Miracle-dz/UniFAR-FTUs huggingface dataset:
Additive Schwarz algorithms for neural network approximate solutions

Additive Schwarz algorithms are proposed as an iterative procedure for neural network approximate solutions of partial differential equations. Based on the convergence analysis of the additive Schwarz algorithms in a general Hilbert space setting, the convergence of the neural network approximate solutions is analyzed for the one-level and two-level iterative schemes. Numerical results of the proposed methods are presented for test examples.
Expressivity and Trainability of Quadratic Networks

—Inspired by diversity of biological neurons, quadratic artiﬁcial neurons can play an important role in deep learning models. The type of quadratic neurons of our interest replaces the inner-product operation in the conventional neuron with a quadratic function. Despite promising results so far achieved by networks of quadratic neurons, there are important issues not well addressed. Theoretically, the superior expressivity of a quadratic network over either a conventional network or a conventional network via quadratic activation is not fully elucidated, which makes the use of quadratic networks not well grounded. Practically, although a quadratic network can be trained via generic backpropagation, it can be subject to a higher risk of collapse than the conventional counterpart. To address these issues, we ﬁrst apply the spline theory and a measure from algebraic geometry to give two theorems that demonstrate better model expressivity of a quadratic network than the conventional counterpart with or without quadratic activation. Then, we propose an effective and efﬁcient training strategy referred to as ReLinear to stabilize the training process of a quadratic network, thereby unleashing the full potential in its associated machine learning tasks. Comprehensive experiments on popular datasets are performed to support our ﬁndings and evaluate the performance of quadratic deep learning.
The MediaMill TRECVID 2005 Semantic Video Search Engine (Draft Version)

In this paper we describe our TRECVID 2005 experiments. The UvA-MediaMill team participated in four tasks. For the detection of camera work (runid: A CAM) we investigate the bene(cid:12)t of using a tessellation of detectors in combination with supervised learning over a standard approach using global image information. Experiments indicate that average precision results increase drastically, especially for pan (+51%) and tilt (+28%). For concept detection we propose a generic approach using our semantic path(cid:12)nder. Most important novelty compared to last years sys-tem is the improved visual analysis using proto-concepts based on Wiccest features. In addition, the path selection mechanism was extended. Based on the semantic path(cid:12)nder architecture we are currently able to detect an unprecedented lexicon of 101 semantic concepts in a generic fashion. We performed a large set of experiments (runid: B vA). The results show that an optimal strategy for generic multimedia analysis is one that learns from the training set on a per-concept basis which tactic to follow. Experiments also indicate that our visual analysis approach is highly promising. The lexicon of 101 semantic concepts forms the basis for our search experiments (runid: B 2 A-MM). We participated in automatic, manual (using only visual information), and interactive search. The lexicon-driven retrieval paradigm aids substantially in all search tasks. When coupled with interaction, exploiting several novel browsing schemes of our semantic video search engine, results are excellent. We obtain a top-3 result for 19 out of 24 search topics. In addition, we obtain the highest mean average precision of all search participants. We exploited the technology developed for the above tasks to explore the BBC rushes. Most intriguing result is that from the lexicon of 101 visual-only models trained for news data 25 concepts perform reasonably well on BBC data also.
The Minimal Control Principle Predicts Strategy Shifts in the Abstract Decision Making Task

Invited Abstract: The minimal control principle (Taatgen, 2007) predicts that people strive for problem- solving strategies that require as few internal control states as possible. In an experiment with the Abstract Decision Making task (ADM task; Joslyn & Hunt, 1998) the reward structure was manipulated to make either a low-control strategy or a high-strategy more feasible. The results show that most participants are sensitive to the shifting demands of the task, consistent with the minimal control principle, but also that individual differ - ences are large.
Implicit Variational Inference: the Parameter and the Predictor Space

Having access to accurate confidence levels along with the predictions allows to determine whether making a decision is worth the risk. Under the Bayesian paradigm, the posterior distribution over parameters is used to capture model uncertainty, a valuable information that can be translated into predictive uncertainty. However, computing the posterior distribution for high capacity predictors, such as neural networks, is generally intractable, making approximate methods such as variational inference a promising alternative. While most methods perform inference in the space of parameters, we explore the benefits of carrying inference directly in the space of predictors. Relying on a family of distributions given by a deep generative neural network, we present two ways of carrying variational inference: one in \emph{parameter space}, one in \emph{predictor space}. Importantly, the latter requires us to choose a distribution of inputs, therefore allowing us at the same time to explicitly address the question of \emph{out-of-distribution} uncertainty. We explore from various perspectives the implications of working in the predictor space induced by neural networks as opposed to the parameter space, focusing mainly on the quality of uncertainty estimation for data lying outside of the training distribution. We compare posterior approximations obtained with these two methods to several standard methods and present results showing that variational approximations learned in the predictor space distinguish themselves positively from those trained in the parameter space.
In-plane prestressing: Inspiration from a hair clip

