Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Ensemble Loss and Latent Refinement for High-Fidelity Neural Image Compression

Recent advancements in neural compression have surpassed traditional codecs in PSNR and MS-SSIM measurements. However, at low bit-rates, these methods can introduce visually displeasing artifacts, such as blurring, color shifting, and texture loss, thereby compromising perceptual quality of images. To address these issues, this study presents an enhanced neural compression method designed for optimal visual fidelity. We have trained our model with a sophisticated semantic ensemble loss, integrating Charbonnier loss, perceptual loss, style loss, and a non-binary adversarial loss, to enhance the perceptual quality of image reconstructions. Additionally, we have implemented a latent refinement process to generate content-aware latent codes. These codes adhere to bit-rate constraints, balance the trade-off between distortion and fidelity, and prioritize bit allocation to regions of greater importance. Our empirical findings demonstrate that this approach significantly improves the statistical fidelity of neural image compression. On CLIC2024 validation set, our approach achieves a 62% bitrate saving compared to MS-ILLM under FID metric.
Contract Usage and Evolution in Android Mobile Applications

Formal contracts and assertions are effective methods to enhance software quality by enforcing preconditions, postconditions, and invariants. Previous research has demonstrated the value of contracts in traditional software development contexts. However, the adoption and impact of contracts in the context of mobile application development, particularly of Android applications, remain unexplored. To address this, we present the first large-scale empirical study on the presence and use of contracts in Android applications, written in Java or Kotlin. We consider different types of contract elements divided into five categories: conditional runtime exceptions, APIs, annotations, assertions, and other. We analyzed 2,390 Android applications from the F-Droid repository and processed more than 51,749 KLOC to determine 1) how and to what extent contracts are used, 2) how contract usage evolves, and 3) whether contracts are used safely in the context of program evolution and inheritance. Our findings include: 1) although most applications do not specify contracts, annotation-based approaches are the most popular among practitioners; 2) applications that use contracts continue to use them in later versions, but the number of methods increases at a higher rate than the number of contracts; and 3) there are many potentially unsafe specification changes when applications evolve and in subtyping relationships, which indicates a lack of specification stability. Our findings show that it would be desirable to have libraries that standardize contract specifications in Java and Kotlin, and tools that aid practitioners in writing stronger contracts and in detecting contract violations in the context of program evolution and inheritance.
AST-2: Single and bi-layered 2-D acoustic soft tactile skin

—This paper aims to present an innovative and cost-effective design for Acoustic Soft Tactile (AST) Skin, with the primary goal of significantly enhancing the accuracy of 2-D tactile feature estimation. The existing challenge lies in achieving precise tactile feature estimation, especially concerning contact geometry characteristics, using cost-effective solutions. We hypothesise that by harnessing acoustic energy through dedicated acoustic channels in 2 layers beneath the sensing surface and analysing amplitude modulation, we can effectively decode interactions on the sensory surface, thereby improving tactile feature estimation. Our approach involves the distinct separation of hardware components responsible for emitting and receiving acoustic signals, resulting in a modular and highly customisable skin design. Practical tests demonstrate the effectiveness of this novel design, achieving remarkable precision in estimating contact normal forces (MAE < 0.8 N), 2D contact localisation (MAE < 0.7 mm), and contact surface diameter (MAE < 0.3 mm). In conclusion, the AST skin, with its innovative design and modular architecture, successfully addresses the challenge of tactile feature estimation. The presented results showcase its ability to precisely estimate various tactile features, making it a practical and cost-effective solution for robotic applications.
Neural Sinkhorn Gradient Flow

