Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-supervised Batch Learning From Logged Data

Off-policy learning methods are intended to learn a policy from logged data, which includes context, action, and feedback (cost or reward) for each sample point. In this work, we build on the counterfactual risk minimization framework, which also assumes access to propensity scores. We propose learning methods for problems where feedback is missing for some samples, so there are samples with feedback and samples missing-feedback in the logged data. We refer to this type of learning as semi-supervised batch learning from logged data, which arises in a wide range of application domains. We derive a novel upper bound for the true risk under the inverse propensity score estimator to address this kind of learning problem. Using this bound, we propose a regularized semi-supervised batch learning method with logged data where the regularization term is feedback-independent and, as a result, can be evaluated using the logged missing-feedback data. Consequently, even though feedback is only present for some samples, a learning policy can be learned by leveraging the missing-feedback samples. The results of experiments derived from benchmark datasets indicate that these algorithms achieve policies with better performance in comparison with logging policies.
FALSIFICATION OF CYBER-PHYSICAL SYSTEMS

The aim of this technical report is to investigate the effect of input parameters on the falsification of cyber-physical systems (CPSs). Falsification is a practical testing method that can be used to improve confidence in the correctness of the system. During the falsification process, only the input-output behavior of the system under test (SUT) can be observed. The falsification process only requires that the SUT can be simulated and formal specifications exist. By associating a quantitative semantics with a formal specification, it becomes possible to formulate the falsification problem as an optimization problem. Thus, reducing the number of simulations necessary to falsify a specification is an important goal.
Dissociation of heavy quarkonia at finite \mu

We studied the properties of the heavy quarkonia in the presence of finite quark-chemical potential for different number of flavors by using the quasi particle approach. The effect of the finite quark-chemical potential has been incorporated through the quasi-particle Debye mass to examine the binding energies of the quarkonium states. From the imaginary part of the potential we have calculated the thermal width of the ground state of the quarkonia and found that the thermal width increases with finite quark-chemical potential. The dissociation temperature (T_D) of the J/psi and Upsilon have been calculated in the presence of finite quark-chemical potential for different flavors (i.e., N_f = 1, 2 and 3). The effect of the finite quark-chemical potential on the mass spectra of the quarkonium states has been also studied.
Encrypted distributed state estimation via affine averaging

Distributed state estimation arises in many applications such as position estimation in robot swarms, clock synchronization for processor networks, and data fusion. One characteristic is that agents only have access to noisy measurements of deviations between their own and neighboring states. Still, estimations of their actual state can be obtained in a fully distributed manner using algorithms such as affine averaging. However, running this algorithm, requires that the agents exchange their current state estimations, which can be a privacy issue (since they eventually reveal the actual states). To counteract this threat, we propose an encrypted version of the affine averaging algorithm in this paper. More precisely, we use homomorphic encryption to realize an encrypted implementation, where only one “leader” agent has access to its state estimation in plaintext. One main challenge (which often arises for recursive encrypted computations) is to prevent overflow w.r. t. the bounded message space of the cryptosystem. We solve this problem by periodically resetting the agents’ states with the help of the leader. We study the resulting system dynamics with respect to different reset strategies and support our findings with extensive numerical simulations.
Neural-iLQR: A Learning-Aided Shooting Method for Trajectory Optimization

Iterative linear quadratic regulator (iLQR) has gained wide popularity in addressing trajectory optimization problems with nonlinear system models. However, as a model-based shooting method, it relies heavily on an accurate system model to update the optimal control actions and the trajectory determined with forward integration, thus becoming vulnerable to inevitable model inaccuracies. Recently, substantial research efforts in learning-based methods for optimal control problems have been progressing significantly in addressing unknown system models, particularly when the system has complex interactions with the environment. Yet an incremental machine learning method or a deep neural network is normally required to fit a substantial scale of sampling motion data. Existing works mainly focus on capturing the whole picture of the motion pattern from trial data (which is essentially noisy and hard to identify with any simple predefined structures) for complex robot systems. Instead, we present Neural-iLQR, a learning-aided shooting method over the unconstrained control space, in which a neural network with a simple structure is used to drive the optimization process towards optimal direction. Through comprehensive evaluations on two illustrative control tasks using three different kinds of lightweight network structures, the proposed method is shown to outperform the conventional iLQR significantly with fast and continuous cost convergence in the presence of inaccuracies in system models, which demonstrates the generalizability and robustness of the proposed learning-aided method.
State-dressed local operators in the AdS/CFT correspondence

