Miracle-dz/UniFAR-FTUs huggingface dataset:
Closed-Loop Neural Prostheses With On-Chip Intelligence: A Review and a Low-Latency Machine Learning Model for Brain State Detection

The application of closed-loop approaches in systems neuroscience and therapeutic stimulation holds great promise for revolutionizing our understanding of the brain and for developing novel neuromodulation therapies to restore lost functions. Neural prostheses capable of multi-channel neural recording, on-site signal processing, rapid symptom detection, and closed-loop stimulation are critical to enabling such novel treatments. However, the existing closed-loop neuromodulation devices are too simplistic and lack sufficient on-chip processing and intelligence. In this paper, we first discuss both commercial and investigational closed-loop neuromodulation devices for brain disorders. Next, we review state-of-the-art neural prostheses with on-chip machine learning, focusing on application-specific integrated circuits (ASIC). System requirements, performance and hardware comparisons, design trade-offs, and hardware optimization techniques are discussed. To facilitate a fair comparison and guide design choices among various on-chip classifiers, we propose a new energy-area (E-A) efficiency figure of merit that evaluates hardware efficiency and multi-channel scalability. Finally, we present several techniques to improve the key design metrics of tree-based on-chip classifiers, both in the context of ensemble methods and oblique structures. A novel Depth-Variant Tree Ensemble (DVTE) is proposed to reduce processing latency (e.g., by 2.5× on seizure detection task). We further develop a cost-aware learning approach to jointly optimize the power and latency metrics. We show that algorithm-hardware co-design enables the energy- and memory-optimized design of tree-based models, while preserving a high accuracy and low latency. Furthermore, we show that our proposed tree-based models feature a highly interpretable decision process that is essential for safety-critical applications such as closed-loop stimulation.
Robust Convergence of Parareal Algorithms with Arbitrarily High-order Fine Propagators

The aim of this paper is to analyze the robust convergence of a class of parareal algorithms for solving parabolic problems. The coarse propagator is fixed to the backward Euler method and the fine propagator is a high-order single step integrator. Under some conditions on the fine propagator, we show that there exists some critical $J_*$ such that the parareal solver converges linearly with a convergence rate near $0.3$, provided that the ratio between the coarse time step and fine time step named $J$ satisfies $J \ge J_*$. The convergence is robust even if the problem data is nonsmooth and incompatible with boundary conditions. The qualified methods include all absolutely stable single step methods, whose stability function satisfies $|r(-\infty)|<1$, and hence the fine propagator could be arbitrarily high-order. Moreover, we examine some popular high-order single step methods, e.g., two-, three- and four-stage Lobatto IIIC methods, and verify that the corresponding parareal algorithms converge linearly with a factor $0.31$ and the threshold for these cases is $J_* = 2$. Intensive numerical examples are presented to support and complete our theoretical predictions.
Data Generation Method for Learning a Low-dimensional Safe Region in Safe Reinforcement Learning

Safe reinforcement learning aims to learn a control policy while ensuring that neither the system nor the environment gets damaged during the learning process. For implementing safe reinforcement learning on highly nonlinear and high-dimensional dynamical systems, one possible approach is to find a low-dimensional safe region via data-driven feature extraction methods, which provides safety estimates to the learning algorithm. As the reliability of the learned safety estimates is data-dependent, we investigate in this work how different training data will affect the safe reinforcement learning approach. By balancing between the learning performance and the risk of being unsafe, a data generation method that combines two sampling methods is proposed to generate representative training data. The performance of the method is demonstrated with a three-link inverted pendulum example.
Physics-AI symbiosis

