Miracle-dz/UniFAR-FTUs huggingface dataset:
FREIGHT: Fast Streaming Hypergraph Partitioning

Partitioning the vertices of a (hyper)graph into k roughly balanced blocks such that few (hyper)edges run between blocks is a key problem for large-scale distributed processing. A current trend for partitioning huge (hyper)graphs using low computational resources are streaming algorithms. In this work, we propose FREIGHT: a Fast stREamInG Hypergraph parTitioning algorithm which is an adaptation of the widely-known graph-based algorithm Fennel. By using an efficient data structure, we make the overall running of FREIGHT linearly dependent on the pin-count of the hypergraph and the memory consumption linearly dependent on the numbers of nets and blocks. The results of our extensive experimentation showcase the promising performance of FREIGHT as a highly efficient and effective solution for streaming hypergraph partitioning. Our algorithm demonstrates competitive running time with the Hashing algorithm, with a difference of a maximum factor of four observed on three fourths of the instances. Significantly, our findings highlight the superiority of FREIGHT over all existing (buffered) streaming algorithms and even the in-memory algorithm HYPE, with respect to both cut-net and connectivity measures. This indicates that our proposed algorithm is a promising hypergraph partitioning tool to tackle the challenge posed by large-scale and dynamic data processing.
New method for overcoming ill-conditioning in vanishing-point-based camera calibration

A common problem in vanishing-point-based camera calibration methods is that they are ill-conditioned when the parallel lines for calibration in the world coordinates also appear to be parallel or near-parallel in the image coordinates. Then the camera parameters are not calculable or incur substantial error because one of the vanishing points approaches infinity. We propose a new method that overcomes the ill-conditioning. Using lane markings on a road to form a rectangular calibration pattern with two vanishing points, we first derive a set of equations based on one vanishing point without assuming any extrinsic parameters being known a priori. Because only one vanishing point can approach infinity at any one time, ill-conditioning can be avoided completely by selecting the vanishing point that does not suffer from it. This enables us to produce a set of extrinsic camera parameters that maps the world and image coordinates accurately. Simulation and real image tests show that the proposed method conquers ill-conditioning successfully and is insensitive to pixel offset on the calibration pattern.
On Second-Order Derivatives of Rigid-Body Dynamics: Theory & Implementation

Model-based control for robots has increasingly been dependent on optimization-based methods like Differential Dynamic Programming and iterative LQR (iLQR). These methods can form the basis of Model-Predictive Control (MPC), which is commonly used for controlling legged robots. Computing the partial derivatives of the dynamics is often the most expensive part of these algorithms, regardless of whether analytical methods, Finite Difference, Automatic Differentiation (AD), or Chain-Rule accumulation is used. Since the second-order derivatives of dynamics result in tensor computations, they are often ignored, leading to the use of iLQR, instead of the full second-order DDP method. In this paper, we present analytical methods to compute the second-order derivatives of inverse and forward dynamics for open-chain rigid-body systems with multi-DoF joints and fixed/floating bases. An extensive comparison of accuracy and run-time performance with AD and other methods is provided, including the consideration of code-generation techniques in C/C++ to speed up the computations. For the 36 DoF ATLAS humanoid, the second-order Inverse, and the Forward dynamics derivatives take approx 200 mu s, and approx 2.1 ms respectively, resulting in a 3x speedup over the AD approach.
$\alpha$-induction for bi-unitary connections

The tensor functor called $\alpha$-induction arises from a Frobenius algebra object, or a Q-system, in a braided unitary fusion category. In the operator algebraic language, it gives extensions of endomorphism of $N$ to $M$ arising from a subfactor $N\subset M$ of finite index and finite depth giving a braided fusion category of endomorpshisms of $N$. It is also understood in terms of Ocneanu's graphical calculus. We study this $\alpha$-induction for bi-unitary connections, which give a characterization of finite-dimensional nondegenerate commuting squares and gives certain 4-tensors appearing in recent studies of 2-dimensional topological order. We show that the resulting $\alpha$-induced bi-unitary connections are flat if we have a commutative Frobenius algebra, or a local Q-system. Examples related to chiral conformal field theory and the Dynkin diagrams are presented.
Investigating the Effect of Relative Positional Embeddings on AMR-to-Text Generation with Structural Adapters

Text generation from Abstract Meaning Representation (AMR) has substantially benefited from the popularized Pretrained Language Models (PLMs). Myriad approaches have linearized the input graph as a sequence of tokens to fit the PLM tokenization requirements. Nevertheless, this transformation jeopardizes the structural integrity of the graph and is therefore detrimental to its resulting representation. To overcome this issue, Ribeiro et al. (2021b) have recently proposed StructAdapt, a structure-aware adapter which injects the input graph connectivity within PLMs using Graph Neural Networks (GNNs). In this paper, we investigate the influence of Relative Position Embeddings (RPE) on AMR-to-Text, and, in parallel, we examine the robustness of StructAdapt. Through ablation studies, graph attack and link prediction, we reveal that RPE might be partially encoding input graphs. We suggest further research regarding the role of RPE will provide valuable insights for Graph-to-Text generation.
Support Generation for Robot-Assisted 3D Printing with Curved Layers

