Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Compensation of Traction System Actuator Failures for High-Speed Trains

In this paper, an adaptive failure compensation problem is addressed for high-speed trains with longitudinal dynamics and traction system actuator failures. Considered the time-varying parameters of the train motion dynamics caused by time-varying friction characteristics, a new piecewise constant model is introduced to describe the longitudinal dynamics with variable parameters. For both the healthy piecewise constant system and the system with actuator failures, the adaptive controller structure and conditions are derived to achieve the plant-model matching. The adaptive laws are designed to update the adaptive controller parameters, in the presence of the system piecewise constant parameters and actuator failure parameters which are unknown. Based on Lyapunov functions, the closed-loop stability and asymptotic state tracking are proved. Simulation results on a high-speed train model are presented to illustrate the performance of the developed adaptive actuator failure compensation control scheme.
Testing Identifiability of Causal Effects

This paper concerns the probabilistic evaluation of the effects of actions in the presence of unmeasured variables. We show that the identification of causal effect between a singleton variable X and a set of variables Y can be accomplished systematically, in time polynomial in the number of variables in the graph. When the causal effect is identifiable, a closed-form expression can be obtained for the probability that the action will achieve a specified goal, or a set of goals.
The Stability and Anti-apoptotic Function of A1 Are Controlled by Its C Terminus*

Most Bcl-2 family members can localize to intracellular membranes via hydrophobic sequences within their C-terminal portion. We found that the C terminus of the anti-apoptotic family member A1 did not function as a membrane anchor. Instead, this stretch of the protein rendered A1 highly unstable by mediating its polyubiquitination and rapid proteasomal degradation. Moreover, the domain did not only function independently of its position within the A1 protein but when transfered could even destabilize unrelated proteins like enhanced green fluorescent protein and caspase-3. A1 was, however, much more stable in the presence of the Bcl-2 homology-only protein BimEL, suggesting that direct interaction of A1 with pro-apoptotic members of the Bcl-2 family strongly reduces its rate of turnover. We further show that the C-terminal end of A1 also contributes to the anti-apoptotic capacity of the protein. In conclusion, our data demonstrate that the C terminus serves a dual function by controlling the stability of A1 and by amplifying the capacity of the protein to protect cells against apoptosis.
Carbon Monoxide Prevents Hypertension and Proteinuria in an Adenovirus sFlt-1 Preeclampsia-Like Mouse Model

Preeclampsia (PE) remains a leading cause of maternal and neonatal morbidity and mortality worldwide. Smoking cigarettes is associated with a decreased incidence of PE. Based on this observation and previous work, we hypothesize that women who smoke have a lower risk of developing PE because of elevated levels of carbon monoxide (CO) in their blood. The objective of this study was to determine if low-dose CO in ambient air could attenuate the late pregnancy hypertension (HTN) and proteinuria in the Adenovirus (Ad) sFlt-1 PE-like mouse model. Continuous low-dose CO treatment (250 ppm) was started on E10.5 and maintained until E17.5. Compared to control and Ad empty vector, AdsFlt-1 mice displayed late- gestation HTN (E14.5–17.5) (P<0.05), proteinuria (P<0.05) and reduced Bowman's space which were all prevented with CO treatment. Use of the Ad (with/without sFlt-1) or CO had no effect (p>0.05) on litter size, fetal resorption numbers and fetal or placental weights. This study shows that treatment with CO can prevent HTN and proteinuria in a mouse model of PE. It provides a possible mechanism for the reduced incidence of PE in smoking women, and supports the possibility of using CO as a future treatment for PE.
NLSdb—major update for database of nuclear localization signals and nuclear export signals

