Miracle-dz/UniFAR-FTUs huggingface dataset:
2-Hydroxy-11-methyl-16-[(E)-4-methylbenzylidene]-13-(4-methylphenyl)-1,11-diazapentacyclo[12.3.1.02,10.03,8.010,14]octadeca-3(8),4,6-triene-9,15-dione

In the title compound, C32H30N2O3, the piperidin-4-one and the two fused pyrrolidine rings adopt envelope conformations. The two methylphenyl rings are oriented at dihedral angle of 20.36 (7) and 56.24 (7)°, respectively, with respect to the indanone ring system. In the crystal structure, intermolecular O—H⋯N and C—H⋯O hydrogen bonds link the molecules into chains propagating along [001]. Weak C—H⋯π interactions are also observed.
Virtual Network Embedding Approximations: Leveraging Randomized Rounding

The Virtual Network Embedding Problem (VNEP) captures the essence of many resource allocation problems. In the VNEP, customers request resources in the form of Virtual Networks. An embedding of a virtual network on a shared physical infrastructure is the joint mapping of (virtual) nodes to physical servers together with the mapping of (virtual) edges onto paths in the physical network connecting the respective servers. This work initiates the study of approximation algorithms for the VNEP for general request graphs. Concretely, we study the offline setting with admission control: given multiple requests, the task is to embed the most profitable subset while not exceeding resource capacities. Our approximation is based on the randomized rounding of Linear Programming (LP) solutions. Interestingly, we uncover that the standard LP formulation for the VNEP exhibits an inherent structural deficit when considering general virtual network topologies: its solutions cannot be decomposed into valid embeddings. In turn, focusing on the class of cactus request graphs, we devise a novel LP formulation, whose solutions can be decomposed. Proving performance guarantees of our rounding scheme, we obtain the first approximation algorithm for the VNEP in the resource augmentation model. We propose different types of rounding heuristics and evaluate their performance in an extensive computational study. Our results indicate that good solutions can be achieved even without resource augmentations. Specifically, heuristical rounding achieves 77.2% of the baseline’s profit on average while respecting capacities.
Some New Constructions and Bounds for Locally Generated Quantum Codes

The existence of quantum locally generated codes is a long standing open problem in quantum information theory. In this thesis, we consider a bound concerning this conjecture as well as a few constructions with codes that are `barely non-local'. We establish a complementary result to [30], and show quantum codes which are strongly not embeddable into nite dimensional lattices must also have poor distance. Along the way we derive some results concerning pseudorandom classical codes. Given that quantum codes seem to be di cult to construct, it seems useful to examine bad quantum codes for applications in information and communication. Indeed, most of the work in quantum error correction by researchers today is in this direction. We construct a nearly local quantum erasure code which can achieve the capacity of the quantum erasure channel. This code has very poor (adversarial) distance, but still manages to correct random erasure errors with high probability. The codes use random Erdos-Renyi graphs to construct quantum states which are nearly local, but also highly entangled across xed cuts with high probability. We derive some new results concerning classical codes with log-sparse parity check matrices which may be of independent interest. iii Inspired by this construction, we are able to construct new approximate unitary 2-designs or scramblers . The study of scrambling is the study of the mixing properties of di erent distributions of random unitaries. There is an inherent duality between the study of scrambling and the study of error correction: A good quantum code will make a good scrambler and vice versa. We study the scrambling properties of our random Erdos-Renyi graph state encoding circuits. We are able to show that these circuits, when supplemented by some local Expander Graph quantum circuits, form approximate unitary 2-designs. This construction, strictly speaking, does not achieve more e cient parameters than existing approximate 2-designs, but might have implementation advantages over other designs and points to a conjecture which could yield approximate unitary designs with time independent qubit-to-qubit coupling. This would be an extremely interesting construction in the context of experimental randomized benchmarking.
Background subtraction using Multi-Channel Fused Lasso

Background subtraction is a fundamental problem in computer vision. Despite having made signiﬁcant progress over the past decade, accurate foreground extraction in complex scenarios is still challenging. Recently, sparse signal recovery has attracted a considerable attention due to the fact that moving objects in videos are sparse. Considering the coherent of the foreground in spatial and temporal domain, many works use the structured sparsity or fused sparsity to regularize the foreground signals. However, existing methods ignore the group prior of foreground signals on multi-channels (such as the RGB). In fact, a pixel should be considered as a multi-channel signal. If a pixel is equal to the adjacent ones that means all the three RGB coefﬁcients should be equal. In this paper, we propose a Multi-Channel Fused Lasso regularizer to explore the smoothness of multi-channels signals. The proposed method is validated on various challenging video sequences. Experiments demonstrate that our approach effectively works on a wide range of complex scenarios, and achieves a state-of-the-art performance.
Data Mining for Managing Intrinsic Quality of Service in Digital Mobile Telecommunications Networks

