Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing mean field spin glasses with external field

We consider the Hamiltonians of mean-field spin glasses, which are certain random functions $H_N$ defined on high-dimensional cubes or spheres in $\mathbb R^N$. The asymptotic maximum values of these functions were famously obtained by Talagrand and later by Panchenko and by Chen. The landscape of approximate maxima of $H_N$ is described by various forms of replica symmetry breaking exhibiting a broad range of possible behaviors. We study the problem of efficiently computing an approximate maximizer of $H_N$. We give a two-phase message pasing algorithm to approximately maximize $H_N$ when a no overlap-gap condition holds. This generalizes several recent works by allowing a non-trivial external field. For even Ising spin glasses with constant external field, our algorithm succeeds exactly when existing methods fail to rule out approximate maximization for a wide class of algorithms. Moreover we give a branching variant of our algorithm which constructs a full ultrametric tree of approximate maxima.
Neural Computation of Capacity Region of Memoryless Multiple Access Channels

This paper provides a numerical framework for computing the achievable rate region of memoryless multiple-access channel (MAC) with a continuous alphabet from data. In particular, we use recent results on variational lower bounds on mutual information and KL-divergence to compute the boundaries of the rate region of MAC using a set of functions parameterized by neural networks. Our method relies on a variational lower bound on KL-divergence and an upper bound on KL-divergence based on the $f$-divergence inequalities. Unlike previous work, which computes an estimate on mutual information, which is neither a lower nor an upper bound, our method estimates a lower bound on mutual information. Our numerical results show that the proposed method provides tighter estimates compared to the MINE-based estimator at large SNRs while being computationally more efficient. Finally, we apply the proposed method to the optical intensity MAC and obtain a new achievable rate boundary tighter than prior works.
Improving Document Representations by Generating Pseudo Query Embeddings for Dense Retrieval

Recently, the retrieval models based on dense representations have been gradually applied in the first stage of the document retrieval tasks, showing better performance than traditional sparse vector space models. To obtain high efficiency, the basic structure of these models is Bi-encoder in most cases. However, this simple structure may cause serious information loss during the encoding of documents since the queries are agnostic. To address this problem, we design a method to mimic the queries to each of the documents by an iterative clustering process and represent the documents by multiple pseudo queries (i.e., the cluster centroids). To boost the retrieval process using approximate nearest neighbor search library, we also optimize the matching function with a two-step score calculation procedure. Experimental results on several popular ranking and QA datasets show that our model can achieve state-of-the-art results while still remaining high efficiency.
Synchronization Control With Adaptive Friction Compensation of Treadmill-Based Testing Apparatus for Wheeled Planetary Rover

This article studies synchronization control of a novel devised treadmill-based testing apparatus for wheeled planetary rover roaming at low speed. To offer satisfactory tracking performance and to ameliorate internal conflicts amongst individual treadmills during rover-treadmill interaction, a decentralized synchronization control strategy integrating an adaptive friction compensation scheme is proposed. To overcome the nonlinear friction effect at low-velocity scenarios, the LuGre model based friction compensation scheme is constructed with a dual-observer structure that can handle parametric uncertainties without recourse to the high-resolution encoder-required parameter identification. With the proposed control design, asymptotic stability of the closed-loop system is guaranteed with the tracking error and synchronization error converging to zero in presence of parametric uncertainties. Experimental results demonstrate the effectiveness of the proposed control strategy, which significantly improves the tracking performance in comparison with the existing proportional differential (PD)-type controller and synchronization controller without friction compensation.
Singular and regular vortices on top of a background pulled to the center

