Miracle-dz/UniFAR-FTUs huggingface dataset:
On Protocols for Monotone Feasible Interpolation

Dag-like communication protocols, a generalization of the classical tree-like communication protocols, are useful objects in the realm of proof complexity (most importantly for monotone feasible interpolation) and circuit complexity. We consider three kinds of protocols in this article (d is the degree of a protocol): — IEQ-d-dags: feasible sets of these protocols are described by inequality which means that the feasible sets are combinatorial triangles; these protocols are also called triangle-dags in the literature, — EQ-d-dags: feasible sets are described by equality, and — c-IEQ-d-dags: feasible sets are described by a conjunction of c inequalities.Garg, Göös, Kamath, and Sokolov (Theory of Computing, 2020) mentioned all these protocols, and they noted that EQ-d-dags are a special case of c-IEQ-d-dags. The exact relationship between these types of protocols is unclear. As our main contribution, we prove the following statement: EQ-2-dags can efficiently simulate c-IEQ-d-dags when c and d are constants. This implies that EQ-2-dags are at least as strong as IEQ-d-dags and that EQ-2-dags have the same strength as c-IEQ-d-dags for c ≥ 2 (because 2-IEQ-2-dags can trivially simulate EQ-2-dags). Hrubeš and Pudlák (Information Processing Letters, 2018) proved that IEQ-d-dags over the monotone Karchmer-Wigderson relation are equivalent to monotone real circuits which implies that we have exponential lower bounds for these protocols. Lower bounds for EQ-2-dags would directly imply lower bounds for the proof system R(LIN).
Pursuing 3-D Scene Structures With Optical Satellite Images From Affine Reconstruction to Euclidean Reconstruction

How to use multiple optical satellite images to recover the 3-D scene structure is a challenging and important problem in the remote sensing field. Most existing methods in literature have been explored based on the classical rational polynomial coefficients (RPCs) camera model which requires at least 39 ground control points (GCPs), however, it is nontrivial to obtain such a large number of GCPs in many real scenes. Addressing this problem, we propose a hierarchical reconstruction framework based on multiple optical satellite images, which needs only four GCPs to fully-automated reconstruct the 3-D scene structure. The proposed framework is independent of the RPC model and composed of a dense affine reconstruction stage and a followed affine-to-Euclidean upgrading stage: At the dense affine reconstruction stage, a dense affine reconstruction approach is explored for pursuing the 3-D affine scene structure without any GCP from input satellite images. Then at the affine-to-Euclidean upgrading stage, the obtained 3-D affine structure is upgraded to a Euclidean one with four GCPs. Experimental results on two public datasets demonstrate that the proposed method significantly outperforms several state-of-the-art methods in most cases.
Weighting and Pruning based Ensemble Deep Random Vector Functional Link Network for Tabular Data Classification

In this paper, we first introduce batch normalization to the edRVFL network. This re-normalization method can help the network avoid divergence of the hidden features. Then we propose novel variants of Ensemble Deep Random Vector Functional Link (edRVFL). Weighted edRVFL (WedRVFL) uses weighting methods to give training samples different weights in different layers according to how the samples were classified confidently in the previous layer thereby increasing the ensemble's diversity and accuracy. Furthermore, a pruning-based edRVFL (PedRVFL) has also been proposed. We prune some inferior neurons based on their importance for classification before generating the next hidden layer. Through this method, we ensure that the randomly generated inferior features will not propagate to deeper layers. Subsequently, the combination of weighting and pruning, called Weighting and Pruning based Ensemble Deep Random Vector Functional Link Network (WPedRVFL), is proposed. We compare their performances with other state-of-the-art deep feedforward neural networks (FNNs) on 24 tabular UCI classification datasets. The experimental results illustrate the superior performance of our proposed methods.
Observing how deep neural networks understand physics through the energy spectrum of one-dimensional quantum mechanics

