Miracle-dz/UniFAR-FTUs huggingface dataset:
Link recommendations: Their impact on network structure and minorities

Network-based people recommendation algorithms are widely employed on the Web to suggest new connections in social media or professional platforms. While such recommendations bring people together, the feedback loop between the algorithms and the changes in network structure may exacerbate social biases. These biases include rich-get-richer effects, filter bubbles, and polarization. However, social networks are diverse complex systems and recommendations may affect them differently, depending on their structural properties. In this work, we explore five people recommendation algorithms by systematically applying them over time to different synthetic networks. In particular, we measure to what extent these recommendations change the structure of bi-populated networks and show how these changes affect the minority group. Our systematic experimentation helps to better understand when link recommendation algorithms are beneficial or harmful to minority groups in social networks. In particular, our findings suggest that, while all algorithms tend to close triangles and increase cohesion, all algorithms except Node2Vec are prone to favor and suggest nodes with high in-degree. Furthermore, we found that, especially when both classes are heterophilic, recommendation algorithms can reduce the visibility of minorities.
Development of Safety Protocol, Features, and Fail-Safes for a Laboratory-Scale Manufacturing Process

The manufacturing of composite materials has many associated hazards – health, physical, fire/explosive, and environmental hazards, to name a few. With general laboratory safety training and common sense, some hazards are readily apparent and easily detected by the students working on a project. However, many of the dangers associated with volatile or toxic chemicals cannot be seen and consequently go unnoticed until an incident occurs. This document details the work done by undergraduate students, faculty and staff at Western Washington University to develop appropriate safety features for a laboratory scale solventbased prepreg manufacturing machine. This manufacturing process has a variety of associated hazards including physical hazards (moving parts on the machine), health hazards (use of toxic/sensitizing chemicals), fire/explosive hazards (volatilization of organic solvents during manufacturing process), and environmental hazards (use of ecologically harmful materials), making it an excellent case study for the development of safety protocol by an academic institution. To date there has not been any documentation centered around the challenges associated with creating a solvent-based prepreg treater system. The safety feature and protocol development process is explained in detail and may serve useful for other institutions undertaking similar projects.
Spin-induced dynamical scalarization, descalarization, and stealthness in scalar-Gauss-Bonnet gravity during a black hole coalescence

Particular couplings between a scalar ﬁeld and the Gauss-Bonnet invariant lead to spontaneous scalarization of black holes. Here we continue our work on simulating this phenomenon in the context of binary black hole systems. We consider a negative coupling for which the black-hole spin plays a major role in the scalarization process. We ﬁnd two main phenomena: (i) dynamical descalarization, in which initially scalarized black holes form an unscalarized remnant, and (ii) dynamical scalarization, whereby the late merger of initially unscalarized black holes can cause scalar hair to grow. An important consequence of the latter case is that modiﬁcations to the gravitational waveform due to the scalar ﬁeld may only occur post-merger, as its presence is hidden during the entirety of the inspiral. However, with a suﬃciently strong coupling, we ﬁnd that scalarization can occur before the remnant has even formed. We close with a discussion of observational implications for gravitational-wave tests of general relativity.
Stabilizer Inactivation for Message-Passing Decoding of Quantum LDPC Codes

We propose a post-processing method for message-passing (MP) decoding of CSS quantum LDPC codes, called stabilizer-inactivation (SI). It relies on inactivating a set of qubits, supporting a check in the dual code, and then running the MP decoding again. This allows MP decoding to converge outside the inactivated set of qubits, while the error on these is determined by solving a small, constant size, linear system. Compared to the state of the art post-processing method based on ordered statistics decoding (OSD), we show through numerical simulations that MP-SI outperforms MP-OSD for different quantum LDPC code constructions, different MP decoding algorithms, and different MP scheduling strategies, while having a significantly reduced complexity.
Sudakov shoulder resummation for thrust and heavy jet mass

When the allowed range of an observable grows order-by-order in perturbation theory, its perturbative expansion can have discontinuities (as in the $C$ parameter) or discontinuities in its derivatives (as in thrust or heavy jet mass) called Sudakov shoulders. We explore the origin of these logarithms using both perturbation theory and effective field theory. We show that for thrust and heavy jet mass, the logarithms arise from kinematic configurations with narrow jets and deduce the next-to-leading logarithmic series. The left-shoulder logarithms in heavy jet mass ($\rho$) of the form $r\ \alpha_s^n \ln^{2n}r $ with $r=\frac{1}{3}-\rho$ are particularly dangerous, because they invalidate fixed order perturbation theory in regions traditionally used to extract $\alpha_s$. Although the factorization formula shows there are no non-global logarithms, we find Landau-pole like singularities in the resummed distribution associated with the cusp anomalous dimension, and that power corrections are exceptionally important.
Annealed Langevin Dynamics for Massive MIMO Detection

