Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Single-System Illusion in Software-Defined Vehicles -- Automated, AI-Powered Workflow

We propose a novel model- and feature-based approach to development of vehicle software systems, where the end architecture is not explicitly defined. Instead, it emerges from an iterative process of search and optimization given certain constraints, requirements and hardware architecture, while retaining the property of single-system illusion, where applications run in a logically uniform environment. One of the key points of the presented approach is the inclusion of modern generative AI, specifically Large Language Models (LLMs), in the loop. With the recent advances in the field, we expect that the LLMs will be able to assist in processing of requirements, generation of formal system models, as well as generation of software deployment specification and test code. The resulting pipeline is automated to a large extent, with feedback being generated at each step.
An Optimal PID Control Algorithm for Training Feedforward Neural Networks

The training problem of feedforward neural networks (FNNs) is formulated into a proportional integral and derivative (PID) control problem of a linear discrete dynamic system in terms of the estimation error. The robust control approach greatly facilitates the analysis and design of robust learning algorithms for multiple-input-multiple-output (MIMO) FNNs using robust control methods. The drawbacks of some existing learning algorithms can therefore be revealed clearly, and an optimal robust PID-learning algorithm is developed. The optimal learning parameters can be found by utilizing linear matrix inequality optimization techniques. Theoretical analysis and examples including function approximation, system identification, exclusive-or (XOR) and encoder problems are provided to illustrate the results.
Conditional Tuning Network for Few-Shot Adaptation of Segmentation Anything Model

The recent Segment Anything Model (SAM) has demonstrated remarkable zero-shot capability and flexible geometric prompting in general image segmentation. However, SAM often struggles when handling various unconventional images, such as aerial, medical, and non-RGB images. This paper presents CAT-SAM, a ConditionAl Tuning network that adapts SAM toward various unconventional target tasks with just few-shot target samples. CAT-SAM freezes the entire SAM and adapts its mask decoder and image encoder simultaneously with a small number of learnable parameters. The core design is a prompt bridge structure that enables decoder-conditioned joint tuning of the heavyweight image encoder and the lightweight mask decoder. The bridging maps the prompt token of the mask decoder to the image encoder, fostering synergic adaptation of the encoder and the decoder with mutual benefits. We develop two representative tuning strategies for the image encoder which leads to two CAT-SAM variants: one injecting learnable prompt tokens in the input space and the other inserting lightweight adapter networks. Extensive experiments over 11 unconventional tasks show that both CAT-SAM variants achieve superior target segmentation performance consistently even under the very challenging one-shot adaptation setup. Project page: https://xiaoaoran.github. io/projects/CAT-SAM
Inkjet Printing—Process and Its Applications

In this Progress Report we provide an update on recent developments in inkjet printing technology and its applications, which include organic thin‐film transistors, light‐emitting diodes, solar cells, conductive structures, memory devices, sensors, and biological/pharmaceutical tasks. Various classes of materials and device types are in turn examined and an opinion is offered about the nature of the progress that has been achieved.
Ion Trap as a Frequency Standard Measurement of Ba+ HFS Frequency Fluctuations

The 8-GHz "field independent" F = 1, mF = 0 to F = 2, mF = 0 ground level Zeeman transition frequency in 137Ba+ ions has been compared with a commercial Cs beam standard. About 10+ ions were stored in a RF quadrupole trap for several hours and cooled by He buffer gas. Hyperfine pumping was achieved by a pulsed tunable dye laser and the transition between the Zeeman sublevels was induced by a microwave signal locked to the primary frequency standard. The transition was observed via changes in the ionic fluorescence intensity. The observed frequency fluctuations ¿v/v of < 2 × 10-12 over a 5-h measurement period are compatible with the specified fluctuations of the reference. Possible sources of frequency shifts were. analyzed and found to be controllable within the precision set by the atomic frequency reference.
deepGOLSA: Goal-directed planning with subgoal reduction models human brain activity