Digital mobile telecommunication networks are highly complex systems and thus their planning, management and optimization are challenging tasks. Subscribers of a mobile network expect network availability, connection throughput and affordability. Moreover, their connection should not degrade or be lost abruptly as they move within the network area. The user expectations constitute the Quality of Service (QoS). To gain a competitive edge on other operators, the operating personnel have to measure the network in terms of the QoS. By analyzing the information in the measurements, they can manage and improve the quality of service. But sometimes they find it difficult to elicit essential information from complex data to improve the QoS. It is here that the recently developed data mining methods come in handy. Three data mining methods were applied to actual GSM network performance measurements. In this thesis, the methods were chosen to help the operating staff to find the essential information in network quality performance measurements. The personnel's prior knowledge of the network and their possible inexperience of the theory behind the methods was taken into account. The results show that the analyst can make good use of rough sets and Classification And Regression Trees (CART), because their information can be expressed in plain language rules that preserve the variable names of the original measurement. In addition, the CART and the Self-Organizing Map (SOM) provide effective visual means for interpreting the data set, for the operator can understand both the rules and the visualization information with little effort. Undoubtedly, the methods we used are applicable also to the next generation, UMTS network, because similar performance measurements are used in that network environment.
Cross Spectral Periocular Matching using ResNet Features

Periocular recognition has gained attention in the last years thanks to its high discrimination capabilities in less constraint scenarios than other ocular modalities. In this paper we propose a method for periocular verification under different light spectra using CNN features with the particularity that the network has not been trained for this purpose. We use a ResNet-101 pretrained model for the ImageNet Large Scale Visual Recognition Challenge to extract features from the IIITD Multispectral Periocular Database. At each layer the features are compared using χ2 distance and cosine similitude to carry on verification between images, achieving an improvement in the EER and accuracy at 1% FAR of up to 63.13% and 24.79% in comparison to previous works that employ the same database. In addition to this, we train a neural network to match the best CNN feature layer vector from each spectrum. With this procedure, we achieve improvements of up to 65% (EER) and 87% (accuracy at 1% FAR) in cross-spectral verification with respect to previous studies.
Profile-Based Resource Allocation for Virtualized Network Functions

The virtualization of compute and network resources enables an unseen flexibility for deploying network services. A wide spectrum of emerging technologies allows an ever-growing range of orchestration possibilities in cloud-based environments. But in this context it remains challenging to rhyme dynamic cloud configurations with deterministic performance. The service operator must somehow map the performance specification in the Service Level Agreement (SLA) to an adequate resource allocation in the virtualized infrastructure. We propose the use of a VNF profile to alleviate this process. This is illustrated by profiling the performance of four example network functions (a virtual router, switch, firewall and cache server) under varying workloads and resource configurations. We then compare several methods to derive a model from the profiled datasets. We select the most accurate method to further train a model which predicts the services’ performance, in function of incoming workload and allocated resources. Our presented method can offer the service operator a recommended resource allocation for the targeted service, in function of the targeted performance and maximum workload specified in the SLA. This helps to deploy the softwarized service with an optimal amount of resources to meet the SLA requirements, thereby avoiding unnecessary scaling steps.
Home exercise in rheumatoid arthritis functional class II: goal setting versus pain attention.

OBJECTIVE: To evaluate the effects of a 12-week home exercise and cognitive treatment program in functionally independent patients with rheumatoid arthritis. METHODS: Forty-two patients were assessed with the Arthritis Self-efficacy Scale, the Stanford Health Assessment Questionnaire, the Ritchie articular index, measurement of joint mobility, and registration of capacity and pain in functional tasks. The patients were then randomized to either a "goal-setting" subgroup, in which individual goals for the exercise were set and exercise encouraged despite pain, or to a "pain attention" subgroup, where advice to decrease exercise load in case of pain was given. All patients used the same home exercise program aiming at improved range of motion, muscle function and aerobic capacity. RESULTS: After the intervention period, exercise had conferred better self-efficacy for "other symptoms," increased capacity in most functional tasks, decreased activity induced pain, lowered Ritchie index, and increased joint mobility. Some improvements regarding pain were larger in the goal-setting subgroup. CONCLUSION: Home exercise influences self-efficacy for mood and fatigue, physical capacity, and pain. Additional cognitive treatment seems to positively influence the perception of pain.
Trends in self‐poisoning with drugs in Newcastle, New South Wales, 1980‐1982