A recent analysis has revealed singular but physically relevant localized two-dimensional vortex states with density ∼r −4/3 at r→0 and a convergent total norm, which are maintained by the interplay of the potential of the attraction to the center, ∼−r −2, and a self-repulsive quartic nonlinearity, produced by the Lee-Huang-Yang correction to the mean-field dynamics of Bose–Einstein condensates. In optics, a similar setting, with the density singularity ∼r −1, is realized with the help of quintic self-defocusing. Here we present physically relevant antidark singular-vortex states in these systems, existing on top of a flat background. Numerical solutions for them are very accurately approximated by the Thomas–Fermi wave function. Their stability exactly obeys an analytical criterion derived from the consideration of small perturbations. The singular-vortex states exist as well in the case when the effective potential is weakly repulsive. It is demonstrated that the singular vortices can be excited by the input in the form of the ordinary nonsingular vortices, hence the singular modes can be created in the experiment. We also consider regular (dark) vortices maintained by the flat background, under the action of the repulsive central potential ∼+r −2. The dark modes with vorticities l = 0 and 1 are completely stable. In the case when the central potential is attractive, but the effective one, which includes the centrifugal term, is repulsive, and, in addition, a weak trapping potential ∼r 2 is applied, dark vortices with l = 1 feature an intricate pattern of alternating stability and instability regions. Under the action of the instability, states with l = 1 travel along tangled trajectories, which stay in a finite area defined by the trap. The analysis is also reported for dark vortices with l = 2, which feature a complex structure of alternating intervals of stability and instability against splitting. Lastly, simple but novel flat vortices are found at the border between the anidark and dark ones.
Temporal Lobe Epilepsy and Alzheimer’s Disease: From Preclinical to Clinical Evidence of a Strong Association

Increasing evidence coming from both experimental and humans’ studies strongly suggest the existence of a link between epilepsy, in particular temporal lobe epilepsy (TLE), and Alzheimer’s disease (AD). Patients with mild cognitive impairment and AD are more prone to have seizures, and seizures seem to facilitate amyloid-β and tau deposits, thus promoting neurodegenerative processes. Consistent with this view, long-lasting drug-resistant TLE and AD have been shown to share several pathological and neuroimaging features. Even if studies addressing prevalence of interictal and subclinical epileptiform activity in these patients are not yet conclusive, their findings raise the possibility that epileptiform activity might negatively impact memory and hasten cognitive decline, either directly or by association with unrecognized silent seizures. In addition, data about detrimental effect of network hyperexcitability in temporal regions in the premorbid and early stages ofADopen up newtherapeutic opportunities for antiseizure medications and/or antiepileptic strategies that might complement or enhance existing therapies, and potentially modify disease progression. Here we provide a review of evidence linking epileptiform activity, network hyperexcitability, and AD, and their role promoting and accelerating neurodegenerative process. Finally, the effects of antiseizure medications on cognition and their optimal administration in patients with AD are summarized.
Novel Nanocomposites of Polypyrrole Doped with Fullerene C60

ABSTRACT This paper reports on the structural, thermal, and dielectric properties of polypyrrole/fullerene C60 nanocomposites synthesized by a interfacial polymerization method. Fourier transform infrared (FT-IR) and ultraviolet-visible (UV-vis) analyses clearly indicated the existence of interactions between polypyrrole (PPy) and the fullerene C60 nanoparticles. Thermal analyses indicated that the extrapolated onset degradation temperature (Tdeg) of PPy increased with increasing doping level. Scanning electron microscopy (SEM) images showed that the fullerene C60 changed the morphology of PPy. Dielectric analyses showed a temperature dependent dielectric relaxation behavior. The relaxation time of the nanocomposites with high doping levels tended to increase with increasing temperature. This behavior of the polypyrrole/fullerene C60 nanocomposites indicated that they could be used as a high temperature ultrasonic transducer.
The impact of ownership structure on dividend policy of listed firms in Jordan