Goal-directed planning presents a challenge for classical Reinforcement Learning (RL) algorithms due to the vastness of combinatorial state and goal spaces. Humans and animals adapt to complex environments especially with diverse, non-stationary objectives, often employing intermediate goals for long-horizon tasks. Here we propose a novel method for effectively deriving subgoals from arbitrary and distant original goals, called the deep Goal Oriented Learning and Selection of Action, or deepGOLSA model. Using a loop-removal technique, the method distills high-quality subgoals from a replay buffer, all without the need of prior environmental knowledge. This generalizable and scalable solution applies across different domains. Simulations show that the model can be integrated into existing RL frameworks like Deep Q Networks and Soft Actor-Critic models. DeepGOLSA accelerates performance in both discrete and continuous tasks, such as grid world navigation and robotic arm manipulation, relative to existing RL models. Moreover, the subgoal reduction mechanism, even without iterative training, outperforms its integrated deep RL counterparts when solving a navigation task. The goal reduction mechanism also models human problem-solving. Comparing the model’s performance and activation with human behavior and fMRI data in a treasure hunting task, we found matching representational patterns between specific deepGOLSA model components and corresponding human brain areas, particularly the vmPFC and basal ganglia. The results suggest a new computational framework for examining goal-directed behaviors in humans.
Structure and dynamics of the autoantigen GAD65 in complex with the human autoimmune polyendocrine syndrome type 2-associated autoantibody b96.11

The enzyme glutamate decarboxylase (GAD) produces the neurotransmitter GABA, using pyridoxal-5’-phosphate. GAD exists as two isoforms, GAD65 and GAD67. Only GAD65 acts as a major autoantigen, with its autoantibodies frequently found in type 1 diabetes and other autoimmune diseases. Here we characterize the structure and dynamics of GAD65 and its interaction with the autoimmune polyendocrine syndrome type 2-associated autoantibody b96.11. Combining hydrogen-deuterium exchange mass spectrometry (HDX), X-ray crystallography, cryo-electron microscopy and computational approaches, we dissect the conformational dynamics of the inactive apo- and the active holo-forms of GAD65, as well as the structure of the GAD65-autoantibody complex. HDX reveals the time-resolved, local dynamics that accompany autoinactivation, with the catalytic loop playing a key role in promoting collective dynamics at the interface between CTD and PLP domains. In the GAD65-b96.11 complex, heavy chain CDRs dominate the interaction, with the relatively long CDRH3 at the interface centre and uniquely bridging the GAD65 dimer via extensive electrostatic interactions with the 260PEVKEK265 motif. The autoantibody bridges structural elements on GAD65 that contribute to conformational change in GAD65, thus connecting the unique and intrinsic conformational flexibility that governs the autoinactivation mechanism of the enzyme to its autoantigenicity. The intrinsic dynamics, rather than sequence differences within epitopes, appear to be responsible for the contrasting autoantigenicities of GAD65 and GAD67. Our data thus reveal insights into the structural and dynamic differences between GAD65 and GAD67 that dictate their contrasting autoantibody reactivities, provide a new structural rationalisation for the nature of the autoimmune response to GAD65, and may have broader implications for antigenicity in general.
Gallium Nitride as an Electromechanical Material

Gallium nitride (GaN) is a wide bandgap semiconductor material and is the most popular material after silicon in the semiconductor industry. The prime movers behind this trend are LEDs, microwave, and more recently, power electronics. New areas of research also include spintronics and nanoribbon transistors, which leverage some of the unique properties of GaN. GaN has electron mobility comparable with silicon, but with a bandgap that is three times larger, making it an excellent candidate for high-power applications and high-temperature operation. The ability to form thin-AlGaN/GaN heterostructures, which exhibit the 2-D electron gas phenomenon leads to high-electron mobility transistors, which exhibit high Johnson's figure of merit. Another interesting direction for GaN research, which is largely unexplored, is GaN-based micromechanical devices or GaN microelectromechanical systems (MEMS). To fully unlock the potential of GaN and realize new advanced all-GaN integrated circuits, it is essential to cointegrate passive devices (such as resonators and filters), sensors (such as temperature and gas sensors), and other more than Moore functional devices with GaN active electronics. Therefore, there is a growing interest in the use of GaN as a mechanical material. This paper reviews the electromechanical, thermal, acoustic, and piezoelectric properties of GaN, and describes the working principle of some of the reported high-performance GaN-based microelectromechanical components. It also provides an outlook for possible research directions in GaN MEMS.
Direct and Indirect Costs of Dinitrogen Fixation in Crocosphaera watsonii WH8501 and Possible Implications for the Nitrogen Cycle