Robot-assisted 3D printing has drawn a lot of attention by its capability to fabricate curved layers that are optimized according to different objectives. However, the support generation algorithm based on a fixed printing direction for planar layers cannot be directly applied for curved layers as the orientation of material accumulation is dynamically varied. In this paper, we propose a skeleton-based support generation method for robot-assisted 3D printing with curved layers. The support is represented as an implicit solid so that the problems of numerical robustness can be effectively avoided. The effectiveness of our algorithm is verified on a dual-material printing platform that consists of a robotic arm and a newly designed dual-material extruder. Experiments have been successfully conducted on our system to fabricate a variety of freeform models.
Maximum Mass and Stability of Differentially Rotating Neutrons Stars

We present our study of stability of differentially rotating, axisymmetric neutron stars described by a polytropic equation of state with $\Gamma = 2$. We focus on quasi-toroidal solutions with a degree of differential rotation $\widetilde A=1$. Our results show that for a wide range of parameters hypermassive, quasi-toroidal neutron stars are dynamically stable against quasi-radial perturbations, which may have implications for newly born neutron stars and binary neutron stars mergers.
Calculation and Uncertainty of Fast Radio Burst Structure Based on Smoothed Data

Studies of the time-domain structure of fast radio bursts (FRBs) require an accurate estimate of the FRB dispersion measure in order to recover the intrinsic burst shape. Furthermore, the exact dispersion measure is itself of interest when studying the time evolution of the medium through which multiple bursts from repeating FRBs propagate. A commonly used approach to obtain the dispersion measure is to take the value that maximizes the FRB structure in the time domain. However, various authors use differing methods to obtain this structure parameter and do not document the smoothing method used. Furthermore, there are no quantitative estimates of the error in this procedure in the FRB literature. In this article, we present a smoothing filter based on the discrete cosine transform, and show that computing the structure parameter by summing the squares of the derivatives and taking the square root (that is, the 2-norm, Σ(d/dt)2 ) immediately lends itself to the calculation of the uncertainty of the structure parameter. We illustrate this with FRB 20181112A and FRB 20210117A data, which were detected by the Australian Square Kilometre Array Pathfinder, and for which high-time-resolution data are available.
Removing splitting/modeling error in projection/penalty methods for Navier-Stokes simulations with continuous data assimilation

We study continuous data assimilation (CDA) applied to projection and penalty methods for the Navier-Stokes (NS) equations. Penalty and projection methods are more efficient than consistent NS discretizations, however are less accurate due to modeling error (penalty) and splitting error (projection). We show analytically and numerically that with measurement data and properly chosen parameters, CDA can effectively remove these splitting and modeling errors and provide long time optimally accurate solutions.
Improving trapped-ion-qubit memories via code-mediated error-channel balancing

The high-fidelity storage of quantum information is crucial for quantum computation and communication. Many experimental platforms for these applications exhibit highly biased noise, with good resilience to spin depolarisation undermined by high dephasing rates. In this work, we demonstrate that the memory performance of a noise-biased trapped-ion qubit memory can be greatly improved by incorporating error correction of dephasing errors through teleportation of the information between two repetition codes written on a pair of qubit registers in the same trap. While the technical requirements of error correction are often considerable, we show that our protocol can be achieved with a single global entangling phase gate of remarkably low fidelity, leveraging the fact that the gate errors are also dominated by dephasing-type processes. By rebalancing the logical spin-flip and dephasing error rates, we show that for realistic parameters our memory can exhibit error rates up to two orders of magnitude lower than the unprotected physical qubits, thus providing a useful means of improving memory performance in trapped ion systems where field-insensitive qubits are not available.
Revisiting the history of the evolution of the universe from radiation dominated to dark energy dominated

Most of our knowledge of the universe has been obtained from the anisotropy spectrum of the cosmic microwave background and observations of large-scale structures. During the history of the Universe, neutrinos from the early Universe evolve from a relativistic phase at very early times to a massive-particle behavior at later times. The mass of neutrinos affects the history of the expansion of the universe and the growth of the disturbances of the various components of the cosmic microwave background, therefore the anisotropy spectrum of the cosmic radiation and the observations of the large-scale structures. In this article, by using the coupling of neutrinos with dark energy, we investigate the cosmic evolution from the era of matter dominated to the era of dark energy dominated and show that neutrinos can play an important role in the evolution from radiation dominated to matter dominated and the evolution from Matter dominated to dark energy dominated. Also, we investigate the effect of non-relativistic neutrino on bulk flow and show that the direction of bulk flow has a little difference in scales smaller than 0.1, and the more we consider the scales higher than the local universe, the more difference is observed in the direction of bulk flow.
Suction effect on an impulsively started circular cylinder: Vortex structure and drag reduction

