Miracle-dz/UniFAR-FTUs huggingface dataset:
Design Methodologies for Central Pattern Generators: An Application to Crawling Humanoids

Systems of coupled nonlinear oscillators inspired from animal central pattern generators (CPGs) are increasingly used for the control of locomotion in robots, in particular for online trajectory generation. Indeed, such systems present interesting characteristics like limit cycle behavior (i.e. stability), synchronization, and the possibility to be entrained and modulated by external signals. There are now good methodologies for designing systems that exhibit specific gaits, i.e. specific phase relations between oscillators, however techniques to modulate the shape of the rhythmic signals in a controlled way are still missing. In this article, we present a method for shaping the signals of an oscillatory system according to several criteria that are relevant for locomotion control (but which could also be useful for other applications). These criteria include being able to adjust the relative durations of ascending and descending phases in a cycle, and to temporarily modulate the dynamics of one oscillator according to the states of another one. The first criterion is important for locomotion in order to adjust the duration of swing and stance phases, while the second allows one to introduce signal shape variations to deal with proper inter-limb coordination. We apply the method to the design of a system of coupled oscillators used to control crawling in a simulated humanoid robot. Using some key characteristics of signal shapes extracted from recordings of baby crawling, we design the system to produce stable trot-like crawling gaits. Insights from symmetry groups’ theory are used to design the right phase lags. The oscillators are designed such that the speed of locomotion can be adjusted by varying the duration of the stance phase while keeping the duration of the swing phase constant, like in most tetrapod animals.
The risk of revision in total knee arthroplasty is not affected by previous high tibial osteotomy

Background and purpose — Previous studies have found different outcomes after revision of knee arthroplasties performed after high tibial osteotomy (HTO). We evaluated the risk of revision of total knee arthroplasty with or without previous HTO in a large registry material. Patients and methods — 31,077 primary TKAs were compared with 1,399 TKAs after HTO, using Kaplan-Meier 10-year survival percentages and adjusted Cox regression analysis. Results — The adjusted survival analyses showed similar survival in the 2 groups. The Kaplan-Meier 10-year survival was 93.8% in the primary TKA group and 92.6% in the TKA-post-HTO group. Adjusted RR was 0.97 (95% CI: 0.77–1.21; p = 0.8). Interpretation — In this registry-based study, previous high tibial osteotomy did not appear to compromise the results regarding risk of revision after total knee arthroplasty compared to primary knee arthroplasty.
A Neural Network Based Intrusion Detection Data Fusion Model

The abilities of summarization, learning and self-fitting and inner-parallel computing make artificial neural networks suitable for intrusion detection. On the other hand, data fusion based IDS has been used to solve the problem of distorting rate and failing-to-report rate and improve its performance. However, Multi-sensor input-data makes the IDS lose its efficiency. The research of neural network based data fusion IDS tries to combine the strong process ability of neural network with the advantages of data fusion IDS. A neural network is designed to realize the data fusion and intrusion analysis and pruning algorithm of neural networks is used for filtering information from multi-sensors.
IT-user-aligned Business Model Innovation (ITUA) in the Sharing Economy: A Dynamic Capabilities Perspective

We introduce the sharing economy as an IT-enabled phenomenon that is distinct from existing socioeconomic modes of interaction. While research on the consumer perspective is growing, studies examining the complementary organizational perspective seem to be lagging behind. Our study therefore aims to investigate the firm stance by analyzing how successful sharing economy organizations manage their business models. We choose the business model as a unit of analysis, as it allows incorporating IT as an integral component of organizational activities into our research. Our case study uses multiple data sources and elicitation methods and provides detailed rigorously collected information that is not typical for case studies. Our findings suggest that consumer needs in the sharing economy lead to conflicting requirements for business model configuration: innovativeness and acceptability. To carefully balance the two, firms need to constantly integrate novel technologies into their business model while not deviating too heavily from already existing models. We term our emergent theory ITuser-aligned business model innovation (ITUA). It offers the unique contribution of conceptualizing the process of business model innovation as a dynamic capability. Our study further highlights the fundamental role of IT in enabling a potential reconciliation of economic and societal value creation.
Enhancing symbolic execution with veritesting

