Miracle-dz/UniFAR-FTUs huggingface dataset:
Algorithmic Recourse for Anomaly Detection in Multivariate Time Series

Anomaly detection in multivariate time series has received extensive study due to the wide spectrum of applications. An anomaly in multivariate time series usually indicates a critical event, such as a system fault or an external attack. Therefore, besides being effective in anomaly detection, recommending anomaly mitigation actions is also important in practice yet under-investigated. In this work, we focus on algorithmic recourse in time series anomaly detection, which is to recommend fixing actions on abnormal time series with a minimum cost so that domain experts can understand how to fix the abnormal behavior. To this end, we propose an algorithmic recourse framework, called RecAD, which can recommend recourse actions to flip the abnormal time steps. Experiments on two synthetic and one real-world datasets show the effectiveness of our framework.
The City Connection: Migration and Family Interdependence in the Philippines

In the rural Philippines, a scarcity of resources and opportunities has made migration to urban areas one part of family and household strategies for mobility and survival. The City Connection examines the ways in which families form networks of maintenance and support that span rural and urban areas. Based on Lillian Trager's observation of and interviews with persons both in Dagupan City and in their rural Philippine hometowns, The City Connection focuses on the stories of individual migrants and their families, setting these stories within the context of regional, social, economic, and cultural forces. Trager examines how households use their resources, connections, and information in the effort to make often minimal incomes slightly less minimal, and demonstrates how the continuing and complex interactions between socioeconomic situation, cultural values, and decisions and behavior of migrants and their families result in patterns of migration and continuing family ties.
Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy

The aim of this study is to analyze the effect of serum metabolites on diabetic nephropathy (DN) and predict the prevalence of DN through a machine learning approach. The dataset consists of 548 patients from April 2018 to April 2019 in the Second Affiliated Hospital of Dalian Medical University (SAHDMU). We select the optimal 38 features through a least absolute shrinkage and selection operator (LASSO) regression model and a 10-fold cross-validation. We compare four machine learning algorithms, including extreme gradient boosting (XGB), random forest, decision tree, and logistic regression, by AUC-ROC curves, decision curves, and calibration curves. We quantify feature importance and interaction effects in the optimal predictive model by Shapley additive explanation (SHAP) method. The XGB model has the best performance to screen for DN with the highest AUC value of 0.966. The XGB model also gains more clinical net benefits than others, and the fitting degree is better. In addition, there are significant interactions between serum metabolites and duration of diabetes. We develop a predictive model by XGB algorithm to screen for DN. C2, C5DC, Tyr, Ser, Met, C24, C4DC, and Cys have great contribution in the model and can possibly be biomarkers for DN.
A New 1-mg Fast Unimorph SMA-Based Actuator for Microrobotics

We present a new unimorph actuator for micro-robotics, which is driven by thin shape-memory alloy (SMA) wires. Using a passive-capillary-alignment technique and existing SMA-microsystem fabrication methods, we developed an actuator that is 7 mm long, has a volume of 0.45 mm3, weighs 0.96 mg, and can achieve operation frequencies of up to 40 Hz as well as lift 155 times its own weight. To demonstrate the capabilities of the proposed actuator, we created an 8-mg crawler, the MiniBug, and a bioinspired 56-mg controllable water-surface-tension crawler, the WaterStrider. The MiniBug is 8.5 mm long, can locomote at speeds as high as 0.76 BL/s (body-lengths per second), and is the lightest fully-functional crawling microrobot of its type ever created. The WaterStrider is 22 mm long, and can locomote at speeds of up to 0.28 BL/s as well as execute turning maneuvers at angular rates on the order of 0.144 rad/s. The WaterStrider is the lightest controllable SMA-driven water-surface-tension crawler developed to date.
Harnessing Digital Twin Technology for Adaptive Traffic Signal Control: Improving Signalized Intersection Performance and User Satisfaction

