Miracle-dz/UniFAR-FTUs huggingface dataset:
Steiner Connectivity Augmentation and Splitting-off in Poly-logarithmic Maximum Flows

We give an almost-linear time algorithm for the Steiner connectivity augmentation problem: given an undirected graph, find a smallest (or minimum weight) set of edges whose addition makes a given set of terminals $\tau$-connected (for any given $\tau>0$). The running time of our algorithm is dominated by polylogarithmic calls to any maximum flow subroutine; using the recent almost-linear time maximum flow algorithm (Chen et al., FOCS 2022), we get an almost-linear running time for our algorithm as well. This is tight up to the polylogarithmic factor even for just two terminals. Prior to our work, an almost-linear (in fact, near-linear) running time was known only for the special case of global connectivity augmentation, i.e., when all vertices are terminals (Cen et al., STOC 2022). We also extend our algorithm to the closely related Steiner splitting-off problem, where the edges incident on a vertex have to be {\em split-off} while maintaining the (Steiner) connectivity of a given set of terminals. Prior to our work, a nearly-linear time algorithm was known only for the special case of global connectivity (Cen et al., STOC 2022). The only known generalization beyond global connectivity was to preserve all pairwise connectivities using a much slower algorithm that makes $n$ calls to an all-pairs maximum flow (or Gomory-Hu tree) subroutine (Lau and Yung, SICOMP 2013), as against polylog(n) calls to a (single-pair) maximum flow subroutine in this work.
DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems

While there have been a number of remarkable breakthroughs in machine learning (ML), much of the focus has been placed on model development. However, to truly realize the potential of machine learning in real-world settings, additional aspects must be considered across the ML pipeline. Data-centric AI is emerging as a unifying paradigm that could enable such reliable end-to-end pipelines. However, this remains a nascent area with no standardized framework to guide practitioners to the necessary data-centric considerations or to communicate the design of data-centric driven ML systems. To address this gap, we propose DC-Check, an actionable checklist-style framework to elicit data-centric considerations at different stages of the ML pipeline: Data, Training, Testing, and Deployment. This data-centric lens on development aims to promote thoughtfulness and transparency prior to system development. Additionally, we highlight specific data-centric AI challenges and research opportunities. DC-Check is aimed at both practitioners and researchers to guide day-to-day development. As such, to easily engage with and use DC-Check and associated resources, we provide a DC-Check companion website (https://www.vanderschaar-lab.com/dc-check/). The website will also serve as an updated resource as methods and tooling evolve over time.
Heating of accretion-disk coronae and jets by general relativistic magnetohydrodynamic turbulence

Turbulence in an accretion disk launches Alfvén waves (AWs) that propagate away from the disk along magnetic-field lines. Because the Alfvén speed varies with distance from the disk, the AWs undergo partial non-WKB reflection, and counter-propagating AWs subsequently interact, causing AW energy to cascade to small scales and dissipate. To investigate this process, we introduce an Elsasser-like formulation of general relativistic magnetohydrodynamics (GRMHD) and develop the theory of general relativistic reduced MHD in an inhomogeneous medium. We then derive a set of equations for the mean-square AW amplitude $M_{+}$ and turbulent heating rate $Q$ under the assumption that, in the plasma rest frame, AWs propagating away from the disk are much more energetic than AWs propagating toward the disk. For the case in which the background flow is axisymmetric and time independent, we solve these equations analytically to determine $M_{+}$ and $Q$ as functions of position. We find that, for an idealized thin disk threaded by a large-scale poloidal magnetic field, the AW energy flux is ${\sim}(\unicode[STIX]{x1D70C}_{\text{b}}/\unicode[STIX]{x1D70C}_{\text{d}})^{1/2}\unicode[STIX]{x1D6FD}_{\text{net,d}}^{-1/2}$ times the disk’s radiative flux, where $\unicode[STIX]{x1D70C}_{\text{b}}$ and $\unicode[STIX]{x1D70C}_{\text{d}}$ are the mass densities at the coronal base and disk midplane, respectively, and $\unicode[STIX]{x1D6FD}_{\text{net,d}}$ is the ratio (evaluated at the disk midplane) of plasma-plus-radiation pressure to the pressure of the average vertical magnetic field. This energy flux could have a significant impact on disk coronae and outflows. To lay the groundwork for future global simulations of turbulent disk coronae and jets, we derive a set of averaged GRMHD equations that account for reflection-driven AW turbulence using a sub-grid model.
EvEntS ReaLM: Event Reasoning of Entity States via Language Models