We present MergePoint, a new binary-only symbolic execution system for large-scale and fully unassisted testing of commodity off-the-shelf (COTS) software. MergePoint introduces veritesting, a new technique that employs static symbolic execution to amplify the effect of dynamic symbolic execution. Veritesting allows MergePoint to find twice as many bugs, explore orders of magnitude more paths, and achieve higher code coverage than previous dynamic symbolic execution systems. MergePoint is currently running daily on a 100 node cluster analyzing 33,248 Linux binaries; has generated more than 15 billion SMT queries, 200 million test cases, 2,347,420 crashes, and found 11,687 bugs in 4,379 distinct applications.
AI-Ckpt: leveraging memory access patterns for adaptive asynchronous incremental checkpointing

With increasing scale and complexity of supercomputing and cloud computing architectures, faults are becoming a frequent occurrence, which makes reliability a difficult challenge. Although for some applications it is enough to restart failed tasks, there is a large class of applications where tasks run for a long time or are tightly coupled, thus making a restart from scratch unfeasible. Checkpoint-Restart (CR), the main method to survive failures for such applications faces additional challenges in this context: not only does it need to minimize the performance overhead on the application due to checkpointing, but it also needs to operate with scarce resources. Given the iterative nature of the targeted applications, we launch the assumption that first-time writes to memory during asynchronous checkpointing generate the same kind of interference as they did in past iterations. Based on this assumption, we propose novel asynchronous checkpointing approach that leverages both current and past access pattern trends in order to optimize the order in which memory pages are flushed to stable storage. Large scale experiments show up to 60% improvement when compared to state-of-art checkpointing approaches, all this achievable with an extra memory requirement of less than 5% of the total application memory.
Host reactive donor T cells are associated with lung injury after experimental allogeneic bone marrow transplantation.

Noninfectious lung injury is common after allogeneic bone marrow transplantation (BMT), but its association with acute graft-versus-host disease (GVHD) is unclear. Using a murine BMT system where donor and host differ by multiple minor histocompatibility (H) antigens, we investigated the nature of lung injury and its relationship both to systemic GVHD and host-reactive donor T cells. Lethally irradiated CBA hosts received syngeneic BMT or allogeneic (B10.BR) T-cell-depleted (TCD) bone marrow (BM) with and without the addition of T cells. Six weeks after BMT, significant pulmonary histopathology was observed in animals receiving allogeneic BMT compared with syngeneic controls. Lung damage was greater in mice that received allogeneic T cells and developed GVHD, but it was also detectable after TCD BMT when signs of clinical and histologic acute GVHD were absent. In each setting, lung injury was associated with significant alterations in pulmonary function. Mature, donor (Vbeta6(+) and Vbeta3(+)) T cells were significantly increased in the broncho-alveolar lavage (BAL) fluid of all allogeneic BMT recipients compared with syngeneic controls, and these cells proliferated and produced interferon-gamma (IFN-gamma) to host antigens in vitro. These in vitro responses correlated with increased IFN-gamma and tumor necrosis factor-alpha (TNF-alpha) in the BAL fluid. We conclude that alloreactive donor lymphocytes are associated with lung injury in this allogeneic BMT model. The expansion of these cells in the BAL fluid and their ability to respond to host antigens even when systemic tolerance has been established (ie, the absence of clinical GVHD) suggest that the lung may serve as a sanctuary site for these host reactive donor T cells. These findings may have important implications with regard to the evaluation and treatment of pulmonary dysfunction after allogeneic BMT even when clinical GVHD is absent.
Subthalamic nucleus long-range synchronization—an independent hallmark of human Parkinson's disease

