Miracle-dz/UniFAR-FTUs huggingface dataset:
Central News Theorem Predicting Online News Popularity

In this study, we want to investigate what features about articles, such as topic or number of links, best predict online popularity. This issue is relevant because of the phenomenon with fake news spreading across social media, and understanding popularity predictors can mitigate the effects of fake news. To identify what features best predict popularity, we run a logistic regression model on a dataset about articles published by Mashable. Our final model is fitted with the logit link stepwise BIC procedure. The model has an AUC score of . 697 and predicts that popular articles are more likely to be published on the weekend, have more keywords and links, be more subjective and opinionated, and be about technology or social media.
Teams AND Up-Front Testing for Development of Safety-Critical Systems with Agile

There are multiple issues/challenges that must be addressed for making agile more suitable for safety-critical systems. In this paper we will address the issues relevant to team formation, communication and testing. Up-front testing goes well in line with agile principles, this is the reason we choose it for our proposed approach. Safety-critical systems are defined as those systems whose failure can cause harm [Mwadulo 2016]. The system is considered safety-critical if its failure can lead to unacceptable circumstances such as loss of human lives or damage to the environment[Mwadulo 2016]. Development of these systems in an agile way can be very beneficial in terms of time and cost. The basic principles of agile say that there should be rapid development, strong communication among all stake holders and changes should be welcome at any stage of development [Alliance 2001]. As there is a lot of focus on people so every individual in team should be motivated and must be given suitable environment and support to perform their jobs [Stavru 2014]. Hence team building and communication is an important aspect for this approach. [Stålhane-IDI ] To build a successfully functioning team It is extremely necessary that whole team is on board regarding safety aspects of the system. [Leite 2017] Agile teams provide room for requirement changes during the development process. In case of safety-critical systems this needs to be handled with great care. Agile has major focus on team collaborations and interaction among all stakeholders[McHugh et al. 2011] and promotes self organizing and cross functional teams [Boehm 2002]. Along with team formation and communication testing is another crucial aspect of safety-critical systems. It poses many challenges to incorporate rigorous testing in short time span and within budget limits. Efficiency
The Smog-Reduction Road: Remote Sensing vs. The Clean Air Act

The 1992 amendments to the Clean Air Act mandated that local governments that violate federal ozone (urban smog) standards abide by a dizzying array of regulations, many of the most controversial of which--centralized state inspection and maintenance programs, carpooling requirements, zero-emission vehicle sales quotas, use of alternative fuels, and new-vehicle emission standards--are intended to control automobile emissions.
Determination of ethyl para methoxycinnamate content and anti-inflammatory test of Kaempferia galangal, L rhizome extract by inhibition of protein denaturation method

Kencur (Kempferia galanga, L) is one of the rhizomes that is often used as a constituent component in traditional medicinal formulas. One of the kencur's pharmacological activities is antiinflammatory. The active compound as an anti-inflammatory is ethyl p-methoxycinnamate (EPMC). This study aims to determine the content and ascertainment of EPMC compounds and the antiinflammatory activity of kencur rhizome extract. Kencur extract can be obtained by the maceration method using 96% ethanol solvent. The EPMC content determination was held using the TLCdensitometry method, while the EPMC compounds were confirmed using the GC MS method. The anti-inflammatory activity test was done using inhibiting protein denaturation methods. The extract results obtained 12.67% yield. The result of EPMC content in kencur extract was 10.05 ± 4.57. The GCMS kencur extract results showed an abundance of EPMC compounds at a retention time of 7.088 with a peak area of 72290779. The results of IC50 anti-inflammatory for the standard substance (EPMC) were 5.306 ± 5.028. The results of IC50 anti-inflammatory the sample (kencur extract) was 303.487 ± 1.201. Ethanol extract of kencur contains ethyl p-methoxycinnamate (EPMC) and has antiinflammatory activity by inhibiting protein denaturation.
Emerging trends in the developments of low power design technique

In this paper is introduced a low power design technique for developing more reliable, functional, and more cost-effective handheld cellular telephones, portable computers, and peripherals. The portability requirements of handheld computers and other portable devices have placed tremendous pressure on electronic equipment designers, who need to deal with restrictions in the size of electronic units and power consumption. Even though battery technology is continuously improving, including reduced power consumption of processors and displays, extensive and continuous use of network services aggravates these issues. Now the onus is on the research and industrial communities to extend battery life and reduce weight. Equally, research on new techniques and technologies continues, to carefully manage energy consumption in mobile devices, while still providing continuous and fast connections to services and applications. This paper also discusses the novel trends in the developments and advancements in the area of low power Very Large Scale Integration (VLSI) design, dynamic power dissipation static power loss in Complementary Metal Oxide Semiconductor (CMOS), and advanced low power technique. Though low power as a well-established domain that undergone lots of developments from transistor sizing, process shrinkage, voltage scaling, clock gating, etc., to adiabatic logic are elaborated.
Sources and biodegradability of dissolved organic matter in two headwater peatland catchments at the Marcell Experimental Forest, northern Minnesota, USA