This paper investigates models of event implications. Specifically, how well models predict entity state-changes, by targeting their understanding of physical attributes. Nominally, Large Language models (LLM) have been exposed to procedural knowledge about how objects interact, yet our benchmarking shows they fail to reason about the world. Conversely, we also demonstrate that existing approaches often misrepresent the surprising abilities of LLMs via improper task encodings and that proper model prompting can dramatically improve performance of reported baseline results across multiple tasks. In particular, our results indicate that our prompting technique is especially useful for unseen attributes (out-of-domain) or when only limited data is available.
An Inclusive Notion of Text

Natural language processing (NLP) researchers develop models of grammar, meaning and communication based on written text. Due to task and data differences, what is considered text can vary substantially across studies. A conceptual framework for systematically capturing these differences is lacking. We argue that clarity on the notion of text is crucial for reproducible and generalizable NLP. Towards that goal, we propose common terminology to discuss the production and transformation of textual data, and introduce a two-tier taxonomy of linguistic and non-linguistic elements that are available in textual sources and can be used in NLP modeling. We apply this taxonomy to survey existing work that extends the notion of text beyond the conservative language-centered view. We outline key desiderata and challenges of the emerging inclusive approach to text in NLP, and suggest community-level reporting as a crucial next step to consolidate the discussion.
Model-independent constraints on modified gravity from current data and from the Euclid and SKA future surveys

The aim of this paper is to constrain modified gravity with redshift space distortion observations and supernovae measurements. Compared with a standard ΛCDM analysis, we include three additional free parameters, namely the initial conditions of the matter perturbations, the overall perturbation normalization, and a scale-dependent modified gravity parameter modifying the Poisson equation, in an attempt to perform a more model-independent analysis. First, we constrain the Poisson parameter Y (also called Geff) by using currently available fσ8 data and the recent SN catalog JLA. We find that the inclusion of the additional free parameters makes the constraints significantly weaker than when fixing them to the standard cosmological value. Second, we forecast future constraints on Y by using the predicted growth-rate data for Euclid and SKA missions. Here again we point out the weakening of the constraints when the additional parameters are included. Finally, we adopt as modified gravity Poisson parameter the specific Horndeski form, and use scale-dependent forecasts to build an exclusion plot for the Yukawa potential akin to the ones realized in laboratory experiments, both for the Euclid and the SKA surveys.
NEON: Enabling Efficient Support for Nonlinear Operations in Resistive RAM-based Neural Network Accelerators

—Resistive Random-Access Memory (RRAM) is well-suited to accelerate neural network (NN) workloads as RRAM-based Processing-in-Memory (PIM) architectures natively support highly-parallel multiply-accumulate (MAC) operations that form the backbone of most NN workloads. Unfortunately, NN workloads such as transformers require support for non-MAC operations (e.g., softmax) that RRAM cannot provide natively. Consequently, state-of-the-art works either integrate additional digital logic circuits to support the non-MAC operations or ofﬂoad the non-MAC operations to CPU/GPU, resulting in signiﬁcant performance and energy efﬁciency overheads. In this work, we propose NEON, a novel compiler optimization to enable the end-to-end execution of the NN workload in RRAM. The key idea of NEON is to transform each non- MAC operation into a lightweight yet highly-accurate neural network. Utilizing neural networks to approximate the non- MAC operations provides two advantages: 1) We can exploit the key strength of RRAM, i.e., highly-parallel MAC operation, to ﬂexibly and efﬁciently execute non-MAC operations in memory. 2) We can simplify RRAM’s microarchitecture by eliminating the additional digital logic circuits while reducing the data movement overheads. Acceleration of the non-MAC operations in memory enables NEON to achieve a 2.28x speedup compared to an idealized digital logic-based RRAM. We analyze the trade-offs associated with the transformation and demonstrate feasible use cases for NEON across different substrates.
SWTF: Sparse Weighted Temporal Fusion for Drone-Based Activity Recognition