We examine aspects of locality in perturbative quantum gravity and how information can be localized in subregions. In the framework of AdS/CFT, we consider the algebra of single-trace operators defined in a short time band. We conjecture that, if the state has large energy variance, then this algebra will have a commutant in the 1/N expansion. We provide evidence for this by identifying operators that commute with the conformal field theory Hamiltonian to all orders in 1/N, thus resolving an apparent tension with the gravitational Gauss law. The bulk interpretation is that these operators are gravitationally dressed with respect to features of the state rather than the boundary. We comment on observables in certain black hole microstates and the gravitational dressing in the island proposal.
FRANS: Automatic Feature Extraction for Time Series Forecasting

Feature extraction methods help in dimensionality reduction and capture relevant information. In time series forecasting (TSF), features can be used as auxiliary information to achieve better accuracy. Traditionally, features used in TSF are handcrafted, which requires domain knowledge and significant data-engineering work. In this research, we first introduce a notion of static and dynamic features, which then enables us to develop our autonomous Feature Retrieving Autoregressive Network for Static features (FRANS) that does not require domain knowledge. The method is based on a CNN classifier that is trained to create for each series a collective and unique class representation either from parts of the series or, if class labels are available, from a set of series of the same class. It allows to discriminate series with similar behaviour but from different classes and makes the features extracted from the classifier to be maximally discriminatory. We explore the interpretability of our features, and evaluate the prediction capabilities of the method within the forecasting meta-learning environment FFORMA. Our results show that our features lead to improvement in accuracy in most situations. Once trained our approach creates features orders of magnitude faster than statistical methods.
Evaluation of long-term sequelae by cardiopulmonary exercise testing 12 months after hospitalization for critical COVID-19

Background: Cardiopulmonary exercise testing (CPET) is an important clinical tool that provides a global assessment of the respiratory, circulatory and metabolic responses to exercise which are not adequately reflected through the measurement of individual organ system function at rest. In the context of critical COVID-19, CPET is an ideal approach for assessing long term sequalae. Methods: In this prospective single-center study, we performed CPET in 60 patients, 12 months after a critical COVID-19 infection that required intensive care unit (ICU) treatment. Lung function at rest and chest computed tomography (CT) scan were also performed. Results: Twelve months after severe COVID-19 pneumonia, the majority of the patients had a peak O2 uptake (VO2) considered within normal limits. However, length of ICU stay remained an independent predictor of VO2. Surprisingly, more than half of the patients with a normal peak predicted VO2 showed ventilatory inefficiency during exercise (high VE/VCO2 ratio and high VE/VCO2 slope) with increased physiological dead space (VD/Vt) and low end-tidal CO2 partial pressure (PETCO2) values. This impairment was even more pronounced in patients with persistent dyspnea. Notably, peak VD/Vt values were positively correlated with peak D-Dimer plasma concentrations from blood samples collected during ICU stay. Conclusions: Even if reduced exercise capacity was rare 12 months after critical COVID-19, more than half of the patients with normal exercise capacity showed ventilatory inefficiency.
Identification of alanine aminotransferase 1 interaction network via iTRAQ-based proteomics in alternating migration, invasion, proliferation and apoptosis of HepG2 cells