The recent detection of heterotrophic nitrogen (N2) fixation in deep waters of the southern Californian and Peruvian OMZ questions our current understanding of marine N2 fixation as a process confined to oligotrophic surface waters of the oceans. In experiments with Crocosphaera watsonii WH8501, a marine unicellular diazotrophic (N2 fixing) cyanobacterium, we demonstrated that the presence of high nitrate concentrations (up to 800 μM) had no inhibitory effect on growth and N2 fixation over a period of 2 weeks. In contrast, the environmental oxygen concentration significantly influenced rates of N2 fixation and respiration, as well as carbon and nitrogen cellular content of C. watsonii over a 24-h period. Cells grown under lowered oxygen atmosphere (5%) had a higher nitrogenase activity and respired less carbon during the dark cycle than under normal oxygen atmosphere (20%). Respiratory oxygen drawdown during the dark period could be fully explained (104%) by energetic needs due to basal metabolism and N2 fixation at low oxygen, while at normal oxygen these two processes could only account for 40% of the measured respiration rate. Our results revealed that under normal oxygen concentration most of the energetic costs during N2 fixation (∼60%) are not derived from the process of N2 fixation per se but rather from the indirect costs incurred for the removal of intracellular oxygen or by the reversal of oxidative damage (e.g., nitrogenase de novo synthesis). Theoretical calculations suggest a slight energetic advantage of N2 fixation relative to assimilatory nitrate uptake, when oxygen supply is in balance with the oxygen requirement for cellular respiration (i.e., energy generation for basal metabolism and N2 fixation). Taken together our results imply the existence of a niche for diazotrophic organisms inside oxygen minimum zones, which are predicted to further expand in the future ocean.
Post-transcriptional RNA stabilization of telomere-proximal RNAs FRG2, DBET, D4Z4 at human 4q35 in response to genotoxic stress and D4Z4 macrosatellite repeat length

Background Reduced copy number of the D4Z4 macrosatellite at human chromosome 4q35 is associated with facioscapulohumeral muscular dystrophy (FSHD). A pervasive idea is that chromatin alterations at the 4q35 locus following D4Z4 repeat unit deletion lead to disease via inappropriate expression of nearby genes. Here, we sought to analyze transcription and chromatin characteristics across 4q35 and how these are affected by D4Z4 deletions and exogenous stresses. Results We found that the 4q subtelomere is subdivided into discrete domains, each with characteristic chromatin features associated with distinct gene expression profiles. Centromere-proximal genes within 4q35 (ANT1, FAT1 and FRG1) display active histone marks at their promoters. In contrast, poised or repressed markings are present at telomere-proximal loci including FRG2, DBE-T and D4Z4. We discovered that these discrete domains undergo region-specific chromatin changes upon treatment with chromatin enzyme inhibitors or genotoxic drugs. We demonstrated that the 4q35 telomere-proximal FRG2, DBE-T and D4Z4-derived transcripts are induced upon DNA damage to levels inversely correlated with the D4Z4 repeat number, are stabilized through post-transcriptional mechanisms upon DNA damage, and are bound to chromatin. Conclusion Our study reveals unforeseen biochemical features of RNAs from clustered transcription units within the 4q35 subtelomere. Specifically, the FRG2, DBE-T and D4Z4-derived transcripts are chromatin-associated and are stabilized post-transcriptionally after induction by genotoxic stress. Remarkably, the extent of this response is modulated by the copy number of the D4Z4 repeats, raising new hypotheses about their regulation and function in human biology and disease.
Multiplexed DNA-PAINT Imaging of the Heterogeneity of Late Endosome/Lysosome Protein Composition