In this study, a digital twin (DT) technology based Adaptive Traffic Signal Control (ATSC) framework is presented for improving signalized intersection performance and user satisfaction. Specifically, real-time vehicle trajectory data, future traffic demand prediction and parallel simulation strategy are considered to develop two DT-based ATSC algorithms, namely DT1 (Digital Twin 1) and DT2 (Digital Twin 2). DT1 uses the delay experienced by each vehicle from all approaches connected to the subject intersection, while DT2 uses the delay of each vehicle that occurred in all the approaches connected to the subject intersection as well as immediate adjacent intersection. To demonstrate the effectiveness of these algorithms, the DT-based ATSC algorithms are evaluated with varying traffic demands at intersection, and individual user level. Evaluation results show that both DT1 and DT2 performs significantly better compared to the density-based baseline algorithm in terms of control delay reductions ranging from 1% to 52% for low traffic demands. DT1 outperforms baseline algorithm for moderate traffic demands, achieving reduction in control delay ranging from 3% to 19%, while the performance of DT2 declines with increasing demand. For high traffic demands, DT1 achieved control delay reduction ranging from 1% to 45% and DT2 achieved 8% to 36% compared to the baseline algorithm. Moreover, DT1 and DT2 effectively distribute the delay per vehicle among all the vehicles, which approach towards intersection, compared to the baseline ATSC algorithm. This helps to improve user satisfaction by reducing prolonged delays at a traffic signal, specifically, for moderate and high traffic demands.
Equatorial plasma fountain and its effects over three locations: Evidence for an additional layer, the F 3 layer

The equatorial plasma fountain and equatorial anomaly in the ionospheres over Jicamarca (77°W), Trivandrum (77°E), and Fortaleza (38°W) are presented using the Sheffield University plasmasphere-ionosphere model under magnetically quiet equinoctial conditions at high solar activity. The daytime plasma fountain and its effects in the regions outside the fountain lead to the formation of an additional layer, the F3 layer, at latitudes within about plus or minus 10° of the magnetic equator in each ionosphere. The maximum plasma concentration of the F3 layer, which occurs at about 550 km altitude, becomes greater than that of the F2 layer for a short period of time before noon when the vertical E × B drift is large. Within the F3 layer the plasma temperature decreases by as much as 100 K. The ionograms recorded at Fortaleza on January 15, 1995, provide observational evidence for the development and decay of an F3 layer before noon. The neutral wind, which causes large north–south asymmetries in the plasma fountain in each ionosphere during both daytime and nighttime, becomes least effective during the prereversal strengthening of the upward drift. During this time the plasma fountain is symmetrical with respect to the magnetic equator and rises to over 1200 km altitude at the equator, with accompanying plasma density depletions in the bottomside of the underlying F region. The north–south asymmetries of the equatorial plasma fountain and equatorial anomaly are more strongly dependent upon the displacement of the geomagnetic and geographic equators (Jicamarca and Trivandrum) than on the magnetic declination angle (Fortaleza).
Ocorrência de uma nova bentonita brasileira nos basaltos intemperizados da formação Mosquito, bacia do Parnaíba, sul do Maranhão (Occurrence of a new brazilian bentonite in the weathered basalts of the Mosquito formation, Parnaíba basin, southern Maranhão, Brazil)

Resumo No municipio de Formosa da Serra Negra, sul do Maranhao, as margens da rodovia MA006, encontram-se afloramentos de basaltos intemperizados da Formacao Mosquito, apresentando textura bastante argilosa e potencial de ser um material bentonitico. Neste trabalho, esse material que vem sendo considerado uma nova ocorrencia de bentonita no Brasil e, batizado de Formosa, foi caracterizado por difracao de raios X, fluorescencia de raios X, microscopia eletronica de varredura, espectroscopia Mossbauer e capacidade de troca cationica, em amostra total e apos separacao granulometrica, e ainda, comparada com duas das principais bentonitas brasileiras, Chocolate e Bofe. Apesar das variacoes quimicas e mineralogicas encontradas, muito comuns nesse tipo de argila, as semelhancas observadas convencem o promissor uso da argila Formosa como bentonita, uma vez que o ponto principal da caracterizacao esta na presenca dominante de montmorillonita e baixa concentracao de outras fases minerais, mostrando que esse material apresenta mineralogia importante do ponto de vista tecnologico. Palavras-chave: bentonita, argila, esmectita, montmorillonita.
Nanowire breakup via a morphological instability enhanced by surface electromigration

