Miracle-dz/UniFAR-FTUs huggingface dataset:
Plug-and-Play Secondary Control for Safety of LTI Systems under Attacks

We consider the problem of controller design for linear time-invariant cyber-physical systems (CPSs) controlled via networks. Specifically, we adopt the set-up that a controller has already been designed to stabilize the plant. However, the closed loop system may be subject to actuator and sensor attacks. We first perform a reachability analysis to see the effect of potential attacks. To further ensure the safety of the states of the system, we choose a subset of sensors that can be locally secured and made free of attacks. Using these limited resources, an extra controller is designed to enhance the safety of the new closed loop. The safety of the system will be characterized by the notion of safe sets. Lyapunov based analysis will be used to derive sufficient conditions that ensure the states always stay in the safe set. The conditions will then be stated as convex optimization problems which can be solved efficiently. Lastly, our theoretical results are illustrated through numerical simulations.
Can AI detect pain and express pain empathy? A review from emotion recognition and a human-centered AI perspective

Sensory and emotional experiences such as pain and empathy are essential for mental and physical health. Cognitive neuroscience has been working on revealing mechanisms underlying pain and empathy. Furthermore, as trending research areas, computational pain recognition and empathic artificial intelligence (AI) show progress and promise for healthcare or human-computer interaction. Although AI research has recently made it increasingly possible to create artificial systems with affective processing, most cognitive neuroscience and AI research do not jointly address the issues of empathy in AI and cognitive neuroscience. The main aim of this paper is to introduce key advances, cognitive challenges and technical barriers in computational pain recognition and the implementation of artificial empathy. Our discussion covers the following topics: How can AI recognize pain from unimodal and multimodal information? Is it crucial for AI to be empathic? What are the benefits and challenges of empathic AI? Despite some consensus on the importance of AI, including empathic recognition and responses, we also highlight future challenges for artificial empathy and possible paths from interdisciplinary perspectives. Furthermore, we discuss challenges for responsible evaluation of cognitive methods and computational techniques and show approaches to future work to contribute to affective assistants capable of empathy.
Interfacial Superconductivity and Zero Bias Peak in Quasi‐One‐Dimensional Bi2Te3/Fe1+yTe Heterostructure Nanostructures

Bismuth telluride/iron telluride (Bi2Te3/Fe1+yTe) heterostructures are known to exhibit interfacial superconductivity between two non‐superconducting materials: Fe1+yTe as the parent compound of Fe‐based superconducting materials and the topological insulator Bi2Te3. Here, a top‐down approach is presented starting from 2D heterostructures to fabricate 1D Bi2Te3/Fe1+yTe nanowires or narrow nanoribbons. It is demonstrated that the Bi2Te3/Fe1+yTe heterostructure remains intact in nanostructures of widths on the order of 100 nm and the interfacial superconductivity is preserved, as evidenced by electrical transport and Andreev reflection point contact spectroscopy experiments measured at the end of the nanowire. The differential conductance shows a similar superconducting twin‐gap structure as in 2D heterostructures, but with enhanced fluctuation effects due to the lower dimensionality. A zero‐bias conductance peak indicates the presence of an Andreev bound state and, given the involvement of the topological Bi2Te3 surface state, a possible topological nature of superconductivity is discussed with strong interplay with an emerging ferromagnetism due to the interstitial excess iron (Fe) in the Fe1+yTe layer, developing in parallel with superconductivity at low temperatures.
Meteorology of the Southern Hemisphere

A comprehensive monograph of the meteorology of the Southern Hemisphere was originally published by the American Meteorological Society in 1972. That monograph was, of necessity, preliminary in nature because the available time series of observational data was short. In the quarter century that has passed since the first monograph, much has happened to warrant an updated edition: new observational techniques based on satellites, anchored and drifting buoys, and more ground-based stations have expanded the observational network to cover the whole hemisphere. The time is right, therefore, for a fresh look at the circulation features of the Southern Hemisphere, both for the atmosphere and oceans.
Ion Coulomb explosion of clusters by a Gaussian laser beam

