Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Efficient Resource Allocation Optimization in Fog Radio Access Networks With Outdated Channel Knowledge

Fog Radio Access Networks (F-RAN) are gaining worldwide interests for enabling mobile edge computing for Beyond 5G. However, to realize the future real-time and delay-sensitive applications, F-RAN tailored radio resource allocation and interference management become necessary. This work investigates user association and beamforming issues for providing energy efficient F-RANs. We formulate the energy efficiency maximization problem, where the F-RAN specific constraint to guarantee local edge processing is explicitly considered. To solve this intricate problem, we design an algorithm based on the Augmented Lagrangian (AL) method. Then, to alleviate the computational complexity, a heuristic low-complexity strategy is developed, where the tasks are split in two parts: one solving for user association and Fog Access Points (F-AP) activation in a centralized manner at the cloud, based on global but outdated user Channel State Information (CSI) to account for fronthaul delays, and the second solving for beamforming in a distributed manner at each active F-AP based on perfect but local CSIs. Simulation results show that the proposed heuristic method achieves an appreciable performance level as compared to the AL-based method, while largely outperforming the energy efficiency of the baseline F-RAN scheme and limiting the sum-rate degradation compared to the optimized sum-rate maximization algorithm.11Part of this article has been presented at IEEE CCNC 2019 [1].
Measuring the Gravitomagnetic Distortion from Rotating Halos. I. Methods

Source galaxy images are distorted not only by a static gravitational potential, but also by frame-dragging induced by massive rotating objects like clusters of galaxies. Such an effect is well understood theoretically; it is therefore of great interest to estimate its detectability for future surveys. In this work, we analyze the lensing convergence κ around rotating dark matter halos. The rotation of the massive objects generates a gravitomagnetic potential giving rise to an anisotropic contribution to the lensing potential. We construct an estimator δ κ to describe the difference between the symmetric enhancement and reduction of κ around the halo rotation axis, finding that it is well approximated by a function proportional to the halo velocity dispersion squared times a dimensionless angular momentum parameter. Using simulation mocks with realistic noise level for a survey like the Legacy Survey of Space and Time (LSST), we test our estimator, and show that the signal from frame-dragging of stacked rotating lenses is consistent with zero within 1σ. However, we find that the most massive cluster in SDSS DR7 spectroscopic selected group catalog has a line-of-sight rotation velocity of 195.0 km s−1 and velocity dispersion of 667.8 km s−1, which is at 1.2 × 10−8 odds according to the angular momentum probability distribution inferred from N-body simulations. By studying SDSS DR7 spectroscopic selected group catalog, we show how rotating clusters can be identified, and, finding that fast rotating clusters might be more abundant than in estimates based on simulations, a detection of gravitomagnetic distortion may be at reach in future surveys.
Grid-Connected Pv-Fc Hybrid System Power Control Using Mppt And Boost Converter

This paper proposes a method for operating a grid connected hybrid system. This system composed of a Photovoltaic (PV) array and a Proton exchange membrane fuel cell (PEMFC) is considered. As the variations occur in temperature and irradiation during power delivery to load, Photo voltaic (PV) system becomes uncontrollable. In coordination with PEMFC, the hybrid system output power becomes controllable. Two operation modes are the unit-power control (UPC) mode and the feeder-flow control (FFC) mode, can be applied to the hybrid system. All MPPT methods follow the same goal that is maximizing the PV system output power by tracking the maximum power on every operating condition. Maximum power point tracking technique (Incremental conductance) for photovoltaic systems was introduced to maximize the produced energy. The coordination of two control modes, coordination of the PV array and the PEMFC in the hybrid system, and determination of reference parameters are presented. The proposed operating strategy systems with a flexible operation mode change always operate the PV array at maximum output power and the PEMFC in its high efficiency performance band. Also thus improving the performance of system operation, enhancing system stability, and reducing the number of operating mode changes.
Influence of Initial Seeding Density on Gene Expression during Neuronal Priming

Derivation of neurons from pluripotent stem cells has moved from a niche expertise to a widely used technology, prompting a surge to create novel and timely treatments for neurologic disorders such as Parkinson’s disease. However, current methods for derivation of neurons, though widely available, remain inefficient, resulting in sub-optimal yields and purities of potentially therapeutic cells. Optimising cell processing methods to improve yield and purity is necessary and in this work, we assessed the impact of Initial Cell Seeding Density (ICSD) on differentiation of pluripotent cells (human embryonic and human induced pluripotent stem cells) towards a neuronal specification using a recognised laboratory protocol. We found small but significant differences in expression of specific genes related with pluripotency and neuronal lineage commitment associated with ICSD. By reducing ICSD, it may be possible to improve yield from less start material.
Assessment of post-operative pain by nest complexity scoring in mice

