Miracle-dz/UniFAR-FTUs huggingface dataset:
Curiosity-Driven Learning of Joint Locomotion and Manipulation Tasks

: Learning complex locomotion and manipulation tasks presents significant challenges, often requiring extensive engineering of, e.g., reward functions or curricula to provide meaningful feedback to the Reinforcement Learning (RL) algorithm. This paper proposes an intrinsically motivated RL approach to reduce task-specific engineering. The desired task is encoded in a single sparse reward, i.e., a reward of “+1” is given if the task is achieved. Intrinsic motivation enables learning by guiding exploration toward the sparse reward signal. Specifically, we adapt the idea of Random Network Distillation (RND) to the robotics domain to learn holistic motion control policies involving simultaneous locomotion and manipulation. We investigate opening doors as an exemplary task for robotic applications. A second task involving package manipulation from a table to a bin highlights the generalization capabilities of the presented approach. Finally, the resulting RL policies are executed in real-world experiments on a wheeled-legged robot in biped mode. We experienced no failure in our experiments, which consisted of opening push doors (over 15 times in a row) and manipulating packages (over 5 times in a row).
DEFT: Dexterous Fine-Tuning for Hand Policies

: Dexterity is often seen as a cornerstone of complex manipulation. Hu-mans are able to perform a host of skills with their hands, from making food to operating tools. In this paper, we investigate these challenges, especially in the case of soft, deformable objects as well as complex, relatively long-horizon tasks. However, learning such behaviors from scratch can be data inefficient. To circumvent this, we propose a novel approach, DEFT ( DE xterous F ine-T uning for Hand Policies), that leverages human-driven priors, which are executed directly in the real world. In order to improve upon these priors, DEFT involves an efficient online optimization procedure. With the integration of human-based learning and online fine-tuning, coupled with a soft robotic hand, DEFT demonstrates success across various tasks, establishing a robust, data-efficient pathway toward general dexterous manipulation. Please see our website at https://dexterous-finetuning.github. io for video results.
Equivariant Motion Manifold Primitives

: Existing movement primitive models for the most part focus on representing and generating a single trajectory for a given task, limiting their adaptability to situations in which unforeseen obstacles or new constraints may arise. In this work we propose Motion Manifold Primitives (MMP), a movement primitive paradigm that encodes and generates, for a given task, a continuous manifold of trajectories each of which can achieve the given task. To address the challenge of learning each motion manifold from a limited amount of data, we exploit inherent symmetries in the robot task by constructing motion manifold primitives that are equivariant with respect to given symmetry groups. Under the assumption that each of the MMPs can be smoothly deformed into each other, an autoencoder framework is developed to encode the MMPs and also generate solution trajectories. Experiments involving synthetic and real-robot examples demonstrate that our method outperforms existing manifold primitive methods by significant margins. Code is available at https://github.com/dlsfldl/EMMP-public .
Intelligent Ofﬂoading in Blockchain-Based Mobile Crowdsensing Using Deep Reinforcement Learning

—Mobile Crowdsensing (MCS) utilizes sensing data collected from users’ mobile devices (MDs) to provide high-quality and personalized services, such as trafﬁc monitoring, weather prediction, and service recommendation. In return, users who participate in crowdsensing (i.e., MCS participants) get payment from cloud service providers (CSPs) according to the quality of their shared data. Therefore, it is vital to guarantee the security of payment transactions between MCS participants and CSPs. As a distributed ledger, the blockchain technology is effective in providing secure transactions among users without a trusted third party, which has found many promising applications such as virtual currency and smart contract. In a blockchain, the proof-of-work (PoW) executed by users plays an essential role in solving consensus issues. However, the complexity of PoW severely obstructs the application of blockchain in MCS due to the limited computational capacity of MDs. To solve this issue, we propose a new framework based on Deep Reinforcement Learning (DRL) for ofﬂoading computation-intensive tasks of PoW to edge servers in a blockchain-based MCS system. The proposed framework can be used to obtain the optimal ofﬂoading policy for PoW tasks under the complex and dynamic MCS environment. Simulation results demonstrate that our method can achieve a lower weighted cost of latency and power consumption compared to benchmark methods.
Memory-Based Sequential Attention

Computational models of sequential attention often use recurrent neural networks, which may lead to information loss over accumulated glimpses and an inability to dynamically reweigh glimpses at each step. Addressing the former limitation should result in greater performance, while addressing the latter should enable greater interpretability. In this work, we propose a biologically-inspired model of sequential attention for image classification. Specifically, our algorithm contextualizes the history of observed locations from within an image to inform future gaze points, akin to scanpaths in the biological visual system. We achieve this by using a transformer-based memory module coupled with a reinforcement learning-based learning algorithm, improving both task performance and model interpretability. In addition to empirically evaluating our approach on classical vision tasks, we demonstrate the robustness of our algorithm to different initial locations in the image and provide interpretations of sampled locations from within the trajectory.
Selective adsorption of tetracycline by β -CD- immobilizing sodium alginate aerogel coupled with ultra�ltration for reclaimed water