A review has been carried out of 747 cases of self‐poisoning with drugs which were treated in the three major Newcastle hospitals over the three‐year period 1980–1982. The data obtained have been compared with earlier published data for the years 1976–1979. The number of patients who were admitted to hospital fell from 290 in 1980 to 226 in 1982. The most common drugs used were benzodiazepine (34.5% of cases) and antidepressant agents (9.1%), paracetamol (7.9%), barbiturates (7.1%) and pheno‐thiazines (3.8%). Although this indicated a gratifying drop in the use of barbiturate drugs, the use of several drugs in combination in self‐poisoning rose disturbingly. Five patients died in hospital; 23 who committed suicide with drugs were not admitted to hospital but were investigated by the Regional Forensic Pathologist.
A Revised Trajectory Approach for the Worst-Case Delay Analysis of an AFDX Network

AFDX (Avionics Full-Duplex Switched Ethernet) standardized as ARINC 664 is a major upgrade for safety-critical applications in avionics systems. Worst-case delay analysis of all the flows transmitted on the AFDX network is mandatory for certification reasons. Different approaches have been proposed for end-to-end (ETE) delay upper bound, such as network calculus and trajectory approach, but these methods still introduce some pessimism in the computations and overestimate the exact worst-case delay of the flows. In addition, the existing trajectory approaches may underestimate the exact worst-case delay of the flows for some corner cases. In this article, we revised the trajectory approach to make the flow analysis more accurate for the computation of the worst-case ETE delay. The results had been shown that the worst-case delay analysis of an AFDX network can be improved by using our revised trajectory approach.
The students' readiness to engage with mobile learning apps

Purpose A relevant literature review suggests that today’s children are increasingly immersing themselves in ubiquitous technologies, including interactive media and digital games. Therefore, this paper aims to investigate the primary school students’ intrinsic and extrinsic motivations toward learning via gameplay through their mobile devices, at home and at school. Design/methodology/approach This study was carried out among primary school students in a small European state. It used valid and reliable measures that comprised the technology acceptance model’s key constructs. However, the empirical investigation also explored the students’ perceived enjoyment and social influences, as plausible antecedents for their behavioral intention to engage with the educational applications (apps). Findings The findings reported that there were strong correlations between the students’ perceived usefulness of the mobile technologies and their behavioral intention to use them for their learning. The results also indicated that there was no significant relationship between the perceived ease of use and the children’s enjoyment in engaging with the educational apps that were used at school. Originality/value To the best of the authors’ knowledge, there is no other study in academia that has explored the children’s technology acceptance, normative pressures and their intrinsic motivations to use mobile learning technologies in the context of primary education. Therefore, this contribution opens future research directions, as this study can be replicated in other contexts.
Progressive speech deterioration and dysphagia in amyotrophic lateral sclerosis: case report.

: Amyotrophic lateral sclerosis (ALS) is a degenerative neurologic disease having both upper and lower motor neuron signs and symptoms. When the speech musculature is involved, a mixed dysarthria and dysphagia usually result. In a 49-year-old man with ALS, dysarthria and dysphagia progressed from mild to severe forms over 17 months. Eleven months after the patient first experienced symptoms, neurologic examination showed fasciculations of the extremities and tongue, limb weakness, and hyperreflexia of the limbs and velopharyngeal mechanism. Tongue strength was one-fourth that of normal. Lingual alternate motions rates for consonant-vowel syllables were also reduced. To enhance lingual strength and swallowing, a tongue-strengthening program was developed for use with articulation training; to augment velopharyngeal function, a palatal lift was fitted; and to increase extremity strength, physical therapy was initiated. Six months after the initial neurologic examination, medical and speech reevaluation showed progressive weakness of the body parts affected initially; continued decline in tongue strength and lingual alternate motion rate; hypoactive reflex activity, indicative of progressive involvement of the lower motor neuron system; and continued deterioration of articulation and phonation owing to the progressive nature of the disease.
Optimizing the structure of software systems supporting logistics at the design stage