The phenomenal success of physics in explaining nature and engineering machines is predicated on low dimensional deterministic models that accurately describe a wide range of natural phenomena. Physics provides computational rules that govern physical systems and the interactions of the constituents therein. Led by deep neural networks, artificial intelligence (AI) has introduced an alternate data-driven computational framework, with astonishing performance in domains that do not lend themselves to deterministic models such as image classification and speech recognition. These gains, however, come at the expense of predictions that are inconsistent with the physical world as well as computational complexity, with the latter placing AI on a collision course with the expected end of the semiconductor scaling known as Moore’s Law. This paper argues how an emerging symbiosis of physics and AI can overcome such formidable challenges, thereby not only extending AI’s spectacular rise but also transforming the direction of engineering and physical science.
Evolution of Neutron Star Magnetic Fields

Neutron stars are natural physical laboratories allowing us to study a plethora of phenomena in extreme conditions. In particular, these compact objects can have very strong magnetic fields with non-trivial origin and evolution. In many respects, its magnetic field determines the appearance of a neutron star. Thus, understanding the field properties is important for the interpretation of observational data. Complementing this, observations of diverse kinds of neutron stars enable us to probe parameters of electro-dynamical processes at scales unavailable in terrestrial laboratories. In this review, we first briefly describe theoretical models of the formation and evolution of the magnetic field of neutron stars, paying special attention to field decay processes. Then, we present important observational results related to the field properties of different types of compact objects: magnetars, cooling neutron stars, radio pulsars, and sources in binary systems. After that, we discuss which observations can shed light on the obscure characteristics of neutron star magnetic fields and their behaviour. We end the review with a subjective list of open problems.
A Survey on Multi-modal Summarization

The new era of technology has brought us to the point where it is convenient for people to share their opinions over an abundance of platforms. These platforms have a provision for the users to express themselves in multiple forms of representations, including text, images, videos, and audio. This, however, makes it difficult for users to obtain all the key information about a topic, making the task of automatic multi-modal summarization (MMS) essential. In this article, we present a comprehensive survey of the existing research in the area of MMS, covering various modalities such as text, image, audio, and video. Apart from highlighting the different evaluation metrics and datasets used for the MMS task, our work also discusses the current challenges and future directions in this field.
Concave Utility Reinforcement Learning with Zero-Constraint Violations

We consider the problem of tabular infinite horizon concave utility reinforcement learning (CURL) with convex constraints. For this, we propose a model-based learning algorithm that also achieves zero constraint violations. Assuming that the concave objective and the convex constraints have a solution interior to the set of feasible occupation measures, we solve a tighter optimization problem to ensure that the constraints are never violated despite the imprecise model knowledge and model stochasticity. We use Bellman error-based analysis for tabular infinite-horizon setups which allows analyzing stochastic policies. Combining the Bellman error-based analysis and tighter optimization equation, for $T$ interactions with the environment, we obtain a high-probability regret guarantee for objective which grows as $\Tilde{O}(1/\sqrt{T})$, excluding other factors. The proposed method can be applied for optimistic algorithms to obtain high-probability regret bounds and also be used for posterior sampling algorithms to obtain a loose Bayesian regret bounds but with significant improvement in computational complexity.
Potential-based Reward Shaping in Sokoban

Learning to solve sparse-reward reinforcement learning problems is difficult, due to the lack of guidance towards the goal. But in some problems, prior knowledge can be used to augment the learning process. Reward shaping is a way to incorporate prior knowledge into the original reward function in order to speed up the learning. While previous work has investigated the use of expert knowledge to generate potential functions, in this work, we study whether we can use a search algorithm(A*) to automatically generate a potential function for reward shaping in Sokoban, a well-known planning task. The results showed that learning with shaped reward function is faster than learning from scratch. Our results indicate that distance functions could be a suitable function for Sokoban. This work demonstrates the possibility of solving multiple instances with the help of reward shaping. The result can be compressed into a single policy, which can be seen as the first phrase towards training a general policy that is able to solve unseen instances.
Physics-based Deep Learning