A collisionless model of ion Coulomb explosion of clusters, due to an intense short pulse laser with Gaussian radial intensity distribution, is developed. The laser quickly converts the clusters into plasma balls via tunnel ionization. The free electrons of a cluster execute oscillatory motion with large excursion Δ and partially move out of the cluster. The unbalanced positive charge within the cluster leads to ion Coulomb explosion. The ion energy distribution function due to a single cluster rises monotonically with energy e up to a cutoff energy em and vanishes beyond it. em depends on electron excursion, hence decreases with r. The cumulative ion energy distribution due to all the clusters of the laser channel turns out to be a decreasing function of energy as observed in recent experiments. The ions so produced can generate neutrons with reasonably high yields. The scheme may also find application in tokamak fusion.
Predicting Overall Survival Using Machine Learning Algorithms in Oral Cavity Squamous Cell Carcinoma

Background/Aim: Machine learning (ML) models are often modelled to predict cancer prognosis but rarely consider spatial factors in a region. Hence this study explored machine learning algorithms utilising Local Government Areas (LGAs) in Queensland, Australia to spatially predict 3- and 5-year prognosis of oral cancer patients and provide clinical interpretability of the predicted outcome made by the ML model. Patients and Methods: Data from a total of 3,841 oral cancer patients were retrieved from the Queensland Cancer Registry (QCR). Synthesizing minority oversampling technique together with edited nearest neighbours (SMOTE-ENN) was used to pre-process unbalanced datasets. Five ML models: logistic regression, random forest classifier, XGBoost, Gaussian Naïve Bayes and Voting Classifier were trained. Predictive features were age, sex, LGAs, tumour site and differentiation. Outcomes were 3- and 5-year overall survival of patients. Model performances on test set were evaluated using area under the curve and F1 scores. SHapley Additive exPlanations (SHAP) method was applied to the best performing model for model interpretation of the predicted outcome. Results: The Voting Classifier was the best performing model with F1 score of 0.58 and 0.64 for 3- and 5-year overall survival, respectively. Age was the most important feature in the Voting Classifier in 3- and 5-year prognosis prediction. LGAs at diagnosis was the top 3 predictive feature for both 3- and 5-year models. Conclusion: The Voting Classifier demonstrated the best overall performance in classifying both 3- and 5-year overall survival of oral cancer patients in Queensland. SHAP method provided clinical understanding of the predictive features of the Voting Classifier.
Is the arsenic incorporation kinetics important when growing GaAs(001), (110), and (111)A films?

The incorporation coefficients of As2 and As4, obtained from reflection high-energy electron diffraction intensity oscillations in the As-limited growth regime, are compared for the growth of GaAs on (001), (110), and (111)A surfaces by molecular beam epitaxy. The kinetic results are remarkably similar for (110) and (111)A, but very different from those obtained on (001). The incorporation coefficients decrease with increasing temperature for all three surfaces, with the effect being much more dramatic on (110) and (111) A. The low- and temperature-dependent incorporation coefficients on (110) and (111)A explain the need for high As:Ga flux ratios and low substrate temperatures in the preparation of high-quality GaAs epitaxial layers.
Impact of Corticosteroids for IrAEs on the Clinical Outcome of Immunotherapy in Patients With NSCLC

Background/Aim: The impact of corticosteroids for the treatment of immune-related adverse events (irAEs) on the antitumor effect of programmed cell death-1 (PD-1) inhibitor is unclear. Patients and Methods: A total of 172 patients with non-small cell lung cancer (NSCLC) treated with PD-1 inhibitors were retrospectively reviewed. Patients were divided into four groups: those who did not develop irAEs [1] and those who developed irAE and were either not treated with corticosteroids [2] or treated with low [3] or high doses [4], and overall survival (OS) was analyzed by the time of corticosteroid treatment. Landmark analysis was performed using Cox proportional hazard model with time-dependent covariates. Results: A high-dose steroid treatment within 60 days correlated with a significantly worse OS than that of the group with irAEs without steroids (p=0.004). Moreover, there was no significant difference in OS between the irAE without steroid and low-dose steroid groups. Conclusion: Early severe irAEs and high-dose corticosteroid treatment were poor prognostic factors in patients with NSCLC treated with PD-1 inhibitors.
The Restricted Least Squares Estimator: A Pedagogical Note