Abstract NLSdb is a database collecting nuclear export signals (NES) and nuclear localization signals (NLS) along with experimentally annotated nuclear and non-nuclear proteins. NES and NLS are short sequence motifs related to protein transport out of and into the nucleus. The updated NLSdb now contains 2253 NLS and introduces 398 NES. The potential sets of novel NES and NLS have been generated by a simple ‘in silico mutagenesis’ protocol. We started with motifs annotated by experiments. In step 1, we increased specificity such that no known non-nuclear protein matched the refined motif. In step 2, we increased the sensitivity trying to match several different families with a motif. We then iterated over steps 1 and 2. The final set of 2253 NLS motifs matched 35% of 8421 experimentally verified nuclear proteins (up from 21% for the previous version) and none of 18 278 non-nuclear proteins. We updated the web interface providing multiple options to search protein sequences for NES and NLS motifs, and to evaluate your own signal sequences. NLSdb can be accessed via Rostlab services at: https://rostlab.org/services/nlsdb/
Exploiting Experts’ Knowledge for Structure Learning of Bayesian Networks

Learning Bayesian network structures from data is known to be hard, mainly because the number of candidate graphs is super-exponential in the number of variables. Furthermore, using observational data alone, the true causal graph is not discernible from other graphs that model the same set of conditional independencies. In this paper, it is investigated whether Bayesian network structure learning can be improved by exploiting the opinions of multiple domain experts regarding cause-effect relationships. In practice, experts have different individual probabilities of correctly labeling the inclusion or exclusion of edges in the structure. The accuracy of each expert is modeled by three parameters. Two new scoring functions are introduced that score each candidate graph based on the data and experts’ opinions, taking into account their accuracy parameters. In the first scoring function, the experts’ accuracies are estimated using an expectation-maximization-based algorithm and the estimated accuracies are explicitly used in the scoring process. The second function marginalizes out the accuracy parameters to obtain more robust scores when it is not possible to obtain a good estimate of experts’ accuracies. The experimental results on simulated and real world datasets show that exploiting experts’ knowledge can improve the structure learning if we take the experts’ accuracies into account.
Large-scale fading model for mobile communications in disaster and salvage scenarios

Within a running project, I-LOV, wireless communication channel model for mobile radio propagation in disaster and salvage scenarios is developed. This contribution focuses on a semi-empirical large-scale fading (LSF) model, which is able to estimate signal power loss between base station and mobile phone under debris. Due to multitude of diverse materials appearing in burying scenarios, the signal loss factor varies heavily depending on electromagnetic properties of involved materials. Additionally, highly variable shape and dimension of rubbles impact strongly the loss factor. Besides numerous LSF models investigated in urban environments, only a few are in ruins. This paper presents a novel modeling concept of LSF in catastrophe scenarios. The proposed LSF model handles the radio propagation way in two parts: outside ruins and through the ruins. Measurements have been conducted at GSM 900 and GSM 1800 bands in collapsed buildings of stratified rubbles, as well as in an avalanche scenario. The model predication fits the measured data well.
Filtering images contaminated with pep and salt type noise with pulse-coupled neural networks

Pulse coupled neural network (PCNN) has a specific feature that the fire of one neuron can capture its adjacent neurons to fire due to their spatial proximity and intensity similarity. In this paper, it is indicated that this feature itself is a very good mechanism for image filtering when the image is damaged with pep and salt (PAS) type noise. An adaptive filtering method, in which the noisy pixels are first located and then filtered based on the output of the PCNN, is presented. The threshold function of a neuron in the PCNN is designed when it is used for filtering random PAS and extreme PAS noise contaminated image respectively. The filtered image has no distortion for noisy pixels and only less mistiness for non-noisy pixels, compared with the conventional window-based filtering method. Excellent experimental results show great effectiveness and efficiency of the proposed method, especially for heavy-noise contaminated images.
Shifting Paradigms in the Treatment of Metastatic Spine Disease