We investigated how neural networks(NNs) understand physics using one-dimensional quantum mechanics. After training an NN to accurately predict energy eigenvalues from potentials, we used it to confirm the NN’s understanding of physics from four different aspects. The trained NN could predict energy eigenvalues of different kinds of potentials than the ones learned, predict the probability distribution of the existence of particles not used during training, reproduce untrained physical phenomena, and predict the energy eigenvalues of potentials with unknown matter effect. These results show that NNs can learn the physical laws from experimental data, predict the results of experiments under conditions different from those used for training, and predict physical quantities of types not provided during training. Because NNs understand physics in a different way than humans, they will be a powerful tool for advancing physics by complementing the human way of understanding.
Quantum Fluctuations and Lineshape Anomaly in a High‐ β$\beta$ Silver‐Coated InP‐Based Metallic Nanolaser

Metallic nanocavity lasers provide important technological advancement toward even smaller integrable light sources. They give access to widely unexplored lasing physics in which the distinction between different operational regimes, like those of thermal or coherent light emission, becomes increasingly challenging upon approaching a device with a near‐perfect spontaneous‐emission coupling factor β$\beta$ . In fact, quantum‐optical studies have to be employed to reveal a transition to coherent emission in the intensity fluctuation behavior of nanolasers when the input–output characteristic appears thresholdless for β=1$\beta = 1$ nanolasers. Here, a new indicator for lasing operation in high‐ β$\beta$ lasers is identified by showing that stimulated emission can give rise to a lineshape anomaly manifested as a transition from a Lorentzian to a Gaussian component in the emission linewidth that dominates the spectrum above the lasing threshold.
Block Policy Mirror Descent

In this paper, we present a new policy gradient (PG) methods, namely the block policy mirror descent (BPMD) method for solving a class of regularized reinforcement learning (RL) problems with (strongly)-convex regularizers. Compared to the traditional PG methods with a batch update rule, which visits and updates the policy for every state, BPMD method has cheap per-iteration computation via a partial update rule that performs the policy update on a sampled state. Despite the nonconvex nature of the problem and a partial update rule, we provide a unified analysis for several sampling schemes, and show that BPMD achieves fast linear convergence to the global optimality. In particular, uniform sampling leads to comparable worst-case total computational complexity as batch PG methods. A necessary and sufficient condition for convergence with on-policy sampling is also identified. With a hybrid sampling scheme, we further show that BPMD enjoys potential instance-dependent acceleration, leading to improved dependence on the state space and consequently outperforming batch PG methods. We then extend BPMD methods to the stochastic setting, by utilizing stochastic first-order information constructed from samples. With a generative model, $\tilde{\mathcal{O}}(\left\lvert \mathcal{S}\right\rvert \left\lvert \mathcal{A}\right\rvert /\epsilon)$ (resp. $\tilde{\mathcal{O}}(\left\lvert \mathcal{S}\right\rvert \left\lvert \mathcal{A} \right\rvert /\epsilon^2)$) sample complexities are established for the strongly-convex (resp. non-strongly-convex) regularizers, where $\epsilon$ denotes the target accuracy. To the best of our knowledge, this is the first time that block coordinate descent methods have been developed and analyzed for policy optimization in reinforcement learning, which provides a new perspective on solving large-scale RL problems.
Efficient Generation of Membrane and Solvent Tetrahedral Meshes for Ion Channel Finite Element Calculation

A finite element solution of an ion channel dielectric continuum model such as Poisson-Boltzmann equation (PBE) and a system of Poisson-Nernst-Planck equations (PNP) requires tetrahedral meshes for an ion channel protein region, a membrane region, and an ionic solvent region as well as an interface fitted irregular tetrahedral mesh of a simulation box domain. However, generating these meshes is very difficult and highly technical due to the related three regions having very complex geometrical shapes. Currently, an ion channel mesh generation software package developed in Lu's research group is one available in the public domain. To significantly improve its mesh quality and computer performance, in this paper, new numerical schemes for generating membrane and solvent meshes are presented and implemented in Python, resulting in a new ion channel mesh generation software package. Numerical results are then reported to demonstrate the efficiency of the new numerical schemes and the quality of meshes generated by the new package for ion channel proteins with ion channel pores having different geometric complexities.
WIS LJ 351 03_Cerra.indd

