Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonparametric Decentralized Detection and Sparse Sensor Selection via Multi-Sensor Online Kernel Scalar Quantization

Signal classification problems arise in a wide variety of applications, and their demand is only expected to grow. In this paper, we focus on the wireless sensor network signal classification setting, where each sensor forwards quantized signals to a fusion center to be classified. Our primary goal is to train a decision function and quantizers across the sensors to maximize the classification performance in an online manner. Moreover, we are interested in sparse sensor selection using a marginalized weighted kernel approach to improve network resource efficiency by disabling less reliable sensors with minimal effect on classification performance. To achieve our goals, we develop a multi-sensor online kernel scalar quantization (MSOKSQ) learning strategy that operates on the sensor outputs at the fusion center. Our theoretical analysis reveals how the proposed algorithm affects the quantizers across the sensors. Additionally, we provide a convergence analysis of our online learning approach by studying its relationship to batch learning. We conduct numerical studies under different classification and sensor network settings which demonstrate the accuracy gains from optimizing different components of MSOKSQ and robustness to reduction in the number of sensors selected.
A Model-Driven-Engineering Approach for Detecting Privilege Escalation in IoT Systems

Software vulnerabilities in access control models can represent a serious threat in a system. In fact, OWASP lists broken access control as number 5 in severity among the top 10 vulnerabilities. In this paper, we study the permission model of an emerging Smart-Home platform, SmartThings, and explore an approach that detects privilege escalation in its permission model. Our approach is based on Model Driven Engineering (MDE) in addition to static analysis. This approach allows for better coverage of privilege escalation detection than static analysis alone, and takes advantage of analyzing free-form text that carries extra permissions details. Our experimental results demonstrate a very high accuracy for detecting over-privilege vulnerabilities in IoT applications
Characterising the use of varenicline: an analysis of the Australian dispensing claims data

Abstract Background and aims In Australia, patterns of use of smoking cessation medications and factors associated with their dispensing are currently not known. This study aimed to measure the demographic and clinical factors associated with varenicline dispensing compared with nicotine replacement therapy (NRT) and bupropion among first‐time users of Pharmaceutical Benefits Scheme (PBS) subsidised smoking cessation medicines in Australia and to characterise those who discontinued varenicline treatment prematurely. Design Retrospective, population‐based study. Logistic regression was used to identify factors associated with varenicline dispensing compared with NRT and bupropion. Sensitivity analyses estimated the proportion of individuals who completed the recommended 12 weeks of varenicline treatment. Setting and participants First‐time users of PBS subsidised smoking cessation medicines in Australia. Individuals first dispensed a smoking cessation medicine between 2011 and 2019 were identified from a 10% random sample of the national dispensing claims data. Measurements The outcome for the regression analysis was the dispensing of varenicline compared with NRT and bupropion. The dispensing of a smoking cessation medicine was identified using the World Health Organization Anatomical Therapeutic Chemical Classification System and PBS item codes. Independent variables included demographic and clinical characteristics such as sex, age, concessional status, year of treatment initiation and comorbidities identified using the Rx‐Risk index. The proportion of people who discontinued varenicline treatment after the initiation pack was determined using prescription refill data. Findings A total of 94 532 people had their first PBS subsidised smoking cessation medicine. Of these, 62 367 (66.0%) were dispensed varenicline, 29 949 (31.7%) NRT and 2216 (2.3%) bupropion. The odds of varenicline dispensing were higher in males (OR, 1.18; 95% CI, 1.14–1.21), but lower in older adults (0.86 [0.82–0.90] in above 30 years to 0.49 [0.47–0.52] in 61 years and above), among concession beneficiaries (0.44; 0.43–0.46), and those with congestive heart failure (0.60; 0.53–0.68), depression (0.61; 0.54–0.69), anxiety (0.70; 0.66–0.73), psychotic illness (0.39; 0.37–0.42), and chronic obstructive pulmonary disease (0.87; 0.82–0.92). The majority (37 670; 60.4%) of those dispensed varenicline discontinued treatment after the initiation pack. Anxiety and psychotic illnesses were significantly more prevalent in those who discontinued treatment. Only 2804 (4.5%) of those dispensed varenicline completed 12 weeks of treatment. Conclusion Individuals dispensed varenicline in Australia appear to be healthier compared with those who are dispensed nicotine replacement therapy or bupropion.
StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models