Pain in laboratory mice is difficult to detect with conventional methods as mice do not obviously show symptoms of mild to moderate pain. Here we investigated the feasi-bility and reliability of nest building performance under various conditions as a meth-od to detect mild to moderate post-operative pain in laboratory mice and aimed to standardise this method for the routine use. Female mice were randomly allocated into control (anaesthesia +/- analgesia) and surgery groups (minor laparotomy +/- analgesia) in two housing conditions. Animals were observed before (baseline) and after treatments (experiment). The nests were scored at seven time points with a numeric scoring system and latency of nest build-ing was measured as well as consumption of the nesting material. Baseline nest scores were always higher than experimental scores and a reliable discrimination was found at three to nine hours after the start of the measurements. A clear graduation in nest complexity was seen after experiments, with higher nest scores in control groups than in surgery groups. Latency in the baseline was always shorter than in the experiment and latency in control groups was shorter than in sur-gery groups during experimental measurements. Pair housed mice had a slightly higher consumption of the nesting material than individually housed mice. In conclusion, scoring of nest complexity at three to nine hours after laparotomy was useful to identify post-operative impairment, caused by moderate pain.
THE TRANSITION FROM THE MIDDLE TO THE UPPER PALEOLITHIC IN THE SWABIAN JURA,

The Swabian Jura in southwestern Germany is a key region for the study of the late Middle Paleolithic and the evolution of the early Upper Paleolithic or Aurignacian in central Europe. Several cave sites in the Ach Valley, such as Hohle Fels, Geißenklösterle, and Sirgenstein, and in the Lone Valley, such as Vogelherd, Hohlenstein-Stadel, and Bockstein-Törle, have yielded considerable stratigraphic sequences with both Middle and Upper Paleolithic deposits. While some of the Middle Paleolithic assemblages can be attributed to the Keilmessergruppe or Micoquian, most of them can only in general be classified as "Swabian Mousterian". The find density in most Middle Paleolithic sites of Swabia is low and indicates that Neandertals visited most caves only sporadically and for short stays. The Aurignacian of Swabia, which started fully developed more than 40,000 years ago, marks a clear break and a radical shift in material culture. A similar shift can be observed with regard to the occupation intensity, which increases strongly with the Aurignacian. New technologies and tool types, made of both lithic and organic raw materials, are characteristic for the Aurignacian assemblages, which are supposed to have been produced by anatomically modern humans. Of particular importance are a broad variety of personal ornaments carved from mammoth ivory, bone and ivory flutes, and, most spectacular, ivory figurines. With ages of down to 40,000 years, the art objects and the musical instruments from the Swabian Jura represent the oldest examples of their kinds known from the archaeological record and prove that the Swabian Jura was one key center of innovations in the early Upper Paleolithic.
Toward a Thermodynamics of Meaning

As language models such as GPT-3 become increasingly successful at generating realistic text, questions about what purely text-based modeling can learn about the world have become more urgent. Is text purely syntactic, as skeptics argue? Or does it in fact contain some semantic information that a sufficiently sophisticated language model could use to learn about the world without any additional inputs? This paper describes a new model that suggests some qualified answers to those questions. By theorizing the relationship between text and the world it describes as an equilibrium relationship between a thermodynamic system and a much larger reservoir, this paper argues that even very simple language models do learn structural facts about the world, while also proposing relatively precise limits on the nature and extent of those facts. This perspective promises not only to answer questions about what language models actually learn, but also to explain the consistent and surprising success of cooccurrence prediction as a meaning-making strategy in AI.
Estimating Bulk Transfer Capacity

— Many applications wish to predict what bandwidth TCP can achieve on a path between Internet hosts without performing extensive measurements. More precisely, they would like to know what the bulk transfer capacity (BTC) of a given path is. This paper investigates the differences between available bandwidth (the unused capacity along a path) and BTC over a wide range of network conditions. We use Pathload [1] to obtain available bandwidth estimates over all paths between machines in the RON [2] testbed. The median BTC rate is under 40% of the available band-width on these paths. The BTC rate was less than one ﬁfth the available bandwidth on a full third of paths. This paper also introduces a method, squeezed pairs , to characterize queuing delays along paths. We show that squeezed pairs allows us to make BTC estimates from available bandwidth estimates with accuracy comparable to BTC estimates from short TCP connections.
Sequence-to-Sequence Load Disaggregation Using Multiscale Residual Neural Network