Using a recent continuum model of a single-crystal nanowire morphological evolution in the applied axial electric field, an axisymmetric evolution of a microscopically rough nanowire surface is computed. Morphological evolution results in a wire breakup into a cylindrical segments (particles). Breakup time and the number of particles are characterized for various levels of the radial and axial surface roughness. It is shown that electromigration and larger surface roughness lead to a shorter breakup time and the increased number of particles.
GRANDE: Gradient-Based Decision Tree Ensembles

Despite the success of deep learning for text and image data, tree-based ensemble models are still state-of-the-art for machine learning with heterogeneous tabular data. However, there is a significant need for tabular-specific gradient-based methods due to their high flexibility. In this paper, we propose $\text{GRANDE}$, $\text{GRA}$die$\text{N}$t-Based $\text{D}$ecision Tree $\text{E}$nsembles, a novel approach for learning hard, axis-aligned decision tree ensembles using end-to-end gradient descent. GRANDE is based on a dense representation of tree ensembles, which affords to use backpropagation with a straight-through operator to jointly optimize all model parameters. Our method combines axis-aligned splits, which is a useful inductive bias for tabular data, with the flexibility of gradient-based optimization. Furthermore, we introduce an advanced instance-wise weighting that facilitates learning representations for both, simple and complex relations, within a single model. We conducted an extensive evaluation on a predefined benchmark with 19 classification datasets and demonstrate that our method outperforms existing gradient-boosting and deep learning frameworks on most datasets.
SCALE: Synergized Collaboration of Asymmetric Language Translation Engines

In this paper, we introduce SCALE, a collaborative framework that connects compact Specialized Translation Models (STMs) and general-purpose Large Language Models (LLMs) as one unified translation engine. By introducing translation from STM into the triplet in-context demonstrations, SCALE unlocks refinement and pivoting ability of LLM, thus mitigating language bias of LLM and parallel data bias of STM, enhancing LLM speciality without sacrificing generality, and facilitating continual learning without expensive LLM fine-tuning. Our comprehensive experiments show that SCALE significantly outperforms both few-shot LLMs (GPT-4) and specialized models (NLLB) in challenging low-resource settings. Moreover, in Xhosa to English translation, SCALE experiences consistent improvement by a 4 BLEURT score without tuning LLM and surpasses few-shot GPT-4 by 2.5 COMET score and 3.8 BLEURT score when equipped with a compact model consisting of merely 600M parameters. SCALE could also effectively exploit the existing language bias of LLMs by using an English-centric STM as a pivot for translation between any language pairs, outperforming few-shot GPT-4 by an average of 6 COMET points across eight translation directions. Furthermore we provide an in-depth analysis of SCALE's robustness, translation characteristics, and latency costs, providing solid foundation for future studies exploring the potential synergy between LLMs and more specialized, task-specific models.
On Security Strategies for Addressing Potential Vulnerabilities in 6G Technologies Deployable in Healthcare

Researchers are now focusing on 6G as a new network technology that will bring significant gains over the previous generations while many sectors are still implementing the 5G network in their business processes and operations. Meanwhile, key technological fields that will be influenced by 6G networks have been identified. These include distributed artificial intelligence, intelligent radio, real-time intelligent edge computing, and 3D intercoms. Additionally, each area and potential application of 6G is supported by relevant emerging technologies. Nevertheless, these 6G technology and applications have significant security vulnerabilities that must be addressed before the complete adoption of 6G networks. The healthcare is one of the sectors that are benefiting from the great features introduced in the 5G networks that enhance digital communications and data protection; that notwithstanding, there are still security flaws in 5G technologies that can be transferred to the 6G networks if not properly addressed. This paper highlights the key areas of 6G networks that would provide grand support for the development of healthcare systems. It also identifies certain vulnerabilities in the previous cellular networks that are transferable to 6G networks, and suggests security strategies including zero trust initiatives that could be implemented to address the security concerns.
Refined Kolmogorov Complexity of Analog, Evolving and Stochastic Recurrent Neural Networks