Knowledge and language understanding of models evaluated through question answering (QA) has been usually studied on static snapshots of knowledge, like Wikipedia. However, our world is dynamic, evolves over time, and our models' knowledge becomes outdated. To study how semi-parametric QA models and their underlying parametric language models (LMs) adapt to evolving knowledge, we construct a new large-scale dataset, StreamingQA, with human written and generated questions asked on a given date, to be answered from 14 years of time-stamped news articles. We evaluate our models quarterly as they read new articles not seen in pre-training. We show that parametric models can be updated without full retraining, while avoiding catastrophic forgetting. For semi-parametric models, adding new articles into the search space allows for rapid adaptation, however, models with an outdated underlying LM under-perform those with a retrained LM. For questions about higher-frequency named entities, parametric updates are particularly beneficial. In our dynamic world, the StreamingQA dataset enables a more realistic evaluation of QA models, and our experiments highlight several promising directions for future research.
Effects of connection to nature on residents’ perceptions of conservation policy justice of Natura 2000

We examined the effects of human connection to nature on residents’ concerns about justice in conservation policies of Natura 2000. Expansion of Natura 2000 conservation network has resulted in local communities having to consider Natura 2000 in their development plans, and justice concerns have been strong in some communities near Natura 2000 sites. We conceptualized Natura 2000 justice within a framework composed of 3 domains of conservation justice: distribution, recognition, and representation. To examine the effect of nature connection on perceived justice of Natura 2000, we conducted a door‐to‐door survey of rural resident (80.09% response rate) in 3 municipalities of Pomerania in Poland. The effect of connection to nature on perceived distribution of Natura 2000 benefits was positive (b = 0.187, t = 7.057, p < 0.001); perceived communication about Natura 2000 was positive (b = 0.089, t = 2.940 p < 0.01); perception of limitations was positive (b = 0.078, t = 2.416, p < 0.01); perceived recognition was positive (b = 0.117, t = 3.367, p < 0.001); and perceived representation was positive (b = 0.123, t = 5.015, p < 0.001). Our results suggest local residents’ bonds with nature matter and they should be considered when new conservation approaches, such as Natura 2000, are introduced.
Spin network entanglement: coarse-graining

In loop quantum gravity, partitioning graph introduces boundaries and entanglement between spin sub-networks, reflecting non-local degrees of freedom and correlation amongst spatial regions. This gives rise to the view of coarse-graining, reducing the degrees of freedom that are unnecessary to be considered. The present work sets coarse-graining in the framework of bulk-boundary relation. We investigates the spin network entanglement, showing that the entanglement is invariant under the coarse-graining at kinematical level. Moreover, we build the transformation between holonomy operators based on finer graph and coarser graph, and reveal the preservation of the coarse-graining method under the evolution generated by the holonomy operator. This leads to a holographical perspective for the entanglement issue in loop quantum gravity.
Termination of superradiance from a binary companion

We study the impact of a binary companion on black hole superradiance at orbital frequencies away from the gravitational-collider-physics (GCP) resonance bands. A superradiant state can couple to a strongly absorptive state via the tidal perturbation of the companion, thereby acquiring a suppressed superradiance rate. Below a critical binary separation, this superradiance rate becomes negative, and the boson cloud gets absorbed by the black hole. This critical binary separation leads to tight constraints on GCP. Especially, a companion with mass ratio q > 10 − 3 invalidates all GCP ﬁne structure transitions, as well as almost all Bohr transitions except those from the | ψ 211 (cid:105) state. Meanwhile, the backreaction on the companion manifests itself as a torque acting on the binary, producing ﬂoating/sinking orbits that can be veriﬁed via pulsar timing. In addition, the possible termination of cloud growth may help to alleviate the current bounds on the ultralight boson mass from various null detections.
Species- and strain-level assessment using rrn long-amplicons suggests donor’s influence on gut microbial transference via fecal transplants in metabolic syndrome subjects