Objective: To investigate the mechanism of alanine aminotransferase 1 (ALT1) in the progression of HCC, the differentially expressed proteins (DEPs) in the ALT1 interaction network were identified by targeted proteomic analysis. Methods: Wound healing and transwell assays were conducted to assess the effect of ALT1 on cellular migration and invasion. Cell Counting Kit-8 (CCK-8), colony formation, and flow cytometry assays were performed to identify alterations in proliferation and apoptosis. After coimmunoprecipitation processing, mass spectrometry with iso-baric tags for relative and absolute quantitation was utilized to explore the protein interactions in ALT1 knockdown HepG2 cells. Results: The results showed that ALT1 knockdown inhibits the migration, invasion, proliferation of HepG2 cells, and promotes apoptosis. A total of 116 DEPs were identified and the bioinformatics analysis suggested that the ALT1-interacting proteins were primarily associated with cellular and metabolic processes. Knockdown of ALT1 in HepG2 cells reduced the expression of Ki67 and epithelial cell adhesion molecule (EP-CAM), while the expression of apoptosis-stimulating protein 2 of p53 (ASPP2) was increased significantly. Suppression of the ALT1 and EP-CAM expression contributed to alterations in epithelial–mesenchymal transition (EMT) -associated markers and matrix metalloproteinases (MMPs). Additionally, inhibition of ALT1 and Ki67 also decreased the expression of apoptosis and proliferation factors. Furthermore, inhibition of ALT1 and ASPP2 also changed the expression of P53, which may be the signaling pathway by which ALT regulates these biological behaviors. Conclusions: This study indicated that the ALT1 protein interaction network is associated with the biological behaviors of HepG2 cells via the p53 signaling pathway.
Assessment of tools for RNA secondary structure prediction and extraction: a final-user perspective

Abstract The study of RNA structure is fundamental to clarify the RNA molecular functioning. The flexible RNA nature, the huge number of expressed RNAs, and the variety of functions make it challenging to obtain a quantity of structural information comparable to what is already available for proteins. The in silico prediction of RNA 3D structures is of particular relevance, to understand the fundamental features of the structure-function relationship, because the 3D structure drives the molecular interaction with DNA or protein complexes. The quality of the prediction of the RNA 3D structure is determined by the knowledge of a properly predicted or measured secondary structure. In this paper, we comparatively evaluate computational tools to model RNA secondary structure, focusing our investigation, among the dozens of methods in literature, on tools which are freely available and implemented in web-server versions, providing a more direct access to the final users, not necessarily bioinformatics experts. Our focus is on assessing performances for long sequences, with the final aim of selecting best methods for perspective lncRNAs investigation. Indeed, among RNAs, the non-coding and long non-coding RNAs (lncRNAs, with sequence length larger than 200 nts) assume special relevance, due to their function in regulatory mechanisms, which is still largely unexplored in the case of lncRNAs. As lncRNA experimental structures are at present missing, other families of large RNAs are here used as test cases, to establish the reliability of predictive bioinformatics tools and their perspective applicability to the case of lncRNAs. Communicated by Ramaswamy H. Sarma
A new facet of category theory: cognitive disadvantage and its implications for safety in the cockpit

Abstract The typicality effect suggests typical category members provide a cognitive advantage, such as being quicker and easier to recognise and describe. The reverse effect has not been explored in an applied environment. Non-typical flight safety events appear to pose problems for pilots, leading to delayed recognition and ineffective use of checklists. Fifty-six airline pilots completed an experiment that tested a real-world typicality gradient, comparing pilot performance on a group of four non-typical events against four randomly selected events. Non-typical flight safety events elicited a greater number of response errors and a greater response latency when compared with a random selection of safety events. We specify and measure cognitive disadvantage and suggest innovations in pilot education, such as locating troublesome events and improving recognition guidance. Our new findings can be used to better prepare pilots for event diversity and inform safety in other work systems of interest to ergonomics. Practitioner summary: Typical safety events in work environments provide a cognitive dividend, supporting effective recognition and response. In this study, we frame and measure the opposite effect, the cognitive disadvantages of non-typical events. Non-typical events pose significant risk in work systems such as air transport, and we suggest innovations in pilot knowledge and training that make use of this approach.
Alternative decohering histories in quantum mechanics