The authors obtain expressions for the restricted least squares estimator and its covariance matrix in the classical regression model when the matrix of regressors is not necessarily of full rank. The standard expressions for the restricted least squares estimator are not usable in the short rank case because they rely on the unrestricted estimator. But, in the presence of restrictions, the restricted least squares estimator may be computable even if the unrestricted estimator is not. The authors' derivation produces some additional, useful algebraic results for least squares computation. Copyright 1991 by MIT Press.
Impact of Post-progression Survival on Outcomes of Lenvatinib Treatment for Unresectable Hepatocellular Carcinoma: A Systematic Review and Retrospective Cohort Study

Background/Aim: Lenvatinib is a tyrosine kinase inhibitor (TKI) more effective against hepatocellular carcinoma (HCC) than sorafenib, making lenvatinib a first-line treatment option for patients with unresectable HCC. In patients treated with sorafenib, post-progression survival (PPS) rather than progression-free survival (PFS) is essential for overall survival (OS). However, the importance of PPS for OS in patients treated with lenvatinib is uncertain, and optimal treatment after lenvatinib failure has not yet been established. Patients and Methods: The present study investigated the correlations of PFS and PPS with OS in studies of HCC patients treated with lenvatinib by weighted linear regression analysis. Furthermore, the contribution of treatment regimens after lenvatinib failure to OS were evaluated in daily clinical practice. Results: An analysis of 20 studies with 4,054 patients found that PPS had a stronger correlation with OS (r=0.869, p<0.001) than did PFS (r=0.505, p=0.007). Analysis of 79 patients with unresectable HCC treated with first-line lenvatinib showed that subsequent treatment was the most significant contributor to OS. Second-line sorafenib was administered to 25 patients, with late transition to third-line treatment being highest among patients who received second-line treatment. Conclusion: PPS contributes significantly to OS in HCC treatment with TKIs, with multi-sequential treatment being a key determinant of longer OS.
Correlation Between Tumor Uptake on FDG-PET and Malignant Features in Esophageal Squamous Cell Carcinoma After Neoadjuvant Chemotherapy Followed by Surgery

Background/Aim: To identify the correlations between the 18F-fluorodeoxyglucose (FDG) uptake on positron emission tomography (PET) images and the pathological features and recurrence among patients with esophageal squamous cell carcinoma (ESCC) who were administered neoadjuvant chemotherapy (NAC) followed by surgery. Patients and Methods: We assessed the correlations between the maximum standardized uptake value (SUVmax) of primary tumors as reflected on preoperative FDG-PET images, the pathological features, and cancer recurrence in 124 patients with locally advanced ESCC, who were treated with NAC and esophagectomy. Results: The pre-NAC SUVmax significantly differed for the ypT status and venous invasion (VI). The post-NAC SUVmax (post-SUVmax) significantly differed for the ypT and ypN status, VI, lymphatic invasion (LI), pathological tumor response, down-staging, and recurrence. The decrease in SUVmax before and after NAC (ΔSUVmax) significantly differed for ypT status, LI, VI, pathological tumor response, down-staging, and recurrence. The survivals were significantly stratified according to the optimal cutoffs of SUVmax for predicting recurrence (post- and ΔSUVmax cutoffs: 4.2 and 30, respectively; all p<0.0001). Moreover, multivariate analysis showed that the post- and ΔSUVmax were independent predictive factors for recurrence-free survival. Conclusion: The SUVmax on preoperative FDG-PET can predict the degree of aggressiveness of the tumor in locally advanced ESCC treated with NAC.
HOL4P4: semantics for a verified data plane