Study Design. Systematic review and evidence appraisal. Objective. To evaluate the optimal treatment for patients with spinal cord compression secondary to solid metastases and in patients with solitary renal metastases, without spinal cord compression. Methods. Focused Medline and OVID database searches were conducted using relevant keywords. Only clinical articles that evaluated specific end points of interest were included in the literature review. The quality of evidence provided by each article was assessed using the ATS guidelines. The expert opinion was synthesized based on the evidence and rated as strong or weak, depending on the quality of the supporting literature. Results. Twelve surgical and 7 radiation clinical series were identified that evaluated post-treatment ambulation in patients with metastatic spinal cord compression. Only 1 surgical article met the criteria for moderate quality evidence while the remaining surgical and radiation articles presented very low quality of evidence. All articles that evaluated treatment of solitary renal metastases presented very low quality of evidence. Conclusion. A strong recommendation is made for patients with high-grade cord compression due to solid tumor metastases to undergo surgical decompression with stabilization followed by radiation therapy. A weak recommendation is made for patients with solitary renal metastases without spinal cord compression to undergo spinal stereotactic radiosurgery.
Knowledge Assimilation at Foreign Subsidiaries of Japanese MNCs through Political Sensegiving and Sensemaking

We analyse political sensegiving and sensemaking by expatriates and host country employees through exportive, contestative and integrative stages of knowledge assimilation at two China-based subsidiaries of different Japanese MNCs. Comparative case study analysis indicated that efforts by expatriates and HQ-based experts to convey, routinize and standardize home country practices during the exportive and contestative stages, while involving traditional ‘one way’ knowledge transfer, can provide a foundation for a subsequent integrative stage, during which host country employees’ locally embedded knowledge is assimilated despite geopolitical asymmetry between home and host countries. Without this foundation, knowledge assimilation can remain ‘frozen’ at the contestative stage, with host country employees resisting importation of good practices from the HQ, and expatriates marginalizing host country employees’ contributions unless these are exceptionally compelling.
Semantics and proof rules of invariant based programs

Invariant based programming is an approach where we start to construct a program by first identifying the basic situations (pre- and postconditions as well as invariants) that could arise during the execution of the algorithm. These situations are identified before any code is written. After that, we identify the transitions between the situations, which will give us the flow of control in the program. The transitions are verified at the time when they are constructed. The correctness of the program is thus established as part of constructing the program. The program structure in invariant based programs is determined by the information content of the situations, using nested invariant diagrams. The control structure is secondary to the situation structure, and will usually not be well-structured in the classical sense, i.e., it is not necessarily built out of single-entry single-exit program constructs. We study in this paper the semantics and proof rules for invariant-based programs. The total correctness of an invariant diagram is established by proving that each transition preserves the invariants and decreases a global variant. The proof rules for invariant-based programs are shown to be correct and complete with respect to an operational semantics. The proof of correctness and completeness introduces the weakest precondition semantics for invariant diagrams, and uses a special construction, based on well-ordered sets, of the least fixpoint of a monotonic function on a complete lattice. The results presented in this paper have been mechanically verified in the PVS theorem prover.
Utility of Framingham Coronary Heart Disease Risk Score for Predicting Cardiac Risk After Stroke

Background and Purpose— Coronary heart disease (CHD) is a major cause of mortality among stroke patients after the acute period. Simple risk stratification of stroke patients without known CHD may permit prompt implementation of CHD-specific management strategies for those who are at high risk for cardiac events. We assessed the utility of the Framingham Coronary Heart Disease Risk Score (FCRS) as a prognosticator in stroke patients without known CHD. Methods— Post hoc analysis of a trial dataset of 3509 recent ischemic stroke patients who were aged 35 years or older, recruited from 56 centers, and followed-up for 2 years. Patients were categorized as having known CHD, high FCRS (≥20%), and low/intermediate FCRS (<20%). The predictive values between baseline FCRS and primary (myocardial infarction [MI]), secondary (MI or vascular death), and tertiary (recurrent stroke) outcomes were assessed in multivariate analyses. Results— Rates of first MI at 2 years were 6.34%, 4.65%, and 1.44% for the known CHD, high FCRS, and low/intermediate FCRS groups. Compared with stroke patients with low/intermediate FCRS, individuals with high FCRS had a higher risk of MI (adjusted hazard ratio, 3.70; 95% confidence interval, 2.14–6.38) and MI or vascular death (adjusted hazard ratio, 2.21; 95% confidence interval, 1.48–3.28). High FCRS did not predict recurrent stroke. Conclusion— Among patients with a recent ischemic stroke without known CHD, high FCRS was associated with a higher risk of MI and vascular death, but not stroke. FCRS could be a simple way to identify recent stroke patients who may benefit from additional CHD-specific management.
Comparison of the Phenolic Compounds, Carotenoids and Tocochromanols Content in Wheat Grain under Organic and Mineral Fertilization Regimes