This digital book contains a practical and comprehensive introduction of everything related to deep learning in the context of physical simulations. As much as possible, all topics come with hands-on code examples in the form of Jupyter notebooks to quickly get started. Beyond standard supervised learning from data, we'll look at physical loss constraints, more tightly coupled learning algorithms with differentiable simulations, as well as reinforcement learning and uncertainty modeling. We live in exciting times: these methods have a huge potential to fundamentally change what computer simulations can achieve.
Long non-coding RNA LINC01296 acts as a migration and invasion promoter in head and neck squamous cell carcinoma and predicts poor prognosis

ABSTRACT Long non-coding RNA (lncRNAs) can participate in gene expression regulation. LINC01296 is abnormally expressed in different tumors and promotes tumorigenesis and development. However, the role of LINC01296 in head and neck squamous cell carcinoma (HNSCC) remains not entirely clear. Thus, to explore LINC01296 expression, biological function and potential mechanism in HNSCC, we used GEPIA and GEO database. QRT-PCR was used to detect the knockout efficiency by LINC01296 inhibition with siRNA. Transwell assay was used to detect the migration and invasion capacity of tumor cells. Then enrichment and immunophenotype correlation analyses were carried out to explore the LINC01296 mechanism in HNSCC. To investigate why LINC01296 was up-regulated in HNSCC, DNA methylation analysis was performed using the DiseaseMeth database. LINC01296 expression was notably up-regulated in HNSCC, which was associated with promoter hypomethylation. Also, it was positively correlated with the HNSCC pathological stage and patients with higher LINC01296 expression levels had a poor prognosis. LINC01296 silencing inhibits HNSCC cell migration and invasion. LINC01296 also participate in the HNSCC progression mainly through protein phosphorylation and microtubule-based process regulation. Overall, LINC01296 was highly expressed in HNSCC, promoted tumor cells’ migration and invasion, and might be a potential diagnostic and prognostic marker in HNSCC patients. Graphical Abstract
A Scalable Last-Mile Delivery Service: From Simulation to Scaled Experiment

In this paper, we investigate the problem of a last-mile delivery service that selects up to $N$ available vehicles to deliver $M$ packages from a centralized depot to $M$ delivery locations. The objective of the last-mile delivery service is to jointly maximize customer satisfaction (minimize delivery time) and minimize operating cost (minimize total travel time) by selecting the optimal number of vehicles to perform the deliveries. We model this as an assignment (vehicles to packages) and path planning (determining the delivery order and route) problem, which is equivalent to the NP-hard multiple traveling salesperson problem. We propose a scalable heuristic algorithm, which sacrifices some optimality to achieve a reasonable computational cost for a high number of packages. The algorithm combines hierarchical clustering with a greedy search. To validate our approach, we compare the results of our simulation to experiments in a 1:25 scale robotic testbed for future mobility systems.
ArtiBoost: Boosting Articulated 3D Hand-Object Pose Estimation via Online Exploration and Synthesis

Estimating the articulated 3D hand-object pose from a single RGB image is a highly ambiguous and challenging problem, requiring large-scale datasets that contain diverse hand poses, object types, and camera viewpoints. Most real-world datasets lack these diversities. In contrast, data synthesis can easily ensure those diversities separately. However, constructing both valid and diverse hand-object interactions and efficiently learning from the vast synthetic data is still challenging. To address the above issues, we propose ArtiBoost, a lightweight online data enhancement method. ArtiBoost can cover diverse hand-object poses and camera viewpoints through sampling in a Composited hand-object Configuration and View-point space (CCV-space) and can adaptively enrich the current hard-discernable items by loss-feedback and sample re-weighting. ArtiBoost alternatively performs data exploration and synthesis within a learning pipeline, and those synthetic data are blended into real-world source data for training. We apply ArtiBoost on a simple learning baseline network and witness the performance boost on several hand-object benchmarks. Our models and code are available at https://github.com/lixiny/ArtiBoost.
Photosensitized Protein Damage by DiethyleneglycoxyP(V)tetrakis(p‐n‐butoxyphenyl)porphyrin Through Electron Transfer: Activity Control Through Self‐aggregation and Dissociation