Abstract This study investigates the impact of ownership structure on the dividend policy in Jordan. In particular, it aims to uncover the effects of family ownership, institutional ownership, foreign ownership, and state ownership on dividend decisions for a sample of 66 Jordanian industrial and service firms listed on the Amman Stock Exchange (ASE) for the period 2014–2017. Tobit Panel Regression is used to test the hypotheses of the study. The results show a significant positive association between institutional ownership and dividend yield, while foreign ownership is associated with a less likelihood of paying dividends. No evidence is found to support that family ownership and state ownership have an impact on dividend yield. Hence, the study provides a clear evidence that high institutional ownership as an external control mechanism increases the need to pay dividends. The results also indicate that Jordanian listed firms have highly concentrated ownership structures and are mainly dominated by families followed by financial institutions, then foreign investors while the state shows relatively lower ownership. The study recommends that investors should take into account ownership structure when making investment decisions to help them choose the best investment opportunities.
Applications of Auction and Mechanism Design in Edge Computing: A Survey

Edge computing as a promising technology provides lower latency, more efficient transmission, and faster speed of data processing since the edge servers are closer to the user devices. Each edge server with limited resources can offload latency-sensitive and computation-intensive tasks from nearby user devices. However, edge computing faces challenges such as resource allocation, energy consumption, security and privacy issues, etc. Auction mechanisms can well characterize bidirectional interactions between edge servers and user devices under the above constraints in edge computing. As demonstrated by the existing works, auction and mechanism design approaches are outstanding on achieving optimal allocation strategy while guaranteeing mutual satisfaction among edge servers and user devices, especially for scenarios with scarce resources. In this paper, we introduce a comprehensive survey of recent researches that apply auction approaches in edge computing. Firstly, a brief overview of edge computing including three common edge computing paradigms, i.e., cloudlet, fog computing and mobile edge computing, is presented. Then, we introduce fundamentals and backgrounds of auction schemes commonly used in edge computing systems. After then, a comprehensive survey of applications of auction-based approaches applied for edge computing is provided, which is categorized by different auction approaches. Finally, several open challenges and promising research directions are discussed.
Co-creation: the new kid on the block in public governance

This article has three objectives. The first is to show that while co-production was originally tied to service production, co-creation has broader applications in the field of public governance and involves a broader range of actors and activities. The second objective is to demonstrate how the co-creation concept both builds on and extends the concept of collaborative governance, thus adding new dimensions to an already well-established literature. The final objective is to show how a strategic turn to co-creation introduces a new type of ‘generative governance’ aimed at solving complex problems by constructing platforms enabling the formation of arenas for co-creation that bring together a plethora of public and private actors, including citizens, in creative problem-solving processes. The three objectives are achieved through prospective theoretical analysis aimed at providing a conceptual foundation for analysing cutting-edge societal developments that are not yet commonplace.
Probabilistic Modeling of Hurricane Wind-Induced Damage in Infrastructure Systems

This paper presents a modeling approach for probabilistic estimation of hurricane wind-induced damage to infrastructural assets. In our approach, we employ a Nonhomogeneous Poisson Process (NHPP) model for estimating spatially-varying probability distributions of damage as a function of hurricane wind eld velocities. Speci cally, we consider a physically-based, quadratic NHPP model for failures of overhead assets in electricity distribution systems. The wind eld velocities are provided by Forecasts of Hurricanes using Large-Ensemble Outputs (FHLO), a framework for generating probabilistic hurricane forecasts. We use FHLO in conjunction with the NHPP model, such that the hurricane forecast uncertainties represented by FHLO are accounted for in estimating the probability distributions of damage. Furthermore, we evaluate the spatial variability and extent of hurricane damage under key wind eld parameters (intensity, size, and asymmetries). By applying our approach to prediction of power outages (loss-of-service) in northwestern Florida due to Hurricane Michael (2018), we demonstrate a statistically signi cant relationship between outage rate and failure rate. Finally, we formulate parametric models that relate total damage and nancial losses to the hurricane parameters of intensity and size. Overall, this paper’s ndings suggest that our approach is well-suited to jointly account for spatial variability and forecast uncertainty in the damage estimates, and is readily applicable to prediction of system loss-of-service due to the damage.
Optimized Bioconversion of Xylose Derived from Pre-Treated Crop Residues into Xylitol by Using Candida boidinii