Late endosomes/lysosomes (LELs) are crucial for numerous physiological processes and their dysfunction is linked to many diseases. Proteomic analyses have identified hundreds of LEL proteins, however, whether these proteins are uniformly present on each LEL, or if there are cell-type dependent LEL sub-populations with unique protein compositions is unclear. We employed a quantitative, multiplexed DNA-PAINT super-resolution approach to examine the distribution of six key LEL proteins (LAMP1, LAMP2, CD63, TMEM192, NPC1 and LAMTOR4) on individual LELs. While LAMP1 and LAMP2 were abundant across LELs, marking a common population, most analyzed proteins were associated with specific LEL subpopulations. Our multiplexed imaging approach identified up to eight different LEL subpopulations based on their unique membrane protein composition. Additionally, our analysis of the spatial relationships between these subpopulations and mitochondria revealed a cell-type specific tendency for NPC1-positive LELs to be closely positioned to mitochondria. Our approach will be broadly applicable to determining organelle heterogeneity with single organelle resolution in many biological contexts. Summary This study develops a multiplexed and quantitative DNA-PAINT super-resolution imaging pipeline to investigate the distribution of late endosomal/lysosomal (LEL) proteins across individual LELs, revealing cell-type specific LEL sub-populations with unique protein compositions, offering insights into organelle heterogeneity at single-organelle resolution.
Mycobacterium dormancy and antibiotic tolerance within the retinal pigment epithelium of ocular tuberculosis

Tuberculosis (TB) is a leading cause of death among infectious diseases worldwide due to latent TB infection, which is the critical step for the successful pathogenic cycle. In this stage, Mycobacterium tuberculosis resides inside the host in a dormant and antibiotic-tolerant state. Latent TB infection can lead to a multisystemic diseases because M. tuberculosis invades virtually all organs, including ocular tissues. Ocular tuberculosis (OTB) occurs when the dormant bacilli within ocular tissues reactivate, originally seeded by hematogenous spread from pulmonary TB. Timely and accurate diagnosis as well as efficient chemotherapies are crucial in preventing poor visual outcomes of OTB patients. Histological evidence suggests that retinal pigment epithelium (RPE) cells play a central role in immune privilege and in the protection from the antibiotic effects, making them an anatomical niche for invading M. tuberculosis. RPE cells exhibit high tolerance to environmental redox stresses, allowing phagocytosed M. tuberculosis bacilli to maintain viability in a dormant state. However, the microbiological and metabolic mechanisms determining the interaction between the RPE intracellular environment and phagocytosed M. tuberculosis are largely unknown. Here, liquid chromatography mass spectrometry (LC-MS) metabolomics was used to illuminate the metabolic state within RPE cells reprogrammed to harbor dormant M. tuberculosis bacilli and enhance the antibiotic tolerance. The results have led to propose a novel therapeutic option to synthetically kill the dormant M. tuberculosis inside the RPE cells by modulating the phenotypic state of M. tuberculosis, thus laying the foundation for a new, innovative regimen for treating OTB. Importance Understanding the metabolic environment within the retinal pigment epithelium (RPE) cells altered by infection with M. tuberculosis and mycobacterial dormancy is crucial to identify new therapeutic methods to cure OTB. The present study showed that RPE cellular metabolism is altered to foster intracellular M. tuberculosis to enter into the dormant and drug tolerant state, thereby blunting the efficacy of anti-TB chemotherapy. RPE cells serve as an anatomical niche as the cells protect invading bacilli from antibiotic treatment. LC-MS metabolomics of RPE cells after co-treatment with H2O2 and M. tuberculosis infection showed that intracellular environment within RPE cells is enriched with greater level of oxidative stress. The antibiotic tolerance of intracellular M. tuberculosis within RPE cells can be restored by a metabolic manipulation strategy such as co-treatment of antibiotic with the most downstream glycolysis metabolite, phosphoenolpyruvate.
Traitement coopératif des requêtes RDF dans le contexte des bases de connaissances incertaines