Wasserstein Gradient Flows (WGF) with respect to specific functionals have been widely used in the machine learning literature. Recently, neural networks have been adopted to approximate certain intractable parts of the underlying Wasserstein gradient flow and result in efficient inference procedures. In this paper, we introduce the Neural Sinkhorn Gradient Flow (NSGF) model, which parametrizes the time-varying velocity field of the Wasserstein gradient flow w.r. t. the Sinkhorn divergence to the target distribution starting a given source distribution. We utilize the velocity field matching training scheme in NSGF, which only requires samples from the source and target distribution to compute an empirical velocity field approximation. Our theoretical analyses show that as the sample size increases to infinity, the mean-field limit of the empirical approximation converges to the true underlying velocity field. To further enhance model efficiency on high-dimensional tasks, a two-phase NSGF++ model is devised, which first follows the Sinkhorn flow to approach the image manifold quickly ($\le 5$ NFEs) and then refines the samples along a simple straight flow. Numerical experiments with synthetic and real-world benchmark datasets support our theoretical results and demonstrate the effectiveness of the proposed methods.
Charged gravastar model in Rastall theory of gravity

Gravastars are considered as one of the prime exotic type compact objects which may be found at the end state of gravitational collapse of massive stars with a view to resolve the complexities that are pertinent in case of a black hole \cite{Mazur}-\cite{Mazur2}. In this paper, we analyse the role of charge on the possible formation of isotropic spherically symmetric gravastar configuration in the framework of Rastall gravity. Gravastar contains three distinct layers {\it viz.} i) Interior region, ii) Thin shell and iii) Exterior region. The interior region is characterised by the equation of state $p=-\rho$ that defines the repulsive outward pressure in radial direction at all points on the thin shell. The thin shell, contains ultra-relativistic stiff fluid which is denoted by the equation of state $p=\rho$ following Zel'dovich's criteria \cite{Zeldovich,Zeldovich1} for cold baryonic universe, can withstand the repulsive pressure exerted by the interior region. The exterior region is the vacuum space-time represented by the Reissner-Nordstr$\ddot{o}$m solution. In view of the above specifications, we construct and analyse a charged gravastar model in Rastall theory of gravity which represents several salient features. The basic physical attributes, {\it viz.} proper length, energy, entropy and equation of state parameter of the shell are investigated. In this model, it is interesting to note that for large value of the radius of hyper-surface (R) the EoS parameter of the thin shell corresponds to dark energy EoS with $\mathcal{W}(R)\rightarrow-1$. However, for small value of $R$ the EoS parameter $\mathcal{W}(R)\rightarrow0$, defines a dust shell. The stability of the model is ensured through the study of gravitational surface redshift and maximisation of shell entropy within the framework of Rastall theory of gravity.
Automatic Tissue Traction with Haptics-Enabled Forceps for Minimally Invasive Surgery

A common limitation of autonomous tissue manipulation in robotic minimally invasive surgery (MIS) is the absence of force sensing and control at the tool level. Recently, our team has developed haptics-enabled forceps that can simultaneously measure the grasping and pulling forces during tissue manipulation. Based on this design, here we further present a method to automate tissue traction with controlled grasping and pulling forces. Specifically, the grasping stage relies on a controlled grasping force, while the pulling stage is under the guidance of a controlled pulling force. Notably, during the pulling process, the simultaneous control of both grasping and pulling forces is also enabled for more precise tissue traction, achieved through force decoupling. The force controller is built upon a static model of tissue manipulation, considering the interaction between the haptics-enabled forceps and soft tissue. The efficacy of this force control approach is validated through a series of experiments comparing targeted, estimated, and actual reference forces. To verify the feasibility of the proposed method in surgical applications, various tissue resections are conducted on ex vivo tissues employing a dual-arm robotic setup. Finally, we discuss the benefits of multi-force control in tissue traction, evidenced through comparative analyses of various ex vivo tissue resections. The results affirm the feasibility of implementing automatic tissue traction using micro-sized forceps with multi-force control, suggesting its potential to promote autonomous MIS. A video demonstrating the experiments can be found at https://youtu.be/8fe8o8IFrjE.
Optimal design of unimorph-type cantilevered piezoelectric energy harvesters using level set-based topology optimization by considering manufacturability