Crop residues can serve as low-cost feedstocks for microbial production of xylitol, which offers many advantages over the commonly used chemical process. However, enhancing the efficiency of xylitol fermentation is still a barrier to industrial implementation. In this study, the effects of oxygen transfer rate (OTR) (1.1, 2.1, 3.1 mmol O2/(L × h)) and initial xylose concentration (30, 55, 80 g/L) on xylitol production of Candida boidinii NCAIM Y.01308 on xylose medium were investigated and optimised by response surface methodology, and xylitol fermentations were performed on xylose-rich hydrolysates of wheat bran and rice straw. High values of maximum xylitol yields (58–63%) were achieved at low initial xylose concentration (20–30 g/L) and OTR values (1.1–1.5 mmol O2/(L × h)). The highest value for maximum xylitol productivity (0.96 g/(L × h)) was predicted at 71 g/L initial xylose and 2.7 mmol O2/(L × h) OTR. Maximum xylitol yield and productivity obtained on wheat bran hydrolysate were 60% and 0.58 g/(L × h), respectively. On detoxified and supplemented hydrolysate of rice straw, maximum xylitol yield and productivity of 30% and 0.19 g/(L × h) were achieved. This study revealed the terms affecting the xylitol production by C. boidinii and provided validated models to predict the achievable xylitol yields and productivities under different conditions. Efficient pre-treatments for xylose-rich hydrolysates from rice straw and wheat bran were selected. Fermentation using wheat bran hydrolysate and C. boidinii under optimized condition is proved as a promising method for biotechnological xylitol production.
Preparation and characterization of new nanocomposites synthesized of Au and Au-Ag bimetallic nanoparticles encapsoulated in functionalized multiwalled carbon nanotubes

Highly dispersed Au and Au-Ag nanoparticles supported on carbon nanotubes are generated using an easy synthesis. MWCNT was opened and its derivative containing -COOH and -OH functional groups was obtained. Polycondensation of monohydrate citric acid in the presence of functionalized MWCNT in the melting state was lead to nanotube -graftpoly (citric acid) (CNT-g-PCA) nanocomposites. The CNTg-PCA were soluble in water freely and stirring their water solution and metal at room temperature lead to the CNT-gPCA containing encapsulated metallic nanoparticles in their polymeric shell. The structure and composition of the resulting MWCNT/Au and Au-Ag nanoparticle hybrids were characterized with TEM and spectroscopy methods. The results imply that the obtained CNT-g-PCA-Au and CNT-g-PCA-Au/ Ag nanocomposites have a good application potential in catalysis, sensor, and fuel cells.
A Contract Theory Approach to Islamic Financial Securities with an Application to Diminishing Mushārakah

This paper demonstrates how the contract theory framework can and should complement standard financial mathematics for analysing Islamic financial securities (IFSs). It is motivated by the perception that most valuations of IFSs are rather simplistic and are as simple as risk and reward, leading to very simplistic investment strategies, especially by buyers. In fact, there are more dimensions to IFSs and IF in general which can only be properly analysed with more advanced approaches, such as contractual issues which are well-recognised and discussed in the fields of Islamic commercial law and contract theory but not always considered in valuation models. Contract theory can bring together financial mathematics and contractual issues, providing a more sophisticated framework for analysing IFSs. This paper aims to demonstrate this by providing a brief outline of the contract theory approach, followed by a simple demonstration of its use in the analysis of diminishing mushārakah (DM) contracts. The resulting model led to three main conclusions regarding DM contracts: That (i) finance seekers have no ready incentive to spend on asset maintenance, (ii) finance seekers will only spend on asset maintenance if their marginal benefit from the asset’s appreciation is greater than the financier’s share of the asset, and (iii) if the magnitude of asset appreciation and depreciation is equal, an increase in either will also increase the optimal level of spending on asset maintenance.
AdS/BCFT correspondence and BTZ black hole thermodynamics within Horndeski gravity