Solving the optimal symbol detection problem in multiple-input multiple-output (MIMO) systems is known to be NP-hard. Hence, the objective of any detector of practical relevance is to get reasonably close to the optimal solution while keeping the computational complexity in check. In this work, we propose a MIMO detector based on an annealed version of Langevin (stochastic) dynamics. More precisely, we define a stochastic dynamical process whose stationary distribution coincides with the posterior distribution of the symbols given our observations. In essence, this allows us to approximate the maximum a posteriori estimator of the transmitted symbols by sampling from the proposed Langevin dynamic. Furthermore, we carefully craft this stochastic dynamic by gradually adding a sequence of noise with decreasing variance to the trajectories, which ensures that the estimated symbols belong to a pre-specified discrete constellation. Based on the proposed MIMO detector, we also design a robust version of the method by unfolding and parameterizing one term– the score of the likelihood– by a neural network. Through numerical experiments in both synthetic and real-world data, we show that our proposed detector yields state-of-the-art symbol error rate performance and the robust version becomes noise-variance agnostic.
Generalizations and variants of Knuth's old sum

We extend the Reed Dawson identity for Knuth’s old sum with a complex parameter, and we oﬀer two separate hypergeometric series-based proofs of this generalization, and we apply this generalization to introduce binomial-harmonic sum identities. We also provide another 2 F 1 (2)-generalization of the Reed Dawson identity involving a free parameter. We then apply Fourier–Legendre theory to obtain an identity involving odd harmonic numbers that resembles the formula for Knuth’s old sum, and the modiﬁed Abel lemma on summation by parts is also applied.
Tutorial: Analog Matrix Computing (AMC) with Crosspoint Resistive Memory Arrays

Matrix computation is ubiquitous in modern scientific and engineering fields. Due to the high computational complexity in conventional digital computers, matrix computation represents a heavy workload in many data-intensive applications, e.g., machine learning, scientific computing, and wireless communications. For fast, efficient matrix computations, analog computing with resistive memory arrays has been proven to be a promising solution. In this Tutorial, we present analog matrix computing (AMC) circuits based on crosspoint resistive memory arrays. AMC circuits are able to carry out basic matrix computations, including matrix multiplication, matrix inversion, pseudoinverse and eigenvector computation, all with one single operation. We describe the main design principles of the AMC circuits, such as local/global or negative/positive feedback configurations, with/without external inputs. Mapping strategies for matrices containing negative values will be presented. The underlying requirements for circuit stability will be described via the transfer function analysis, which also defines time complexity of the circuits towards steady-state results. Lastly, typical applications, challenges, and future trends of AMC circuits will be discussed.
Subgroup discovery of Parkinson's Disease by utilizing a multi-modal smart device system

In recent years, sensors from smart consumer devices have shown great diagnostic potential in movement disorders. In this context, data modalities such as electronic questionnaires, hand movement and voice captures have successfully captured biomarkers and allowed discrimination between Parkinson's disease (PD) and healthy controls (HC) or differential diagnosis (DD). However, to the best of our knowledge, a comprehensive evaluation of assessments with a multi-modal smart device system has still been lacking. In a prospective study exploring PD, we used smartwatches and smartphones to collect multi-modal data from 504 participants, including PD patients, DD and HC. This study aims to assess the effect of multi-modal vs. single-modal data on PD vs. HC and PD vs. DD classification, as well as on PD group clustering for subgroup identification. We were able to show that by combining various modalities, classification accuracy improved and further PD clusters were discovered.
On the Capacity-Achieving Input of the Gaussian Channel With Polar Quantization

The polar receiver architecture is a receiver design that captures the envelope and phase information of the signal rather than its in-phase and quadrature components. Several studies have demonstrated the robustness of polar receivers to phase noise and other nonlinearities. Yet, the information-theoretic limits of polar receivers with finite-precision quantizers have not been investigated in the literature. The main contribution of this work is to identify the optimal signaling strategy for the additive white Gaussian noise (AWGN) channel with polar quantization at the output. More precisely, we show that the capacity-achieving modulation scheme has an amplitude phase shift keying (APSK) structure. Using this result, the capacity of the AWGN channel with polar quantization at the output is established by numerically optimizing the probability mass function of the amplitude. The capacity of the polar-quantized AWGN channel with $b_{1}$ -bit phase quantizer and optimized single-bit magnitude quantizer is also presented. Our numerical findings suggest the existence of signal-to-noise ratio (SNR) thresholds, above which the number of amplitude levels of the optimal APSK scheme and their respective probabilities change abruptly. Moreover, the manner in which the capacity-achieving input evolves with increasing SNR depends on the number of phase quantization bits.
The Mechanism of Prediction Head in Non-contrastive Self-supervised Learning