Fecal microbiota transplantation (FMT) is currently used for treating Clostridium difficile infection and explored for other clinical applications in experimental trials. However, the effectiveness of this therapy could vary, and partly depend on the donor’s bacterial species engraftment, whose evaluation is challenging because there are no cost-effective strategies for accurately tracking the microbe transference. In this regard, the precise identification of bacterial species inhabiting the human gut is essential to define their role in human health unambiguously. We used Nanopore-based device to sequence bacterial rrn operons (16S-ITS-23S) and to reveal species-level abundance changes in the human gut microbiota of a FMT trial. By assessing the donor and recipient microbiota before and after FMT, we further evaluated whether this molecular approach reveals strain-level genetic variation to demonstrate microbe transfer and engraftment. Strict control over sequencing data quality and major microbiota covariates was critical for accurately estimating the changes in gut microbial species abundance in the recipients after FMT. We detected strain-level variation via single-nucleotide variants (SNVs) at rrn regions in a species-specific manner. We showed that it was possible to explore successfully the donor-bacterial strain (e.g., Parabacteroides merdae) engraftment in recipients of the FMT by assessing the nucleotide frequencies at rrn-associated SNVs. Our findings indicate that the engraftment of donors’ microbiota is to some extent correlated with the improvement of metabolic health in recipients and that parameters such as the baseline gut microbiota configuration, sex, and age of donors should be considered to ensure the success of FMT in humans. Trial registration The study was prospectively registered at the Dutch Trial registry - NTR4488 (https://www.trialregister.nl/trial/4488).
Distributed Energy Resources Cybersecurity Outlook: Vulnerabilities, Attacks, Impacts, and Mitigations

The digitization and decentralization of the electric power grid are key thrusts for an economically and environmentally sustainable future. Toward this goal, distributed energy resources (DER), including rooftop solar panels, battery storage, electric vehicles, etc., are becoming ubiquitous in power systems. Power utilities benefit from DERs as they minimize operational costs; at the same time, DERs grant users and aggregators control over the power they produce and consume. DERs are interconnected, interoperable, and support remotely controllable features; thus, their cybersecurity is of cardinal importance. DER communication dependencies and the diversity of DER architectures widen the threat surface and aggravate the cybersecurity posture of power systems. In this work, we focus on security oversights that reside in the cyber and physical layers of DERs and can jeopardize grid operations. The existing works have underlined the impact of cyberattacks targeting DER assets; however, they either focus on specific system components (e.g., communication protocols), do not consider the mission-critical objectives of DERs, or neglect the adversarial perspective (e.g., adversary/attack models) altogether. To address these omissions, we comprehensively analyze adversarial capabilities and objectives when manipulating DER assets, and then present how protocol and device-level vulnerabilities can materialize into cyberattacks impacting power system operations. Finally, we provide mitigation strategies to thwart adversaries and directions for future DER cybersecurity research.
Navigating the pathway to ceasing driving—A voyage of discovery