DiethyleneglycoxyP(V)tetrakis(p‐n‐butoxyphenyl)porphyrin (EGP(V)TBPP) forms a self‐aggregation in an aqueous solution, and the photoexcited state of this molecule was effectively deactivated. Association with human serum albumin (HSA), a water‐soluble protein, causes dissociation of the self‐aggregation, resulting in recovery of the photosensitizer activity of EGP(V)TBPP. Under visible light irradiation, EGP(V)TBPP photosensitized HSA oxidation. The photosensitized singlet oxygen–generating activity of EGP(V)TBPP was confirmed by near‐infrared emission measurement. A singlet oxygen quencher, sodium azide, partially inhibited the HSA photodamage; however, the quenching effect was estimated to be 57%. Another 43% of the HSA photodamage could be explained by the electron transfer mechanism. The redox potential of EGP(V)TBPP and the calculated Gibbs energy of electron transfer from tryptophan to photoexcited EGP(V)TBPP demonstrated the possibility of HSA oxidation through electron extraction. Fluorescence lifetime measurements of EGP(V)TBPP verified the electron transfer from HSA. The photosensitizer activity of EGP(V)TBPP can be controlled through an association with biomolecules, such as protein, and the electron transfer–mediated biomolecule photooxidation plays an important role in photodynamic therapy under hypoxia.
Genetic counseling for congenital heart disease – Practice resource of the National Society of Genetic Counselors

Congenital heart disease (CHD) is an indication which spans multiple specialties across various genetic counseling practices. This practice resource aims to provide guidance on key considerations when approaching counseling for this particular indication while recognizing the rapidly changing landscape of knowledge within this domain. This resource was developed with consensus from a diverse group of certified genetic counselors utilizing literature relevant for CHD genetic counseling practice and is aimed at supporting genetic counselors who encounter this indication in their practice both pre‐ and postnatally.
A mouse model of hypoplastic left heart syndrome demonstrating left heart hypoplasia and retrograde aortic arch flow

ABSTRACT In hypoplastic left heart syndrome (HLHS), the mechanisms leading to left heart hypoplasia and their associated fetal abnormalities are largely unknown. Current animal models have limited utility in resolving these questions as they either do not fully reproduce the cardiac phenotype, do not survive to term and/or have very low disease penetrance. Here, we report the development of a surgically induced mouse model of HLHS that overcomes these limitations. Briefly, we microinjected the fetal left atrium of embryonic day (E)14.5 mice with an embolizing agent under high-frequency ultrasound guidance, which partially blocks blood flow into the left heart and induces hypoplasia. At term (E18.5), all positively embolized mice exhibit retrograde aortic arch flow, non-apex-forming left ventricles and hypoplastic ascending aortas. We thus report the development of the first mouse model of isolated HLHS with a fully penetrant cardiac phenotype and survival to term. Our method allows for the interrogation of previously intractable questions, such as determining the mechanisms of cardiac hypoplasia and fetal abnormalities observed in HLHS, as well as testing of mechanism-based therapies, which are urgently lacking.
Review of consensus test methods in medical imaging and current practices in photoacoustic image quality assessment