In this study, we proposed a design methodology for a piezoelectric energy-harvesting device optimized for maximal power generation at a designated frequency using topology optimization. The proposed methodology emphasizes the design of a unimorph-type piezoelectric energy harvester, wherein a piezoelectric film is affixed to a singular side of a silicon cantilever beam. Both the substrate and the piezoelectric film components underwent concurrent optimization. Constraints were imposed to ensure that the resultant design is amenable to microfabrication, with specific emphasis on the etchability of piezoelectric energy harvesters. Several numerical examples were provided to validate the efficacy of the proposed method. The results showed that the proposed method derives both the substrate and piezoelectric designs that maximize the electromechanical coupling coefficient and allows the eigenfrequency of the device and minimum output voltage to be set to the desired values. Furthermore, the proposed method can provide solutions that satisfy the cross-sectional shape, substrate-depend, and minimum output voltage constraints. The solutions obtained by the proposed method are manufacturable in the field of microfabrication.
MTRGL: Effective Temporal Correlation Discerning through Multi-modal Temporal Relational Graph Learning

In this study, we explore the synergy of deep learning and financial market applications, focusing on pair trading. This market-neutral strategy is integral to quantitative finance and is apt for advanced deep-learning techniques. A pivotal challenge in pair trading is discerning temporal correlations among entities, necessitating the integration of diverse data modalities. Addressing this, we introduce a novel framework, Multi-modal Temporal Relation Graph Learning (MTRGL). MTRGL combines time series data and discrete features into a temporal graph and employs a memory-based temporal graph neural network. This approach reframes temporal correlation identification as a temporal graph link prediction task, which has shown empirical success. Our experiments on real-world datasets confirm the superior performance of MTRGL, emphasizing its promise in refining automated pair trading strategies.
Traces of vanishing H\"older spaces

For an arbitrary subset $E\subset\mathbb{R}^n$ and a modulus of continuity $\omega$, we define the subspaces of vanishing jets $\dot{\operatorname{VJ}}^{m,\omega}_{\Gamma}(E)$ of the jet spaces $\dot{\operatorname{J}}^{m,\omega}(E),$ for the vanishing scales $\Gamma\in \{\operatorname{small},\operatorname{large},\operatorname{far}\},$ and up to order $m\in \mathbb{N} \cup \lbrace 0 \rbrace. $ We show that each $\Gamma$-vanishing jet of order $m$ is obtained by restricting a \emph{globally} defined function whose $m$th derivative is in the $\Gamma$-vanishing H\"older space. This amounts to proving that the linear Whitney extension operator preserves separately each of the vanishing scales from $E$ to the whole ambient space $\mathbb{R}^n. $ Further results will soon appear in a second version of this manuscript.
BayesPrompt: Prompting Large-Scale Pre-Trained Language Models on Few-shot Inference via Debiased Domain Abstraction

As a novel and effective fine-tuning paradigm based on large-scale pre-trained language models (PLMs), prompt-tuning aims to reduce the gap between downstream tasks and pre-training objectives. While prompt-tuning has yielded continuous advancements in various tasks, such an approach still remains a persistent defect: prompt-tuning methods fail to generalize to specific few-shot patterns. From the perspective of distribution analyses, we disclose that the intrinsic issues behind the phenomenon are the over-multitudinous conceptual knowledge contained in PLMs and the abridged knowledge for target downstream domains, which jointly result in that PLMs mis-locate the knowledge distributions corresponding to the target domains in the universal knowledge embedding space. To this end, we intuitively explore to approximate the unabridged target domains of downstream tasks in a debiased manner, and then abstract such domains to generate discriminative prompts, thereby providing the de-ambiguous guidance for PLMs. Guided by such an intuition, we propose a simple yet effective approach, namely BayesPrompt, to learn prompts that contain the domain discriminative information against the interference from domain-irrelevant knowledge. BayesPrompt primitively leverages known distributions to approximate the debiased factual distributions of target domains and further uniformly samples certain representative features from the approximated distributions to generate the ultimate prompts for PLMs. We provide theoretical insights with the connection to domain adaptation. Empirically, our method achieves state-of-the-art performance on benchmarks.
Agriculturally developed areas reduce genetic connectivity for a keystone neotropical ungulate

