Miracle-dz/UniFAR-FTUs huggingface dataset:
Faster coloring and embedding in dense hypergraphs via stability

The classical Andr\'{a}sfai-Erd\H{o}s-S\'{o}s Theorem states that for $\ell\ge 2$, every $n$-vertex $K_{\ell+1}$-free graph with minimum degree greater than $\frac{3\ell-4}{3\ell-1}n$ must be $\ell$-partite. We establish a simple criterion for $r$-graphs, $r \geq 2$, to exhibit an Andr\'{a}sfai-Erd\H{o}s-S\'{o}s-type property (AES), leading to a classification of most previously studied hypergraph families with this property. For every AES $r$-graph $F$, we present a simple algorithm to decide the $F$-freeness of an $n$-vertex $r$-graph with minimum degree greater than $(\pi(F) - \varepsilon_F)\binom{n}{r-1}$ in time $O(n^r)$, where $\varepsilon_F>0$ is a constant. In particular, for the complete graph $K_{\ell+1}$, we can take $\varepsilon_{K_{\ell+1}} = (3\ell^2-\ell)^{-1}$. Based on a result by Chen-Huang-Kanj-Xia, we show that for every fixed $C>0$, this problem cannot be solved in time $n^{o(\ell)}$ if we replace $\varepsilon_{K_{\ell+1}}$ with $(C\ell)^{-1}$ unless ETH fails. Furthermore, we establish an algorithm to decide the $K_{\ell+1}$-freeness of an $n$-vertex graph with $\mathrm{ex}(n,K_{\ell+1})-k$ edges in time $(\ell+1)n^2$ for $k \le n/30\ell$ and $\ell \le \sqrt{n/6}$, partially improving upon the recently provided running time of $2.49^k n^{O(1)}$ by Fomin--Golovach--Sagunov--Simonov. Moreover, we show that for every fixed $\delta>0$, this problem cannot be solved in time $n^{o(\ell)}$ if $k$ is of order $n^{1+\delta}$ unless ETH fails. As an intermediate step, we show that for a specific class of $r$-graphs $F$, the (surjective) $F$-coloring problem can be solved in time $O(n^r)$, provided the input $r$-graph has $n$ vertices and a large minimum degree, refining several previous results.
Polynomial Chaos Expansions on Principal Geodesic Grassmannian Submanifolds for Surrogate Modeling and Uncertainty Quantification

In this work we introduce a manifold learning-based surrogate modeling framework for uncertainty quantification in high-dimensional stochastic systems. Our first goal is to perform data mining on the available simulation data to identify a set of low-dimensional (latent) descriptors that efficiently parameterize the response of the high-dimensional computational model. To this end, we employ Principal Geodesic Analysis on the Grassmann manifold of the response to identify a set of disjoint principal geodesic submanifolds, of possibly different dimension, that captures the variation in the data. Since operations on the Grassmann require the data to be concentrated, we propose an adaptive algorithm based on Riemanniann K-means and the minimization of the sample Frechet variance on the Grassmann manifold to identify"local"principal geodesic submanifolds that represent different system behavior across the parameter space. Polynomial chaos expansion is then used to construct a mapping between the random input parameters and the projection of the response on these local principal geodesic submanifolds. The method is demonstrated on four test cases, a toy-example that involves points on a hypersphere, a Lotka-Volterra dynamical system, a continuous-flow stirred-tank chemical reactor system, and a two-dimensional Rayleigh-Benard convection problem
Two Regimes of Asymptotic Fall-off of a Massive Scalar Field in the Schwarzschild-de Sitter Spacetime