—Drone-camera based human activity recognition (HAR) has received signiﬁcant attention from the computer vision research community in the past few years. A robust and efﬁcient HAR system has a pivotal role in ﬁelds like video surveillance, crowd behavior analysis, sports analysis, and human-computer interaction. What makes it challenging are the complex poses, understanding different viewpoints, and the environmental scenarios where the action is taking place. To address such complexities, in this paper, we propose a novel Sparse Weighted Temporal Fusion (SWTF) module to utilize sparsely sampled video frames for obtaining global weighted temporal fusion outcome. The proposed SWTF is divided into two components. First, a temporal segment network that sparsely samples a given set of frames. Second, weighted temporal fusion, that incorporates a fusion of feature maps derived from optical ﬂow, with raw RGB images. This is followed by base network, which comprises a convolutional neural network module along with fully connected layers that provide us with activity recognition. The SWTF network can be used as a plug-in module to the existing deep CNN architectures, for optimizing them to learn temporal information by eliminating the need for a separate temporal stream. It has been evaluated on three publicly available benchmark datasets, namely Okutama, MOD20, and Drone-Action. The proposed model has received an accuracy of 72.76%, 92.56%, and 78.86% on the respective datasets thereby surpassing the previous state-of-the-art performances by a signiﬁcant margin.
An Interacting Two-Fluid Scenario for Dark Energy in an FRW Universe

We study the evolution of the dark energy parameter within the scope of a spatially homogeneous and isotropic Friedmann—Robertson—Walker (FRW) model filled with barotropic fluid and dark energy. To obtain the deterministic solution we choose the scale factor , which yields a time-dependent deceleration parameter (DP). In doing so, we consider the case minimally coupled with dark energy to the perfect fluid as well as direct interaction with it.
Deterministic physical systems under uncertain initial conditions: the case of maximum entropy applied to projectile motion

The kinematics and dynamics of deterministic physical systems have been a foundation of our understanding of the world since Galileo and Newton. For real systems, however, uncertainty is largely present via external forces such as friction or lack of precise knowledge about the initial conditions of the system. In this work we focus on the latter case and describe the use of inference methodologies in solving the statistical properties of classical systems subject to uncertain initial conditions. In particular we describe the application of the formalism of maximum entropy (MaxEnt) inference to the problem of projectile motion, given information about the average horizontal range over many realizations. By using MaxEnt we can invert the problem and use the provided information on the average range to reduce the original uncertainty in the initial conditions. Also, additional insight into the initial condition's probabilities, and the projectile path distribution itself, can be achieved based on the value of the average horizontal range. The wide applicability of this procedure, as well as its ease of use, reveals a useful tool with which to revisit a large number of physics problems, from classrooms to frontier research.
External Inverse-compton and Proton Synchrotron Emission from the Reverse Shock as the Origin of VHE Gamma Rays from the Hyper-bright GRB 221009A

