Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Resource Scaling for Explicit State Model Checking on Cloud

Model checking is now a standard technology for verifying large and complex systems. While there are a range of tools and techniques to verify various properties of a system under consideration, in this work, we restrict our attention to safety checking procedures using explicit state space generation. The necessary hardware resources required in this approach depends on the model complexity and the resulting state transition graph that gets generated. This cannot be estimated apriori. For reasonably realistic models, the available main memory in even high end servers may not be sufficient. Hence, we have to use distributed safety verification approaches on a cluster of nodes. However, the problem of estimating the minimum number of nodes in the cluster for the verification procedure to complete successfully remains unsolved. In the thesis, we propose a dynamically scalable model checker using an actor based architecture. Using the proposed approach, an end user can invoke a model checker hosted on a cloud platform in a push button fashion. Our safety verification procedures automatically expands the cluster by requesting more virtual machines from the cloud provider. Finally, the user gets to pay only for the hardware resources he rented for the duration of the verification procedure. We refer to this as Model Checking as Service. We approach this problem by proposing an asynchronous algorithm for safety checking in actor framework. The actor based approach allows for scaling the resources on a need basis and redistributes the work load transparently through state migration. We tested our approach by developing a distributed version of SpinJA model checker using Akka actor framework. We conducted our experiments on Google Cloud Engine (GCE) platform wherein we scale our resources automatically using the GCE API. On large models such as anderson.8 from BEEM benchmark suite, our approach reduced the model checking cost in dollars by 8.6x while reducing the wall clock time to complete the safety checking procedure 5.5x times.
Salinity stress cross-tolerance of Ostrea lurida after exposure to acidified ocean conditions in a simulated freshet discharge

Ocean acidification is widely regarded as a serious danger to marine organisms, especially calcifying invertebrates, but how this might interact with other abiotic stressors in realistic environmental events is less well modeled. Low salinity events in Washington state caused by snowmelt may increase in severity due to climate change, adding another stressor onto the backdrop of an already acidifying ocean. To test the effect of these asynchronous multi-stressors, we acclimated Olympia oysters Ostrea lurida to a gradient of pH conditions, after which we restored the pH and exposed them to low salinity, simulating a river freshet discharge. Our data indicate that shell thickness and respiration rates decrease with pH, regardless of salinity shock conditions. Calcification rates decreased with pH for oysters exposed only to ambient salinity, while calcification rates trended towards an increase in oysters exposed to the salinity shock. These findings could indicate a possible cross-tolerance in O. lurida, where exposure to increasingly acidic conditions could prepare them for short-term hyposaline shock.
MGRO J2019+37’s VHE radiation mechanism study by multi-wavelength observation

Milagro observations of the Cygnus region at 12-35 TeV have revealed a bright and extended source, MGRO J2019+37. This source is almost as bright as Crab Nebula in northern sky, while it is not confirmed by ARGO-YBJ around TeV. Up to now, no obvious counterpart at low energy wavelengths has been found. For this reason, MGRO J2019+37 becomes mysterious and the origin of its VHE γ-ray emission mechanism is attractive. In this paper, a brief summary of the multi-wavelength observations from Radio to γ-ray is presented. All the available data from the INTEGRAL X-ray and the Fermi-LAT γ-ray bands are used to get constraint on its emission flux at low energy wavelengths. Then, its possible counterparts and the VHE emission mechanism are
An analysis of physiological signals as a measure of engagement in rehabilitation using machine learning algorithms