Recently the surprising discovery of the Bootstrap Your Own Latent (BYOL) method by Grill et al. shows the negative term in contrastive loss can be removed if we add the so-called prediction head to the network. This initiated the research of non-contrastive self-supervised learning. It is mysterious why even when there exist trivial collapsed global optimal solutions, neural networks trained by (stochastic) gradient descent can still learn competitive representations. This phenomenon is a typical example of implicit bias in deep learning and remains little understood. In this work, we present our empirical and theoretical discoveries on non-contrastive self-supervised learning. Empirically, we find that when the prediction head is initialized as an identity matrix with only its off-diagonal entries being trainable, the network can learn competitive representations even though the trivial optima still exist in the training objective. Theoretically, we present a framework to understand the behavior of the trainable, but identity-initialized prediction head. Under a simple setting, we characterized the substitution effect and acceleration effect of the prediction head. The substitution effect happens when learning the stronger features in some neurons can substitute for learning these features in other neurons through updating the prediction head. And the acceleration effect happens when the substituted features can accelerate the learning of other weaker features to prevent them from being ignored. These two effects enable the neural networks to learn all the features rather than focus only on learning the stronger features, which is likely the cause of the dimensional collapse phenomenon. To the best of our knowledge, this is also the first end-to-end optimization guarantee for non-contrastive methods using nonlinear neural networks with a trainable prediction head and normalization.
LSI: a learned secondary index structure

Learned index structures have been shown to achieve favorable lookup performance and space consumption compared to their traditional counterparts such as B-trees. However, most learned index studies have focused on the primary indexing setting, where the base data is sorted. In this work, we investigate whether learned indexes sustain their advantage in the secondary indexing setting. We introduce Learned Secondary Index (LSI), a first attempt to use learned indexes for indexing unsorted data. LSI works by building a learned index over a permutation vector, which allows binary search to performed on the unsorted base data using random access. We additionally augment LSI with a fingerprint vector to accelerate equality lookups. We show that LSI achieves comparable lookup performance to state-of-the-art secondary indexes while being up to 6x more space efficient.
Sparseloop: An Analytical Approach To Sparse Tensor Accelerator Modeling

In recent years, many accelerators have been proposed to efficiently process sparse tensor algebra applications (e.g., sparse neural networks). However, these proposals are single points in a large and diverse design space. The lack of systematic description and modeling support for these sparse tensor accelerators impedes hardware designers from efficient and effective design space exploration. This paper first presents a unified taxonomy to systematically describe the diverse sparse tensor accelerator design space. Based on the proposed taxonomy, it then introduces Sparseloop, the first fast, accurate, and flexible analytical modeling framework to enable early-stage evaluation and exploration of sparse tensor accelerators. Sparseloop comprehends a large set of architecture specifications, including various dataflows and sparse acceleration features (e.g., elimination of zero-based compute). Using these specifications, Sparseloop evaluates a design’s processing speed and energy efficiency while accounting for data movement and compute incurred by the employed dataflow, including the savings and overhead introduced by the sparse acceleration features using stochastic density models. Across representative accelerator designs and workloads, Sparseloop achieves over 2000× faster modeling speed than cycle-level simulations, maintains relative performance trends, and achieves 0.1% to 8% average error. The paper also presents example use cases of Sparseloop in different accelerator design flows to reveal important design insights.
Sharp turns in axion monodromy: primordial black holes and gravitational waves

Large turns in multifield inflation can lead to a very rich phenomenology, but are difficult to realise in supergravity, and typically require large field space curvatures. In this work, we present a mechanism to realise multiple sharp turns, and therefore strong non-geodesic trajectories, from transient violations of slow-roll without the requirement of large field space curvatures in supergravity inflation. Such turning rates can strongly source the adiabatic fluctuations, resulting in an enhanced scalar power spectrum with resonant features and a large peak amplitude. If the growth of the scalar power spectrum at small scales is large enough, primordial black holes can be produced in abundance. These large scalar fluctuations induce a characteristic large spectrum of gravitational waves for a wide range of frequencies, which inherits the resonant features. We illustrate this mechanism in a supergravity model of axion monodromy, which provides the first concrete model to realise such resonant features.The model can sustain inflation for around 60 e-folds, leading to considerable production of very light primordial black holes, and large gravitational wave spectra, which could be detectable by multiple upcoming gravitational wave surveys. For the set of parameter we consider, large oscillations occur at all scales. This represents a challenge for the model at large scales and motivates further investigation to reconcile this class of models with Planck data.
Concise tensors of minimal border rank