Background: Computer software, widely used to support a broad range of logistics activities, is characterized both by increasing functionality and increasing complexity. For this reason, the process of software production, including the stages of specification of requirements, design, programming and testing, is time-consuming and expensive. The main goal of the software design phase is to determine the software architecture that identifies all software components and defines links and connections between them. The design phase also includes the development of the so-called internal logic of all extracted components, that is, detailed elaboration of algorithms for their operation and defining the structure of data used. It should be emphasized that the results of the software design process depend greatly on the knowledge and experience of the designer, because there are no universal behavioral patterns in this area. The main goal of the approach proposed at work is to reduce the role of the subjective factor in the results of the software development process. The focus of this work is on this software development process within logistics processes. Methods: The basic research method used in the work is mathematical modeling. The paper proposes a formal method of assigning the modular structure of the computer program by formulating and solving the corresponding double-criterion optimization problem. The module strength coefficient and module-coupling coefficients were established as modularization criteria of the program. Results: The main result of the work is the method of determining the modular structure of the designed program by determining the solution of the two-criterion optimization problem. The numerical example developed to illustrate this entirel confirms the possibilities of its practical application. The modular structure of the program, based on the solution of the formulated polyoptimization task, is characterized by the maximum value of the modular power coefficient and the minimum value of the modular strength coefficient. According to the latest trends in software engineering, it is the optimal structure. The method can be useful in the process of designing software for systems supporting the implementation of logistics processes. Conclusions: The author's method of determining the modular structure of the program, presented in the article, is an unprecedented attempt in the literature to use formal methods in the software design process, which could be implemented practically in the logistics processes. The lack of similar attempts probably results from the very low compliance of the software design stage with attempts to formalize it. In order to increase the possibility of practical application of the proposed method, it seems reasonable to conduct further work to implement the methods of developing software requirements specifications in a formalized form, e.g. with the use of mathematical notation.
In-Game Actions to Promote Game-Based Math Learning Engagement

Game-based learning (GBL) has increasingly been used to promote students’ learning engagement. Although prior GBL studies have highlighted the significance of learning engagement as a mediator of students’ meaningful learning, the existing accounts failed to capture specific evidence of how exactly students’ in-game actions in GBL enhance learning engagement. Hence, this mixed-method study was designed to examine whether middle school students’ in-game actions are likely to promote certain types of learning engagement (i.e., content and cognitive engagement). This study used and examined the game E-Rebuild, a single-player three-dimensional architecture game that requires learners’ application of math knowledge. Using in-depth gameplay behavior analysis, this study sampled a total of 92 screen-recorded and video-captured gameplay sessions attended by 25 middle school students. We adopted two analytic approaches: sequential analysis and thematic analysis. Whereas sequential analysis explored which in-game actions by students were likely to promote each type of learning engagement, the thematic analysis depicted how certain gameplay contexts contributed to students’ enhanced learning engagement. The study found that refugee allocation and material trading actions promoted students’ content engagement, whereas using in-game building tools and learning support boosted their cognitive engagement. This study also found that students’ learning engagement was associated with their development of mathematical thinking in a GBL context.
Fault Ride through Capability Improvement of DFIG Using SMES Unit during Short Circuit

Catastrophic impacts of conventional based power plants on the environment has forced many nations to concern more on exploring renewable energy (RE) source for power plants. One of the most popular RE sources is wind, where the wind has been installed about 430 GW in 2015. Among wind turbines generator that available in the market niche, Doubly Fed Induction Generator (DFIG) is the most popular type. DFIG type dominates the total wind turbine installed capacity by about 17.5% in 2015 according to the report of JRC (Joint Research Center). Although DFIG is capable in extracting 5% more energy if compared to the fixed speed type, it is however, very sensitive to the fault that may violate the Fault Ride Through (FRT) which leads to the disconnecting of the DFIG from the grid. In this paper, a Superconducting Magnetic Energy Storage (SMES) unit is applied to improve the FRT capability of the system with DFIG during a short circuit event in the distribution lines. A Fuzzy Logic Type-2 is employed on the SMES to obtain the most optimum response of the DFIG during the fault. 
Inter-Turn Short-Circuit Failure of PMSM Indicator based on Kalman Filtering in Operational Behavior

In the next twenty years traffic aircraft will be doubled. Thus, avionic devices will become more and more electric and the aircrafts become lighter in order to save more fuel. Thus, the more electric aircraft will face a great challenge that of the predictive maintenance of its electrical equipments. A key component of these devices is the Permanent Magnet Synchronous Motor (PMSM). In this article we are interested in one of the most recurrent failure of electric motor, that of the inter-turn short circuit failure. The purpose of this study, therefore, is to develop an interturn short-circuit sensitive indicator. It’s based on a linear Kalman filter for a healthy model to estimate residual voltage drops in the rotor reference (d,q). The proposed study shows a high sensitive indicator to the inter-turn short-circuit fault even under external disturbances. As well, several features can result from it, especially the signal energy, spectral and statistical information, etc. These features can highlight aging laws that will be used as patterns for Prognosis and Health Management (PHM) of inter-turn short-circuit failure.