β -CD-immobilizing sodium alginate aerogel ( β -CD/NaAlg) was fabricated using β -cyclodextrin ( β -CD) as a modi�cation reagent to eliminate tetracycline antibiotics from wastewater. β -CD/NaAlg aerogel with the β -CD/NaAlg molar ratio of 9 achieved the maximum removal rate of more than 70% for tetracycline in 8 h, 1.5 g/L of adsorbent dosage, and pH of 4. Tetracycline adsorption on β -CD/NaAlg aerogel was aligned with the Freundlich and pseudo-second-order model, showing that this process was mostly controlled by chemisorption. According to XPS analyses and DFT simulations, the tetracycline adsorption mechanism via β -CD/NaAlg aerogel is ascribed to electrostatic attraction through carboxyl groups and amido groups, hydrogen bonds connecting hydroxyl groups, as well as van der Waals forces. Besides, the as-prepared aerogel had a good reusability with the removal e�ciencies of 55.3% for tetracycline even after repeating �ve times. Additionally, the couple of β -CD/NaAlg aerogel greatly improved selective separation performance for tetracycline antibiotics of the ultra�ltration membrane. In general, the synthesized β - CD/NaAlg aerogel is constructive for strategies to develop novel green, metal-free and stable adsorbent materials.
The Discovery of Giant Magnetoresistance

____________________________________________________________________________________________________ The Royal Swedish Academy of Sciences, Information Department, Box 50005, SE-104 05 Stockholm, Sweden Phone: +46 8 673 95 00, Fax: +46 8 15 56 70, E-mail: info@kva.se, Website: www.kva.se Scientific Background on the Nobel Prize in Physics 2007 The Discovery of Giant Magnetoresistance compiled by the Class for Physics of the Royal Swedish Academy of Sciences
Genome-Wide Identi�cation and Characterization of Lipases from Ascomycetes, and Molecular Docking Analysis with Various Plastics

The widespread utilization and inadequate management of plastic products have contributed to a signi�cant increase of microplastics (MPs) in natural environments. MPs persist in the environment and are resistant to degradation. However, certain microorganisms possess the ability to degrade them. This study focuses on the in silico identi�cation and molecular docking analysis of different lipases found in various fungal species, speci�cally aiming to assess their catalytic potential for microplastic degradation. In addition to observing enzyme-substrate interactions at the active site, hydrophobic interactions, highest binding a�nity and hydrogen bonds were also examined. A total of 71 lipases were identi�ed from 13 fungal species on the basis of presence of the lipase 3 domain. Most of the proteins were predicted to be extracellularly localized. Based on the results of molecular dockings, in terms of binding a�nities, polycarbonate (PC) was found to have the highest binding a�nities with all the docked proteins which suggests that it is the most biodegradable plastic type. However, polyvinyl chloride (PVC) exhibited low binding energies with all the lipases indicating its resistance against degradation via fungal lipases. Key amino acids involved in binding interactions of PC were found to be glycine, alanine and valine. The binding interactions encompass hydrogen bonding, Van der Waals forces and Pi-interactions. These �ndings highlighted the potential of enzymes sourced from fungal species for microplastic degradation purposes. The role of lipase in the germination of A. oryzae was also predicted under soy sauce koji fermentation. It was found that 4 proteins were upregulated whereas 4 proteins were downregulated.
PREVALENCE OF CARDIAC DISEASES IN SMALL ANIMALS: A FIVE-YEAR SINGLE-CENTRE RETROSPECTIVE STUDY PREVALENȚA PATOLOGIILOR CARDIACE LA ANIMALELE DE COMPANIE: UN STUDIU RETROSPECTIV, UNICENTRIC, PE

%), pleural effusion (3.6%), and aortic thromboembolism (2.2%). To the best of the author's knowledge, this is the largest epidemiological retrospective study on cardiac diseases in Romania. This study describes the epidemio - logical data of the most common cardiac diseases in dogs and cats from a single veterinary cardiology centre large database, over 5 years
Optimizing Network Latency: Unveiling the Impact of Reflection Server Tuning