Drag reduction on an impulsively started circular cylinder by surface suction has been investigated with a joint numerical and experimental study. Two suction slots entraining mass with various rates were located symmetrically in a range of angles downstream of the points of zero shear stress. The Reynolds number Re of this study was between 500 and 2000, while the suction coefficient Ṁ varied from 0 to 0.08. Analysis based on the force contributed by fluid elements with nonvanishing vorticity [Chang, Proc. R. Soc. London Ser. A 437, 517 (1992)] has provided a clear view of the physical mechanism causing the drag reduction. For the impulsive flow with surface suction, the initial drag reduction is due to the strengthening of the front boundary layer, while the slender vortices in the near wake due to suction then become the dominant factor in reduction of the drag force in later stages. Close agreements were found between the experimental and numerical results, including streakline flow visualization and .
Optimizing Orthogonalized Tensor Deflation via Random Tensor Theory

This paper tackles the problem of recovering a low-rank signal tensor with possibly correlated components from a random noisy tensor, or so-called spiked tensor model. When the underlying components are orthogonal, they can be recovered efficiently using tensor deflation which consists of successive rank-one approximations, while non-orthogonal components may alter the tensor deflation mechanism, thereby preventing efficient recovery. Relying on recently developed random tensor tools, this paper deals precisely with the non-orthogonal case by deriving an asymptotic analysis of a parameterized deflation procedure performed on an order-three and rank-two spiked tensor. Based on this analysis, an efficient tensor deflation algorithm is proposed by optimizing the parameter introduced in the deflation mechanism, which in turn is proven to be optimal by construction for the studied tensor model. The same ideas could be extended to more general low-rank tensor models, e.g., higher ranks and orders, leading to more efficient tensor methods with a broader impact on machine learning and beyond.
Analyzing DCTCP and Cubic Buffer Sharing under Diverse Router Configurations

In this work, we look at the impact of router configurations on DCTCP and Cubic traffic when both algorithms share router buffers in the data center. Modern data centers host traffic with mixed congestion controls, including DCTCP and Cubic traffic. Both DCTCP and Cubic in the data center can compete with each other and potentially starve and/or be unfair to each other when sharing buffer space in the data center. This happens since both algorithms are at odds with each other in terms of buffer utilization paradigms where DCTCP attempts to limit buffer utilization while Cubic generally fills buffers to obtain high throughput. As a result, we propose methods for a measurement-driven analysis of DCTCP and Cubic performance when sharing buffers in data center routers via simulation. We run around 10000 simulation experiments with unique router configurations and network conditions. Afterwards, we present a generalizable ML model to capture the effect that different buffer settings have on DCTCP and Cubic streaming traffic in the data center. Finally, we suggest that this model can be used to tune buffer settings in the data center.
On Structural and Spectral Properties of Distance Magic Graphs

A graph $G=(V,E)$ is said to be distance magic if there is a bijection $f$ from a vertex set of $G$ to the first $|V(G)|$ natural numbers such that for each vertex $v$, its weight given by $\sum_{u \in N(v)}f(u)$ is constant, where $N(v)$ is an open neighborhood of a vertex $v$. In this paper, we introduce the concept of $p$-distance magic labeling and establish the necessary and sufficient condition for a graph to be distance magic. Additionally, we introduce necessary and sufficient conditions for a connected regular graph to exhibit distance magic properties in terms of the eigenvalues of its adjacency and Laplacian matrices. Furthermore, we study the spectra of distance magic graphs, focusing on singular distance magic graphs. Also, we show that the number of distance magic labelings of a graph is, at most, the size of its automorphism group.
LDPTrace: Locally Differentially Private Trajectory Synthesis

Trajectory data has the potential to greatly benefit a wide-range of real-world applications, such as tracking the spread of the disease through people's movement patterns and providing personalized location-based services based on travel preference. However, privacy concerns and data protection regulations have limited the extent to which this data is shared and utilized. To overcome this challenge, local differential privacy provides a solution by allowing people to share a perturbed version of their data, ensuring privacy as only the data owners have access to the original information. Despite its potential, existing point-based perturbation mechanisms are not suitable for real-world scenarios due to poor utility, dependence on external knowledge, high computational overhead, and vulnerability to attacks. To address these limitations, we introduce LDPTrace, a novel locally differentially private trajectory synthesis framework. Our framework takes into account three crucial patterns inferred from users' trajectories in the local setting, allowing us to synthesize trajectories that closely resemble real ones with minimal computational cost. Additionally, we present a new method for selecting a proper grid granularity without compromising privacy. Our extensive experiments using real-world as well as synthetic data, various utility metrics and attacks, demonstrate the efficacy and efficiency of LDPTrace.
RFC-Net: Learning High Resolution Global Features for Medical Image Segmentation on a Computational Budget