Modified landscapes can restrict the movement of organisms, leading to isolation and reduced population viability, particularly for species with extensive home ranges and long-distance travel, such as white-lipped peccaries (WLPs, Tayassu pecari). Previous studies have indicated that forested areas favor WLP herd movements, but the impact of the non-forested areas on their genetic connectivity is unknown. In this study, we used land cover, the Brazilian Roads Map, and population genetic data to investigate the impact of non-forested matrices on WLP’s genetic connectivity in the Pantanal floodplain and surrounding Cerrado plateau of central-west Brazil. We compared isolation-by-distance (IBD), isolation-by-barrier, and isolation-by-resistance models and tested 39 hypotheses within a modeling framework. Finally, we identified the optimal areas for ecological corridors based on the most effective landscape model. Barrier and landscape resistance were more strongly correlated with genetic relatedness than the IBD model. The model that received the most robust support considered only forest as habitat. All other classes formed a matrix that impeded gene flow, including agriculture, grassland, savannah, and paved and unpaved roads. WLP herds living in landscapes with reduced forest cover are more vulnerable to the effects of genetic isolation. To maintain gene flow, it is essential to establish connections between habitats throughout the landscape. Conservation programs should prioritize strategies that strengthen connections between habitats, including facilitating wildlife road-crossing structures and creating/restoring ecological corridors to link isolated habitat fragments.
Bioengineering a plant NLR immune receptor with a robust binding interface towards a conserved fungal pathogen effector

Bioengineering of plant immune receptors has emerged as a key strategy for generating novel disease resistance traits to counteract the expanding threat of plant pathogens to global food security. However, current approaches are limited by rapid evolution of plant pathogens in the field and may lack durability when deployed. Here, we show that the rice nucleotide-binding, leucine-rich repeat (NLR) immune receptor Pik-1 can be engineered to respond to a conserved family of effectors from the multihost blast fungus pathogen Magnaporthe oryzae. We switched the effector binding and response profile of the Pik NLR from its cognate rice blast effector AVR-Pik to the host-determining factor Pwl2 by installing a putative host target, OsHIPP43, in place of the native integrated HMA domain (generating Pikm-1OsHIPP43). This chimeric receptor also responded to other PWL alleles from diverse blast isolates. The crystal structure of the Pwl2/OsHIPP43 complex revealed a multifaceted, robust interface that cannot be easily disrupted by mutagenesis, and may therefore provide durable, broad resistance to blast isolates carrying PWL effectors in the field. Our findings highlight how the host targets of pathogen effectors can be used to bioengineer new recognition specificities that have more robust properties compared to naturally evolved disease resistance genes.
Conditional knockout of Shank3 in the ventral CA1 by quantitative in vivo genome-editing impairs social memory

Individuals with autism spectrum disorder (ASD) have a higher prevalence of social memory impairment. A series of our previous studies revealed that hippocampal ventral CA1 (vCA1) neurons possess social memory engram and that the neurophysiological representation of social memory in the vCA1 neurons is disrupted in ASD-associated Shank3 knockout mice. However, whether the dysfunction of Shank3 in vCA1 causes the social memory impairment observed in ASD remains unclear. In this study, we found that vCA1-specific Shank3 conditional knockout (cKO) by the adeno-associated virus (AAV)- or specialized extracellular vesicle (EV)-mediated in vivo gene editing was sufficient to recapitulate the social memory impairment in male mice. Furthermore, the utilization of EV-mediated Shank3-cKO allowed us to quantitatively examine the role of Shank3 in social memory. Our results suggested that there is a certain threshold for the proportion of Shank3-cKO neurons required for social memory disruption. Thus, our study provides insight into the population coding of social memory in vCA1, as well as the pathological mechanisms underlying social memory impairment in ASD.
Burkholderia cenocepacia epigenetic regulator M.BceJIV simultaneously engages two DNA recognition sequences for methylation