We introduce a formal semantics of P4 for the HOL4 interactive theorem prover. We exploit properties of the language, like the absence of call by reference and the copy-in/copy-out mechanism, to define a heapless small-step semantics that is abstract enough to simplify verification, but that covers the main aspects of the language: interaction with the architecture via externs, table match, and parsers. Our formalization is written in the Ott metalanguage, which allows us to export definitions to multiple interactive theorem provers. The exported HOL4 semantics allows us to establish machine-checkable proofs regarding the semantics, properties of P4 programs, and soundness of analysis tools.
Symbolic Reachability Analysis of Distributed Systems using Narrowing and Heuristic Search

A concurrent system specified as a rewrite theory can be symbolically analyzed using narrowing-based reachability analysis. Narrowing-based approaches have been applied to formally analyze cryptographic protocols and parameterized protocols. However, existing narrowing-based techniques, based on a breadth-first-search strategy, cannot deal with generic distributed systems with objects and messages due to the symbolic state-space explosion problem. This paper proposes a heuristic search approach for narrowing-based reachability analysis to guide the search for counterexamples involving a small number of objects. As a result, our method can effectively find a counterexample if an error state is reachable. We demonstrate the effectiveness of our technique using a nontrivial distributed consensus algorithm.
MetaABR: environment-adaptive video streaming system with meta-reinforcement learning

This work focuses on a video bitrate algorithm that quickly adapts to new and various environments with just a few update steps. This aspect is especially important for large-scale video streaming services used by a wide variety of users in different environments. Our proposed model is based on a neural network and employs meta-reinforcement learning to train it. After training, it can be easily customized for a variety of new environments with a few update steps, providing a user-specific streaming service.
Specializing Scope Graph Resolution Queries

To warrant programmer productivity, type checker results should be correct and available quickly. Correctness can be provided when a type checker implementation corresponds to a declarative type system specification. Statix is a type system specification language which achieves this by automatically deriving type checker implementations from declarative typing rules. A key feature of Statix is that it uses scope graphs for declarative specification of name resolution. However, compared to hand-written type checkers, type checkers derived from Statix specifications have sub-optimal run time performance. In this paper, we identify and resolve a performance bottleneck in the Statix solver, namely part of the name resolution algorithm, using partial evaluation. To this end, we introduce a tailored procedural intermediate query resolution language, and provide a specializer that translates declarative queries to this language. Evaluating this specializer by comparing type checking run time performance on three benchmarks (Apache Commons CSV, IO, and Lang3), shows that our specializer improves query resolution time up to 7.7x, which reduces the total type checking run time by 38 - 48%.
BACKORDERS: using random forests to detect DDoS attacks in programmable data planes

Networks and the services they support form the communication backbone of our society, and it is important that potential Distributed Denial of Service (DDoS) attacks are detected quickly, in order to avoid or minimize the impact they may have on the availability of services. Recent technological advances in programmable networks - specifically the programmability of data planes in switches and routers, have made available new ways of detecting such attacks. By relying on this newfound possibility, this paper proposes the utilization of a Random Forest (RF) to aid in quickly and accurately detecting DDoS attacks in a programmable switch. Random forests utilize several classification trees, each of them for independently classifying an input as one of a set of classes. Here, each decision tree will classify a network flow as potentially malicious, i.e. part of a DDoS attack, or a legitimate user flow. Despite utilizing multiple classification trees to improve accuracy, random forests are relatively lightweight, with each tree requiring few and simple computations to arrive at a classification. Our results show that even small RFs, requiring as few as 63 match+action table entries, can achieve F1-Scores of over 90%.
OTEC: an optimized transcoding task scheduler for cloud and fog environments

Encoding and transcoding videos into multiple codecs and representations is a significant challenge that requires seconds or even days on high-performance computers depending on many technical characteristics, such as video complexity or encoding parameters. Cloud computing offering on-demand computing resources optimized to meet the needs of customers and their budgets is a promising technology for accelerating dynamic transcoding workloads. In this work, we propose OTEC, a novel multi-objective optimization method based on the mixed-integer linear programming model to optimize the computing instance selection for transcoding processes. OTEC determines the type and number of cloud and fog resource instances for video encoding and transcoding tasks with optimized computation cost and time. We evaluated OTEC on AWS EC2 and Exoscale instances for various administrator priorities, the number of encoded video segments, and segment transcoding times. The results show that OTEC can achieve appropriate resource selections and satisfy the administrator's priorities in terms of time and cost minimization.
A new perspective on Australian snow

