Miracle-dz/UniFAR-FTUs huggingface dataset:
That's All Folks: a KG of Values as Commonsense Social Norms and Behaviors

Values, as intended in ethics, determine the shape and validity of moral and social norms, grounding our everyday individual and community behavior on commonsense knowledge. Formalising latent moral content in human interaction is an appealing perspective that would enable a deeper understanding of both social dynamics and individual cognitive and behavioral dimension. To tackle this problem, several theoretical frameworks offer different values models, and organize them into different taxonomies. The problem of the most used theories is that they adopt a cultural-independent perspective while many entities that are considered"values"are grounded in commonsense knowledge and expressed in everyday life interaction. We propose here two ontological modules, FOLK, an ontology for values intended in their broad sense, and That's All Folks, a module for lexical and factual folk value triggers, whose purpose is to complement the main theories, providing a method for identifying the values that are not contemplated by the major value theories, but which nonetheless play a key role in daily human interactions, and shape social structures, cultural biases, and personal beliefs. The resource is tested via performing automatic detection of values from text with a frame-based approach.
DOLOS: A Novel Architecture for Moving Target Defense

Moving Target Defense and Cyber Deception emerged in recent years as two key proactive cyber defense approaches, contrasting with the static nature of the traditional reactive cyber defense. The key insight behind these approaches is to impose an asymmetric disadvantage for the attacker by using deception and randomization techniques to create a dynamic attack surface. Moving Target Defense (MTD) typically relies on system randomization and diversification, while Cyber Deception is based on decoy nodes and fake systems to deceive attackers. However, current Moving Target Defense techniques are complex to manage and can introduce high overheads, while Cyber Deception nodes are easily recognized and avoided by adversaries. This paper presents DOLOS, a novel architecture that unifies Cyber Deception and Moving Target Defense approaches. DOLOS is motivated by the insight that deceptive techniques are much more powerful when integrated into production systems rather than deployed alongside them. DOLOS combines typical Moving Target Defense techniques, such as randomization, diversity, and redundancy, with cyber deception and seamlessly integrates them into production systems through multiple layers of isolation. We extensively evaluate DOLOS against a wide range of attackers, ranging from automated malware to professional penetration testers, and show that DOLOS is effective in slowing down attacks and protecting the integrity of production systems. We also provide valuable insights and considerations for the future development of MTD techniques based on our findings.
Influence of Spattering on In-process Layer Surface Roughness during Laser Powder Bed Fusion

Laser powder bed fusion (LPBF) holds promise to efficiently produce metal parts. However, LPBF incurs stochastic melt pool (MP) spattering, which would roughen workpiece in-process surface, thus weakening inter-layer bonding and causing issues like porosity, powder contamination, and recoater intervention. Understanding the consequential effect of MP spattering on layer surface remains difficult due to the lack of process monitoring capability for concurrently tracking MP spatters and characterizing layer surfaces. In this work, using our lab-designed LPBF-specific fringe projection profilometry and an off-axis camera, we quantify the correlation between MP spattering and in-process layer surface roughness for the first time to reveal the influence of MP spatters on process anomaly and part defects. A method of extracting and registering MP spattering metrics is developed by machine learning of the in-situ imaging data. Each image is analyzed to obtain the MP center location and the spatter count and ejection angle. These MP spatter signatures are registered for each monitored MP across each layer. Regression modeling is used to correlate registered MP spatter signature and its processing parameters with layer surface topography measured by the in-situ FPP. We find that the attained MP spatter feature profile can help predict the layer surface roughness more accurately (>50% less error), in contrast to the conventional approaches that would only use nominal process setting. This is because the spatter information can reflect key process changes including the deviations in actual laser scan parameters and their effects. The results corroborate the importance of spatter monitoring and the distinct influence of spattering on layer surface roughness. Our work paves a foundation to thoroughly elucidate and effectively control the role of MP spattering in defect formation during LPBF.
Revisiting the Kepler problem with linear drag using the blowup method and normal form theory

In this paper, we revisit the Kepler problem with linear drag. With dissipation, the energy and the angular momentum are both decreasing, but in Margheri et al (2017 Celest. Mech. Dyn. Astron. 127 35–48) it was shown that the eccentricity vector has a well-defined limit in the case of linear drag. This limiting eccentricity vector defines a conserved quantity, and in the present paper, we prove that the corresponding invariant sets are smooth manifolds. These results rely on normal form theory and a blowup transformation, which reveals that the invariant manifolds are (nonhyperbolic) stable sets of (limiting) periodic orbits. Moreover, we identify a separate invariant manifold which corresponds to a zero limiting eccentricity vector. This manifold is obtained as a generalized center manifold over the zero eigenspace of a zero-Hopf point. Finally, we present a detailed blowup analysis, which provides a geometric picture of the dynamics. We believe that our approach and results will have general interest in problems with blowup dynamics, including the Kepler problem with generalized nonlinear drag.
On the Capacity Region of Optical Intensity Broadcast Channels