We continue our efforts to understand, within the framework of the quantum mechanics of the universe as a whole, the quasiclassical realm of familiar experience as a feature emergent from the Hamiltonian of the elementary particles and the initial condition of the universe. Quantum mechanics assigns probabilities to exhaustive sets of alternative decoherent histories of the universe. We introduce and define the notion of strong decoherence. We replace the notion of maximal sets of alternative decohering histories by defining the more useful concept of "full" sets of alternative strongly decohering histories. These full sets fall into equivalence classes each of which is characterized by a basis in Hilbert space. Finally we describe our continuing efforts to find measures of classicality --- measures that could be applied to such full sets of alternative strongly decohering so as to characterize a quasiclassical realm.
Baseline Serum Autoantibody Signatures Predict Recurrence and Toxicity in Melanoma Patients Receiving Adjuvant Immune Checkpoint Blockade

Abstract Purpose: Adjuvant immunotherapy produces durable benefit for patients with resected melanoma, but many develop recurrence and/or immune-related adverse events (irAE). We investigated whether baseline serum autoantibody (autoAb) signatures predicted recurrence and severe toxicity in patients treated with adjuvant nivolumab, ipilimumab, or ipilimumab plus nivolumab. Experimental Design: This study included 950 patients: 565 from CheckMate 238 (408 ipilimumab versus 157 nivolumab) and 385 from CheckMate 915 (190 nivolumab versus 195 ipilimumab plus nivolumab). Serum autoAbs were profiled using the HuProt Human Proteome Microarray v4.0 (CDI Laboratories, Mayaguez, PR). Analysis of baseline differentially expressed autoAbs was followed by recurrence and severe toxicity signature building for each regimen, testing of the signatures, and additional independent validation for nivolumab using patients from CheckMate 915. Results: In the nivolumab independent validation cohort, high recurrence score predicted significantly worse recurrence-free survival [RFS; adjusted HR (aHR), 3.60; 95% confidence interval (CI), 1.98–6.55], and outperformed a model composed of clinical variables including PD-L1 expression (P < 0.001). Severe toxicity score was a significant predictor of severe irAEs (aHR, 13.53; 95% CI, 2.59–86.65). In the ipilimumab test cohort, high recurrence score was associated with significantly worse RFS (aHR, 3.21; 95% CI, 1.38–7.45) and severe toxicity score significantly predicted severe irAEs (aHR, 11.04; 95% CI, 3.84–37.25). In the ipilimumab plus nivolumab test cohort, high autoAb recurrence score was associated with significantly worse RFS (aHR, 6.45; 95% CI, 1.48–28.02), and high severe toxicity score was significantly associated with severe irAEs (aHR, 23.44; 95% CI, 4.10–212.50). Conclusions: Baseline serum autoAb signatures predicted recurrence and severe toxicity in patients treated with adjuvant immunotherapy. Prospective testing of the signatures that include datasets with longer follow-up and rare but more severe toxicities will help determine their generalizability and potential clinical utility. See related commentary by Hassel and Luke, p. 3914
Thymoquinone improves folliculogenesis, sexual hormones, gene expression of apoptotic markers and antioxidant enzymes in polycystic ovary syndrome rat model

