Miracle-dz/UniFAR-FTUs huggingface dataset:
The Weighted Markov-Dubins Problem

In this letter, a variation of the classical Markov-Dubins problem is considered, which deals with curvature-constrained least-cost paths in a plane with prescribed initial and final configurations, different bounds for the sinistral and dextral curvatures, and penalties <inline-formula><tex-math notation="LaTeX">$\mu _{L}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\mu _{R}$</tex-math></inline-formula> for the sinistral and dextral turns, respectively. The addressed problem generalizes the classical Markov-Dubins problem and the asymmetric sinistral/dextral Markov-Dubins problem. The proposed formulation can be used to model an Unmanned Aerial Vehicle (UAV) with a penalty associated with a turn due to the required additional thrust to maintain altitude and airspeed while turning, or a UAV with different curvature bounds and costs for the sinistral and dextral turns due to hardware failures. Using optimal control theory, the main result of this letter shows that the optimal path belongs to a set of at most 21 candidate paths, each comprising of at most five segments. Unlike in the classical Markov-Dubins problem, the <inline-formula><tex-math notation="LaTeX">$CCC$</tex-math></inline-formula> path, which is a candidate path for the classical Markov-Dubins problem, is not optimal for the weighted Markov-Dubins problem. Moreover, the obtained list of candidate paths for the weighted Markov-Dubins problem reduces to the standard <inline-formula><tex-math notation="LaTeX">$CSC$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$CCC$</tex-math></inline-formula> paths and the corresponding degenerate paths when <inline-formula><tex-math notation="LaTeX">$\mu _{L}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\mu _{R}$</tex-math></inline-formula> approach zero.
Nucleon electromagnetic form factors from the covariant Faddeev equation

We compute the electromagnetic form factors of the nucleon in the Poincare-covariant Faddeev framework based on the Dyson-Schwinger equations of QCD. The general expression for a baryon's electromagnetic current in terms of three interacting dressed quarks is derived. Upon employing a rainbow-ladder gluon-exchange kernel for the quark-quark interaction, the nucleon's Faddeev amplitude and electromagnetic form factors are computed without any further truncations or model assumptions. The form factor results show clear evidence of missing pion-cloud effects below a photon momentum transfer of ~2 GeV^2 and in the chiral region whereas they agree well with experimental data at higher photon momenta. Thus, the approach reflects the properties of the nucleon's quark core.
Serial Chain Hinge Support for Soft, Robust and Effective Grasp

This paper presents a serial chain hinge support, a rigid yet flexible structure that improves the mechanical performance and robustness of soft-fingered grippers. Gravity can reduce the integrity of soft fingers in horizontal approach, resulting in lower maximum payload caused by a large deflection of fingers. To substantiate our claim we performed multiple experiments on the payload and deflection of the SofIA gripper under both horizontal and vertical approaches. In addition, we show that this reinforcement does not impede the original compliant behavior of the gripper, maintaining the original kinematic model functionality. Also, we showcase the proprioceptive and exteroceptive capabilities for two opposing manipulation problems: grasping small and large objects. Finally, we validated the improved SofIA gripper in agricultural and everyday activities.
Semantic Similarity Models for Depression Severity Estimation

Depressive disorders constitute a severe public health issue worldwide. However, public health systems have limited capacity for case detection and diagnosis. In this regard, the widespread use of social media has opened up a way to access public information on a large scale. Computational methods can serve as support tools for rapid screening by exploiting this user-generated social media content. This paper presents an efficient semantic pipeline to study depression severity in individuals based on their social media writings. We select test user sentences for producing semantic rankings over an index of representative training sentences corresponding to depressive symptoms and severity levels. Then, we use the sentences from those results as evidence for predicting users' symptom severity. For that, we explore different aggregation methods to answer one of four Beck Depression Inventory (BDI) options per symptom. We evaluate our methods on two Reddit-based benchmarks, achieving 30\% improvement over state of the art in terms of measuring depression severity.
Dealing with missing data using attention and latent space regularization