The detection of the hyper-bright gamma-ray burst (GRB) 221009A enables us to explore the nature of the GRB emission and the origin of very high-energy gamma rays. We analyze the Fermi Large Area Telescope (Fermi-LAT) data of this burst and investigate the GeV–TeV emission in the framework of the external reverse-shock model. We show that the early ∼1–10 GeV emission can be explained by the external inverse-Compton mechanism via upscattering MeV gamma rays by electrons accelerated at the reverse shock, in addition to the synchrotron self-Compton component. The predicted early optical flux could have been brighter than that of the naked-eye GRB 080319B. We also show that proton synchrotron emission from accelerated ultrahigh-energy cosmic rays (UHECRs) is detectable and could potentially explain ≳TeV photons detected by LHAASO or constrain the UHECR acceleration mechanism. Our model suggests that the detection of (10TeV) photons with energies up to ∼18 TeV is possible for reasonable models of the extragalactic background light without invoking new physics and predicts anticorrelations between MeV photons and TeV photons, which can be tested with the LHAASO data.
Suzaku Observations of Spectral Variations of the Ultra Luminous X-ray Source Holmberg IX X-1

Observations of the Ultra Luminous X-ray source (ULX) Holmberg IX X-1 were carried out with Suzaku twice, once on 2012 April 13 and then on 2012 October 24, with exposures of 180 ks and 217 ks, respectively. The source showed a hard power-law shape spectrum with a mild cutoff at $\sim 8$ keV, which is typical of ULXs when they are relatively dim. On both occasions, the 0.6-11 keV spectrum was explained successfully in terms a cool ($\sim 0.2$ keV) multi-color disk blackbody emission model and a thermal Comptonization emission produced by an electron cloud with a relatively low temperature and high optical depth, assuming that a large fraction of the disk-blackbody photons are Comptonized whereas the rest is observed directly. The 0.5-10 keV luminosity was $1.2\times10^{40}$ erg s$^{-1}$ in April, and $\sim 14\%$ higher in October. This brightening was accompanied by spectral softening in $\ge 2$ keV, with little changes in the $\le 2$ keV spectral shape. This behavior can be understood if the accretion disk remains unchanged, while the electron cloud covers a variable fraction of the disk. The absorbing column density was consistent with the galactic line-of sight value, and did not vary by more than $1.6\times 10^{21}$ cm$^{-2}$. Together with the featureless spectra, these properties may not be reconciled easily with the super-critical accretion scenario of this source.
An Empirical Study on Clustering Pretrained Embeddings: Is Deep Strictly Better?

Recent research in clustering face embeddings has found that unsupervised, shallow, heuristic-based methods -- including $k$-means and hierarchical agglomerative clustering -- underperform supervised, deep, inductive methods. While the reported improvements are indeed impressive, experiments are mostly limited to face datasets, where the clustered embeddings are highly discriminative or well-separated by class (Recall@1 above 90% and often nearing ceiling), and the experimental methodology seemingly favors the deep methods. We conduct a large-scale empirical study of 17 clustering methods across three datasets and obtain several robust findings. Notably, deep methods are surprisingly fragile for embeddings with more uncertainty, where they match or even perform worse than shallow, heuristic-based methods. When embeddings are highly discriminative, deep methods do outperform the baselines, consistent with past results, but the margin between methods is much smaller than previously reported. We believe our benchmarks broaden the scope of supervised clustering methods beyond the face domain and can serve as a foundation on which these methods could be improved. To enable reproducibility, we include all necessary details in the appendices, and plan to release the code.
ClassPruning: Speed Up Image Restoration Networks by Dynamic N: M Pruning

Image restoration tasks have achieved tremendous performance improvements with the rapid advancement of deep neural networks. However, most prevalent deep learning models perform inference statically, ignoring that different images have varying restoration difficulties and lightly degraded images can be well restored by slimmer subnetworks. To this end, we propose a new solution pipeline dubbed ClassPruning that utilizes networks with different capabilities to process images with varying restoration difficulties. In particular, we use a lightweight classifier to identify the image restoration difficulty, and then the sparse subnetworks with different capabilities can be sampled based on predicted difficulty by performing dynamic N:M fine-grained structured pruning on base restoration networks. We further propose a novel training strategy along with two additional loss terms to stabilize training and improve performance. Experiments demonstrate that ClassPruning can help existing methods save approximately 40% FLOPs while maintaining performance.
QCNN: Quadrature Convolutional Neural Network with Application to Unstructured Data Compression