Climate change is coming to every city, and many cities will experience dramatic climate change– associated extremes in temperature and precipitation, with serious impacts on human and natural systems. This article shares a studio approach used at both the graduate and undergraduate level for investigating the many impacts of climate change and promoting a more comprehensive approach to climateadaptive design, especially for inland city locations. Adaptation strategies in the framework include (a) floodplain storage; (b) low impact development; (c) resilient planting design/ ecological resilience; (d) landscape connectivity; (e) urban heat island mitigation; and (f) multimodal mobility. The framework draws from existing techniques and methods that are currently utilized in design practice and urban ecological planning, yet not typically configured as a comprehensive scope of services for climate adaptation. Such a framework for climateadaptive design in noncoastal locations could have practical applications for landscape architects.
1285-01 Economia/Paolera

A n Argentine economic experiment has come to an ignominious end. The attempt to establish a stable currency has given way to an unplanned float, and a fourfold devaluation has rendered null any chance of restoring the old parity. In addition to a widespread economic malaise following some years of a boom, one of the major casualties of the crisis is the banking sector, where the search for a long-run cure has barely begun. The fiscal health of the government remains under a cloud of suspicion. Foreign investors in government bonds, badly burned, are ill disposed toward the country. Most observers agree that the political class has failed, and some have suggested that nothing less than placing the country in “economic receivership” and turning its affairs over to an independent foreign administrator will rectify the situation:
Using Machine Learning Based Models for Personality Recognition

Personality is a stable tendency and characteristic that determines the similarities and differences in psychological behaviors (thoughts, emotions, and actions) of individuals. In other words, personality not only shows an individual's behavioral pattern, thought, and interpersonal communications but also has a great impact on various life aspects, such as happiness, preference, physical, and mental health [1] and [2]. The evolution of personality theories and their progress in measurement methods and statistical analysis has led to the emergence of one of the most influential contemporary personality theories, known as the Five Factor Model (FFM) or Big Five, which contains five primary traits and has been considered by many psychologists in recent years as a popular and Using Machine Learning-Based Models for Personality Recognition
Enhancement in quality and productivity: a riskless approach based on optimum selection of tolerance and improvement strategies

The need of higher quality products from the customers and the tendency of industries to quality improvement for maintaining their competitive position over the long run are the main motives of using improvement methodologies. However, any use of improvement methodology besides its additional costs will also change the total variability of the process. Changes in total variability will affect the optimal value of tolerance as well as the quality of the product. Therefore, one of the main concerns of the producers is to find an effective way to manufacture their goods in a manner that reduces the production costs and gains customers’ satisfaction. To meet these goals, this study introduces an algorithm to propose a riskless approach of improvement that results in the maximum amount of net savings. It is shown that using appropriate improvement strategies simultaneous with a proper selection of tolerance have an important impact to enhance productivity and quality.
Explaining and Measuring Functionalities of Malware Detectors

Numerous open-source and commercial malware detectors are available. However, their efficacy is threatened by new adversarial attacks, whereby malware attempts to evade detection, e.g., by performing feature-space manipulation. In this work, we propose an explainability-guided and model-agnostic framework for measuring the ability of malware to evade detection. The framework introduces the concept of Accrued Malicious Magnitude (AMM) to identify which malware features should be manipulated to maximize the likelihood of evading detection. We then use this framework to test several state-of-the-art malware detectors’ ability to detect manipulated malware. We find that (i) commercial antivirus engines are vulnerable to AMM-guided manipulated samples; (ii) the ability of a manipulated malware generated using one detector to evade detection by another detector ( i.e., transferability) depends on the overlap of features with large AMM values between the different detectors; and (iii) AMM values effectively measure the importance of features and explain the ability to evade detection. Our findings shed light on the weaknesses of current malware detectors, as well as how they can be improved.
Towards Communication-Efficient and Privacy-Preserving Federated Representation Learning