The decay behavior of a massless scalar field in the Schwarzschild-de Sitter spacetime is well-known to follow an exponential law at asymptotically late times $t \rightarrow \infty$. In contrast, a massive scalar field in the asymptotically flat Schwarzschild background exhibits a decay with oscillatory (sinusoidal) tails enveloped by a power law. We demonstrate that the asymptotic decay of a massive scalar field in the Schwarzschild-de Sitter spacetime is exponential. Specifically, if $\mu M \gg 1$, where $\mu$ and $M$ represent the mass of the field and the black hole, respectively, the exponential decay is also oscillatory. Conversely, in the regime of small $\mu M$, the decay is purely exponential without oscillations. This distinction in decay regimes underscores the fact that, for asymptotically de Sitter spacetimes, a particular branch of quasinormal modes, instead of a"tail", governs the decay at asymptotically late times. There are two branches of quasinormal modes for the Schwarzschild-de Sitter spacetime: the modes of an asymptotically flat black hole corrected by a non-zero $\Lambda$-term, and the modes of an empty de Sitter spacetime corrected by the presence of a black hole. We show that the latter branch is responsible for the asymptotic decay. When $\mu M$ is small, the modes of pure de Sitter spacetime are purely imaginary (non-oscillatory), while at intermediate and large $\mu M$ they have both real and imaginary parts, what produces the two pictures of the asymptotic decay. In addition, we show that the asymptotic decay of charged and higher dimensional black hole is also exponential.
Driven bright solitons on a mid-infrared laser chip

Despite the ongoing progress in integrated optical frequency comb technology, compact sources of short bright pulses in the mid-infrared wavelength range from 3 {\mu}m to 12 {\mu}m so far remained beyond reach. The state-of-the-art ultrafast pulse emitters in the mid-infrared are complex, bulky, and inefficient systems based on the downconversion of near-infrared or visible pulsed laser sources. Here we show a purely DC-driven semiconductor laser chip that generates one picosecond solitons at the center wavelength of 8.3 {\mu}m at GHz repetition rates. The soliton generation scheme is akin to that of passive nonlinear Kerr resonators. It relies on a fast bistability in active nonlinear laser resonators, unlike traditional passive mode-locking which relies on saturable absorbers or active mode-locking by gain modulation in semiconductor lasers. Monolithic integration of all components - drive laser, active ring resonator, coupler, and pump filter - enables turnkey generation of bright solitons that remain robust for hours of continuous operation without active stabilization. Such devices can be readily produced at industrial laser foundries using standard fabrication protocols. Our work unifies the physics of active and passive microresonator frequency combs, while simultaneously establishing a technology for nonlinear integrated photonics in the mid-infrared.
The Circular Polarization Reverse in Magnetar Bursts

Magnetars, whose magnetic fields are strongest in the neutron stars, show diverse bursting activities. The shape of the magnetar deviates from an exactly pure spherical shape because of its strong magnetic field, and it can be a triaxial body. We study the free precession of the triaxial magnetar focusing on the unstable situation, in which the main rotation axis turns upside-down. This flip is the so-called Dzhanibekov effect. We find that during the flip, the Euler force can suddenly disturb the force balance on the magnetar surface, which may result in a crack at the surface. We suggest that the crack can trigger bursting activities of the magnetar, such as X-ray short bursts and flares with sufficiently large baryon fractions. In the burst with a large baryon fraction, right- or left-handed circularly polarized photons are selectively scattered, depending on the magnetic field direction. The association between the flip and the X-ray bursts will be confirmed by the observations of X-ray circular polarization. We also discuss the association of fast radio bursts, ultrahigh-energy cosmic rays, and gravitational waves.
Outline of an Independent Systematic Blackbox Test for ML-based Systems

This article proposes a test procedure that can be used to test ML models and ML-based systems independently of the actual training process. In this way, the typical quality statements such as accuracy and precision of these models and system can be verified independently, taking into account their black box character and the immanent stochastic properties of ML models and their training data. The article presents first results from a set of test experiments and suggest extensions to existing test methods reflecting the stochastic nature of ML models and ML-based systems.
InteractOut: Leveraging Interaction Proxies as Input Manipulation Strategies for Reducing Smartphone Overuse