Beta-band synchronous oscillations in the dorsolateral region of the subthalamic nucleus (STN) of human patients with Parkinson's disease (PD) have been frequently reported. However, the correlation between STN oscillations and synchronization has not been thoroughly explored. The simultaneous recordings of 2390 multi-unit pairs recorded by two parallel microelectrodes (separated by fixed distance of 2 mm, n = 72 trajectories with two electrode tracks >4 mm STN span) in 57 PD patients undergoing STN deep brain stimulation surgery were analyzed. Automatic procedures were utilized to divide the STN into dorsolateral oscillatory and ventromedial non-oscillatory regions, and to quantify the intensity of STN oscillations and synchronicity. Finally, the synchronicity of simultaneously vs. non-simultaneously recorded pairs were compared using a shuffling procedure. Synchronization was observed predominately in the beta range and only between multi-unit pairs in the dorsolateral oscillatory region (n = 615). In paired recordings between sites in the dorsolateral and ventromedial (n = 548) and ventromedial-ventromedial region pairs (n = 1227), no synchronization was observed. Oscillation and synchronicity intensity decline along the STN dorsolateral-ventromedial axis suggesting a fuzzy border between the STN regions. Synchronization strength was significantly correlated to the oscillation power, but synchronization was no longer observed following shuffling. We conclude that STN long-range beta oscillatory synchronization is due to increased neuronal coupling in the Parkinsonian brain and does not merely reflect the outcome of oscillations at similar frequency. The neural synchronization in the dorsolateral (probably the motor domain) STN probably augments the pathological changes in firing rate and patterns of subthalamic neurons in PD patients.
Teaching Forward , Understanding Backward : In Search of Theorized Practice

In this study, teacher generated metaphors were closely aligned with three major educological themes: behavioral change, assisted performance, and radical constructivism. Teacher observations, in contrast, revealed much more complexity. A factor analysis of the follow-up survey results exposed nine distinctive teacher practice profiles. Each of these three strategies -metaphor analysis, observations, and survey data -provided teachers with unique topographical perspectives for uncovering the subterranean landscapes of the mind. Rather than viewing their work as an eclectic collection of strategies, teachers were able to identify the components of their own distinctive profiles and analyze their implications. Their analyses revealed that daily activities did not always reflect underlying patterns.
Information Theory Based Feature Valuing for Logistic Regression for Spam Filtering

Discriminative learning models such as Logistic Regression (LR) has shown good performance in spam filtering tasks. While most previous researches on LR have used binary features, this discards much useful information. To overcome this problem, information theory based feature valuing method for LR instead of traditional binary features is presented. The effectiveness of our approach has been evaluated on TREC, CEAS, and SEWM test sets. Results show that the proposed method outperforms the traditional binary features in the most test sets.
Swab Materials and Bacillus anthracis Spore Recovery from Nonporous Surfaces

Four swab materials were evaluated for their efficiency in recovery of Bacillus anthracis spores from steel coupons. Cotton, macrofoam, polyester, and rayon swabs were used to sample coupons inoculated with a spore suspension of known concentration. Three methods of processing for the removal of spores from the swabs (vortexing, sonication, or minimal agitation) and two swab preparations (premoistened and dry) were evaluated. Results indicated that premoistened swabs were more efficient at recovering spores than dry swabs (14.3% vs. 4.4%). Vortexing swabs for 2 min during processing resulted in superior extraction of spores when compared to sonicating them for 12 min or subjecting them to minimal agitation. Premoistened macrofoam and cotton swabs that were vortexed during processing recovered the greatest proportions of spores with a mean recovery of 43.6% (standard deviation [SD] 11.1%) and 41.7% (SD 14.6%), respectively. Premoistened and vortexed polyester and rayon swabs were less efficient, at 9.9% (SD 3.8%) and 11.5% (SD 7.9%), respectively.
Macrolide therapy suppresses key features of experimental steroid-sensitive and steroid-insensitive asthma