This paper investigates the feasibility of federated representation learning under the constraints of communication cost and privacy protection. Existing works either conduct annotation-guided local training which requires frequent communication or aggregates the client models via weight averaging which has potential risks of privacy exposure. To tackle the above problems, we first identify that self-supervised contrastive local training is robust against the non-identically distributed data, which provides the feasibility of longer local training and thus reduces the communication cost. Then based on the aforementioned robustness, we propose a novel Federated representation Learning framework with Ensemble Similarity Distillation~(FLESD) that utilizes this robustness. At each round of communication, the server first gathers a fraction of the clients' inferred similarity matrices on a public dataset. Then it ensembles the similarity matrices and train the global model via similarity distillation. We verify the effectiveness of FLESD by a series of empirical experiments and show that, despite stricter constraints, it achieves comparable results under multiple settings on multiple datasets.
Separable Expansions for Covariance Estimation

The non-parametric estimation of covariance lies at the heart of functional data analysis, whether for curve or surface-valued data. The case of a two-dimensional domain poses both statistical and computational challenges, which are typically alleviated by assuming separability. However, separability is often questionable, sometimes even demonstrably inadequate. We propose a framework for the analysis of covariance operators of random surfaces that generalises separability, while retaining its major advantages. Our approach is based on the expansion of the covariance into a series of separable terms. The expansion is valid for any covariance over a two-dimensional domain. Leveraging the key notion of the partial inner product, we extend the power iteration method to general Hilbert spaces and show how the aforementioned expansion can be efficiently constructed in practice. Truncation of the expansion and retention of the leading terms automatically induces a non-parametric estimator of the covariance, whose parsimony is dictated by the truncation level. The resulting estimator can be calculated, stored and manipulated with little computational overhead relative to separability. Consistency and rates of convergence are derived under mild regularity assumptions, illustrating the trade-off between bias and variance regulated by the truncation level. The merits and practical performance of the proposed methodology are demonstrated in a comprehensive simulation study and on classification of EEG signals. AMS 2000 subject classifications: Primary 62G05, 62M40; secondary 65F45.
To the memory of Professor Kirill C. H. Mackenzie ON THE REDUCED SPACE OF MULTIPLICATIVE MULTIVECTORS

With any Lie groupoid G is associated a strict Lie 2-algebra Γ(∧A) T → X mult (G) where Γ(∧A) is the Schouten algebra of the tangent Lie algebroid A of G and X• mult (G) is the space of multiplicative multivectors on G. The quotient R• mult := X• mult (G)/imgT is a Morita invariant of G and called the reduced space of multiplicative multivectors. We prove a canonical decomposition formula of elements in X mult (G) and establish a key relation between R mult and the cohomology H(JG,∧A) where JG is the jet groupoid of G and 1 ≤ k ≤ rankA. We also study the infinitesimal of R mult , i.e. the reduced space of Lie algebroid differentials on A.
An Empirical Study on the Impact of Deep Parameters on Mobile App Energy Usage