With the increased demand on economy and efficiency of measurement technology, nonintrusive load monitoring (NILM) has received more and more attention as a cost-effective way to monitor electricity and provide feedback to users. Deep neural networks have been showing a great potential in the field of load disaggregation. In this article, first, a new convolutional model based on residual blocks is proposed to avoid the degradation problem whose traditional networks more or less suffer from when network layers are increased in order to learn more complex features. Second, we propose dilated convolution to curtail the excessive quantity of model parameters and obtain bigger receptive field and multiscale structure to learn mixed data features in a more targeted way. Third, we give details about generating training and test set under certain rules. Finally, the algorithm is tested on real-house public data set, UK Domestic Application Level Electric (UK-DALE), with three existing neural networks. The results are compared and analyzed, and the proposed model shows improvements on F1 score, MAE, as well as model complexity across different appliances.
GBRT Traffic Accident Prediction Model Based on Time Series Relationship

Road traffic accidents are a concrete manifestation of road traffic safety levels. In the current traffic accident prediction work, there is an insufficient mining of the time series relationship in the data, the predicted time period is too macroscopic, and the influencing factors related to traffic accidents are missing. Aiming at the above problems, a gradient boosted regression tree (GBRT) traffic accident model based on time series relationship is proposed. The model predicts the number of daily traffic accidents, deaths, and the number of vehicles involved in Leicester, England, from 2005 to 2 015. Experimental results show that adding the time series relationship helps to improve the prediction accuracy of the model. The prediction results serve as a reference for the decision-making of the traffic management department. The modeling method brings positive reference significance to the modeling work of the same type of prediction problems.
A computational platform for the personalized clinical treatment of glioblastoma multiforme

In this work, we develop a computational tool to predict the patient-speciﬁc evolution of a highly malignant brain tumour, the glioblastoma multiforme (GBM), and its response to therapy. A diffuse-interface mathematical model based on mixture theory is fed by clinical neuroimaging data that provide the anatomical and microstructural characteristics of the patient brain. The model is numerically solved using the ﬁnite element method, on the basis of suitable numerical techniques to deal with the resulting Cahn-Hilliard type equation with degenerate mobility and single-well potential. We report the results of simulations performed on the real geometry of a patient brain, proving how the tumour expansion is actually dependent on the local tissue structure. We also report a sensitivity analysis concerning the effects of the different therapeutic strategies employed in the clinical Stupp protocol. The simulated results are in qualitative agreement with the observed evolution of GBM during growth, recurrence and response to treatment. Taken as a proof-of-concept, these results open the way to a novel personalized approach of mathematical tools in clinical oncology.
A Modular Approach to Distributed Caching

It is essential today organizations to have fast and stable access to information stored in different sources. Last generation of in-memory database demonstrates much better productivity in data processing compared to classical relational database management system. In this paper, an approach for modular distributed caching is proposed. The approach is based on a modular layered architecture, which extends the primary relational based systems and will make it possible to increase the speed of queries processing. Some tests based on a prototype are performed and discussed.
Postcopulatory cost of immune system activation in Poecilia reticulata

The function and maintenance of the immune system impose costs that can take the form of energy consumption and/or physiological side effects. These, in turn, can affect the expression of several traits, including traits closely related to fitness. The interaction of the immune system with other life-history traits (e.g. growth and survival) and with precopulatory sexually selected traits (e.g. male ornaments) has been widely studied, whereas little is known about its interplay with postcopulatory sexually selected traits, such as sperm characteristics. We studied the effects of an immune challenge on the expression of precopulatory and postcopulatory traits in the guppy, Poecilia reticulata. We injected two groups of males with a lipopolysaccharide (LPS) solution or a saline solution (control) and compared the effects of the immune system activation on traits’ expression. The results showed a specific negative effect of the LPS injection on sperm traits, whereas precopulatory ornaments were lowered merely by manipulation (injection). We discuss the possible mechanisms underlying our findings, and the evolutionary implications that follow.
Text and Object Recognition using Deep Learning for Visually Impaired People

The main aim of this paper is to aid the visually impaired people with object detection and text detection using deep learning. Object detection is done using a convolution neural network and text recognition is done by optical character recognition. The detected output is converted into speech using text to the speech synthesizer. Object detection comprises of two methods. One is object localization and the other is image classification. Image classification refers to the prediction of classes of different objects within an image. Object localization infers the location of objects using bounding boxes.
Channel And Power Allocation For Mm-Wave Device-To-Device-Enabled Vehicular Networks