Most practical data science problems encounter missing data. A wide variety of solutions exist, each with strengths and weaknesses that depend upon the missingness-generating process. Here we develop a theoretical framework for training and inference using only observed variables enabling modeling of incomplete datasets without imputation. Using an information and measure-theoretic argument we construct models with latent space representations that regularize against the potential bias introduced by missing data. The theoretical properties of this approach are demonstrated empirically using a synthetic dataset. The performance of this approach is tested on 11 benchmarking datasets with missingness and 18 datasets corrupted across three missingness patterns with comparison against a state-of-the-art model and industry-standard imputation. We show that our proposed method overcomes the weaknesses of imputation methods and outperforms the current state-of-the-art.
Attacking Face Recognition With T-Shirts: Database, Vulnerability Assessment, and Detection

Face recognition systems are widely deployed for biometric authentication. Despite this, it is well-known that, without any safeguards, face recognition systems are highly vulnerable to presentation attacks. In response to this security issue, several promising methods for detecting presentation attacks have been proposed which show high performance on existing benchmarks. However, an ongoing challenge is the generalization of presentation attack detection methods to unseen and new attack types. To this end, we propose a new T-shirt Face Presentation Attack (TFPA) database of 1,608 T-shirt attacks using 100 unique presentation attack instruments. In an extensive evaluation, we show that this type of attack can compromise the security of face recognition systems and that some state-of-the-art attack detection mechanisms trained on popular benchmarks fail to robustly generalize to the new attacks. Further, we propose three new methods for detecting T-shirt attack images, one which relies on the statistical differences between depth maps of bona fide images and T-shirt attacks, an anomaly detection approach trained on features only extracted from bona fide RGB images, and a fusion approach which achieves competitive detection performance.
Equivariance with Learned Canonicalization Functions

Symmetry-based neural networks often constrain the architecture in order to achieve invariance or equivariance to a group of transformations. In this paper, we propose an alternative that avoids this architectural constraint by learning to produce canonical representations of the data. These canonicalization functions can readily be plugged into non-equivariant backbone architectures. We offer explicit ways to implement them for some groups of interest. We show that this approach enjoys universality while providing interpretable insights. Our main hypothesis, supported by our empirical results, is that learning a small neural network to perform canonicalization is better than using predefined heuristics. Our experiments show that learning the canonicalization function is competitive with existing techniques for learning equivariant functions across many tasks, including image classification, $N$-body dynamics prediction, point cloud classification and part segmentation, while being faster across the board.
Branching Random Walks on relatively hyperbolic groups

Let $\Gamma$ be a non-elementary relatively hyperbolic group with a finite generating set. Consider a finitely supported admissible and symmetric probability measure $\mu$ on $\Gamma$ and a probability measure $\nu$ on $\mathbb{N}$ with mean $r$. Let $\mathrm{BRW}(\Gamma,\nu,\mu)$ be the branching random walk on $\Gamma$ with offspring distribution $\nu$ and base motion given by the random walk with step distribution $\mu$. It is known that for $1<r \leq R$ with $R$ the radius of convergence for the Green function of the random walk, the population of $\mathrm{BRW}(\Gamma,\nu,\mu)$ survives forever, but eventually vacates every finite subset of $\Gamma$. We prove that in this regime, the growth rate of the trace of the branching random walk is equal to the growth rate $\omega_\Gamma(r)$ of the Green function of the underlying random walk. We also prove that the Hausdorff dimension of the limit set $\Lambda(r)$, which is the random subset of the Bowditch boundary consisting of all accumulation points of the trace of $\mathrm{BRW}(\Gamma,\nu,\mu)$, is equal to a constant times $\omega_\Gamma(r)$.
Impact of sterile neutrinos on the early time flux from a galactic supernova