Background: Active participation of clients in rehabilitation is a crucial factor for improved outcomes. A few studies have investigated how to measure patient engagement to given rehabilitation therapies compared to intervention developments. In this study, we aimed to examine the possibility of assessing patient engagement by analyzing features computed from autonomic nervous responses with machine learning algorithms, such as naïve bayes classi�er (NB), random forest (RF), support vector machine (SVM), and neural network (NN). Methods: Autonomic nervous responses were collected from 56 older adults with moderate to mild cognitive impairment using a wearable device during rehabilitation therapy sessions under two different conditions designed to induce different levels of engagement. 32 features were obtained from the autonomic nervous system responses and used to determine the client engagement levels. In addition, feature selection methods were applied to reduce redundant information. Client engagement during rehabilitation therapy sessions was rated by therapists using Pittsburgh Rehabilitation Participation Scale (PRPS). In addition, subjective feelings were quanti�ed by participants using Self-Assessment Manikin (SAM). The data collected under the different conditions were labelled as having a higher or lower degree of engagement, and classi�cation performance was tested with the algorithms. Results: It was found that the mean scores of PRPS and the valence dimension of the participants were signi�cantly different between the conditions con�rming the successful induction of varying levels of engagement from the participants. The worst performance was observed when the classi�ers were trained with all features (mean classi�cation accuracy: 81.6%) whereas the highest mean accuracy was observed when classi�ers were trained with the top 5 features (85.6%) selected by minimum-Redundancy-Maximum-Relevance (mRMR) than other combinations of feature sets. The best classi�cation accuracy of different degrees of rehabilitation participation was achieved with SVM (87.7%) in combination with the top 5 ranked features, which indicates that the feature selection methods employed in this study did not only lead to a reduction in the number of features, but also a slight improvement in the classi�cation performance. On the other hand, RF consistently performed better than NB and NN when combined with the mRMR feature selection method, suggesting RF is the most robust classi�er among the tested classi�ers for determining the degree of client engagement in rehabilitation. Conclusions:
Proteomics in Understanding Society: pre-analytic condition impacts on measurement

Proteomic methods have been developed to enable measurements of low levels of proteins in blood samples. In Waves 2 and 3 (2010-2012) of Understanding Society, blood samples were collected in participants’ homes, then posted to the laboratory and subsequently used for the measurement of 184 proteins. It is unknown whether the delay between blood collection and laboratory processing compromises blood sample integrity such that genuine associations between proteins and demographic factors are obscured. This paper describes the protein measurements and the quality checks performed on them. It also describes the proteins’ associations with a range of factors, including pre-analytic factors (time of day of sample collection, transit delay: number of days the sample was in the post, haemolysis of sample) and demographic factors (age, sex, educational attainment, geographical region, ethnicity). A range of standard statistical tests were used. We find that protein measurements in the dataset are highly reproducible, with low levels of missingness. We observe low detectability in around one-tenth of proteins. We identify transit delay as an important source of variation, and we characterise the nature of its relationship to each individual protein. Examining educational attainment as an exemplar, we observe that of the proteins measured, 127 varied by educational attainment, including just under half of the proteins defined with low detectability. The largest associations with educational attainment are found in proteins previously described to be associated with cognitive function. While adjustment for pre-analytic factors usually improved overall model fit, association of proteins with educational attainment were largely unaltered.
RETROSPECTIVE: Detailed Design and Evaluation of Redundant Multithreading Alternatives

Context This paper was part of a stream of work in the area of soft error analysis and tolerance techniques done by two of the co-authors (Mukherjee and Reinhardt) in the early 2000s. This effort started with an ISCA 2000 paper on redundant multithreading (RMT) [1] followed by this paper two years later [2]. A few other papers were published with additional collaborators [3,4], culminating in Mukherjee’s book on the topic [5].
Slow rate fluctuations in a network of noisy neurons with coupling delay

We analyze the emergence of slow rate fluctuations and rate oscillations in a model of a random neuronal network, underpinning the individual roles and interplay of external and internal noise, as well as the coupling delay. We use the second-order finite-size mean-field model to gain insight into the relevant parameter domains and the mechanisms behind the phenomena. In the delay-free case, we find an intriguing paradigm for slow stochastic fluctuations between the two stationary states, which is shown to be associated to noise-induced transitions in a double-well potential. While the basic effect of coupling delay consists in inducing oscillations of mean rate, the coaction with external noise is demonstrated to lead to stochastic fluctuations between the different oscillatory regimes.
Efficient Regression-Based Linear Discriminant Analysis for Side-Channel Security Evaluations Towards Analytical Attacks against 32-bit Implementations