Abstract. Significance: Photoacoustic imaging (PAI) is a powerful emerging technology with broad clinical applications, but consensus test methods are needed to standardize performance evaluation and accelerate translation. Aim: To review consensus image quality test methods for mature imaging modalities [ultrasound, magnetic resonance imaging (MRI), x-ray CT, and x-ray mammography], identify best practices in phantom design and testing procedures, and compare against current practices in PAI phantom testing. Approach: We reviewed scientific papers, international standards, clinical accreditation guidelines, and professional society recommendations describing medical image quality test methods. Observations are organized by image quality characteristics (IQCs), including spatial resolution, geometric accuracy, imaging depth, uniformity, sensitivity, low-contrast detectability, and artifacts. Results: Consensus documents typically prescribed phantom geometry and material property requirements, as well as specific data acquisition and analysis protocols to optimize test consistency and reproducibility. While these documents considered a wide array of IQCs, reported PAI phantom testing focused heavily on in-plane resolution, depth of visualization, and sensitivity. Understudied IQCs that merit further consideration include out-of-plane resolution, geometric accuracy, uniformity, low-contrast detectability, and co-registration accuracy. Conclusions: Available medical image quality standards provide a blueprint for establishing consensus best practices for photoacoustic image quality assessment and thus hastening PAI technology advancement, translation, and clinical adoption.
Population Pharmacokinetics and Pharmacodynamics of the Neutralizing Antibodies Bamlanivimab and Etesevimab in Patients With Mild to Moderate COVID‐19 Infection

Bamlanivimab and etesevimab are neutralizing antibodies indicated for treatment of coronavirus disease 2019 (COVID‐19) in patients with early mild or moderate disease. We present the use of pharmacokinetic/pharmacodynamic (PK/PD) modeling that characterizes the timecourse of viral load obtained from 2,970 patients from 2 phase II clinical trials. The model was used for identification of optimal doses that would result in at least 90% of patients achieving serum drug concentrations that result in 90% of maximum drug effect (IC90) for at least 28 days. The serum IC90 (95% confidence interval) was estimated to be 4.2 (3.2–4.3) µg/mL for bamlanivimab and 12.6 (9.7–12.8) µg/mL for etesevimab. Observed clinical trial data confirmed PK and PK/PD model predictions that doses of 700 mg bamlanivimab and 1,400 mg etesevimab would result in maximum reduction in viral load, with no additional effect seen at higher doses. No dose adjustment is recommended as age, sex, race, baseline viral load, and hepatic impairment did not have a significant impact on the PK of the antibodies. Earlier drug administration resulted in greater reductions in viral load, demonstrating the importance of receiving treatment as soon as possible. Relative to placebo, typical reduction in viral load over a 7‐day period was estimated to be 80 or 93% (drug administered 4 days or 1 day after the onset of symptoms, respectively), P < 0.0001. PK/PD modeling and simulation was pivotal throughout the drug development and emergency use authorization process.
Casein kinase 1 epsilon and circadian misalignment impact affective behaviours in mice

Affective behaviours and mental health are profoundly affected by disturbances in circadian rhythms. Casein kinase 1 epsilon (CSNK1E) is a core component of the circadian clock. Mice with tau or null mutation of this gene have shortened and lengthened circadian period respectively. Here, we examined anxiety‐like, fear, and despair behaviours in both male and female mice of these two different mutants. Compared with wild‐type mice, we found reductions in fear and anxiety‐like behaviours in both mutant lines and in both sexes, with the tau mutants exhibiting the greatest phenotypic changes. However, the behavioural despair had distinct phenotypic patterns, with markedly less behavioural despair in female null mutants, but not in tau mutants of either sex. To determine whether abnormal light entrainment of tau mutants to 24‐h light–dark cycles contributes to these phenotypic differences, we also examined these behaviours in tau mutants on a 20‐h light–dark cycle close to their endogenous circadian period. The normalized entrainment restored more wild‐type‐like behaviours for fear and anxiety, but it induced behavioural despair in tau mutant females. These data show that both mutations of Csnk1e broadly affect fear and anxiety‐like behaviours, while the effects on behavioural despair vary with genetics, photoperiod, and sex, suggesting that the mechanisms by which Csnk1e affects fear and anxiety‐like behaviours may be similar, but distinct from those affecting behavioural despair. Our study also provides experimental evidence in support of the hypothesis of beneficial outcomes from properly entrained circadian rhythms in terms of the anxiety‐like and fear behaviours.