Where they are present in catchments, peatlands are a dominant source of dissolved organic matter (DOM) to surrounding waterways due, in part, to high production rates. Despite the preponderance of peatlands in northern latitudes and expected peatland vulnerability to climate change, little is known about peatland DOM degradation relative to a more comprehensive understanding of degradation when DOM is sourced from upland‐dominated catchments. We compared DOM biodegradability of various sources of stream water in two catchments having peatlands (22%–33% of the area) surrounded by upland forests (70%–90% of the area, either deciduous or coniferous). We measured total organic carbon (TOC), and biodegradable dissolved organic carbon concentrations; bacterial respiration rates; streamflow; and upland runoff during and after snowmelt (March to June, 2009–2011). We also explored if DOM in upland runoff stimulated biodegradation of peatland‐derived DOM (i.e., a priming effect), and if forest cover type affected DOM biodegradability. As expected, the peatlands were the largest sources of both water (72%–80%) and TOC (92%–96%) to the streams although more area in each catchment was in uplands (70%–90%). Several results were unexpected, yet revealing: (1) DOM from peatlands sometimes had the same biodegradability as DOM from uplands, (2) upland sources of DOM had negligible effects on biodegradability in the peatland and downstream, and (3) upland deciduous cover did not yield more degradable DOM than conifer cover. The most pronounced effect of upland runoff was dilution of downstream TOC concentrations when there was upland runoff. Overall, the effects of upland DOM may have been negligible due to the overriding effect of the large amount of biodegradable DOM that originated in bogs. This research highlights that peatland‐sourced DOM has important effects on downstream DOM biodegradability even in catchments where upland area is substantially larger than peatland area.
A survey of recent trends and challenges in MASSIVE MIMO in terms of channel utilization and Antenna selection

The Massive Multiple Input multiple Output (MIMO) antenna is some sort of multiple antennasystems, that is used to massively make better use of spectral efficiency, robustness to interference, increased the bandwidth and reduced latency . and it is used to provide a solution for high demand communication systems like cellular mobile communication that requires high data rate less interference and better Mobile Node coverage and connectivity.The antenna that form the massive antenna system is organized in a somehow that allow a group of antenna to work as a transmitter and other make other jobs may be standby to the transmitter antennas also The channel that connect transmitter and receiver and which estimated with a sequence of signal called pilot signal, is important to simulate and model. Massive MIMO has some main issues that are related to the technique used in the antenna selection and channel utilization, we provide an overview of recent trends concerning channel utilization and antenna selection, we then compare them and provide some open research area.
Reference-based Weak Supervision for Answer Sentence Selection using Web Data

Answer sentence selection (AS2) modeling requires annotated data, i.e., hand-labeled question-answer pairs. We present a strategy to collect weakly supervised answers for a question based on its reference to improve AS2 modeling. Specifically, we introduce Reference-based Weak Supervision (RWS), a fully automatic large-scale data pipeline that harvests high-quality weakly-supervised answers from abundant Web data requiring only a question-reference pair as input. We study the efficacy and robustness of RWS in the setting of TANDA, a recent state-of-the-art fine-tuning approach specialized for AS2. Our experiments indicate that the produced data consistently bolsters TANDA. We achieve the state of the art in terms of P@1, 90.1%, and MAP, 92.9%, on WikiQA.
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models

Existing neural information retrieval (IR) models have often been studied in homogeneous and narrow settings, which has considerably limited insights into their out-of-distribution (OOD) generalization capabilities. To address this, and to facilitate researchers to broadly evaluate the effectiveness of their models, we introduce Benchmarking-IR (BEIR), a robust and heterogeneous evaluation benchmark for information retrieval. We leverage a careful selection of 18 publicly available datasets from diverse text retrieval tasks and domains and evaluate 10 state-of-the-art retrieval systems including lexical, sparse, dense, late-interaction and re-ranking architectures on the BEIR benchmark. Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs. In contrast, dense and sparse-retrieval models are computationally more efficient but often underperform other approaches, highlighting the considerable room for improvement in their generalization capabilities. We hope this framework allows us to better evaluate and understand existing retrieval systems, and contributes to accelerating progress towards better robust and generalizable systems in the future. BEIR is publicly available at https://github.com/UKPLab/beir.
Topological dipole Floquet solitons