32-bit software implementations become increasingly popular for embedded security applications. As a result, profiling 32-bit target intermediate values becomes increasingly needed to evaluate their side-channel security. This implies the need of statistical tools that can deal with long traces and large number of classes. While there are good options to solve these issues separately (e.g., linear regression and linear discriminant analysis), the current state of the art lacks efficient tools to solve them jointly. To the best of our knowledge, the best-known option is to fragment the profiling in smaller parts, which is suboptimal from the information theoretic viewpoint. In this paper, we therefore revisit regression-based linear discriminant analysis, which combines linear regression and linear discriminant analysis, and improve its efficiency so that it can be used for profiling long traces corresponding to 32-bit implementations. Besides introducing the optimizations needed for this purpose, we show how to use regression-based linear discriminant analysis in order to obtain efficient bounds for the perceived information, an information theoretic metric characterizing the security of an implementation against profiled attacks. We also combine this tool with optimizations of soft analytical side-channel attack that apply to bitslice implementations. We use these results to attack a 32-bit implementation of SAP instantiated with Ascon’s permutation, and show that breaking the initialization of its re-keying in one trace is feasible for determined adversaries.
Transformative Features of Teacher Residency Programs: A Textual Narrative Synthesis

: Although teacher residency programs (TRPs) are increasing in prevalence, the scholarly literature on these programs has not been systematically synthesized to demonstrate consensus regarding beneﬁts of teacher residency programs for teacher preparation over traditional models. This literature review provides a textual narrative synthesis of empirical research on teacher residencies from 2014-2019 and summarizes features of TRPs, including enhanced mentoring , immersive resident learning , and a transformative third space for teacher preparation. The literature synthesis conﬁrmed that TRPs have the potential to transform teacher preparation and improve upon university-school partnerships to bolster theory-to-practice connections. As TRPs innovated and developed their residency programs, the reﬂective process was essential.
Analysis of the Effect of Digital Economy Driving High-Quality Development of Regional Brand Economy

The high-quality development of the brand economy is an important part of China's high-quality economic development. Based on China's provincial panel data, this paper constructs an evaluation indicator system from four dimensions of digital infrastructure, digital industry development, digital R&D innovation, and digital governance environment, and constructs an indicator system of high-quality development level of the regional brand economy from two levels of brand value and brand intellectual property. Empirical tests show that the digital economy drives high-quality development of the regional brand economy, And the driving effect shows that the eastern region is stronger than the central and western regions. Finally, it is proposed that China should accelerate the development of the digital economy by strengthening regional digital infrastructure, promoting regional digital industry development, supporting and promoting regional digital R&D innovation, and optimizing the digital governance environment to achieve high-quality development of the regional brand economy.
SHARPENING THE PROFILE AND SCOPE OF THE GLOBAL FINANCIAL SAFETY NET FOR BETTER PERFORMANCE AND GOVERNANCE OF THE FINANCIAL SECTOR

The COVID-19 pandemic has highlighted the need for better global risk management, availability of resources and coordination among multilateral and bilateral donors. In this policy brief, the authors suggest enhancing the concept of the Global Financial Safety Net (GFSN) to better address these issues. They put forward the concept of an enlarged GFSN by including bilateral sovereign financing and a portion of multilateral development banks’ financing. This would help achieve better accountability and more efficient coordination among various financial institutions and their shareholders.
On Partial Satisfaction Planning with Total-Order HTNs

Since its introduction, partial satisfaction planning (PSP), including both oversubscription (OSP) and net-benefit, has received significant attention in the classical planning community. However, hierarchical aspects have been mostly ignored in this context, although several problem domains that form the main motivation for PSP, such as the rover domain, have an inherent hierarchical structure. In this paper, we are taking the necessary steps for facilitating this research direction. First, we formally define hierarchical partial satisfaction planning problems and discuss the usefulness and necessity of this formalism. Second, we present a carefully structured set of benchmarks consisting of OSP and net-benefit problems with hierarchical structure. We describe and analyze the different domains of the benchmark set and the desiderata that are met to provide an interesting and challenging starting point for upcoming research. Third, we introduce various planning techniques that can solve hierarchical OSP problems and investigate their empirical behaviour on our proposed benchmark.
Prediction of Preoperative Scale Score of Dystonia Based on Few-Shot Learning

As a neurological disease, dystonia mainly has symptoms including muscle stiffness, dyskinesia, tremor, muscle spasm, etc. Dystonia score plays an important role in targeted auxiliary diagnosis, treatment plan design, and follow-up evaluation of patients. In this paper, the feature information of brain lateralization is extracted from electroencephalography (EEG) signals by clustering method, while information on time domain, frequency domain, and time sequence are extracted from EEG signals and electromyography (EMG) signals. Various deep-learning models are used to predict dystonia scores. Experiments show that this method can effectively predict dystonia based on the quantitative indicators extracted from few-shot neural signals. The methodology in this paper can help doctors judge the disease more accurately, make personalized treatment plans, and assist in monitoring the treatment effect.
The role of m6A and m6Am RNA modifications in the pathogenesis of diabetes mellitus