Learning High-Resolution representations is essential for semantic segmentation. Convolutional neural network (CNN) architectures with downstream and upstream propagation flow are popular for segmentation in medical diagnosis. However, due to performing spatial downsampling and upsampling in multiple stages, information loss is inexorable. On the contrary, connecting layers densely on high spatial resolution is computationally expensive. In this work, we devise a Loose Dense Connection Strategy to connect neurons in subsequent layers with reduced parameters. On top of that, using a m-way Tree structure for feature propagation we propose Receptive Field Chain Network (RFC-Net) that learns high-resolution global features on a compressed computational space. Our experiments demonstrates that RFC Net achieves state-of-the-art performance on Kvasir and CVC-ClinicDB benchmarks for Polyp segmentation. Our code is publicly available at github.com/sourajitcs/RFC-NetAAAI23.
Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression under Gaussian Marginals

We study the task of agnostically learning halfspaces under the Gaussian distribution. Specifically, given labeled examples $(\mathbf{x},y)$ from an unknown distribution on $\mathbb{R}^n \times \{ \pm 1\}$, whose marginal distribution on $\mathbf{x}$ is the standard Gaussian and the labels $y$ can be arbitrary, the goal is to output a hypothesis with 0-1 loss $\mathrm{OPT}+\epsilon$, where $\mathrm{OPT}$ is the 0-1 loss of the best-fitting halfspace. We prove a near-optimal computational hardness result for this task, under the widely believed sub-exponential time hardness of the Learning with Errors (LWE) problem. Prior hardness results are either qualitatively suboptimal or apply to restricted families of algorithms. Our techniques extend to yield near-optimal lower bounds for related problems, including ReLU regression.
Variational Analysis of the Orthogonally Invariant Norm Cone of Symmetric Matrices

A large number matrix optimization problems are described by orthogonally invariant norms. This paper is devoted to the study of variational analysis of the orthogonally invariant norm cone of symmetric matrices. For a general orthogonally invariant norm cone of symmetric matrices, formulas for the tangent cone, normal cone and second-order tangent set are established. The differentiability properties of the projection operator onto the orthogonally invariant norm cone are developed, including formulas for the directional derivative and the B-subdifferential. Importantly, the directional derivative is characterized by the second-order derivative of the corresponding symmetric function, which is convenient for computation. All these results are specified to the Schatten $p$-norm cone, especially to the second-order cone of symmetric matrices.
Mixed Multi-Model Semantic Interaction for Graph-based Narrative Visualizations

Narrative sensemaking is an essential part of understanding sequential data. Narrative maps are a visual representation model that can assist analysts to understand narratives. In this work, we present a semantic interaction (SI) framework for narrative maps that can support analysts through their sensemaking process. In contrast to traditional SI systems which rely on dimensionality reduction and work on a projection space, our approach has an additional abstraction layer—the structure space—that builds upon the projection space and encodes the narrative in a discrete structure. This extra layer introduces additional challenges that must be addressed when integrating SI with the narrative extraction pipeline. We address these challenges by presenting the general concept of Mixed Multi-Model Semantic Interaction (3MSI)—an SI pipeline, where the highest-level model corresponds to an abstract discrete structure and the lower-level models are continuous. To evaluate the performance of our 3MSI models for narrative maps, we present a quantitative simulation-based evaluation and a qualitative evaluation with case studies and expert feedback. We find that our SI system can model the analysts’ intent and support incremental formalism for narrative maps.
On stability of rainbow matchings

We show that for any integer $k\ge 1$ there exists an integer $t_0(k)$ such that for integers $t, k_1, \ldots, k_{t+1}, n$ with $t>t_0(k)$, $\max\{k_1, \ldots, k_{t+1}\}\le k$, and $n>2k(t+1)$, the following holds: If $F_i \subseteq {[n]\choose k_i}$ and $|F_i|>{n\choose k_i}-{n-t\choose k_i} - {n-t-k \choose k_i-1} + 1$ for all $i \in [t+1]$, then either $\{F_1,\ldots, F_{t+1}\}$ admits a rainbow matching of size $t+1$ or there exists $W\in {[n]\choose t}$ such that $W$ is a vertex cover of $F_i$ for all $i\in [t+1]$. This may be viewed as a rainbow non-uniform extension of the classical Hilton-Milner theorem. We also show that the same holds for every $t$ and $n>2k^3t$, generalizing a recent stability result of Frankl and Kupavskii on matchings to rainbow matchings.