We provide a refined characterization of the super-Turing computational power of analog, evolving, and stochastic neural networks based on the Kolmogorov complexity of their real weights, evolving weights, and real probabilities, respectively. First, we retrieve an infinite hierarchy of classes of analog networks defined in terms of the Kolmogorov complexity of their underlying real weights. This hierarchy is located between the complexity classes $\mathbf{P}$ and $\mathbf{P/poly}$. Then, we generalize this result to the case of evolving networks. A similar hierarchy of Kolomogorov-based complexity classes of evolving networks is obtained. This hierarchy also lies between $\mathbf{P}$ and $\mathbf{P/poly}$. Finally, we extend these results to the case of stochastic networks employing real probabilities as source of randomness. An infinite hierarchy of stochastic networks based on the Kolmogorov complexity of their probabilities is therefore achieved. In this case, the hierarchy bridges the gap between $\mathbf{BPP}$ and $\mathbf{BPP/log^*}$. Beyond proving the existence and providing examples of such hierarchies, we describe a generic way of constructing them based on classes of functions of increasing complexity. For the sake of clarity, this study is formulated within the framework of echo state networks. Overall, this paper intends to fill the missing results and provide a unified view about the refined capabilities of analog, evolving and stochastic neural networks.
2 HANSI JIANG AND CARL MEYER

In this paper the exact linear relation between the leading eigenvector of the unnormalized modularity matrix and the eigenvectors of the adjacency matrix is developed. Based on this analysis a method to approximate the leading eigenvector of the modularity matrix is given, and the relative error of the approximation is derived. A complete proof of the equivalence between normalized modularity clustering and normalized adjacency clustering is also given. A new metric is defined to describe the agreement of two clustering methods, and some applications and experiments are given to illustrate and corroborate the points that are made in the theoretical development.
Reappraisal of the K–T boundary succession at Stevns Klint, Denmark

We present stratigraphical and stable isotope data (δ13C, δ18O) for two successions selected from the K–T boundary succession seen in the cliff sections of Stevns Klint (Denmark). Although the succession at Højerup is well known from other studies, that at Kulstirenden has not been described before. It contains an exceptionally thick (c. 40 cm) development of the boundary clay (or Fish Clay), which includes the ‘red layer’ just above its base. There is clear evidence of shallowing in the latest Maastrichtian in advance of the K–T boundary, together with fluctuations in sea-water temperature. The detailed δ13C record at Kulstirenden provides a record of the environmental changes within the boundary interval with a resolution better than many other successions described in the literature.
Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools

While algorithmic fairness is a thriving area of research, in practice, mitigating issues of bias often gets reduced to enforcing an arbitrarily chosen fairness metric, either by enforcing fairness constraints during the optimization step, post-processing model outputs, or by manipulating the training data. Recent work has called on the ML community to take a more holistic approach to tackle fairness issues by systematically investigating the many design choices made through the ML pipeline, and identifying interventions that target the issue’s root cause, as opposed to its symptoms. While we share the conviction that this pipeline-based approach is the most appropriate for combating algorithmic unfairness on the ground, we believe there are currently very few methods of operationalizing this approach in practice. Drawing on our experience as educators and practitioners, we first demonstrate that without clear guidelines and toolkits, even individuals with specialized ML knowledge find it challenging to hypothesize how various design choices influence model behavior. We then consult the fair-ML literature to understand the progress to date toward operationalizing the pipeline-aware approach: we systematically collect and organize the prior work that attempts to detect, measure, and mitigate various sources of unfairness through the ML pipeline. We utilize this extensive categorization of previous contributions to sketch a research agenda for the community. We hope this work serves as the stepping stone toward a more comprehensive set of resources for ML researchers, practitioners, and students interested in exploring, designing, and testing pipeline-oriented approaches to algorithmic fairness.
Discrete-Choice Model with Generalized Additive Utility Network