Burkholderia cenocepacia is an opportunistic and infective bacterium containing an orphan DNA methyltransferase (M.BceJIV) with roles in regulating gene expression and motility of the bacterium. M.BceJIV recognizes a GTWWAC motif (where W can be an adenine or a thymine) and methylates the N6 of the adenine at the fifth base position (GTWWAC). Here, we present a high-resolution crystal structure of M.BceJIV/DNA/sinefungin ternary complex and allied biochemical, computational, and thermodynamic analyses. Remarkably, the structure shows not one, but two DNA substrates bound to the M.BceJIV dimer, wherein each monomer contributes to the recognition of two recognition sequences. This unexpected mode of DNA binding and methylation has not been observed previously and sets a new precedent for a DNA methyltransferase. We also show that methylation at two recognition sequences occurs independently, and that GTWWAC motifs are enriched in intergenic regions of a strain of B. cenocepacia’s genome. We further computationally assess the interactions underlying the affinities of different ligands (SAM, SAH, and sinefungin) for M.BceJIV, as a step towards developing selective inhibitors for limiting B. cenocepacia infection.
Embedding the Connection Calculus in Satisfiability Modulo Theories

We investigate embedding a broad class of deduction systems in satisﬁability solvers such as Z3. One such deduction system is the connection calculus. Using Z3’s support for user-propagation, proofs in a user-speciﬁed calculus can be found automatically via Z3’s internal satisﬁability procedures. The approach places few constraints on the deduction system, yet allows for domain-speciﬁc optimisations if known. We discuss ramiﬁcations for proof search in the connection calculus.
Introduction to in silico model for proarrhythmic risk assessment under the CiPA initiative

In 2005, the International Council for Harmonization (ICH) established cardiotoxicity assessment guidelines to identify the risk of Torsade de Pointes (TdP). It is focused on the blockade of the human ether-à-go-go-related gene (hERG) channel known to cause QT/QTc prolongation and the QT/QTc prolongation shown on the electrocardiogram. However, these biomarkers are not the direct risks of TdP with low specificity as the action potential is influenced by multiple channels along with the hERG channel. Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative emerged to address limitations of the current model. The objective of CiPA is to develop a standardized in silico model of a human ventricular cell to quantitively evaluate the cardiac response for the cardiac toxicity risk and to come up with a metric for the TdP risk assessment. In silico working group under CiPA developed a standardized and reliable in silico model and a metric that can quantitatively evaluate cellular cardiac electrophysiologic activity. The implementation mainly consists of hERG fitting, Hill fitting, and action potential simulation. In this review, we explained how the in silico model of CiPA works, and briefly summarized current overall CiPA studies. We hope this review helps clinical pharmacologists to understand the underlying estimation process of CiPA in silico modeling.
An adaptive unimodal subclass decomposition (AUSD) learning system for land use and land cover classification using high-resolution remote sensing

Land use and land cover classification is an important application of remote-sensing images. The performances of most classification models are largely limited by the incompleteness of the calibration set and the complexity of spectral features. It is difficult for models to realize continuous learning when the study area is transferred or enlarged. This paper proposed an adaptive unimodal subclass decomposition (AUSD) learning system, which comprises two-level iterative learning controls: The inner loop separates each class into several unimodal Gaussian subclasses; the outer loop utilizes transfer learning to extend the model to adapt to supplementary calibration set collected from enlarged study areas. The proposed model can be efficiently adjusted according to the variability of spectral signatures caused by the increasingly high-resolution imagery. The classification result can be obtained using the Gaussian mixture model by Bayesian decision theory. This AUSD learning system was validated using simulated data with the Gaussian distribution and multi-area SPOT-5 high-resolution images with 2.5-m resolution. The experimental results on numerical data demonstrated the ability of continuous learning. The proposed method achieved an overall accuracy of over 90% in all the experiments, validating the effectiveness as well as its superiority over several widely used classification methods.