Improving software performance through configuration parameter tuning is a common activity during software maintenance. Beyond traditional performance metrics like latency, mobile app developers are interested in reducing app energy usage. Some mobile apps have centralized locations for parameter tuning, similar to databases and operating systems, but it is common for mobile apps to have hundreds of parameters scattered around the source code. The correlation between these “deep” parameters and app energy usage is unclear. Researchers have studied the energy effects of deep parameters in specific modules, but we lack a systematic understanding of the energy impact of mobile deep parameters. In this paper we empirically investigate this topic, combining a developer survey with systematic energy measurements. Our motivational survey of 25 Android developers suggests that developers do not understand, and largely ignore, the energy impact of deep parameters. To assess the potential implications of this practice, we propose a deep parameter energy profiling framework that can analyze the energy impact of deep parameters in an app. Our framework identifies deep parameters, mutates them based on our parameter value selection scheme, and performs reliable energy impact analysis. Applying the framework to 16 popular Android apps, we discovered that deep parameter-induced energy inefficiency is rare. We found only 2 out of 1644 deep parameters for which a different value would significantly improve its app's energy efficiency. A detailed analysis found that most deep parameters have either no energy impact, limited energy impact, or an energy impact only under extreme values. Our study suggests that it is generally safe for developers to ignore the energy impact when choosing deep parameter values in mobile apps.
On asymptotically arbitrage-free approximations of the implied volatility

Following-up Fukasawa and Gatheral (Frontiers of Mathematical Finance, 2022), we prove that the BBF formula, the SABR formula, and the rough SABR formula provide asymptotically arbitrage-free approximations of the implied volatility under, respectively, the local volatility model, the SABR model, and the rough SABR model.
Semantic OcTree Mapping and Shannon Mutual Information Computation for Robot Exploration

Autonomous robot operation in unstructured and unknown environments requires efficient techniques for mapping and exploration using streaming range and visual observations. Information-based exploration techniques, such as Cauchy–Schwarz quadratic mutual information and fast Shannon mutual information, have successfully achieved active binary occupancy mapping with range measurements. However, as we envision robots performing complex tasks specified with semantically meaningful concepts, it is necessary to capture semantics in the measurements, map representation, and exploration objective. This work presents semantic octree mapping and Shannon mutual information computation for robot exploration. We develop a Bayesian multiclass mapping algorithm based on an octree data structure, where each voxel maintains a categorical distribution over semantic classes. We derive a closed-form efficiently computable lower bound of the Shannon mutual information between a multiclass octomap and a set of range-category measurements using semantic run-length encoding of the sensor rays. The bound allows rapid evaluation of many potential robot trajectories for autonomous exploration and mapping. We compare our method against state-of-the-art exploration techniques and apply it in a variety of simulated and real-world experiments.
Autoregressive mixture models for clustering time series

Clustering time series into similar groups can improve models by combining information across like time series. While there is a well developed body of literature for clustering of time series, these approaches tend to generate clusters independently of model training, which can lead to poor model fit. We propose a novel distributed approach that simultaneously clusters and fits autoregression models for groups of similar individuals. We apply a Wishart mixture model so as to cluster individuals while modelling the corresponding autocovariance matrices at the same time. The fitted Wishart scale matrices map to cluster‐level autoregressive coefficients through the Yule–Walker equations, fitting robust parsimonious autoregressive mixture models. This approach is able to discern differences in underlying autocorrelation variation of time series in settings with large heterogeneous datasets. We prove consistency of our cluster membership estimator, asymptotic distributions of coefficients and compare our approach against competing methods through simulation as well as by fitting a COVID‐19 forecast model.
Market Power and Bank Profitability: Evidence from Montenegro and Serbia

Abstract This study investigates the relationship between profitability and market power in the banking sector using data from the financial reports of the banks that operated in Serbia and Montenegro, covering the period from the first quarter of 2010 to the last quarter of 2019. In order to investigate this relationship, determinants of bank profitability are split between internal and external. As the external determinants, selected ratios of concentration were calculated and used in order to measure market power. The total of sixteen panel regression models were applied, eight for each country. The results indicate that variations of return on assets and return on equity in Serbia can be explained by the variations of the ratios of concentration. On the other hand, results of the panel regression model applied for the banking sector of Montenegro does not give enough argument to support such explanation, and bank profitability can be explained by bank efficiency to some extent.