without relying on large, labeled datasets or vast computational resources.
Quantification before Selection: Active Dynamics Preference for Robust Reinforcement Learning

Training a robust policy is critical for policy deployment in real-world systems or dealing with unknown dynamics mismatch in different dynamic systems. Domain Randomization~(DR) is a simple and elegant approach that trains a conservative policy to counter different dynamic systems without expert knowledge about the target system parameters. However, existing works reveal that the policy trained through DR tends to be over-conservative and performs poorly in target domains. Our key insight is that dynamic systems with different parameters provide different levels of difficulty for the policy, and the difficulty of behaving well in a system is constantly changing due to the evolution of the policy. If we can actively sample the systems with proper difficulty for the policy on the fly, it will stabilize the training process and prevent the policy from becoming over-conservative or over-optimistic. To operationalize this idea, we introduce Active Dynamics Preference~(ADP), which quantifies the informativeness and density of sampled system parameters. ADP actively selects system parameters with high informativeness and low density. We validate our approach in four robotic locomotion tasks with various discrepancies between the training and testing environments. Extensive results demonstrate that our approach has superior robustness for system inconsistency compared to several baselines.
Minimizing the AoI in Resource-Constrained Multi-Source Relaying Systems: Dynamic and Learning-Based Scheduling

We consider a multi-source relaying system where independent sources randomly generate status update packets which are sent to the destination with the aid of a relay through unreliable links. We develop transmission scheduling policies to minimize the weighted sum average age of information (AoI) subject to transmission capacity and long-run average resource constraints. We formulate a stochastic control optimization problem and solve it using a constrained Markov decision process (CMDP) approach and a drift-plus-penalty method. The CMDP problem is solved by transforming it into an MDP problem using the Lagrangian relaxation method. We theoretically analyze the structure of optimal policies for the MDP problem and subsequently propose a structure-aware algorithm that returns a practical near-optimal policy. Using the drift-plus-penalty method, we devise a near-optimal low-complexity policy that performs the scheduling decisions dynamically. We also develop a model-free deep reinforcement learning policy for which the Lyapunov optimization theory and a dueling double deep Q-network are employed. The complexities of the proposed policies are analyzed. Simulation results are provided to assess the performance of our policies and validate the theoretical results. The results show up to 91% performance improvement compared to a baseline policy.
The interplay between a galactic bar and a supermassive black hole: nuclear fuelling in a subparsec resolution galaxy simulation

We study the connection between the large-scale dynamics and the gas fueling toward a central black hole via the analysis of a Milky Way-like simulation at sub-parsec resolution. This allows us to follow a set of processes at various scales (e.g., the triggering of inward gas motion towards inner resonances via the large-scale bar, the connection to the central black hole via mini spirals) in a self-consistent manner. This simulation provides further insights on the role of shear for the inhibition of star formation within the bar in regions with significant amount of gas. We also witness the decoupling of the central gas and nuclear cluster from the large-scale disc, via interactions with the black hole. This break of symmetry in the mass distribution triggers the formation of gas clumps organised in a time-varying 250 pc ring-like structure, the black hole being offset by about 70 pc from its centre. Some clumps form stars, while most get disrupted or merge. Supernovae feedback further creates bubbles and filaments, some of the gas being expelled to 100 pc or higher above the galaxy plane. This helps remove angular momentum from the gas, which gets closer to the central dark mass. Part of the gas raining down is being accreted, forming a 10~pc polar disc-like structure around the black hole, leading to an episode of star formation. This gives rise to multiple stellar populations with significantly different angular momentum vectors, and may lead to a natural intermittence in the fueling of the black hole.
Transformer Inertial Poser: Real-time Human Motion Reconstruction from Sparse IMUs with Simultaneous Terrain Generation

Real-time human motion reconstruction from a sparse set of (e.g. six) wearable IMUs provides a non-intrusive and economic approach to motion capture. Without the ability to acquire position information directly from IMUs, recent works took data-driven approaches that utilize large human motion datasets to tackle this under-determined problem. Still, challenges remain such as temporal consistency, drifting of global and joint motions, and diverse coverage of motion types on various terrains. We propose a novel method to simultaneously estimate full-body motion and generate plausible visited terrain from only six IMU sensors in real-time. Our method incorporates 1. a conditional Transformer decoder model giving consistent predictions by explicitly reasoning prediction history, 2. a simple yet general learning target named "stationary body points” (SBPs) which can be stably predicted by the Transformer model and utilized by analytical routines to correct joint and global drifting, and 3. an algorithm to generate regularized terrain height maps from noisy SBP predictions which can in turn correct noisy global motion estimation. We evaluate our framework extensively on synthesized and real IMU data, and with real-time live demos, and show superior performance over strong baseline methods.
Anti‐bacterial effects of functional cosmetics containing silver ions (Ag+) on problematic toenails in Republic of Korea

Problematic toenails caused by various diseases that are infected by various bacteria are discolored or deformed if left unattended for a long time are gradually increasing in modern society. Accordingly, the present study examines the anti‐bacterial effects of LCN products, focused on Pseudomonas aeruginosa and Trichophyton rubrum, which can appear in problematic toenail symptoms. After the treatment, a significant anti‐bacterial effect occurring on problematic toenails when a product containing silver ion (Ag+) will be analyzed.
Cytochrome P450 Transcriptional Regulation by Testis‐Specific Y‐Encoded‐Like Protein: Identification of Novel Upstream Transcription Factors

Cytochrome P450s (CYPs) display significant inter-individual variation in expression, much of which remains unexplained by known CYP single-nucleotide polymorphisms (SNPs). Testis‐specific Y‐encoded‐like proteins (TSPYLs) are transcriptional regulators for several drug-metabolizing CYPs including CYP3A4. However, transcription factors (TFs) that might influence CYP expression through an effect on TSPYL expression are unknown. Therefore, we studied regulators of TSPYL expression in hepatic cell lines and their possible SNP-dependent variation. Specifically, we identified candidate TFs that might influence TSPYL expression using the ENCODE ChIPseq database. Subsequently, the expression of TSPYL1/2/4 as well as that of selected CYP targets for TSPYL regulation were assayed in hepatic cell lines before and after knockdown of TFs that might influence CYP expression through TSPYL-dependent mechanisms. Those results were confirmed by studies of TF binding to TSPYL1/2/4 gene promoter regions. In hepatic cell lines, knockdown of the REST and ZBTB7A TFs resulted in decreased TSPYL1 and TSPYL4 expression and increased CYP3A4 expression, changes reversed by TSPYL1/4 overexpression. Potential binding sites for REST and ZBTB7A on the promoters of TSPYL1 and TSPYL4 were confirmed by chromatin immunoprecipitation. Finally, common SNP variants in upstream binding sites on the TSPYL1/4 promoters were identified and luciferase reporter constructs confirmed SNP-dependent modulation of TSPYL1/4 gene transcription. In summary, we identified REST and ZBTB7A as regulators of the expression of TSPYL genes which themselves can contribute to regulation of CYP expression and—potentially—of drug metabolism. SNP-dependent modulation of TSPYL transcription may contribute to individual variation in both CYP expression and—downstream–drug response phenotypes. SIGNIFICANCE STATEMENT Testis-specific Y‐encoded‐like proteins (TSPYLs) are transcriptional regulators of cytochrome P450 (CYP) gene expression. Here, we report that variation in TSPYL expression as a result of the effects of genetically regulated TSPYL transcription factors is an additional factor that could result in downstream variation in CYP expression and potentially, as a result, variation in drug biotransformation.