This study investigates the dynamics of network latency optimizations, with a focus on the role of reflection server tuning. In an era marked by the demand for precise and low-latency network measurements, our exploration unveils the interplay of diverse parameters in achieving optimal performance. Notably, the implementation of a tuned profile on Linux emerges as a standout strategy, showcasing significant rewards in network efficiency. We highlight the importance of early acceptance of latency-critical traffic in the firewall chain and emphasize the cumulative impact of various optimizations. These findings have practical implications for network administrators and system architects, providing valuable insights for the deployment of efficient and low-latency network infrastructures, essential in the landscape of emerging technologies such as 5G networks and edge computing solutions.
Genetic modiﬁers of menopausal hormone replacement therapy and breast cancer risk: a genome–wide interaction study

Women using menopausal hormone therapy (MHT) are at increased risk of developing breast cancer (BC). To detect genetic modiﬁers of the association between current use of MHT and BC risk, we conducted a meta-analysis of four genome-wide case-only studies followed by replication in 11 case–control studies. We used a case-only design to assess interactions between single-nucleotide polymorphisms (SNPs) and current MHT use on risk of overall and lobular BC. The discovery stage included 2920 cases (541 lobular) from four genome-wide association studies. The top 1391 SNPs showing P values for interaction ( P int ) ! 3.0 ! 10 K 3 were selected for replication using pooled case–control data from 11 studies of the Breast Cancer Association Consortium, including 7689 cases (676 lobular) and 9266 controls. Fixed-effects meta-analysis was used to derive combined P int . No SNP reached genome-wide signiﬁcance in either the discovery or combined stage. We observed effect modiﬁcation of current MHT use on overall BC risk by two SNPs on chr13 near POMP (combined P int % 8.9 ! 10 K 6 ), two SNPs in SLC25A21 (combined P int % 4.8 ! 10 K 5 ), and three SNPs in PLCG2 (combined P int % 4.5 ! 10 K 5 ). The association between lobular BC risk was potentially modiﬁed by one SNP in TMEFF2 (combined P int % 2.7 ! 10 K 5 ), one SNP in CD80 (combined P int % 8.2 ! 10 K 6 ), three SNPs on chr17 near TMEM132E (combined P int % 2.2 ! 10 K 6 ), and two SNPs on chr18 near SLC25A52 (combined P int % 4.6 ! 10 K 5 ). In conclusion, polymorphisms in genes related to solute transportation in mitochondria, transmembrane signaling, and immune cell activation are potentially modifying BC risk associated with current use of MHT. These ﬁndings warrant replication in independent studies.
Digital Commons @ University of Digital Commons @ University of South Florida South Florida

Age-related hearing loss (ARHL) -presbycusis - is the most prevalent neurodegenerative disease and number one communication disorder of our aged population; and affects hundreds of millions of people worldwide. Its prevalence is close to that of cardiovascular disease and arthritis, and can be a precursor to dementia. The auditory perceptual dysfunction is well understood, but knowledge of the biological bases of ARHL is still somewhat lacking. Surprisingly, there are no FDA-approved drugs for treatment. Based on our previous studies of human subjects, where we discovered relations between serum aldosterone levels and the severity of ARHL, we treated middle age mice with aldosterone, which normally declines with age in all mammals. We found that hearing thresholds and suprathreshold responses significantly improved in the aldosterone-treated mice compared to the non-treatment group. In terms of cellular and molecular mechanisms underlying this therapeutic effect, additional experiments revealed that spiral ganglion cell survival was significantly improved, mineralocorticoid receptors were upregulated via post-translational protein modifications, and age-related intrinsic and extrinsic apoptotic pathways were blocked by the aldosterone therapy. Taken together, these novel findings pave the way for translational drug development towards the first medication to prevent the progression of ARHL.
Communications of the Association for Information Systems Communications of the Association for Information Systems

Focus groups to investigate new ideas are widely used in many research fields. The use of focus groups in design research poses interesting opportunities and challenges. Traditional focus group methods must be adapted to meet two specific goals of design research. For the refinement of an artifact design, exploratory focus groups (EFGs) study the artifact to propose improvements in the design. The cycle of build and evaluate using EFGs continues until the artifact is released for field test in the application environment. Then, the field test of the design artifact may employ confirmatory focus groups (CFGs) to establish the utility of the artifact in field use. Rigorous investigation of the artifact requires multiple CFGs to be run with opportunities for quantitative and qualitative data collection and analyses across the multiple CFGs. In this paper, we discuss the adaptation of focus groups to design research projects. We demonstrate the use of both EFGs and CFGs in a design research project in the health care field.
Need for Speed: Leveraging the Power of Functional Encryption for Resource-Constrained Devices