Abstract Background Nowadays, polycystic ovary syndrome (PCOS) is a prevalent cause of infertility affecting women of reproductive age around the world. Thymoquinone is a natural antioxidant, derived from Nigella sativa. Objectives The current study aimed to evaluate the protective effects of thymoquinone on the detrimental effects of PCOS rats induced with letrozole. Methods Thirty‐two female rats were randomly divided into four groups: (1) Control, (2) PCOS, (3) PCOS+5 mg/kg thymoquinone and (4) PCOS+10 mg/kg thymoquinone. Thymoquinone was administered every 3 days for 30 days. Ovaries were histopathologically and stereologically examined, and antioxidant and apoptotic enzymes gene expression in ovaries and sex hormones in serum were measured. Results The number of unilaminar, multilaminar, antral, and graffian follicles, volume density of corpus luteum (p < 0.01), and GPx1 gene expression in ovaries and level of FSH in the blood increased in both thymoquinone groups when compared to untreated PCOS (p < 0.05). Ovaries in thymoquinone groups showed a significant reduction in the number of atretic follicles, ovary weight and volume, volume density of cortex and ovarian cysts, Bax gene expression (p < 0.01) and Bax/Bcl2 ratio as well as levels of luteinizing hormone (LH), LH/FSH ratio and testosterone (p < 0.05) in the blood of female rats when compared to PCOS group. Administration of thymoquinone restored the most detrimental effects of PCOS on ovaries (p < 0.01) and sexual hormones (p < 0.05) in rats. Conclusions These data suggest that thymoquinone has improved effects on ovarian function in the PCOS rat model. Therefore, thymoquinone might be useful as a protective agent and adjunct treatment in PCOS patients.
The metabolomic profile associated with clustering of cardiovascular risk factors—A multi-sample evaluation

Background A clustering of cardiovascular risk factors is denoted the metabolic syndrome (MetS), but the mechanistic underpinnings of this clustering is not clear. Using large-scale metabolomics, we aimed to find a metabolic profile common for all five components of MetS. Methods and findings 791 annotated non-xenobiotic metabolites were measured by ultra-performance liquid chromatography tandem mass spectrometry in five different population-based samples (Discovery samples: EpiHealth, n = 2342 and SCAPIS-Uppsala, n = 4985. Replication sample: SCAPIS-Malmö, n = 3978, Characterization samples: PIVUS, n = 604 and POEM, n = 501). MetS was defined by the NCEP/consensus criteria. Fifteen metabolites were related to all five components of MetS (blood pressure, waist circumference, glucose, HDL-cholesterol and triglycerides) at a false discovery rate of <0.05 with adjustments for BMI and several life-style factors. They represented different metabolic classes, such as amino acids, simple carbohydrates, androgenic steroids, corticosteroids, co-factors and vitamins, ceramides, carnitines, fatty acids, phospholipids and metabolonic lactone sulfate. All 15 metabolites were related to insulin sensitivity (Matsuda index) in POEM, but only Palmitoyl-oleoyl-GPE (16:0/18:1), a glycerophospholipid, was related to incident cardiovascular disease over 8.6 years follow-up in the EpiHealth sample following adjustment for cardiovascular risk factors (HR 1.32 for a SD change, 95%CI 1.07–1.63). Conclusion A complex metabolic profile was related to all cardiovascular risk factors included in MetS independently of BMI. This profile was also related to insulin sensitivity, which provide further support for the importance of insulin sensitivity as an important underlying mechanism in the clustering of cardiovascular risk factors.
Predicting mosquito repellents for clothing application from molecular fingerprint-based artificial neural network SAR models

ABSTRACT Spraying repellents on clothing limits toxicity and allergy problems that can occur when the repellents are directly applied to skin. This also allows the use of higher doses to ensure longer lasting effects. As the number of repellents available on the market is limited, it is necessary to propose new ones, especially by using in silico methods that reduce costs and time. In this context SAR models were built from a dataset of 2027 chemicals for which repellent activity on clothing was measured against Aedes aegypti. The interest of using either the ECFP or MACCS fingerprints as input neurons of a three-layer perceptron was evaluated. Transformation of MACCS bit strings into disjunctive tables led to interesting results. Models obtained with both types of fingerprints were compared to a model including physicochemical and topological descriptors.