Discrete-choice models are a powerful framework for analyzing decision-making behavior to provide valuable insights for policymakers and businesses. Multinomial logit models (MNLs) with linear utility functions have been used in practice because they are ease to use and interpretable. Recently, MNLs with neural networks (e.g., ASU-DNN) have been developed, and they have achieved higher prediction accuracy in behavior choice than classical MNLs. However, these models lack interpretability owing to complex structures. We developed utility functions with a novel neural-network architecture based on generalized additive models, named generalized additive utility network ( GAUNet), for discrete-choice models. We evaluated the performance of the MNL with GAUNet using the trip survey data collected in Tokyo. Our models were comparable to ASU-DNN in accuracy and exhibited improved interpretability compared to previous models.
The Close AGN Reference Survey (CARS): An Interplay between Radio Jets and AGN Radiation in the Radio-quiet AGN HE0040-1105

We present a case study of HE 0040-1105, an unobscured radio-quiet active galactic nucleus (AGN) at a high accretion rate of λ Edd = 0.19 ± 0.04. This particular AGN hosts an ionized gas outflow with the largest spatial offset from its nucleus compared to all other AGNs in the Close AGN Reference Survey. By combining multiwavelength observations from the Very Large Telescope/MUSE, Hubble Space Telescope/Wide Field Camera 3, Very Large Array, and European VLBI Network, we probe the ionization conditions, gas kinematics, and radio emission from host galaxy scales to the central few parsecs. We detect four kinematically distinct components, one of which is a spatially unresolved AGN-driven outflow located within the central 500 pc, where it locally dominates the interstellar medium conditions. Its velocity is too low to escape the host galaxy’s gravitational potential, and may be re-accreted onto the central black hole via chaotic cold accretion. We detect compact radio emission in HE 0040-1105 within the region covered by the outflow, varying on a timescale of ∼20 yr. We show that neither AGN coronal emission nor star formation processes wholly explain the radio morphology/spectrum. The spatial alignment between the outflowing ionized gas and the radio continuum emission on 100 pc scales is consistent with a weak jet morphology rather than diffuse radio emission produced by AGN winds. >90% of the outflowing ionized gas emission originates from the central 100 pc, within which the ionizing luminosity of the outflow is comparable to the mechanical power of the radio jet. Although radio jets might primarily drive the outflow in HE 0040-1105, radiation pressure from the AGN may contribute to this process.
Peptidylarginine deiminase 4 and ADAMTS13 activity in Staphylococcus aureus bacteraemia

Staphylococcus aureus infection is associated with increased levels of neutrophil extracellular traps (NETs) and von Willebrand factor (VWF), and with reduced activity of ADAMTS13 (a disintegrin and metalloproteinase with thrombospondin type 1 motifs, member 13). Peptidylarginine deiminase 4 (PAD4) contributes to NET formation and inactivates ADAMTS13 in vitro. The role of PADs in the dynamics of NETs, VWF and ADAMTS13 has not yet been studied. We thus aimed to assess the longitudinal evolution of NETs, PADs, VWF and ADAMTS13 activity in S. aureus infection. Plasma samples from S. aureus bacteraemia patients were longitudinally collected and analysed for NETs, PAD4/PAD2, VWF and ADAMTS13 activity. Correlation analyses with clinical data were performed. Recombinant PAD4 and S. aureus were assessed in vitro for their potential to modulate ADAMTS13 activity. Sixty-seven patients were included. Plasma levels of NETs, VWF, PAD4 and PAD2 were increased and ADAMTS13 activity was decreased. Levels of PADs were negatively correlated with ADAMTS13 activity. NETs were positively correlated with PADs, and negatively with ADAMTS13 activity. In vitro, recombinant PAD4 but not S. aureus reduced ADAMTS13 activity in plasma. Levels of PAD4 and PAD2 correlate with reduced ADAMTS13 activity, with neutrophils as the likely source of PAD activity in S. aureus bacteraemia. This article is part of the Theo Murphy meeting issue ‘The virtues and vices of protein citrullination’.