Structural instability is a hazard that leads to catastrophic failure and is generally avoided through special designs. A trend, however, has emerged over the past decades pointing to the harnessing of mechanisms with instability. Inspired by the snapping of a hair clip, we are finessing the unique characteristics of the lateral-torsional buckling of beams and the snap-through of pre-buckled dome-like thin-wall structures in a new field: the in-plane prestressed mechanism. Analyses reveal how the 2D-3D assembly of an in-plane prestressed actuator (IPA) is achieved and how the post-buckling energy landscape is pictured. Combining them with soft robotics, we show that the inclusion of a bistable IPA can enormously enhance the performance of an underwater fish robot as well as inspire a finger-like soft gripper. Summary:
A Model of Individual Differences in Skill Acquisition in the Kanfer–ackerman Air Traffic Control Task

Individual differences in skill acquisition are influenced by several architectural factors. According to Ackerman's theory, general intelligence, speed of proceduralization and psychomotor speed influence different stages of skill acquisition. The ACT-R cognitive architecture allows for direct testing of this theory by manipulating parameters that correspond to these factors. The present study discusses an ACT-R model of the Kanfer–Ackerman Air Traffic Control task in which the relevant abilities can be manipulated directly. The model predictions show the same patterns of correlations as the patterns found by Ackerman in the experimental data. 1. Introduction of errors, and can be associated with the autonomous stage. The associate stage is an in-between stage, Skill acquisition is usually characterized as going during which part of the knowledge is declarative through three stages: a cognitive stage, an associative and another part compiled. stage and an autonomous stage (Fitts, 1964). The A problem in the study of complex problem three stages can be characterized by moving from solving, especially in a learning context, is the conscious, slow and error-prone to unconscious, fast vastness of individual differences. In order to study and error-free. Anderson (1982) explains these three the acquisition of complex skills, it is a good stages in terms of a transition from declarative research strategy to have a theory of individual knowledge to procedural knowledge. In the cognitive differences. From the perspective of the cognitive stage knowledge is declarative and needs to be architecture, there are two sources of individual interpreted. Interpreting knowledge is slow, and may differences: architectural differences and knowledge lead to errors if the relevant knowledge cannot be differences (Taatgen, 1999a). Architectural differ-retrieved at the right time. Procedural knowledge on ences are differences in the cognitive architecture the other hand is compiled and therefore fast and free itself. In terms of an architecture like ACT-R, architectural differences can be tied to global param
Output Feedback Stabilization of the Moore-Greitzer Compressor Model

This paper presents a number of facts related to the output feedback stabilization of the Moore-Greitzer compressor model. We show that quadratic feedback stabilization of the surge subsystem of the three-state Moore-Greitzer compressor model, which ensures absence of additional equilibria and cycles in the closed-loop system augmented with stall dynamics, implies convergence of all solutions to the unique equilibrium at the origin. Then some steps in developing such output feedback controller for surge subsystem are discussed.
Identifying the MJO, Equatorial Waves, and Their Impacts Using 32 Years of HIRS Upper-Tropospheric Water Vapor

AbstractThe Madden–Julian oscillation (MJO) and convectively coupled equatorial waves are the dominant modes of synoptic-to-subseasonal variability in the tropics. These systems have frequently been examined with proxies for convection such as outgoing longwave radiation (OLR). However, upper-tropospheric water vapor (UTWV) gives a more complete picture of tropical circulations because it is more sensitive to the drying and warming associated with subsidence. Previous studies examined tropical variability using relatively short (3–7 yr) UTWV datasets. Intersatellite calibration of data from the High Resolution Infrared Radiation Sounder (HIRS) has recently produced a homogeneous 32-yr climate data record of UTWV for 200–500 hPa. This study explores the utility of HIRS UTWV for identifying the MJO and equatorial waves.Spectral analysis shows that the MJO and equatorial waves stand out above the low-frequency background in UTWV, similar to previous findings with OLR. The fraction of variance associated with.
A Robust Secure Hybrid Analog and Digital Receive Beamforming Scheme for Efficient Interference Reduction