: Functional Encryption (FE) is a cutting-edge cryptographic technique that enables a user with a specific functional decryption key to determine a certain function of encrypted data without gaining access to the underlying data. Given its potential and the fact that FE is still a relatively new field, we set out to investigate how it could be applied to resource-constrained environments. This work presents what we believe to be the first lightweight FE scheme explicitly designed for resource-constrained devices. We also propose a use case protocol that demonstrates how our scheme can secure an Internet of Things (IoT) architecture where relevant devices collect data and securely deliver them to a storage server, where an analyst can request access to the encrypted data. Finally, we conduct thorough experiments on two commercially available resource-constrained devices to provide compelling evidence of our approach’s practicality and efficiency. Although the results of our evaluations show that there is room for improvement in the proposed scheme, this work represents one of the first attempts to apply FE to the IoT setting that can directly impact people’s daily lives and the everyday operations of organizations.
Exoplanets and Astrotheology

The discovery of exoplanets is a small part of the array of scientiﬁc arguments for and against the existence of extraterrestrial intelligence. Yet the recent stunning achievement of this program of observational astronomy has had a signiﬁcant effect on scientiﬁc opinion and public interest. It also raises some key theological questions. New observing techniques are leading to the discovery of extrasolar planets daily. Earth-like planets outside of our Solar System can now be identiﬁed and in future years explored for signs of life. This article maps the history of these discoveries and highlights some of the the-ological issues which are important to bring into dialogue with these scientiﬁc insights.
Methodological Insights: Impression management: Developing and illustrating a scheme of analysis for narrative disclosures – a methodological note

Purpose - This paper aims to develop a holistic measure for analysing impression management and for detecting bias introduced into corporate narratives as a result of impression management. Design/methodology/approach - Prior research on the seven impression management methods in the literature is summarised. Four of the less-researched methods are described in detail, and are illustrated with examples from UK annual results' press releases (ARPRs). A method of computing a holistic composite impression management score based on these four impression management methods is developed, based on both quantitative and qualitative data in corporate narrative disclosures. An impression management bias score is devised to capture the extent to which impression management introduces bias into corporate narratives. An example of the application of the composite impression management score and impression management bias score methodology is provided. Findings - While not amounting to systematic evidence, the 21 illustrative examples suggest that impression management is pervasive in corporate financial communications using multiple impression management methods, such that positive information is exaggerated, while negative information is either ignored or is underplayed. Originality/value - Four impression management methods are described in detail, illustrated by 21 examples. These four methods are examined together. New impression management methods are studied in this paper for the first time. This paper extends prior impression management measures in two ways. First, a composite impression management score based on four impression management techniques is articulated. Second, the composite impression management score methodology is extended to capture a measure for bias, in the form of an impression management bias score. This is the first time outside the USA that narrative disclosures in press releases have been studied.
Deep Learning-Based Estimation of Blood Pressure and Anxiety Using PPG and ECG

—In the preoperative period, patients often experience heightened anxiety and stress, which can manifest hypertension or tachycardia, potentially leading to adverse clinical outcomes. Therefore, it is crucial to detect patients in an anxious state to implement effective hypertension management and improve long-term outcomes. In this study, we extract blood pressure and heart rate variability features from both photoplethysmography (PPG) and electrocardiogram (ECG) signals using wavelet transform. Subsequently, we employ deep learning-based models for the classification of hypertension.
Jawthenticate : Microphone-free Speech-based Authentication using Jaw Motion and Facial Vibrations

In this paper, we present Jawthenticate , an earable system that authenticates a user using audible or inaudible speech without us-ing a microphone. This system can overcome the shortcomings of traditional voice-based authentication systems like unreliability in noisy conditions and spoofing using microphone-based replay attacks. Jawthenticate derives distinctive speech-related features from the jaw motion and associated facial vibrations. This combination of features makes Jawthenticate resilient to vocal imitations as well as camera-based spoofing. We use these features to train a two-class SVM classifier for each user. Our system is invariant to the content and language of speech. In a study conducted with 41 subjects, who speak different native languages, Jawthenticate achieves a Balanced Accuracy (BAC) of 97.07%, True Positive Rate (TPR) of 97.75%, and True Negative Rate (TNR) of 96.4% with just 3 seconds of speech data.
Analysis of Kerr frequency combs in micro-resonators with two-photon absorption and free-carriers

We study optical Kerr frequency combs in nonlinear microresonators in the presence of two-photon absorption (TPA) and free-carrier absorption (FCA) described by an extended Lugiato-Lefever system of equations. We give a criterion for modulation instability of constant stationary solutions and establish existence of non-constant solutions. Both results are accompanied by numerical simulations, which show that the abstract conditions both for modulation instability and existence are satisfied in large parts of the parameter regions.
The first

In 2003, Atkin and Garvan initiated the study of rank and crank moments for ordinary partitions. These moments satisfy a strict inequality. We prove that a strict inequality also holds for the ﬁrst rank and crank moments of overpartitions and consider a new combinatorial interpretation in this setting.