Smartphone overuse poses risks to people's physical and mental health. However, current intervention techniques mainly focus on explicitly changing screen content (i.e., output) and often fail to persistently reduce smartphone overuse due to being over-restrictive or over-flexible. We present the design and implementation of InteractOut, a suite of implicit input manipulation techniques that leverage interaction proxies to weakly inhibit the natural execution of common user gestures on mobile devices. We present a design space for input manipulations and demonstrate 8 Android implementations of input interventions. We first conducted a pilot lab study (N=30) to evaluate the usability of these interventions. Based on the results, we then performed a 5-week within-subject field experiment (N=42) to evaluate InteractOut in real-world scenarios. Compared to the traditional and common timed lockout technique, InteractOut significantly reduced the usage time by an additional 15.6% and opening frequency by 16.5% on participant-selected target apps. InteractOut also achieved a 25.3% higher user acceptance rate, and resulted in less frustration and better user experience according to participants' subjective feedback. InteractOut demonstrates a new direction for smartphone overuse intervention and serves as a strong complementary set of techniques with existing methods.
Heavy flavour hadron production in relativistic heavy ion collisions at RHIC and LHC in EPOS4HQ

Employing the recently developed EPOS4HQ event generator, we study the production of different heavy-flavor mesons in relativistic heavy-ion collisions at RHIC and LHC energies. The transverse momentum spectra, yield ratio, nuclear modification factor, and elliptic flow can be well described in the EPOS4HQ framework. We furthermore analyze the processes which modify these observables as compared to $pp$ collisions and are at the origin of the experimentally determined nuclear modification factor $R_{AA}$.
Producers Equilibria and Dynamics in Engagement-Driven Recommender Systems

Online platforms such as YouTube, Instagram, TikTok heavily rely on recommender systems to decide what content to show to which users. Content producers often aim to produce material that is likely to be shown to users and lead them to engage with said producer. To do so, producers try to align their content with the preferences of their targeted user base. In this work, we explore the equilibrium behavior of producers that are interested in maximizing user engagement. We study two variants of the content-serving rule that the platform's recommender system uses, and we show structural results on producers' production at equilibrium. We leverage these structural results to show that, in simple settings, we see specialization naturally arising from the competition among producers trying to maximize user engagement. We provide a heuristic for computing equilibria of our engagement game, and evaluate it experimentally. We show i) the performance and convergence of our heuristic, ii) the producer and user utilities at equilibrium, and iii) the level of producer specialization.
Optimal-Landmark-Guided Image Blending for Face Morphing Attacks

In this paper, we propose a novel approach for conducting face morphing attacks, which utilizes optimal-landmark-guided image blending. Current face morphing attack can be categorized into landmark-based and generation-based approaches. Landmark-based methods use geometric transformations to warp facial regions according to averaged landmarks, but often produce morphed images with poor visual quality. Generation-based methods, which employ generation models to blend multiple face images, can achieve better visual quality, but are often unsuccessful in generating morphed images that can effectively evade state-of-the-art face recognition systems (FRSs). Our proposed method overcomes the limitations of previous approaches by optimizing the morphing landmarks and using Graph Convolutional Networks (GCNs) to combine landmark and appearance features. We model facial landmarks as nodes in a bipartite graph that is fully connected, and utilize GCNs to simulate their spatial and structural relationships. The aim is to capture variations in facial shape and enable accurate manipulation of facial appearance features during the warping process, resulting in morphed facial images that are highly realistic and visually faithful. Experiments on two public datasets prove that our method inherits the advantages of previous landmark-based and generation-based methods and generates morphed images with higher quality, posing a more significant threat to state-of-the-art FRSs.
Revisiting Gradient Pruning: A Dual Realization for Defending against Gradient Attacks

Collaborative learning (CL) is a distributed learning framework that aims to protect user privacy by allowing users to jointly train a model by sharing their gradient updates only. However, gradient inversion attacks (GIAs), which recover users' training data from shared gradients, impose severe privacy threats to CL. Existing defense methods adopt different techniques, e.g., differential privacy, cryptography, and perturbation defenses, to defend against the GIAs. Nevertheless, all current defense methods suffer from a poor trade-off between privacy, utility, and efficiency. To mitigate the weaknesses of existing solutions, we propose a novel defense method, Dual Gradient Pruning (DGP), based on gradient pruning, which can improve communication efficiency while preserving the utility and privacy of CL. Specifically, DGP slightly changes gradient pruning with a stronger privacy guarantee. And DGP can also significantly improve communication efficiency with a theoretical analysis of its convergence and generalization. Our extensive experiments show that DGP can effectively defend against the most powerful GIAs and reduce the communication cost without sacrificing the model's utility.
Keep Your Friends Close, and Your Enemies Closer: Structural Properties of Negative Relationships on Twitter