Background Steroid-insensitive endotypes of asthma are an important clinical problem and effective therapies are required. They are associated with bacterial infection and non-eosinophilic inflammatory responses in the asthmatic lung. Macrolide therapy is effective in steroid-insensitive endotypes, such as non-eosinophilic asthma. However, whether the effects of macrolides are due to antimicrobial or anti-inflammatory mechanisms is not known. Objective To determine and assess the efficacy of macrolide (ie, clarithromycin) and non-macrolide (ie, amoxicillin) antibiotic treatments in experimental models of infection-induced, severe, steroid-insensitive neutrophilic allergic airways disease (SSIAAD), compared with steroid-sensitive AAD and to delineate the antimicrobial and anti-inflammatory effects of macrolide therapy. Methods We developed and used novel mouse models of Chlamydia and Haemophilus lung infection-induced SSIAAD. We used these models to investigate the effects of clarithromycin and amoxicillin treatment on immune responses and airways hyper-responsiveness (AHR) in Ova-induced, T helper lymphocyte (Th) 2 -associated steroid-sensitive AAD and infection-induced Th1/Th17-associated SSIAAD compared with dexamethasone treatment. Results Clarithromycin and amoxicillin had similar antimicrobial effects on infection. Amoxicillin did attenuate some features, but did not broadly suppress either form of AAD. It did restore steroid sensitivity in SSIAAD by reducing infection. In contrast, clarithromycin alone widely suppressed inflammation and AHR in both steroid-sensitive AAD and SSIAAD. This occurred through reductions in Th2 responses that drive steroid-sensitive eosinophilic AAD and tumour necrosis factor α and interleukin 17 responses that induce SSIAAD. Conclusions Macrolides have broad anti-inflammatory effects in AAD that are likely independent of their antimicrobial effects. The specific responses that are suppressed are dependent upon the responses that dominate during AAD.
A New Adaptive Genetic Neural Network Based Active Evolution

Neural network’s constructions and weights are one aspect of the basic questions. A kind of artificial neural network method based on an active evolution genetic algorithm is proposed. Introduce the algorithm’s basic idea. Active evolution genetic algorithm is combined the active evolution algorithm which is advantaged both overcoming the local optimized value and keeping rapidly convergence. Save time and space for the construction of new network, improve the output’s error precision and find the better way to solve how to build the network’s weights and structures at the beginning. The experiment results show that the algorithm is superior to simple genetic neural network algorithm with higher convergent speed, optimization and practical value of structures and weights, and improves network’s forecasting accuracy.
Transcriptome Analysis Showed a Differential Signature between Invasive and Non-invasive Corticotrophinomas

ACTH-dependent hypercortisolism caused by a pituitary adenoma [Cushing’s disease (CD)] is the most common cause of endogenous Cushing’s syndrome. CD is often associated with several morbidities, including hypertension, diabetes, osteoporosis/bone fractures, secondary infections, and increased cardiovascular mortality. While the majority (≈80%) of the corticotrophinomas visible on pituitary magnetic resonance imaging are microadenomas (MICs, <10 mm of diameter), some tumors are macroadenomas (MACs, ≥10 mm) with increased growth potential and invasiveness, exceptionally exhibiting malignant demeanor. In addition, larger and invasive MACs are associated with a significant increased risk of local complications, such as hypopituitarism and visual defects. Given the clinical and molecular heterogeneity of corticotrophinomas, the aim of this study was to investigate the pattern of genetic differential expression between MIC and MAC, including the invasiveness grade as a criterion for categorizing these tumors. In this study, were included tumor samples from patients with clinical, laboratorial, radiological, and histopathological diagnosis of hypercortisolism due to an ACTH-producing pituitary adenoma. Differential gene expression was studied using an Affymetrix microarray platform in 12 corticotrophinomas, classified as non-invasive MIC (n = 4) and MAC (n = 5), and invasive MAC (n = 3), according to modified Hardy criteria. Somatic mutations in USP8 were also investigated and mutations were identified in six cases. Differential expression analysis demonstrated that non-invasive MIC and MAC have a similar genetic signature, while invasive MACs exhibited a differential expression profile. Among the genes differentially expressed, we highlighted CCND2, ZNF676, DAPK1, and TIMP2, and their differential expression was validated through quantitative real-time PCR in another cohort of 15 non-invasive and 3 invasive cortocotrophinomas. We also identified potential biological pathways associated with growth and invasiveness, TGF-β and G protein signaling pathways, DNA damage response pathway, and pathways associated with focal adhesion. Our study revealed a differential pattern of genetic signature in a subgroup of MAC, supporting a genetic influence on corticotrophinomas in patients with CD.
Multi-Level Reputation-Based Greylisting