We study the impact of the existence of an eV-mass scale sterile neutrino---with parameters in the ballpark of what is required to fit the laboratory anomalies---on the early time profile of the electron neutrino and antineutrino fluxes associated to a core-collapse supernova (SN). In particular, we focus on the universal feature of neutronization burst expected in the first tens of ms of the signal: provided that a detector with sufficient sensitivity is available, it is well-known that in the 3 neutrino framework the detection of the neutronization burst in neutrino channel would signal inverted mass hierarchy. This conclusion is dramatically altered in the presence of a sterile neutrino: we study here both analytically and numerically the region in parameter space where this characteristic signal disappears, mimicking normal hierarchy expectations. Conversely, the detection of a peak consistent with expectations for inverted mass hierarchy would exclude the existence of a sterile state over a much wider parameter space than what required by laboratory anomalies fits, or even probed by detectors coming on-line in the near future. Additionally, we show the peculiar alteration in the energy-time double differential flux, with a delayed peak appearing for kinematical reasons, which might offer a remarkable signature in case of favorable parameters and for a high statistics detection of a Galactic SN. We also comment on additional potentially interesting effects in the electron antineutrino channel, if more than one angle in the active-sterile sector is non-vanishing. As an ancillary result that we derived in the technical resolution of the equations, in an appendix we report the Cayley-Hamilton formalism for the evolution of a four neutrino system in matter, generalizing existing results in the literature.
Critical diffraction of irregular structure detonations and their predictability from experimentally obtained D-K data

The present work reports new experiments of detonation diffraction in a 2D channel configuration in stoichiometric mixtures of ethylene, ethane, and methane with oxygen as oxidizer. The flow field details are obtained using high-speed schlieren near the critical conditions of diffraction. The critical initial pressure for successful diffraction is reported for the ethylene, ethane and methane mixtures. The flow field details revealed that the lateral portion of the wave results in a zone of quenched ignition. The dynamics of the laterally diffracting shock front are found in good agreement with the recent model developed by Radulescu et al. (Physics of Fluids 2021). The model provides noticeable improvement over the local models using Whitham's characteristic rule and Wescott, Bdzil and Stewart's model for weakly curved reactive shocks. These models provide a link between the critical channel height and the critical wave curvature. The critical channel heights and global curvatures are found in very good agreement with the critical curvatures measured independently by Xiao and Radulescu (Combust. Flame 2020) in quasi-steady experiments in exponential horns for three mixtures tested. Furthermore, critical curvature data obtained by others in the literature was found to provide a good prediction of critical diffraction in 2D. These findings suggest that the critical diffraction of unstable detonations may be well predicted by a model based on the maximum curvature of the detonation front, where the latter is to be measured experimentally and account for the role of the cellular structure in the burning mechanism. This finding provides support to the view that models for unstable detonations at a meso-scale larger than the cell size, i.e., hydrodynamic average models, are meaningful.
Bayesian Analysis of Linear Contracts

We study a generalization of both the classic single-dimensional mechanism design problem, and the hidden-action principal-agent problem of contract theory [c.f. , Alon et al. 2021]. In this setting, the principal seeks to incentivize an agent with a private Bayesian type to take a costly action. The goal is to design an incentive compatible menu of contracts which maximizes the expected revenue.
Effective algebraicity for solutions of systems of functional equations with one catalytic variable

We study systems of $n \geq 1$ discrete differential equations of order $k\geq1$ in one catalytic variable and provide a constructive and elementary proof of algebraicity of their solutions. This yields effective bounds and a systematic method for computing the minimal polynomials. Our approach is a generalization of the pioneering work by Bousquet-M\'elou and Jehanne (2006).
Compatibilization of HDPE/agar biocomposites with eutectic-based ionic liquid containing surfactant