The rapidly developing research field of epitranscriptomics has recently emerged into the spotlight of researchers due to its vast regulatory effects on gene expression and thereby cellular physiology and pathophysiology. N6-methyladenosine (m6A) and N6,2’-O-dimethyladenosine (m6Am) are among the most prevalent and well-characterized modified nucleosides in eukaryotic RNA. Both of these modifications are dynamically regulated by a complex set of epitranscriptomic regulators called writers, readers, and erasers. Altered levels of m6A and also several regulatory proteins were already associated with diabetic tissues. This review summarizes the current knowledge and gaps about m6A and m6Am modifications and their respective regulators in the pathophysiology of diabetes mellitus. It focuses mainly on the more prevalent type 2 diabetes mellitus (T2DM) and its treatment by metformin, the first-line antidiabetic agent. A better understanding of epitranscriptomic modifications in this highly prevalent disease deserves further investigation and might reveal clinically relevant discoveries in the future.
Automatic Registration of Point Clouds by Combining Local Shape Descriptor and G4PCS Algorithm

Registration is usually the first step for the usage of point cloud data. Registration of point clouds is crucial in numerous practical applications. In order to quickly and accurately align point clouds, we combine the local shape descriptor and generalized four-point congruent set (G4PCS) algorithm. A descriptor with high computation efficiency is chosen to establish the correspondences. Then, the constraints of the G4PCS algorithm are applied and three angular constraints are proposed. These constraints are divided into three groups according to their computation complexity. The three groups of constraints are used to accelerate the searching process of four correct correspondences. The experiments are performed to compare our method with several existing methods. The experimental results show that our registration method has the best registration precision and computation efficiency. The introduction of the local shape descriptor can enhance both registration precision and computation efficiency. The proposed three angular constraints can also further boost the search process.
Investigation on Different Stator Structures of the Axial-Radial Flux Switched Reluctance Motors

In order to improve the torque performance and power density of switched reluctance motor, the combination of the axial flux and the radial flux is a suitable and effective method. Based on its operating principle and the characteristics of the hybrid flux structure, this article proposes the new axial-radial flux switched reluctance motors with different stator structures for electric vehicle applications. First, this article describes the topologies and operating principles of two stator structures. Then, the structural parameters of two kinds of motors are derived based on electromagnetic design principles. In order to evaluate the effects of different stator structures, the overall performance of the two proposed motors is studied using three-dimensional finite-element analysis, including the static magnetic characteristics and dynamic performance. Finally, a prototype motor is trialed, and the experimental results obtained are analyzed with the finite element results to verify the excellent performance of the proposed motor.
HOW TO MAKE ASSOCIATIVE MEMORIES ATTENTIVE

This paper investigates parallel and distributed implementation of a class of associative search where during similarity evaluation the scope of the search is dynamically localizable into a sub-set of the pattern elements. This is an important search type with numerous applications ranging from content-based image retrieval, adaptive pattern matching, digital library to vision. Current parallel and distributed neural models based on scalar product rule of synaptic efficacy can not conveniently realize it. In this paper, we identify the prerequisites, analyze the current models and demonstrate a new paradigm of parallel and distributing computing that can realize this search.
Homogeneous ice formation in convective cloud outflow regions

Homogeneous droplet freezing in the warm cirrus regime (230–240 K) is investigated along idealized convective cloud trajectories using a spectral parcel model developed to track droplet freezing events accurately. The novel model is described and used to study ice formation from rapidly ascending (vertical velocity 0.6–6 m s−1) air parcels containing cloud condensation nuclei (CCN) and liquid water droplets. Homogeneous freezing events in warm cirrus are affected by latent heat exchange and produce a mode of small ice crystals with maximum dimensions 10–100 μm after initial supersaturation quenching. During the formation stage, ice‐crystal number concentrations formed homogeneously in convective cloud outflow are hardly affected by ice‐crystal settling and depend sensitively on vertical velocity. In the case of CCN activation into cloud water droplets prior to or along with freezing, relative humidity variations also result in widely varying ice numbers that are insensitive to CCN solubility. These results offer pointers on how further progress can be achieved in simulating and better understanding the formation of upper tropospheric ice clouds originating from convective detrainment zones.