A field study was performed to evaluate the effect of mineral (NPK) and organic-based fertilizers such as compost (C), manure (FYM) and meat and bone meal (MBM) on the appearance (dimensions and color) of spring wheat kernels and on the total content in grain of main its phytochemicals (polyphenols, carotenoids and tocochromanols) and phenolic acids composition. Total phenolic compounds were determined using the Folin-Ciocalteu assay after alkaline hydrolysis of grain and carotenoids were analyzed spectrophotometrically. Composition of tocochromanols and phenolic acids was determined using RP-HPLC techniques. Only insignificant differences in the appearance of kernels and small changes in the content and composition of grain phytochemicals were noted between the studied fertilization systems. Among the analyzed phytochemicals the greatest variation was observed in the group of polyphenol compounds, with a stated increase of their total content of 6.7 and 11.2% in grain fertilized with MBM and compost, respectively. Simultaneously the grain from organic fertilization contained significantly less phenolic acids, and the decrease in their content ranged from 10.0% for FYM to 24.8% for MBM+EM-1. Organically and conventionally fertilized grain had similar amounts of tocochromanols and carotenoids. Comparison of MBM and MBM+EM-1 variants showed that application of effective microorganisms decreased carotenoids and tocochromanols content by 8.5 and 9.7%, respectively.
Self-Healing Key Distribution Schemes for Wireless Networks: A Survey

The objective of self-healing key distribution is to enable group users to recover session keys by themselves, without requesting additional transmissions from the group manager (GM), even when they miss some broadcast messages. One major benefit of the self-healing key distribution mechanism is the reduction of energy consumption due to the elimination of such additional transmission. Also in some applications, e.g., uni-directional broadcast channel from the GM, the self-healing key distribution mechanism seems to be the ideal solution. Desired features of self-healing key distribution schemes include energy awareness, short broadcast message, efficient users addition, revocation and so on. A primary challenge is managing the trade-off between providing an acceptable level of security and conserving scarce resources in particular energy which is critical for wireless network operations. Over a decade, a great number of self-healing key distribution schemes have been proposed for establishing a group key amongst a dynamic group of users over an unreliable, or lossy, network. In this paper a comprehensive survey is conducted on the state-of-the-art in the field of self-healing key distribution. First, we clarify the security requirements of self-healing key distribution scheme for their special application environment. Then, we present a classification of self-healing key distribution schemes according to different cryptographic primitives, and give an insight to their features and goals. Furthermore, we consider several problems, namely authentication on broadcast messages, sponsorization and mutual-healing, related to the robustness of self-healing key distribution schemes. At last, we delineate their similarities and differences and outline several future research directions.
Machine learning and features selection for semi-automatic ICD-9-CM encoding

This paper describes the architecture of an encoding system which aim is to be implemented as a coding help at the Cliniques universtaires Saint-Luc, a hospital in Brussels. This paper focuses on machine learning methods, more specifically, on the appropriate set of attributes to be chosen in order to optimize the results of these methods. A series of four experiments was conducted on a baseline method: Naive Bayes with varying sets of attributes. These experiments showed that a first step consisting in the extraction of information to be coded (such as diseases, procedures, aggravating factors, etc.) is essential. It also demonstrated the importance of stemming features. Restraining the classes to categories resulted in a recall of 81.1%.
Waveform Synthesis for Diversity-Based Transmit Beampattern Design