Medium-scale or large-scale receive antenna array with digital beamforming can be employed at receiver to make a significant interference reduction but leads to expensive cost and high complexity of the RF-chain circuit. To deal with this issue, classic analog-and-digital beamforming (ADB) structure was proposed in the literature for greatly reducing the number of RF-chains. Based on the ADB structure, in this paper, we propose a robust hybrid ADB scheme to resist directions of arrival (DOAs) estimation errors. The key idea of our scheme is to employ null space projection (NSP) in the analog beamforming domain and diagonal loading (DL) method in digital beamforming domain. The simulation results show that the proposed scheme performs more robustly, and moreover, it has a significant improvement on the receive signal-to-interference-plus-noise ratio compared to NSP ADB scheme and DL method.
An ( Almost ) Constant-Effort Solution-Verification Proof-of-Work Protocol based on Merkle Trees ( extended and colored version of [ 7 ] )

Proof-of-work schemes are economic measures to deter denial-of-service attacks: service requesters compute moderately hard functions the results of which are easy to check by the provider. We present such a new scheme for solution-verification protocols. Although most schemes to date are probabilistic unbounded iterative processes with high variance of the requester effort, our Merkle tree scheme is deterministic with an almost constant effort and null variance, and is computation-optimal.
Adaptive fault detection in real-time GPS positioning

The paper presents an adaptive scheme of a GPS (Global Positioning System) Kalman filter. The adaptive scheme is capable of estimating the covariance in response to the changing dynamics in GPS navigation. When augmenting a Kalman filter, the adaptive scheme may lead to a more accurate position update. Moreover, the estimation method can also be used in integrity monitoring to enhance the selection of threshold for fault detection. The proposed method is verified using simulated data.
Capturing the Diversity of Deprived Areas with Image-Based Features: The Case of Mumbai

Many cities in the Global South are facing rapid population and slum growth, but lack detailed information to target these issues. Frequently, municipal datasets on such areas do not keep up with such dynamics, with data that are incomplete, inconsistent, and outdated. Aggregated census-based statistics refer to large and heterogeneous areas, hiding internal spatial differences. In recent years, several remote sensing studies developed methods for mapping slums; however, few studies focused on their diversity. To address this shortcoming, this study analyzes the capacity of very high resolution (VHR) imagery and image processing methods to map locally specific types of deprived areas, applied to the city of Mumbai, India. We analyze spatial, spectral, and textural characteristics of deprived areas, using a WorldView-2 imagery combined with auxiliary spatial data, a random forest classifier, and logistic regression modeling. In addition, image segmentation is used to aggregate results to homogenous urban patches (HUPs). The resulting typology of deprived areas obtains a classification accuracy of 79% for four deprived types and one formal built-up class. The research successfully demonstrates how image-based proxies from VHR imagery can help extract spatial information on the diversity and cross-boundary clusters of deprivation to inform strategic urban management.
Characterizing fault tolerance in genetic programming

Evolutionary Algorithms (EAs), and particularly Genetic Programming (GP), are techniques frequently employed to solve difficult real-life problems, which can require up to days or months of computation. One approach to reduce the time to solution is to use parallel computing on distributed platforms. Distributed platforms are prone to failures, and when these platforms are large and/or low-cost, failures are expected events rather than catastrophic exceptions. Therefore, fault tolerance and recovery techniques often become necessary. It turns out that Parallel GP (PGP) applications have an inherent ability to tolerate failures. This ability is quantified via simulation experiments performed using failure traces from real-world distributed platforms, namely, desktop grids (DGs), for two well-known GP problems. A simple technique is then proposed by which PGP applications can better tolerate the different, and often high, failures rates seen in different platforms.
The Capacity of T-Private Information Retrieval With Private Side Information

We consider the problem of <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula>-Private Information Retrieval with private side information (TPIR-PSI). In this problem, <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> replicated databases store <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> independent messages, and a user, equipped with a local cache that holds <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> messages as side information, wishes to retrieve one of the other <inline-formula> <tex-math notation="LaTeX">$K-M$ </tex-math></inline-formula> messages. The desired message index and the side information must remain jointly private even if any <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> of the <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> databases collude. We show that the capacity of TPIR-PSI is <inline-formula> <tex-math notation="LaTeX">$\left ({1+\frac {T}{N}+\cdots +\left ({\frac {T}{N}}\right)^{K-M-1}}\right)^{-1}$ </tex-math></inline-formula>. As a special case obtained by setting <inline-formula> <tex-math notation="LaTeX">$T=1$ </tex-math></inline-formula>, this result settles the capacity of PIR-PSI, an open problem previously noted by Kadhe et al. We also consider the problem of symmetric-TPIR with private side information (STPIR-PSI), where the answers from all <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> databases reveal no information about any other message besides the desired message. We show that the capacity of STPIR-PSI is <inline-formula> <tex-math notation="LaTeX">$1-\frac {T}{N}$ </tex-math></inline-formula> if the databases have access to common randomness (not available to the user) that is independent of the messages, in an amount that is at least <inline-formula> <tex-math notation="LaTeX">$\frac {T}{N-T}$ </tex-math></inline-formula> bits per desired message bit. Otherwise, the capacity of STPIR-PSI is zero.
TrajeVAE - Controllable Human Motion Generation from Trajectories