Abstract Background Ceasing driving for older people is a life transition and can be associated with a sense of loss and decreased wellbeing. Nurses can play a key role in supporting the older person to manage the transitions, especially as the work involved in ceasing driving is often hidden. For example, finding alternative and accessible means of travel and maintaining mobility. Travel itself can be viewed as essential, for example attending health appointments and shopping. Discretionary travel may not be viewed as essential, but provides an important means of maintaining activity and social connection. Method A case study is presented of Mike's journey in ceasing driving. The concept of transition helps in co‐creating with Mike his journey of discovery. Semi‐structured interviews took place with Mike over a six‐month period and a journey pathway created, which was subject to further iterations during subsequent interviews. Results Ceasing driving involves a number of transitions encompassing a series of losses, managing uncertainty, problem‐solving and learning new skills. Being able to access and adapt to maintain mobility and travel are influenced by structural and environmental barriers. Conclusion The World Health Organization's strategy to create age‐friendly cities and communities is admirable. Like any other strategy this is a long‐term plan, and in meantime action needs to happen to support older people in ceasing driving and creating a non‐driving life and limit a decrease in well‐being. Starting conversations on transitional travel planning is proposed, with nurses playing a central role in making this process happen.
Spectral Neural Operators

A plentitude of applications in scientific computing requires the approximation of mappings between Banach spaces. Recently introduced Fourier Neural Operator (FNO) and Deep Operator Network (DeepONet) can provide this functionality. For both of these neural operators, the input function is sampled on a given grid (uniform for FNO), and the output function is parametrized by a neural network. We argue that this parametrization leads to 1) opaque output that is hard to analyze and 2) systematic bias caused by aliasing errors in the case of FNO. The alternative, advocated in this article, is to use Chebyshev and Fourier series for both domain and codomain. The resulting Spectral Neural Operator (SNO) has transparent output, never suffers from aliasing, and may include many exact (lossless) operations on functions. The functionality is based on well-developed fast, and stable algorithms from spectral methods. The implementation requires only standard numerical linear algebra. Our benchmarks show that for many operators, SNO is superior to FNO and DeepONet.
On the Price of Fairness of Allocating Contiguous Blocks

In this work, we revisit the problem of fairly allocating a number of indivisible items that are located on a line to multiple agents. A feasible allocation requires that the allocated items to each agent are connected on the line. The items can be goods on which agents have non-negative utilities, or chores on which the utilities are non-positive. Our objective is to understand the extent to which welfare is inevitably sacrificed by enforcing the allocations to be fair, i.e., price of fairness (PoF). We study both egalitarian and utilitarian welfare. Previous works by Suksompong [Discret. Appl. Math., 2019] and H\"ohne and van Stee [Inf. Comput. , 2021] have studied PoF regarding the notions of envy-freeness and proportionality. However, these fair allocations barely exist for indivisible items, and thus in this work, we focus on the relaxations of maximin share fairness and proportionality up to one item, which are guaranteed to be satisfiable. For most settings, we give (almost) tight ratios of PoF and all the upper bounds are proved by designing polynomial time algorithms.
Apsidal motion in massive eccentric binaries: The case of CPD-41° 7742, and HD152218 revisited

Context. This paper is part of a study of the apsidal motion in close eccentric massive binary systems, which aims to constrain the internal structure of the stars. We focus on the binary CPD-41 ◦ 7742 and brieﬂy revisit the case of HD152218. Aims. Independent studies of CPD-41 ◦ 7742 in the past showed large discrepancies in the longitude of periastron of the orbit, hinting at the presence of apsidal motion. We here perform a consistent analysis of all observational data, explicitly accounting for the rate of change of the longitude of periastron. Methods. We make use of the extensive set of spectroscopic and photometric observations of CPD-41 ◦ 7742 to infer values for the fundamental parameters of the stars and of the binary. Applying a disentangling method to the spectra allows us to simultaneously derive the radial velocities (RVs) at the times of observation and reconstruct the individual spectra of the stars. The spectra are analysed by means of the CMFGEN model atmosphere code to determine the stellar properties. We determine the apsidal motion rate in two ways: First, we complement our RVs in periastron longitudes pointed out in past studies of this binary system. The age estimates are in good agreement with estimates obtained for other massive binaries in NGC6231. This study conﬁrms the need for enhanced mixing in the stellar evolution models of the most massive stars to reproduce the observational stellar properties; this points towards larger convective cores than usually considered.
Producing Histopathology Phantom Images using Generative Adversarial Networks to improve Tumor Detection