We determine defining equations for the set of concise tensors of minimal border rank in $${\mathbb {C}}^m{\mathord { \otimes } }{\mathbb {C}}^m{\mathord { \otimes } }{\mathbb {C}}^m$$ C m ⊗ C m ⊗ C m when $$m=5$$ m = 5 and the set of concise minimal border rank $$1_*$$ 1 ∗ -generic tensors when $$m=5,6$$ m = 5 , 6 . We solve the classical problem in algebraic complexity theory of classifying minimal border rank tensors in the special case $$m=5$$ m = 5 . Our proofs utilize two recent developments: the 111-equations defined by Buczyńska–Buczyński and results of Jelisiejew–Šivic on the variety of commuting matrices. We introduce a new algebraic invariant of a concise tensor, its 111-algebra, and exploit it to give a strengthening of Friedland’s normal form for 1-degenerate tensors satisfying Strassen’s equations. We use the 111-algebra to characterize wild minimal border rank tensors and classify them in $${\mathbb {C}}^5{\mathord { \otimes } }{\mathbb {C}}^5{\mathord { \otimes } }{\mathbb {C}}^5$$ C 5 ⊗ C 5 ⊗ C 5 .
This paper is included in the Proceedings of the 31st USENIX Security Symposium.

We demonstrate the ﬁrst downgrade attacks against RPKI. The key design property in RPKI that allows our attacks is the tradeoff between connectivity and security: when networks cannot retrieve RPKI information from publication points, they make routing decisions in BGP without validating RPKI. We exploit this tradeoff to develop attacks that prevent the retrieval of the RPKI objects from the public repositories, thereby disabling RPKI validation and exposing the RPKI-protected networks to preﬁx hijack attacks. We demonstrate experimentally that at least 47% of the public repositories are vulnerable against a speciﬁc version of our attacks, a rate-limiting off-path downgrade attack. We also show that all the current RPKI relying party implementations are vulnerable to attacks by a malicious publication point. This translates to 20.4% of the IPv4 address space. We provide recommendations for preventing our down-grade attacks. However, resolving the fundamental problem is not straightforward: if the relying parties prefer security over connectivity and insist on RPKI validation when ROAs cannot be retrieved, the victim AS may become disconnected from many more networks than just the one that the adversary wishes to hijack. Our work shows that the publication points are a critical infrastructure for Internet connectivity and security. Our main recommendation is therefore that the publication points should be hosted on robust platforms guar-anteeing a high degree of connectivity.
Infrared Line Emissions from Atoms and Atomic Ions in NGC 7027: Improved Wavelength Determinations for Infrared Metal Lines and a Probable Detection of Zn5+

An infrared L- and M-band spectral survey, performed toward the young planetary nebula NGC 7027 with the iSHELL instrument on NASA’s Infrared Telescope Facility (IRTF), has revealed more than 20 vibrational lines of the molecules HeH+, H2, and CH+ and more than 50 spectral lines of atoms and atomic ions. The present paper focuses on the atomic line emissions, the molecular lines having been discussed in two previous publications. The atomic lines detected with high confidence in the 2.951–5.24 μm region covered (incompletely) by this survey comprise (1) six collisionally excited lines of metal ions that had previously been identified in astrophysical nebulae but for which the present observations provide the most accurate wavelength determinations obtained to date; (2) a spectral line at 4.6895 μm, not previously reported, for which the probable identification is the 4 F 7/2–4 F 9/2 fine-structure transition of [Zn vi]; (3) 39 recombination lines of H and He+, with upper states of principal quantum number up to 38 (H) or 24 (He+); (4) 10 recombination lines of the multielectron species He, C2+, and C3+.
CURL: Continuous, Ultra-compact Representation for LiDAR

Increasing the density of the 3D LiDAR point cloud is appealing for many applications in robotics. However, high-density LiDAR sensors are usually costly and still limited to a level of coverage per scan (e.g., 128 channels). Meanwhile, denser point cloud scans and maps mean larger volumes to store and longer times to transmit. Existing works focus on either improving point cloud density or compressing its size. This paper aims to design a novel 3D point cloud representation that can continuously increase point cloud density while reducing its storage and transmitting size. The pipeline of the proposed Continuous, Ultra-compact Representation of LiDAR (CURL) includes four main steps: meshing, upsampling, encoding, and continuous reconstruction. It is capable of transforming a 3D LiDAR scan or map into a compact spherical harmonics representation which can be used or transmitted in low latency to continuously reconstruct a much denser 3D point cloud. Extensive experiments on four public datasets, covering college gardens, city streets, and indoor rooms, demonstrate that much denser 3D point clouds can be accurately reconstructed using the proposed CURL representation while achieving up to 80% storage space-saving. We open-source the CURL codes for the community.