The Australian Alps have a unique climatological, ecological and hydrological environment and play a key role in water supply for southeastern Australia. Using resort observations we compile a new and robust snow accumulation data set. Both maximum snow depth and total snow accumulation have declined over the last 25 years. A significant decreasing trend was observed for the total number of light snow days, whereas the total number of heavy snow occurrences has remained constant. Maximum temperatures are highly related to all snow variables. It is suggested that global warming is already impacting light snowfall events, while heavy events are less affected.
WildLab: A naturalistic free viewing experiment reveals previously unknown electroencephalography signatures of face processing

Neural mechanisms of face perception are predominantly studied in well‐controlled experimental settings that involve random stimulus sequences and fixed eye positions. Although powerful, the employed paradigms are far from what constitutes natural vision. Here, we demonstrate the feasibility of ecologically more valid experimental paradigms using natural viewing behaviour, by combining a free viewing paradigm on natural scenes, free of photographer bias, with advanced data processing techniques that correct for overlap effects and co‐varying non‐linear dependencies of multiple eye movement parameters. We validate this approach by replicating classic N170 effects in neural responses, triggered by fixation onsets (fixation event‐related potentials [fERPs]). Importantly, besides finding a strong correlation between both experiments, our more natural stimulus paradigm yielded smaller variability between subjects than the classic setup. Moving beyond classic temporal and spatial effect locations, our experiment furthermore revealed previously unknown signatures of face processing: This includes category‐specific modulation of the event‐related potential (ERP)'s amplitude even before fixation onset, as well as adaptation effects across subsequent fixations depending on their history.
Analysis of electromagnetic scattering from perfect electric conducting targets using improved characteristic basis function method and fast dipole method

The characteristic basis function method (CBFM) is an efficient approach to analyze electromagnetic problems through the size reduction of the original matrix in the method of moments equation. However, adequate plane waves (PWs) must be set in each sub-block to construct characteristic basis functions (CBFs), thus increasing the number of CBFs and causing higher time consumption in singular value decomposition. In addition, the reduced matrix calculation procedure is time-consuming because numerous vector–matrix–vector products (VMVPs) are contained. To mitigate these problems, an improved CBFM is presented. This method fully considers the mutual coupling effects among sub-blocks to obtain the secondary level characteristic basis function (SCBF). Therefore, the number of PWs, as well as the number of CBFs, is significantly reduced. The fast dipole method is also used to accelerate the matrix–vector products in the construction of SCBFs and VMVPs in the calculation of the reduced matrix. Numerical results demonstrate that the proposed method is accurate and efficient.
Temporal landscape of mitochondrial proteostasis governed by the UPRmt

Breakdown of mitochondrial proteostasis activates quality control pathways including the mitochondrial unfolded protein response (UPRmt) and PINK1/Parkin mitophagy. However, beyond the upregulation of chaperones and proteases, we have a limited understanding of how the UPRmt remodels and restores damaged mito-proteomes. Here, we have developed a functional proteomics framework, termed MitoPQ (Mitochondrial Proteostasis Quantification), to dissect the UPRmt’s role in maintaining proteostasis during stress. We discover essential roles for the UPRmt in both protecting and repairing proteostasis, with oxidative phosphorylation metabolism being a central target of the UPRmt. Transcriptome analyses together with MitoPQ reveal that UPRmt transcription factors drive independent signaling arms that act in concert to maintain proteostasis. Unidirectional interplay between the UPRmt and PINK1/Parkin mitophagy was found to promote oxidative phosphorylation recovery when the UPRmt failed. Collectively, this study defines the network of proteostasis mediated by the UPRmt and highlights the value of functional proteomics in decoding stressed proteomes.