This paper investigates the capacity region of the optical intensity broadcast channels (OI-BCs), where the input is subject to a peak-intensity constraint, an average-intensity constraint, or both. By leveraging the decomposition results of several random variables, i.e., uniform, exponential, and truncated exponential random variables, and adopting a superposition coding (SC) scheme, the inner bound on the capacity region is derived. Then, the outer bound is derived by applying the conditional entropy power inequality (EPI). In the high signal-to-noise ratio (SNR) regime, the inner bound asymptotically matches the outer bound, thus characterizing the high-SNR asymptotic capacity region. The bounds are also extended to the general K-user BCs without loss of high-SNR asymptotic optimality.
REASONER: An Explainable Recommendation Dataset with Multi-aspect Real User Labeled Ground Truths Towards more Measurable Explainable Recommendation

Explainable recommendation has attracted much attention from the industry and academic communities. It has shown great potential for improving the recommendation persuasiveness, informativeness and user satisfaction. Despite a lot of promising explainable recommender models have been proposed in the past few years, the evaluation strategies of these models suffer from several limitations. For example, the explanation ground truths are not labeled by real users, the explanations are mostly evaluated based on only one aspect and the evaluation strategies can be hard to unify. To alleviate the above problems, we propose to build an explainable recommendation dataset with multi-aspect real user labeled ground truths. In specific, we firstly develop a video recommendation platform, where a series of questions around the recommendation explainability are carefully designed. Then, we recruit about 3000 users with different backgrounds to use the system, and collect their behaviors and feedback to our questions. In this paper, we detail the construction process of our dataset and also provide extensive analysis on its characteristics. In addition, we develop a library, where ten well-known explainable recommender models are implemented in a unified framework. Based on this library, we build several benchmarks for different explainable recommendation tasks. At last, we present many new opportunities brought by our dataset, which are expected to shed some new lights to the explainable recommendation field. Our dataset, library and the related documents have been released at https://reasoner2023.github. io/.
On the tightness of graph-based statistics

We establish tightness of graph-based stochastic processes in the space $D[0+\epsilon,1-\epsilon]$ with $\epsilon>0$ that allows for discontinuities of the first kind. The graph-based stochastic processes are based on statistics constructed from similarity graphs. In this setting, the classic characterization of tightness is intractable, making it difficult to obtain convergence of the limiting distributions for graph-based stochastic processes. We take an alternative approach and study the behavior of the higher moments of the graph-based test statistics. We show that, under mild conditions of the graph, tightness of the stochastic process can be established by obtaining upper bounds on the graph-based statistics' higher moments. Explicit analytical expressions for these moments are provided. The results are applicable to generic graphs, including dense graphs where the number of edges can be of higher order than the number of observations.
New Inertial Azimuth Finder Apparatus

Anewdevicefore nding theazimuth ofa rigidbody isdescribed. Thedeviceisbased on thereading ofa rotating, vertically placed accelerometer. The functional relationship between the measured specie c force and the azimuth information is derived for leveled and tilted apparatus when the latter is at rest or when it is in a straight and level motion. A robust algorithm for extracting the azimuth from the accelerometer measurements is presented, an experimental setup is described, and test results are shown and analyzed. It is shown that the new device is accurate, has low sensitivity to common error sources, and yields the azimuth very fast. The device can be used for azimuth e nding in terrestrial missions as well as in orbiting spacecraft.
Nanometer patterning of epitaxial CoSi2/Si(100) for ultrashort channel Schottky barrier metal–oxide–semiconductor field effect transistors

A nanometer patterning method, based on local oxidation of silicide layers, was used to pattern epitaxial CoSi2 layers. A feature size as small as 50 nm was obtained for 20 nm epitaxial CoSi2 layers on Si(100) after patterning by local rapid thermal oxidation in dry oxygen. A Schottky source/drain metal–oxide–semiconductor field effect transistor with epitaxial CoSi2 on p-Si(100) was fabricated using this nanopatterning method to make the 100 nm gate. The device shows good I–V characteristics at 300 K.
DiffQG: Generating Questions to Summarize Factual Changes

Identifying the difference between two versions of the same article is useful to update knowledge bases and to understand how articles evolve. Paired texts occur naturally in diverse situations: reporters write similar news stories and maintainers of authoritative websites must keep their information up to date. We propose representing factual changes between paired documents as question-answer pairs, where the answer to the same question differs between two versions. We find that question-answer pairs can flexibly and concisely capture the updated contents. Provided with paired documents, annotators identify questions that are answered by one passage but answered differently or cannot be answered by the other. We release DiffQG which consists of 759 QA pairs and 1153 examples of paired passages with no factual change. These questions are intended to be both unambiguous and information-seeking and involve complex edits, pushing beyond the capabilities of current question generation and factual change detection systems. Our dataset summarizes the changes between two versions of the document as questions and answers, studying automatic update summarization in a novel way.
Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting

Probabilistic, hierarchically coherent forecasting is a key problem in many practical forecasting applications -- the goal is to obtain coherent probabilistic predictions for a large number of time series arranged in a pre-specified tree hierarchy. In this paper, we present an end-to-end deep probabilistic model for hierarchical forecasting that is motivated by a classical top-down strategy. It jointly learns the distribution of the root time series, and the (dirichlet) proportions according to which each parent time-series is split among its children at any point in time. The resulting forecasts are naturally coherent, and provide probabilistic predictions over all time series in the hierarchy. We experiment on several public datasets and demonstrate significant improvements of up to 26% on most datasets compared to state-of-the-art baselines. Finally, we also provide theoretical justification for the superiority of our top-down approach compared to the more traditional bottom-up modeling.
Canonical Hubble-Tension-Resolving Early Dark Energy Cosmologies Are Inconsistent with the Lyman-α Forest.

Current cosmological data exhibit discordance between indirect and some direct inferences of the present-day expansion rate H_{0}. Early dark energy (EDE), which briefly increases the cosmic expansion rate prior to recombination, is a leading scenario for resolving this "Hubble tension" while preserving a good fit to cosmic microwave background (CMB) data. However, this comes at the cost of changes in parameters that affect structure formation in the late-time universe, including the spectral index of scalar perturbations n_{s}. Here, we present the first constraints on axionlike EDE using data from the Lyman-α forest, i.e., absorption lines imprinted in background quasar spectra by neutral hydrogen gas along the line of sight. We consider two independent measurements of the one-dimensional Lyα forest flux power spectrum from the Sloan Digital Sky Survey (SDSS eBOSS) and from the MIKE/HIRES and X-Shooter spectrographs. We combine these with a baseline dataset comprised of Planck CMB data and baryon acoustic oscillation (BAO) measurements. Combining the eBOSS Lyα data with the CMB and BAO dataset reduces the 95% confidence level (C.L.) upper bound on the maximum fractional contribution of EDE to the cosmic energy budget f_{EDE} from 0.07 to 0.03 and constrains H_{0}=67.9_{-0.4}^{+0.4} km/s/Mpc (68% C.L.), with maximum a posteriori value H_{0}=67.9 km/s/Mpc. Similar results are obtained for the MIKE/HIRES and X-Shooter Lyα data. Our Lyα-based EDE constraints yield H_{0} values that are in >4σ tension with the SH0ES distance-ladder measurement and are driven by the preference of the Lyα forest data for n_{s} values lower than those required by EDE cosmologies that fit Planck CMB data. Taken at face value, the Lyα forest severely constrains canonical EDE models that could resolve the Hubble tension.
Continuous Deep Equilibrium Models: Training Neural ODEs Faster by Integrating Them to Infinity

Implicit models separate the definition of a layer from the description of its solution process. While implicit layers allow features such as depth to adapt automatically to new scenarios and inputs, this adaptivity makes its computational expense challenging to predict. In this manuscript, we increase the “implicitness” of the DEQ by redefining the method in terms of an infinite time neural ODE, which paradoxically decreases the training cost over a standard neural ODE by $2-4\times$. Additionally, we address the question: is there a way to simultaneously achieve the robustness of implicit layers while allowing the reduced computational expense of an explicit layer? To solve this, we develop Skip and Skip Reg. DEQ, an implicit-explicit (IMEX) layer that simultaneously trains an explicit prediction followed by an implicit correction. We show that training this explicit predictor is free and even decreases the training time by $1.11-3.19\times$. Together, this manuscript shows how bridging the dichotomy of implicit and explicit deep learning can combine the advantages of both techniques.
Risk Stratification for Oropharyngeal Squamous Cell Carcinoma Using Texture Analysis on CT – A Step Beyond HPV Status

Background and Purpose: Human papillomavirus-associated oropharyngeal squamous cell carcinoma (OPSCC) is increasingly prevalent. Despite the overall more favorable outcome, the observed heterogeneous treatment response within this patient group highlights the need for additional means to prognosticate and guide clinical decision-making. Promising prediction models using radiomics from primary OPSCC have been derived. However, no model/s using metastatic lymphadenopathy exist to allow prognostication in those instances when the primary tumor is not seen. The aim of our study was to evaluate whether radiomics using metastatic lymphadenopathy allows for the development of a useful risk assessment model comparable to the primary tumor and whether additional knowledge of the HPV status further improves its prognostic efficacy. Materials and Methods: 80 consecutive patients diagnosed with stage III-IV OPSCC between February 2009 and October 2015, known human papillomavirus status, and pre-treatment CT images were retrospectively identified. Manual segmentation of primary tumor and metastatic lymphadenopathy was performed and the extracted texture features were used to develop multivariate assessment models to prognosticate treatment response. Results: Texture analysis of either the primary or metastatic lymphadenopathy from pre-treatment enhanced CT images can be used to develop models for the stratification of treatment outcomes in OPSCC patients. AUCs range from . 78 to . 85 for the various OPSCC groups tested, indicating high predictive capability of the models. Conclusions: This preliminary study can form the basis multi-centre trial that may help optimize treatment and improve quality of life in patients with OPSCC in the era of personalized medicine. Visual Abstract
cis-Golgi phosphate transporters harboring an EXS domain are essential for plant growth and development