In this research, eutectic-based ionic liquid specifically choline chloride/glycerol was prepared at a 1:2 mole ratio. The choline chloride/glycerol was added with the different content of surfactant (hexadecyltrimethylammonium bromide). The choline chloride/glycerol-hexadecyltrimethylammonium bromide was introduced into high-density polyethylene/agar biocomposites through melt mixing. The mechanical testing results indicated that the impact strength and tensile extension of the biocomposites increased with the introduction of the choline chloride/glycerol-hexadecyltrimethylammonium bromide. The scanning electron microscope, differential scanning calorimetry and thermal gravimetric analysis results exhibited that significant decrease in the number of agar fillers pull-out, melting point and thermal decomposition temperatures of the biocomposites are also due to the choline chloride/glycerol-hexadecyltrimethylammonium bromide. The Fourier transform infrared spectra and X-ray diffractometer patterns of the biocomposites introduced with the choline chloride/glycerol-hexadecyltrimethylammonium bromide demonstrate the presence of physical interactions, which contributes to the increase of compatibility between both high-density polyethylene and agar. In conclusion, high-density polyethylene/agar biocomposites could be compatibilized with eutectic-based ionic liquid containing surfactant, choline chloride/glycerol-hexadecyltrimethylammonium bromide.
Collapse and Revival and Cat States with an N Spin System

Department of Physics, Loughborough University,Loughborough, Leics LE11 3TU, United Kingdom. (Dated: February 13, 2013)We discuss collapse and revival of Rabi oscillations in a system comprising a qubit and a \bigspin" (made of Nqubits, or spin-1=2 particles). We demonstrate a regime of behaviour analogousto conventional collapse and revival for a qubit- eld system, employing spin coherent states for theinitial state of the big spin. These dynamics can be used to create a \Schrodinger cat" state ofthe big spin. Even for relatively small values of N, states with signi cant potential for quantummetrology applications can result, giving sensitivity approaching the Heisenberg limit.
Neutron Capture Nucleosynthesis

Heavy elements (beyond iron) are formed in neutron capture nucleosynthesis processes. We have proposed a simple unified model to investigate the neutron capture nucleosynthesis in arbitrary neutron density environment. We have also investigated what neutron density is required to reproduce the measured abundance of nuclei assuming equilibrium processes. We found both of these that the medium neutron density has a particularly important role at neutron capture nucleosynthesis. About these results most of the nuclei can formed at medium neutron capture density environment e.g. in some kind of AGB stars. Besides these observations our model is capable to use educational purpose.
Identifying the perceived local properties of networks reconstructed from biased random walks

Many real-world systems give rise to a time series of symbols. The elements in a sequence can be generated by agents walking over a networked space so that whenever a node is visited the corresponding symbol is generated. In many situations the underlying network is hidden, and one aims to recover its original structure and/or properties. For example, when analyzing texts, the underlying network structure generating a particular sequence of words is not available. In this paper, we analyze whether one can recover the underlying local properties of networks generating sequences of symbols for different combinations of random walks and network topologies. We found that the reconstruction performance is influenced by the bias of the agent dynamics. When the walker is biased toward high-degree neighbors, the best performance was obtained for most of the network models and properties. Surprisingly, this same effect is not observed for the clustering coefficient and eccentric, even when large sequences are considered. We also found that the true self-avoiding displayed similar performance as the one preferring highly-connected nodes, with the advantage of yielding competitive performance to recover the clustering coefficient. Our results may have implications for the construction and interpretation of networks generated from sequences.
C3: Cross-instance guided Contrastive Clustering