We present a new convolution layer for deep learning architectures which we call QuadConv — an approximation to continuous convolution via quadrature. Our operator is developed explicitly for use on unstructured data, and accomplishes this by learning a continuous kernel that can be sampled at arbitrary locations. In the setting of neural compression, we show that a QuadConv-based autoencoder, resulting in a Quadrature Convolutional Neural Network (QCNN), can match the performance of standard discrete convolutions on structured uniform data, as in CNNs, and maintain this accuracy on unstructured data
Variations of the Energy of Free Particles in the pp-Wave Spacetimes

We consider the action of exact plane gravitational waves, or pp-waves, on free particles. The analysis is carried out by investigating the variations of the geodesic trajectories of the particles, before and after the passage of the wave. The initial velocities of the particles are non-vanishing. We evaluate numerically the Kinetic energy per unit mass of the free particles, and obtain interesting, quasi-periodic behaviour of the variations of the Kinetic energy with respect to the width $\lambda$ of the gaussian that represents the wave. The variation of the energy of the free particle is expected to be exactly minus the variation of the energy of the gravitational field, and therefore provides an estimation of the local variation of the gravitational energy. The investigation is carried out in the context of short bursts of gravitational waves, and of waves described by normalised gaussians, that yield impulsive waves in a certain limit.
UAV Traffic Management : A Survey On Communication Security

Unmanned Aerial Systems (UAS) have a wide variety of applications, and their development in terms of capabilities is continuously evolving. Many missions performed by an Unmanned Aerial Vehicle (UAV) require flying in public airspace. This requires very high safety standards, similar to those mandatory in commercial civil aviation. A safe UAV Traffic Management (UTM) requires several communication links between aircraft, their pilots and UTM systems. The integrity of these communication links is critical for the safety of operations. Several security requirements also have to be met on each of these links. Unfortunately, current cryptographic standards used over the internet are most often not suitable to UAS due to their limited resources and dynamic nature. This survey discusses the security required for every communication link in order to enable a safe traffic management. Research works focusing on the security of communication links using cryptographic primitives are then presented and discussed. Authentication protocols developed for UAVs or other constrained systems are compared and evaluated as solutions for UAS security. Symmetrical alternatives to the AES algorithm are also presented. Works to secure current UTM protocols such as ADS-B and RemoteID are discussed. The analysis reveals a need for the development of a complete secure architecture able to provide authentication and integrity to external systems (other aircraft, UTM systems.).
Decomposing the Fundamentals of Creepy Stories

Fear is a universal concept; people crave it in urban legends, scary movies, and modern stories. Open questions remain, however, about why these stories are scary and more gener-ally what scares people. In this study, we explore these questions by analyzing tens of thousands of scary stories on forums (known as subreddits) in a social media website, Reddit. We ﬁrst explore how writing styles have evolved to keep these stories fresh before we analyze the stable core techniques writers use to make stories scary. We ﬁnd that writers have changed the themes of their stories over years from haunted houses to school-related themes, body horror, and diseases. Yet some features remain stable; words associated with pseudo-human nouns, such as clown or devil are more common in scary stories than baselines. In ad-dition, we collect a range of datasets that an-notate sentences containing fear. We use these data to develop a high-accuracy fear detection neural network model, which is used to quantify where people express fear in scary stories. We ﬁnd that sentences describing fear, and words most often seen in scary stories, spike at particular points in a story, possibly as a way to keep the readers on the edge of their seats until the story’s conclusion. These results provide a new understanding of how authors cater to their readers, and how fear may manifest in stories.