We present the idea and implementation details of a highly effective and reliable e-mail filtering technique. At the core of the component-based architecture is a novel combination of an enhanced self-learning variant of greylisting with a reputation-based trust mechanism. These strategies provide separate feature extraction and classification components with the opportunity of utilizing the time between two delivery attempts of an e-mail message. The approach presented features a very high spam blocking rate and also minimizes the workload on the client side, as no responsibility for messages classified as spam is taken. The reputation-based trust mechanism decreases the delay in the transfer process of e-mail messages from reliable senders and also reduces the number of erroneously blocked legitimate messages.
Adaptive routing in mobile ad hoc networks based on decision aid approach

In this paper, we propose a new approach to the problem of adaptive routing in small scale Mobile Ad Hoc Networks (MANET). The proposed approach focuses on leveraging the already existing routing algorithms in real-time through switching from one routing algorithm to another as the conditions change. The contribution of this work is three-fold: (1) nodes use a Multiple Criteria Decision Analysis method called PROMETHEE to make local decisions, (2) the final decision is evaluated as an aggregation of all the local decisions in the MANET through weighted voting, and (3) the network-wide decision process is carried out through a hybrid method that aims to benefit from the advantages of centralized and distributed methods, while limiting their disadvantages. The analyses based on the results extracted from simulations underline that the proposed approach produces stable configurations with minimal interruptions. Furthermore, the completion of global adaptation cycles are performed an order of magnitude faster in comparison to the results outlined in the previous work. These results indicate that the proposed solution is a promising alternative to the existing solutions, and forms a strong basis for further research on larger scale MANETs.
A Compensation-based Approach for Materialized View Maintenance in Distributed Environments

Data integration over multiple heterogeneous data sources has become increasingly important for modern applications. The integrated data is usually stored in materialized views (MV) to allow better access, performance and high availability. MV must be maintained after the data sources change. In a loosely-coupled environment, such as the Data Grid, the data sources are autonomous. Hence the source updates can be concurrent and cause erroneous maintenance results. State-of-the-art maintenance strategies apply compensating queries to correct such errors, making the restricting assumption that all source schemata remain static over time. However, in such dynamic environments, the data sources may change not only their data but also their schema, query capabilities or semantics. Consequently, either the maintenance queries or compensating queries would fail. In this paper, we propose a framework called DyDa that overcomes these limitations and handles both source data updates and schema changes. First, we identify three types of maintenance anomalies, caused by either data updates and/or rename and/or drop schema changes. We propose a compensation algorithm to solve the first two types of anomalies. We identify that the third type of anomaly is caused by the violation of dependencies between the maintenance processes. We propose a detection and correction algorithm to remove such anomalies based on the formalisms of dependencies. A new view adaptation algorithm is designed to incrementally adapt some complex updates introduced by the correction algorithm. Put together, these algorithms are the first complete solution to the concurrency problems for MV maintenance in loosely-coupled environments. We have implemented the DyDa system. The experimental results show that our new concurrency handling strategy imposes a minimal overhead on normal data update processing while allowing for the extended functionality to maintain the materialized views even under concurrent schema changes.