Advance in medical imaging is an important part in deep learning research. One of the goals of computer vision is development of a holistic, comprehensive model which can identify tumors from histology slides obtained via biopsies. A major problem that stands in the way is lack of data for a few cancer-types. In this paper, we ascertain that data augmentation using GANs can be a viable solution to reduce the unevenness in the distribution of diﬀerent cancer types in our dataset. Our demonstration showed that a dataset augmented to a 50 % increase causes an increase in tumor detection from 80 % to 87.5 % .
Transformer in Computer Vision: ViT and its Progress

Transformer, an attention-based encoder-decoder architecture, has not only revolutionized the ﬁeld of natural language processing (NLP), but has also done some pioneering work in the ﬁeld of computer vision (CV). Compared to convolutional neural networks (CNNs), the Vision Trans-former (ViT) relies on excellent modeling capabilities to achieve very good performance on several benchmarks such as ImageNet, COCO
Can spin-lattice dynamics model hysteresis loops?

We use spin-lattice dynamics (SLD) simulations to study the possibility of modeling the low-temperature magnetic hysteresis behavior of a bulk ferromagnetic material. The temporal evolution of the magnetic and mechanical degrees of freedom is obtained through a set of two coupled Langevin equations. Hysteresis loops are calculated for diﬀerent angles between the external ﬁeld and the magnetocrystalline anisotropy axes. The inﬂuence of several relevant parameters is studied, including ﬁeld frequency, magnetic damping, magnetic anisotropy (magnitude and type), magnetic exchange, and system size. The role played by a moving lattice is also discussed. At very low temperatures we ﬁnd that the exchange, and lattice dynamics barely aﬀect the loops, while the ﬁeld frequency and magnetic damping have a large eﬀect on it. The inﬂuence of anisotropy magnitude and symmetry is found to follow the expected behavior. We show that a careful choice of simulations parameters allows for an excellent agreement between SLD and the paradigmatic Stoner-Wohlfarth model, opening the possibility for more accurate inclusion of lattice defects and thermal eﬀects.
On the Cayley-persistence algebra

In this paper, we introduce a persistent (co)homology theory for Cayley digraph grading. We give the algebraic structures of Cayley-persistence object. Specifically, we consider the module structure of persistent (co)homology and show the decomposition of a finitely generated Cayley-persistence module. Moreover, we introduce the persistence-cup product on the Cayley-persistence module and study the twisted structure with respect to the persistence-cup product. As an application on manifolds, we show that the persistent (co)homology is closely related to the persistent map of fundamental classes.
Stop Filtering: Multi-View Attribute-Enhanced Dialogue Learning

There is a growing interest in improving the conversational ability of models by filtering the raw dialogue corpora. Previous filtering strategies usually rely on a scoring method to assess and discard samples from one perspective, enabling the model to enhance the corresponding dialogue attributes (e.g., consistency) more easily. However, the discarded samples may obtain high scores in other perspectives and can provide regularization effects on the model learning, which causes the performance improvement to be sensitive to the filtering ratio. In this work, we propose a multi-view attribute-enhanced dialogue learning framework that strengthens the attribute-related features more robustly and comprehensively. Instead of filtering the raw dataset to train the model, our framework first pre-trains the model on the raw dataset and then fine-tunes it through adapters on the selected sub-sets, which also enhances certain attributes of responses but without suffering from the problems mentioned above. Considering the variety of the dialogue attribute, we further design a multi-view enhancement mechanism, including multi-view selection and inter-view fusion. It groups the high-quality samples from multiple perspectives, respectively, and enhances different attributes of responses with the corresponding sample sets and adapters, keeping knowledge independent and allowing flexible integration. Empirical results and analysis show that our framework can improve the performance significantly in terms of enhancing dialogue attributes and fusing view-specific knowledge.