RÉSUMÉ. De nombreuses larges bases de connaissances (BC) incertaines sont disponibles sur le Web où les faits sont associés avec un degré de conﬁance α . En général, Les utilisateurs de ces BC n’ayant qu’une connaissance partielle de leur contenu, certaines de leurs requêtes échouent, c’est à dire qu’elles ne renvoient aucun résultat. Pour éviter cette situation frustrante, et au lieu de renvoyer un ensemble vide de réponses, nous avons proposé une approche expliquant l’échec (pour un degré α et pour plusieurs degrés) en fournissant un ensemble de α Minimal Failing Subqueries ( α MFS), et en calculant des requêtes alternatives, appelées α MaXimal Succeeding Subqueries ( α XSS), qui sont aussi proches que possible de la requête initiale. Les expérimenta-tions menées sur le benchmark WatDiv montrent l’intérêt de nos approches par rapport à une méthode de référence. ABSTRACT. Several large uncertain Knowledge Bases (KBs) are available on the Web where facts are associated with a certainty degree α . Usually, users have only a partial knowledge of the KBs contents, their queries may be failing i.e., they return no result for the desired certainty. To prevent this frustrating situation, instead of returning an empty set of answers, our approach explains the reasons of the failure (for a single degree α and for several degrees) with a set of α Minimal Failing Subqueries ( α MFSs), and computes alternative relaxed queries, called α MaXimal Succeeding Subqueries ( α XSSs), that are as close as possible to the initial failing query. The conducted experiments on the WatDiv benchmark show the relevance of our approaches compared to a baseline method. MOTS-CLÉS : BC incertaines, Requêtes SPARQL, Réponse vide.
Towards automatic clustering of protein sequences

Analyzing protein sequence data becomes increasingly important recently. Most previous work on this area has mainly focused on building classification models. In this paper we investigate in the problem of automatic clustering of unlabeled protein sequences. As a widely recognized technique in statistics and computer science, clustering has been proven very useful in detecting unknown object categories and revealing hidden correlations among objects. One difficulty, that prevents clustering from being performed directly on protein sequence is the lack of an effective similarity measure that can be computed efficiently. Therefore, we propose a novel model for protein sequence cluster by exploring significant statistical properties possessed by the sequences. The concept of imprecise probabilities are introduced to the original probabilistic suffix tree to monitor the convergence of the empirical measurement and to guide the clustering process. It is demonstrated that the proposed method can successfully discover meaningful families without the necessity of learning models of different families from pre-labeled "training data".
A Graph Matching Algorithm and Its Application to Conceptual System Translation

ABSURDIST II, an extension to ABSURDIST, is an algorithm using attributed graph matching to find translations between conceptual systems. It uses information about the internal structure of systems by itself, or in combination with external information about concept similarities across systems. It supports systems with multiple types of weighted or unweighted, directed or undirected relations between concepts. The algorithm exploits graph sparsity to improve computational efficiency. We present the results of experiments with a number of conceptual systems, including artificially constructed random graphs with introduced distortions.
Influence of the solvent on the stability of bis(terpyridine) structures on graphite

The effect of solvation on the adsorption of organic molecules on graphite at room temperature has been addressed with force-field molecular dynamics simulations. As a model system, the solvation of a bis(terpyridine) isomer in water and 1,2,4-trichlorobenzene was studied with an explicit solvation model. The inclusion of solvation has a noticeable effect on adsorption energies. Although the results of the various considered force fields differ quite significantly, they all agree that the adsorption of BTP from the TCB solvent is almost thermoneutral. The substrate simply acts as a template to allow a planar arrangement of the network, which is stabilized by the intermolecular interaction. Using an atomic thermodynamics approach, the order of the stability of various network structures as a function of the chemical potential is derived yielding a sequence in agreement with the experiment.
An Empirical Comparative Study on Two Large-Scale Hierarchical Text Classification Approaches

s. The entire corpus is publicly available for research purpose. The class label taxonomy here is IPC (International Patent Classification) scheme. Being a complicated hierarchical classification system, IPC categories all the technological fields into 8 sections, 120 classes, 630 subclasses and approximately 69000 groups. Figure 1 illustrates the IPC taxonomy. It is to be noticed that in this IPC hierarchical taxonomy structure, samples are only located at leaf nodes. Note that 115 patent documents without class labels are removed from the original 3496252 patent documents in our experiments. In t. J. C om p. P ro c. L an g. 2 01 1. 23 :3 09 -3 25 . D ow nl oa de d fr om w w w . w or ld sc ie nt if ic . c om by T H E U N IV E R SI T Y O F H O N G K O N G o n 05 /0 8/ 15 . F or p er so na l u se o nl y. January 29, 2013 15:23 WSPC/162-IJCPOL S1793840611002383 An Empirical Comparative Study on Two LSHTC Approaches 317