: The demand for higher data rate services has led to the emergence of 5G wireless networks to offer the limitations of the current cellular communication technologies. The millimeter-wave (mm-wave) communication technologies have evolved into direct Device-to-Device (D2D) single-hop or multi-hop communications. Direct D2D communication can easily be integrated into vehicular communication networks such as vehicle-to-everything (V2X) to offer high-speed data connectivity, very low latency, and reliable services. However, the implementation of D2D enabled vehicular communication networks are characterized by an architecture that causes misalignment of the mm-wave beams from the vehicles thereby causing mutual interference. The mm-wave communication is also having a challenge of high propagation losses, sensitivity to blockage, and directivity. In this regard, the coexistence of cellular users, D2D users together with the vehicular users’ calls for strict QoS requirements since there is the high mobility of the vehicles and the presence of mutual interference. This paper formulates a matching theory-based Hungarian algorithm to perform channel and power allocation that takes into consideration the high rate of channel fluctuations, interference, and latency constraints in the high mobility environment. The proposed Hungarian algorithm was simulated in MATLAB by applying 3GPP TR 37.885 and 38.901 specifications. The Hungarian algorithm is compared with the max-min algorithm. The Hungarian algorithm was found to have a 10.8% better performance than the max-min algorithm when the maximum spectrum efficiency was considered. When the minimum spectrum efficiency was considered the Hungarian algorithm was 17.35% better than the max-min algorithm.
Evaluation of Commercial Real Time PCR and Immunochromatography Techniques in Laboratory Diagnosis of Tuberculosis

This study is a diagnostic evaluation study to real time PCR and Immunochromatography compared to conventional microbiological techniques (represented by Acid fast smear and culture) in laboratory diagnosis of tuberculosis. Whole blood and sputum samples were collected from 136 tuberculosis patients, 124 of them were pulmonary tuberculosis and 12 of them were extrapolmonary tuberculosis in addition to 102 healthy control subjects at consultant clinic for respiratory diseases in Hilla – Babil province-Iraq, during the period from May 2010 to June 2011. Real-time PCR kits, Immunochromatography rapid test in addition to acid fast smear and Lowenstein-Jensen culture were used to diagnose the tuberculosis cases. The results show that the sensitivities of the AFB smear, culture, RT-PCR and ICG were 74%, 43% 88% and 24%, respectively. The sensitivity of 99% was obtained by using RT-PCR and/or AFB smear as a diagnostic criteria. The specificity of AFB smear, culture, RT-PCR was 100%, while the specificity of ICG was 98%. The using of both AFB smear and RT-PCR as diagnostic tests raised the sensitivity to 99% compared with the using of the AFB smear, culture and ICG as a single test.
MEASUREMENT OF MICROCRYSTALLINE SINGLE PARTICLE TRACKING 2 ,

Published online: ABSTRACT The aim of the research was to develop the alpha apparent and the complex shear modulus of microcrystalline cellulose (MCC) solutions based on a single particle tracking technique (SPT) using The temporal displacement of a recorded, trackedand analyzed using custom at 30 fps resulted in the output of radial frequency range from 0.4 rad/s to 10 rad/s. The results from the local measurement in MCC s using rheometer. The measurements were dependent on the local position of the probe in the solution. The results from the developed SPT the common measurement using rheometer.
Small LTRC Samples and Lower Bounds in Hazard Rate Estimation

The proposed estimator is motivated by the asymptotic theory and an intensive numerical study presented in the next section. The cosine basis, used in the asymptotic analysis, performs exceptionally well for a majority of hazards discussed in the literature but can be improved for hazards that change rapidly near boundaries. For instance, there is an important class of bathtub-shaped hazards that can be seen in the bottom row of Figure 1. Note that the hazards are almost vertical near boundaries. According to Jankowski and Welner (2009), all traditional nonparametric estimators do not perform well for bathtub hazards, and hence in that paper a smart procedure of piece-wise approximations is proposed. Our numerical study of bathtub hazards shows that cosine estimators perform well in terms of the ISE, but their visual appeal near boundaries is not satisfactory. To resolve the boundary issue, it is recommended to complement the cosine basis by linear and quadratic terms as is explained in the proof of Theorem 2. This simple method, as we will see shortly, yields satisfactory visualization of bathtub shapes. At the same time, the recommended estimator uses linear and quadratic terms only if there is a statistically significant evidence for the need of a boundary correction.
Discussion on the paper by Gerber and Chopin

I congratulate Gerber and Chopin for this extraordinary contribution and I am very happy that now quasi-Monte-Carlo approaches will achieve wider recognition also in statistics. The numerical experiments in the paper are very promising and I believe that the theoretic results are not yet as far developed as the experiments indicate. I would like to comment on the consistency result of Section 3.4. For me the main challenge would be to derive an explicit error bound instead of the asymptotic statement from theorem 5. To achieve such a result an explicit estimate of the importance sampling procedure (see theorem 1 in Section 3.2) might be useful. As the authors already point out, results in that direction can be found in [1]. However, this is certainly not enough to obtain an explicit error estimate. Since this is a difficult question we might start with asking whether for all T ∈ N and all N ∈ N there is a sequence of deterministic point sets UT,N = (Ut,N)0≤t≤T with Ut,N = {ut , . , ut } ⊂ [0, 1),