The Ego Network Model (ENM) is a model for the structural organisation of relationships, rooted in evolutionary anthropology, that is found ubiquitously in social contexts. It takes the perspective of a single user (Ego) and organises their contacts (Alters) into a series of (typically 5) concentric circles of decreasing intimacy and increasing size. Alters are sorted based on their tie strength to the Ego, however, this is difficult to measure directly. Traditionally, the interaction frequency has been used as a proxy but this misses the qualitative aspects of connections, such as signs (i.e. polarity), which have been shown to provide extremely useful information. However, the sign of an online social relationship is usually an implicit piece of information, which needs to be estimated by interaction data from Online Social Networks (OSNs), making sign prediction in OSNs a research challenge in and of itself. This work aims to bring the ENM into the signed networks domain by investigating the interplay of signed connections with the ENM. This paper delivers 2 main contributions. Firstly, a new and data-efficient method of signing relationships between individuals using sentiment analysis and, secondly, we provide an in-depth look at the properties of Signed Ego Networks (SENs), using 9 Twitter datasets of various categories of users. We find that negative connections are generally over-represented in the active part of the Ego Networks, suggesting that Twitter greatly over-emphasises negative relationships with respect to"offline"social networks. Further, users who use social networks for professional reasons have an even greater share of negative connections.
A large dataset curation and benchmark for drug target interaction

Bioactivity data plays a key role in drug discovery and repurposing. The resource-demanding nature of \textit{in vitro} and \textit{in vivo} experiments, as well as the recent advances in data-driven computational biochemistry research, highlight the importance of \textit{in silico} drug target interaction (DTI) prediction approaches. While numerous large public bioactivity data sources exist, research in the field could benefit from better standardization of existing data resources. At present, different research works that share similar goals are often difficult to compare properly because of different choices of data sources and train/validation/test split strategies. Additionally, many works are based on small data subsets, leading to results and insights of possible limited validity. In this paper we propose a way to standardize and represent efficiently a very large dataset curated from multiple public sources, split the data into train, validation and test sets based on different meaningful strategies, and provide a concrete evaluation protocol to accomplish a benchmark. We analyze the proposed data curation, prove its usefulness and validate the proposed benchmark through experimental studies based on an existing neural network model.
LeftoverLocals: Listening to LLM Responses Through Leaked GPU Local Memory

This paper describes LeftoverLocals: a vulnerability that allows data recovery from GPU memory created by another process on Apple, Qualcomm, and AMD GPUs. LeftoverLocals impacts the security posture of GPU applications, with particular significance to LLMs and ML models that run on impacted GPUs. By recovering local memory, an optimized GPU memory region, we built a PoC where an attacker can listen into another user's interactive LLM session (e.g., llama.cpp) across process or container boundaries.
Dynamic MRI reconstruction using low-rank plus sparse decomposition with smoothness regularization

The low-rank plus sparse (L+S) decomposition model has enabled better reconstruction of dynamic magnetic resonance imaging (dMRI) with separation into background (L) and dynamic (S) component. However, use of low-rank prior alone may not fully explain the slow variations or smoothness of the background part at the local scale. In this paper, we propose a smoothness-regularized L+S (SR-L+S) model for dMRI reconstruction from highly undersampled k-t-space data. We exploit joint low-rank and smooth priors on the background component of dMRI to better capture both its global and local temporal correlated structures. Extending the L+S formulation, the low-rank property is encoded by the nuclear norm, while the smoothness by a general \ell_{p}-norm penalty on the local differences of the columns of L. The additional smoothness regularizer can promote piecewise local consistency between neighboring frames. By smoothing out the noise and dynamic activities, it allows accurate recovery of the background part, and subsequently more robust dMRI reconstruction. Extensive experiments on multi-coil cardiac and synthetic data shows that the SR-L+S model outp
ConChi: Pattern Change Mining from Mobile Context-Aware Data