We theoretically introduce a new type of topological dipole solitons propagating in a Floquet topological insulator based on a kagome array of helical waveguides. Such solitons bifurcate from two edge states belonging to different topological gaps and have bright envelopes of different symmetries: fundamental for one component, and dipole for the other. The formation of dipole solitons is enabled by unique spectral features of the kagome array which allow the simultaneous coexistence of two topological edge states from different gaps at the same boundary. Notably, these states have equal and nearly vanishing group velocities as well as the same sign of the effective dispersion coefficients. We derive envelope equations describing components of dipole solitons and demonstrate in full continuous simulations that such states indeed can survive over hundreds of helix periods without any noticeable radiation into the bulk.
Intermittent Current Interruption Method for Commercial Lithium-Ion Batteries Aging Characterization

In this article, a pioneering study is presented where the intermittent current interruption method is used to characterize the aging behavior of commercial lithium-ion batteries. With a very resource-efficient implementation, this method can track the battery resistive and diffusive behaviors over the entire state-of-charge range and be able to determine the aging throughout the lifetime of the batteries. In addition, the incremental capacity analysis can be carried out with the same dataset. This method can provide measurement results with high repeatability and produce equivalent information as the electrochemical impedance spectroscopy method. In this study, both the resistive and diffusive parameters increase with the battery capacity fading. This method does not require advanced test equipment and even with a 0.1-Hz sampling frequency; it is possible to extract usable parameters by prolonging the interruption length. Therefore, it has the potential to be easily implemented in the charging sequence in electric vehicles or stationary storage batteries for aging diagnostics.
Progressive Encoding for Neural Optimization

We introduce a Progressive Positional Encoding (PPE) layer, which gradually exposes signals with increasing frequencies throughout the neural optimization. In this paper, we show the competence of the PPE layer for mesh transfer and its advantages compared to contemporary surface mapping techniques. Our approach is simple and requires little user guidance. Most importantly, our technique is a parameterization-free method, and thus applicable to a variety of target shape representations, including point clouds, polygon soups, and non-manifold meshes. We demonstrate that the transferred meshing remains faithful to the source mesh design characteristics, and at the same time fits the target geometry well.
LaLaLoc: Latent Layout Localisation in Dynamic, Unvisited Environments

We present LaLaLoc to localise in environments without the need for prior visitation, and in a manner that is robust to large changes in scene appearance, such as a full rearrangement of furniture. Specifically, LaLaLoc performs localisation through latent representations of room layout. LaLaLoc learns a rich embedding space shared between RGB panoramas and layouts inferred from a known floor plan that encodes the structural similarity between locations. Further, LaLaLoc introduces direct, cross-modal pose optimisation in its latent space. Thus, LaLaLoc enables fine-grained pose estimation in a scene without the need for prior visitation, as well as being robust to dynamics, such as a change in furniture configuration. We show that in a domestic environment LaLaLoc is able to accurately localise a single RGB panorama image to within 8.3cm, given only a floor plan as a prior.
Toward a concordance teleparallel cosmology. Part II. Linear perturbation

Late time cosmic acceleration may be achieved by modifying gravity on large scales. This should also have consequences on the evolution of perturbations. We thus extend our study of exponential infrared f(T) teleparallel gravity to examine the viability of the theory at the linear perturbation level, evaluating the full CMB and matter power spectra. As the theory does not introduce extra free parameters, it fits within the minimal six parameter space of standard . Using Planck 2018 CMB (TT+TE+EE+lensing) alone, best fits predict those parameters to be almost identical to ΛCDM, with slightly smaller χ2 min. The resulting H 0=72.24 ± 0.64 km/s/Mpc, which virtually alleviates the tension with local measurements, due to late time phantom behaviour. Inclusion of BAO data however reduces H 0, reflecting furthermore systematic deviations from data that are also present in supernova distances and the growth rate of structure (increasing the apparent tension in the latter case). As the theory, unlike other viable f(T) models, does not reduce to ΛCDM through extra free parameters, those conclusions are generic; applying to any modified gravity or dynamical dark energy with phantom behaviour. With best fit parameters, the present scenario produces a CMB spectrum almost identical to ΛCDM, with slight deviation at low-multipole ℓ < 30, where cosmic variance is large. The matter power spectrum is also quite close to ΛCDM; with percent level scale free modifications affecting modes significantly smaller than the horizon, arising primarily from modified background evolution. More significant deviations appear on larger scales, and may in principle distinguish modified gravity scenarios of the type studied here from dynamical dark energy.
Interpretability in deep learning for finance: a case study for the Heston model