The generation of plausible and controllable 3D human motion animations is a long-standing problem that often requires a manual intervention of skilled artists. Existing machine learning approaches try to semi-automate this process by allowing the user to input partial information about the future movement. However, they are limited in two signiﬁcant ways: they either base their pose prediction on past prior frames with no additional control over the future poses or allow the user to input only a single trajectory that precludes ﬁne-grained control over the output. To mitigate these two issues, we reformulate the problem of future pose prediction into pose completion in space and time where trajectories are represented as poses with missing joints. We show that such a framework can generalize to other neural networks designed for future pose prediction. Once trained in this framework, a model is capable of predicting sequences from any number of trajectories. To leverage this notion, we propose a novel transformer-like architecture, T RAJE VAE , that provides a versatile framework for 3D human animation. We demonstrate that T RAJE VAE outperforms trajectory-based reference approaches and methods that base their predictions on past poses in terms of accuracy. We also show that it can predict reasonable future poses even if provided only with an initial pose.
Hypercubes and Hamiltonian Cycles of Display Sets of Rooted Phylogenetic Networks

In the context of reconstructing phylogenetic networks from a collection of phylogenetic trees, several characterisations and subsequently algorithms have been established to reconstruct a phylogenetic network that collectively embeds all trees in the input in some minimum way. For many instances however, the resulting network also embeds additional phylogenetic trees that are not part of the input. However, little is known about these inferred trees. In this paper, we explore the relationships among all phylogenetic trees that are embedded in a given phylogenetic network. First, we investigate some combinatorial properties of the collection P of all rooted binary phylogenetic trees that are embedded in a rooted binary phylogenetic network N . To this end, we associated a particular graph G , which we call rSPR graph, with the elements in P and show that, if |P| = 2 k , where k is the number of vertices with in-degree two in N , then G has a Hamiltonian cycle. Second, by exploiting rSPR graphs and properties of hypercubes, we turn to the well-studied class of rooted binary level-1 networks and give necessary and suﬃcient conditions for when a set of rooted binary phylogenetic trees can be embedded in a level-1 network without inferring any additional trees. Lastly, we show how these conditions translate into a polynomial-time algorithm to reconstruct such a network if it exists.
Liquid simulation on lattice-based tetrahedral meshes

This paper describes a simulation method for animating the behavior of incompressible liquids with complex free surfaces. The region occupied by the liquid is discretized with a boundary-conforming tetrahedral mesh that grades from fine resolution near the surface to coarser resolution on the interior. At each time-step, semi-Lagrangian techniques are used to advect the fluid and its boundary forward, and a new conforming mesh is then constructed over the fluid-occupied region. The tetrahedral meshes are built using a variation of the body-centered cubic lattice structure that allows octree grading and deviation from the lattice-structure at boundaries. The semi-regular mesh structure can be generated rapidly and allows efficient computation and storage while still conforming well to boundaries and providing a meshquality guarantee. Pressure projection is performed using an algebraic multigrid method, and a thickening scheme is used to reduce volume loss when fluid features shrink below mesh resolution. Examples are provided to demonstrate that the resulting method can capture complex liquid motions that include fine detail on the free surfaces without suffering from excessive amounts volume loss or artificial damping.
Benchmarking the Capability of Symbolic Execution Tools with Logic Bombs

Symbolic execution has become an indispensable technique for software testing and program analysis. However, since several symbolic execution tools are presently available off-the-shelf, there is a need for a practical benchmarking approach. This paper introduces a fresh approach that can help benchmark symbolic execution tools in a fine-grained and efficient manner. The approach evaluates the performance of such tools against known challenges faced by general symbolic execution techniques, e.g., floating-point numbers and symbolic memories. We first survey related papers and systematize the challenges of symbolic execution. We extract 12 distinct challenges from the literature and categorize them into two categories: symbolic-reasoning challenges and path-explosion challenges. Next, we develop a dataset of logic bombs and a framework for benchmarking symbolic execution tools automatically. For each challenge, our dataset contains several logic bombs, each addressing a specific challenging problem. Triggering one or more logic bombs confirms that the symbolic execution tool in question is able to handle the corresponding problem. Real-world experiments with three popular symbolic execution tools, namely, KLEE, angr, and Triton have shown that our approach can reveal the capabilities and limitations of the tools in handling specific issues accurately and efficiently. The benchmarking process generally takes only a few dozens of minutes to evaluate a tool. We have released our dataset on GitHub as open source, with an aim to better facilitate the community to conduct future work on benchmarking symbolic execution tools.