In this work, we study gravity duals of conformal field theories with boundaries known as AdS/BCFT correspondence, put forward by Takayanagi [1], within a scalar-tensor theory, as proposed by Horndeski in 4D [2]. We consider the case of 3D gravity dual to 2D BCFT, take a Gibbons-Hawking surface term modified by Horndeski's theory and find the corresponding 3D (BTZ) black hole solutions. We analyze the effects of Horndeski's gravity on the profile of the extra boundary for the black hole by using an approximate analytic solution. Performing a holographic renormalization, we have calculated the free energy and obtained the total entropy and the corresponding area, as well as the boundary entropy for the black hole. In particular, the boundary entropy found here, can be seen as an extension of the one proposed by Takayanagi. From the free energy, we have done a systematic study of the 3D black hole thermodynamics and present, among our results, an indication of restoration of conformal symmetry for high temperatures. Finally, we present a study on the influence of the Horndeski's gravity on the Hawking-Page phase transition where we can see the stable and unstable phases throughout the plane of free energy {\sl versus} temperature.
Desiccation of Wetlands and Their Influence on the Regional Climate. Case Study: Ciénaga de Aguablanca, Cali, Colombia

The desiccation of wetlands is a process associated with the dynamics of urban growth and expansion of the agricultural frontier. This article aims to evaluate the influence of the desiccation of the Ciénaga de Aguablanca on regional climate. The periodization of the desiccation and urban growth of Cali was reconstructed under the theoretical contributions of environmental history. As complementary sources, maps, aerial photographs, and diagrams of the city were obtained from 1944 to 2020, with which the hydromorphological changes in the Ciénaga de Aguablanca were represented. Data from six hydroclimatological stations were statistically analyzed with Pettitt’s test, trend analysis, and Rclimdex. The results indicate a reduction of 99% in the area of the wetlands, from 19.2 km2 in 1944 to 0.2 km2 by 2020. Additionally, a break point was observed in 1970, preceded by the process of wetland desiccation and waterproofing of the eastern part of Cali, along with significant differences between temperature series inside and outside the urban perimeter. Another break point was detected in 1985 in the flow series, associated with the construction of the La Salvajina dam. Monthly rainfall showed a tendency to increase, but its temporal distribution was uneven, given that rainfall volume showed a tendency to increase over short times. Regional climate changes can occur at a much faster rate than global variations due to the anthropogenic actions of wetland intervention.
Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth

Graph Neural Networks (GNNs) have been studied from the lens of expressive power and generalization. However, their optimization properties are less well understood. We take the first step towards analyzing GNN training by studying the gradient dynamics of GNNs. First, we analyze linearized GNNs and prove that despite the non-convexity of training, convergence to a global minimum at a linear rate is guaranteed under mild assumptions that we validate on real-world graphs. Second, we study what may affect the GNNs' training speed. Our results show that the training of GNNs is implicitly accelerated by skip connections, more depth, and/or a good label distribution. Empirical results confirm that our theoretical results for linearized GNNs align with the training behavior of nonlinear GNNs. Our results provide the first theoretical support for the success of GNNs with skip connections in terms of optimization, and suggest that deep GNNs with skip connections would be promising in practice.
Understanding by Understanding Not: Modeling Negation in Language Models

Negation is a core construction in natural language. Despite being very successful on many tasks, state-of-the-art pre-trained language models often handle negation incorrectly. To improve language models in this regard, we propose to augment the language modeling objective with an unlikelihood objective that is based on negated generic sentences from a raw text corpus. By training BERT with the resulting combined objective we reduce the mean top 1 error rate to 4% on the negated LAMA dataset. We also see some improvements on the negated NLI benchmarks.