Transmit beampattern design is a critically important task in many fields including defense and homeland security as well as biomedical applications. Flexible transmit beampattern designs can be achieved by exploiting the waveform diversity offered by an array of sensors that transmit probing signals chosen at will. Unlike a standard phased-array, which transmits scaled versions of a single waveform, a waveform diversity-based system offers the flexibility of choosing how the different probing signals are correlated with one another. Recently proposed techniques for waveform diversity-based transmit beampattern design have focused on the optimization of the covariance matrix of the waveforms, as optimizing a performance metric directly with respect to the waveform matrix is a more complicated operation. Given an , obtained in a previous optimization stage or simply pre-specified, the problem becomes that of determining a signal waveform matrix whose covariance matrix is equal or close to , and which also satisfies some practically motivated constraints (such as constant-modulus or low peak-to-average-power ratio constraints). We propose a cyclic optimization algorithm for the synthesis of such an , which (approximately) realizes a given optimal covariance matrix under various practical constraints. A numerical example is presented to demonstrate the effectiveness of the proposed algorithm.
Crowdsourcing under Real-Time Constraints

In the recent years we are experiencing the rapid growth of crowdsourcing systems, in which “human workers” are enlisted to perform tasks more effectively than computers, and get compensated for the work they provide. The common belief is that the wisdom of the “human crowd” can greatly complement many computer tasks which are assigned to machines. A significant challenge facing these systems is determining the most efficient allocation of tasks to workers to achieve successful completion of the tasks under real-time constraints. This paper presents REACT, a crowdsourcing system that seeks to address this challenge and proposes algorithms that aim to stimulate user participation and handle dynamic task assignment and execution in the crowdsourcing system. The goal is to determine the most appropriate workers to assign incoming tasks, in such a way so that the realtime demands are met and high quality results are returned. We empirically evaluate our approach and show that REACT meets the requested real-time demands, achieves good accuracy, is efficient, and improves the amount of successful tasks that meet their deadlines up to 61% compared to traditional approaches like AMT.
A 3.8-5.5-GHz Multi-Band CMOS Frequency Synthesizer for WPAN/WLAN Applications

In this paper, we present a 3.8-5.5 GHz multi-band CMOS frequency synthesizer for WLAN and UWB applications. In the multi-band frequency synthesizer, both new multi-mode prescaler and adaptive multi-band LC VCO are proposed. The proposed multi-mode prescaler produces six modes of divide-by-2/3, 4/5, 8/9, 16/17, 32/33, and 64/65. In the adaptive multi-band LC VCO, the gate width of cross-coupled MOS array is tuned to calibrate oscillation amplitude and alleviate 1/f flicker noise of MOS. The multi-band frequency synthesizer represents -121 dBc/Hz at 5 MHz offset from 5.24 GHz carrier. The multi-band frequency synthesizer consumes a total current of 26mA at 1.2 V, and is manufactured in 0.13-mum CMOS process technology
Aggregating Inter-Sentence Information to Enhance Relation Extraction

Previous work for relation extraction from free text is mainly based on intra-sentence information. As relations might be mentioned across sentences, inter-sentence information can be leveraged to improve distantly supervised relation extraction. To effectively exploit inter-sentence information, we propose a ranking based approach, which first learns a scoring function based on a listwise learning-to-rank model and then uses it for multi-label relation extraction. Experimental results verify the effectiveness of our method for aggregating information across sentences. Additionally, to further improve the ranking of high-quality extractions, we propose an effective method to rank relations from different entity pairs. This method can be easily integrated into our overall relation extraction framework, and boosts the precision significantly.