Abstract Cell wall synthesis and protein glycosylation require the import of nucleotide diphosphate–sugar conjugates into the Golgi that must be counterbalanced by phosphate (Pi) export. Numerous Golgi nucleotide-sugar transporters have been characterized, but transporters mediating Golgi Pi export remain poorly understood. We used plant and yeast genetics to characterize the role of 2 Arabidopsis (Arabidopsis thaliana) proteins possessing an EXS domain, namely ERD1A and ERD1B, in Golgi Pi homeostasis. ERD1A and ERD1B localized in cis-Golgi and were broadly expressed in vegetative and reproductive tissues. We identified ERD1 putative orthologs in algae, bryophytes, and vascular plants. Expressing ERD1A and ERD1B in yeast complemented the erd1 mutant phenotype of cellular Pi loss via exocytosis associated with reduced Golgi Pi export. The Arabidopsis erd1a mutant had a similar phenotype of apoplastic Pi loss dependent on exocytosis. ERD1A overexpression in Nicotiana benthamiana and Arabidopsis led to partial mislocalization of ERD1A to the plasma membrane and specific Pi export to the apoplastic space. Arabidopsis erd1a had defects in cell wall biosynthesis, which were associated with reduced shoot development, hypocotyl growth, cell wall extensibility, root elongation, pollen germination, pollen tube elongation, and fertility. We identified ERD1 proteins as Golgi Pi exporters that are essential for optimal plant growth and fertility.
Geometric Tuning of Single‐Atom FeN4 Sites via Edge‐Generation Enhances Multi‐Enzymatic Properties

Single‐atom nanozymes (SAzymes) are considered promising alternatives to natural enzymes. The catalytic performance of SAzymes featuring homogeneous, well‐defined active structures can be enhanced through elucidating structure‐activity relationship and tailoring physicochemical properties. However, manipulating enzymatic properties through structural variation is an underdeveloped approach. Herein, the synthesis of edge‐rich Fe single‐atom nanozymes (FeNC‐edge) via an H2O2‐mediated edge generation is reported. By controlling the number of edge sites, the peroxidase (POD)‐ and oxidase (OXD)‐like performance is significantly enhanced. The activity enhancement results from the presence of abundant edges, which provide new anchoring sites to mononuclear Fe. Experimental results combined with density functional theory (DFT) calculations reveal that FeN4 moieties in the edge sites display high electron density of Fe atoms and open N atoms. Finally, it is demonstrated that FeNC‐edge nanozyme effectively inhibits tumor growth both in vitro and in vivo, suggesting that edge‐tailoring is an efficient strategy for developing artificial enzymes as novel catalytic therapeutics.
Understanding the relationship between income and mental health among 16- to 24-year-olds: Analysis of 10 waves (2009–2020) of Understanding Society to enable modelling of income interventions

A substantial body of evidence suggests that young people, including those at the crucial transition points between 16 and 24, now face severe mental health challenges. In this article, we analyse data from 10 waves of a major UK longitudinal household cohort study, Understanding Society, to examine the relationship between income and anxiety and depression among 16- to 24-year-olds. Using random effects logistic regression (Model 1) allowing for whether the individual was depressed in the previous period as well as sex, age, ethnicity, whether the individual was born in the UK, region, rurality, highest qualification, marital status, employment status and attrition, we find a significant and inversely monotonic adjusted association between average net equivalised household income quintiles and clinical threshold levels of depressive symptoms SF-12 Mental Component Summary (MCS score ≤45.6). This means that being in a higher income group is associated with a reduced likelihood of clinically significant depressive symptoms, allowing for observable confounding variables. Using a ‘within-between’ model (Model 2), we find that apart from among those with the very highest incomes, increases in average net equivalised household income over the course of childhood and adolescence are significantly associated with reduced symptoms of anxiety and depression as measured by a higher SF-12 MCS score. Compared with previous reviews, the data presented here provides an estimate of the magnitude of effect that helps facilitate microsimulation modelling of impact on anxiety and depression from changes in socioeconomic circumstances. This enables a more detailed and complete understanding of the types of socioeconomic intervention that might begin to address some of the causes of youth mental health problems.