Mobile context-aware systems focus on adapting mobile service provisions to the actual user needs. They offer personalized services based on the context in which mobile users’ requests have been submitted. Since contextual information changes over time, the application of established itemset change mining algorithms to context-aware data is an appealing research issue. Change itemset discovery focuses on discovering patterns which represent the temporal evolution of frequent itemsets in consecutive time periods. However, the sparseness of the analyzed data may bias the extraction process, because itemsets are likely to become infrequent at certain time periods. This chapter presents ConChI, a novel contextaware system that performs change itemset mining from context-aware data with the aim at supporting mobile expert decisions. To counteract data sparseness itemset change mining is driven by an analystprovided taxonomy which allows analyzing data correlation changes at different abstraction levels. In particular, taxonomy is exploited to represent the knowledge that becomes infrequent in certain time periods by means of high level (generalized) itemsets. Experiments performed on real contextual data coming from a mobile application show the effectiveness of the proposed system in supporting mobile user and service profiling.
Do deep neural networks utilize the weight space efficiently?

Deep learning models like Transformers and Convolutional Neural Networks (CNNs) have revolutionized various domains, but their parameter-intensive nature hampers deployment in resource-constrained settings. In this paper, we introduce a novel concept utilizes column space and row space of weight matrices, which allows for a substantial reduction in model parameters without compromising performance. Leveraging this paradigm, we achieve parameter-efficient deep learning models. Our approach applies to both Bottleneck and Attention layers, effectively halving the parameters while incurring only minor performance degradation. Extensive experiments conducted on the ImageNet dataset with ViT and ResNet50 demonstrate the effectiveness of our method, showcasing competitive performance when compared to traditional models. This approach not only addresses the pressing demand for parameter efficient deep learning solutions but also holds great promise for practical deployment in real-world scenarios.
Touch-triggered bZIP translocation regulates elongation and secondary wall biosynthesis

Touch-triggered bZIP translocation regulates elongation and secondary wall biosynthesis 1 2 Joshua H. Coomey 1,2 , Kirk J.-M. MacKinnon 1,3 , Pubudu P. Handakumbura 1,2 , Ian W. 3 McCahill 1,2 , Gina M. Trabucco 1,3 , Jessica Mazzola 1 , Nicole A. Leblanc 1 , Rithany Kheam 1 , 4 Miriam Hernandez-Romero 1,2 , Kerrie Barry 4 , Lifeng Liu 4 , Ji E. Lee 4 , John P. Vogel 4 , Ronan C. 5 O'Malley 4 , James J. Chambers 5 , Samuel P. Hazen 1,2,3 6 7 8
A microRNA that controls the emergence of embryonic movement

Movement is a key feature of animal systems, yet its embryonic origins are not fully understood. Here we investigate the genetic basis underlying the embryonic onset of movement in Drosophila focusing on the role played by small non-coding RNAs (microRNAs, miRNAs). To this end, we first develop a quantitative behavioural pipeline capable of tracking embryonic movement in large populations of fly embryos, and using this system, discover that the Drosophila miRNA miR-2b-1 plays a role in the emergence of movement. Through the combination of spectral analysis of embryonic motor patterns, cell sorting and RNA in situs, genetic reconstitution tests, and neural optical imaging we define that miR-2b-1 influences the emergence of embryonic movement by exerting actions in the developing nervous system. Furthermore, through the combination of bioinformatics coupled to genetic manipulation of miRNA expression and phenocopy tests we identify a previously uncharacterised (but evolutionarily conserved) chloride channel encoding gene – which we term Janus – as a genetic target that mechanistically links miR-2b-1 to the onset of movement. Cell-specific genetic reconstitution of miR-2b-1 expression in a null miRNA mutant background, followed by behavioural assays and target gene analyses, suggest that miR-2b-1 affects the emergence of movement through effects in sensory elements of the embryonic circuitry, rather than in the motor domain. Our work thus reports the first miRNA system capable of regulating embryonic movement, suggesting that other miRNAs are likely to play a role in this key developmental process in Drosophila as well as in other species.