Clustering is the task of gathering similar data samples into clusters without using any predefined labels. It has been widely studied in machine learning literature, and recent advancements in deep learning have revived interest in this field. Contrastive clustering (CC) models are a staple of deep clustering in which positive and negative pairs of each data instance are generated through data augmentation. CC models aim to learn a feature space where instance-level and cluster-level representations of positive pairs are grouped together. Despite improving the SOTA, these algorithms ignore the cross-instance patterns, which carry essential information for improving clustering performance. This increases the false-negative-pair rate of the model while decreasing its true-positive-pair rate. In this paper, we propose a novel contrastive clustering method, Cross-instance guided Contrastive Clustering (C3), that considers the cross-sample relationships to increase the number of positive pairs and mitigate the impact of false negative, noise, and anomaly sample on the learned representation of data. In particular, we define a new loss function that identifies similar instances using the instance-level representation and encourages them to aggregate together. Moreover, we propose a novel weighting method to select negative samples in a more efficient way. Extensive experimental evaluations show that our proposed method can outperform state-of-the-art algorithms on benchmark computer vision datasets: we improve the clustering accuracy by 6.6%, 3.3%, 5.0%, 1.3% and 0.3% on CIFAR-10, CIFAR-100, ImageNet-10, ImageNet-Dogs, and Tiny-ImageNet.
Robustifying Deep Vision Models Through Shape Sensitization

Recent work has shown that deep vision models tend to be overly dependent on low-level or"texture"features, leading to poor generalization. Various data augmentation strategies have been proposed to overcome this so-called texture bias in DNNs. We propose a simple, lightweight adversarial augmentation technique that explicitly incentivizes the network to learn holistic shapes for accurate prediction in an object classification setting. Our augmentations superpose edgemaps from one image onto another image with shuffled patches, using a randomly determined mixing proportion, with the image label of the edgemap image. To classify these augmented images, the model needs to not only detect and focus on edges but distinguish between relevant and spurious edges. We show that our augmentations significantly improve classification accuracy and robustness measures on a range of datasets and neural architectures. As an example, for ViT-S, We obtain absolute gains on classification accuracy gains up to 6%. We also obtain gains of up to 28% and 8.5% on natural adversarial and out-of-distribution datasets like ImageNet-A (for ViT-B) and ImageNet-R (for ViT-S), respectively. Analysis using a range of probe datasets shows substantially increased shape sensitivity in our trained models, explaining the observed improvement in robustness and classification accuracy.
Stronger 3-SUM Lower Bounds for Approximate Distance Oracles via Additive Combinatorics

The “short cycle removal” technique was recently introduced by Abboud, Bringmann, Khoury and Zamir (STOC ’22) to prove fine-grained hardness of approximation. Its main technical result is that listing all triangles in an n1/2-regular graph is n2−o(1)-hard even when the number of short cycles is small; namely, when the number of k-cycles is O(nk/2+γ) for γ<1/2. Its corollaries are based on the 3-SUM conjecture and their strength depends on γ, i.e. on how effectively the short cycles are removed. Abboud et al. achieve γ≥ 1/4 by applying structure versus randomness arguments on graphs. In this paper, we take a step back and apply conceptually similar arguments on the numbers of the 3-SUM problem, from which the hardness of triangle listing is derived. Consequently, we achieve the best possible γ=0 and the following lower bound corollaries under the 3-SUM conjecture: * Approximate distance oracles: The seminal Thorup-Zwick distance oracles achieve stretch 2k± O(1) after preprocessing a graph in O(m n1/k) time. For the same stretch, and assuming the query time is no(1) Abboud et al. proved an Ω(m1+1/12.7552 · k) lower bound on the preprocessing time; we improve it to Ω(m1+1/2k) which is only a factor 2 away from the upper bound. Additionally, we obtain tight bounds for stretch 2+o(1) and 3−є and higher lower bounds for dynamic shortest paths. * Listing 4-cycles: Abboud et al. proved the first super-linear lower bound for listing all 4-cycles in a graph, ruling out (m1.1927+t)1+o(1) time algorithms where t is the number of 4-cycles. We settle the complexity of this basic problem by showing that the O(min(m4/3,n2) +t) upper bound is tight up to no(1) factors. Our results exploit a rich tool set from additive combinatorics, most notably the Balog-Szemerédi-Gowers theorem and Rusza’s covering lemma. A key ingredient that may be of independent interest is a truly subquadratic algorithm for 3-SUM if one of the sets has small doubling.