Deep learning is a powerful tool whose applications in quantitative finance are growing every day. Yet, artificial neural networks behave as black boxes and this hinders validation and accountability processes. Being able to interpret the inner functioning and the input-output relationship of these networks has become key for the acceptance of such tools. In this paper we focus on the calibration process of a stochastic volatility model, a subject recently tackled by deep learning algorithms. We analyze the Heston model in particular, as this model's properties are well known, resulting in an ideal benchmark case. We investigate the capability of local strategies and global strategies coming from cooperative game theory to explain the trained neural networks, and we find that global strategies such as Shapley values can be effectively used in practice. Our analysis also highlights that Shapley values may help choose the network architecture, as we find that fully-connected neural networks perform better than convolutional neural networks in predicting and interpreting the Heston model prices to parameters relationship.
Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning

Deep reinforcement learning (DRL) has successfully solved various problems recently, typically with a unimodal policy representation. However, grasping distinguishable skills for some tasks with non-unique optima can be essential for further improving its learning efficiency and performance, which may lead to a multimodal policy represented as a mixture-of-experts (MOE). To our best knowledge, present DRL algorithms for general utility do not deploy this method as policy function approximators due to the potential challenge in its differentiability for policy learning. In this work, we propose a probabilistic mixture-of-experts (PMOE) implemented with a Gaussian mixture model (GMM) for multimodal policy, together with a novel gradient estimator for the indifferentiability problem, which can be applied in generic off-policy and on-policy DRL algorithms using stochastic policies, e.g., Soft Actor-Critic (SAC) and Proximal Policy Optimisation (PPO). Experimental results testify the advantage of our method over unimodal polices and two different MOE methods, as well as a method of option frameworks, based on the above two types of DRL algorithms, on six MuJoCo tasks. Different gradient estimations for GMM like the reparameterisation trick (Gumbel-Softmax) and the score-ratio trick are also compared with our method. We further empirically demonstrate the distinguishable primitives learned with PMOE and show the benefits of our method in terms of exploration.
Multimessenger Implications of Sub-PeV Diffuse Galactic Gamma-Ray Emission

The diffuse Galactic gamma-ray flux between 0.1 and 1 PeV has recently been measured by the Tibet ASγ Collaboration. The flux and spectrum are consistent with the decay of neutral pions from hadronuclear interactions between Galactic cosmic rays and the interstellar medium (ISM). We derive the flux of the Galactic diffuse neutrino emission from the same interaction process that produces the gamma rays. Our calculation accounts for the effect of gamma-ray attenuation inside the Milky Way and uncertainties due to the spectrum and distribution of cosmic rays, gas density, and infrared emission of the ISM. We find that the contribution from the Galactic plane (GP) to the all-sky neutrino flux is ≲5%–10% around 100 TeV. The Galactic and extragalactic neutrino intensities are comparable in the GP region. Our results are consistent with the upper limit reported by the IceCube and ANTARES Collaborations, and predict that next-generation neutrino experiments may observe the Galactic component. We also show that the Tibet ASγ data imply either an additional component in the cosmic-ray nucleon spectrum or contribution from discrete sources, including PeVatrons such as superbubbles and hypernova remnants, and PeV electron accelerators. Future multimessenger observations between 1 TeV and 1 PeV are crucial to decomposing the origin of sub-PeV gamma-rays.
Unsupervised Extractive Summarization by Human Memory Simulation

Summarization systems face the core challenge of identifying and selecting important information. In this paper, we tackle the problem of content selection in unsupervised extractive summarization of long, structured documents. We introduce a wide range of heuristics that leverage cognitive representations of content units and how these are retained or forgotten in human memory. We find that properties of these representations of human memory can be exploited to capture relevance of content units in scientific articles. Experiments show that our proposed heuristics are effective at leveraging cognitive structures and the organization of the document (i.e.\ sections of an article), and automatic and human evaluations provide strong evidence that these heuristics extract more summary-worthy content units.
Search processes with stochastic resetting and partially absorbing targets

We extend the theoretical framework used to study search processes with stochastic resetting to the case of partially absorbing targets. Instead of an absorption event occurring when the search particle reaches the boundary of a target, the particle can diffuse freely in and out of the target region and is absorbed at a rate κ when inside the target. In the context of cell biology, the target could represent a chemically reactive substrate within a cell or a region where a particle can be offloaded onto a nearby compartment. We apply this framework to a partially absorbing interval and to spherically symmetric targets in Rd . In each case, we determine how the mean first passage time (MFPT) for absorption depends on κ, the resetting rate r, and the target geometry. For the given examples, we find that the MFPT is a monotonically decreasing function of κ, whereas it is a unimodal function of r with a unique minimum at an optimal resetting rate r opt. The variation of r opt with κ depends on the spatial dimension d, decreasing in sensitivity as d increases. For finite κ, r opt is a non-trivial function of the target size and distance between the target and the reset point. We also show how our results converge to those obtained previously for problems with totally absorbing targets and similar geometries when the absorption rate becomes infinite. Finally, we generalize the theory to take into account an extended chemical reaction scheme within a target.