Miracle-dz/UniFAR-FTUs huggingface dataset:
N-MPC for Deep Neural Network-Based Collision Avoidance exploiting Depth Images

This paper introduces a Nonlinear Model Predictive Control (N-MPC) framework exploiting a Deep Neural Network for processing onboard-captured depth images for collision avoidance in trajectory-tracking tasks with UAVs. The network is trained on simulated depth images to output a collision score for queried 3D points within the sensor field of view. Then, this network is translated into an algebraic symbolic equation and included in the N-MPC, explicitly constraining predicted positions to be collision-free throughout the receding horizon. The N-MPC achieves real time control of a UAV with a control frequency of 100Hz. The proposed framework is validated through statistical analysis of the collision classifier network, as well as Gazebo simulations and real experiments to assess the resulting capabilities of the N-MPC to effectively avoid collisions in cluttered environments. The associated code is released open-source along with the training images.
Can GNN be Good Adapter for LLMs?

Recently, large language models (LLMs) have demonstrated superior capabilities in understanding and zero-shot learning on textual data, promising significant advances for many text-related domains. In the graph domain, various real-world scenarios also involve textual data, where tasks and node features can be described by text. These text-attributed graphs (TAGs) have broad applications in social media, recommendation systems, etc. Thus, this paper explores how to utilize LLMs to model TAGs. Previous methods for TAG modeling are based on million-scale LMs. When scaled up to billion-scale LLMs, they face huge challenges in computational costs. Additionally, they also ignore the zero-shot inference capabilities of LLMs. Therefore, we propose GraphAdapter, which uses a graph neural network (GNN) as an efficient adapter in collaboration with LLMs to tackle TAGs. In terms of efficiency, the GNN adapter introduces only a few trainable parameters and can be trained with low computation costs. The entire framework is trained using auto-regression on node text (next token prediction). Once trained, GraphAdapter can be seamlessly fine-tuned with task-specific prompts for various downstream tasks. Through extensive experiments across multiple real-world TAGs, GraphAdapter based on Llama 2 gains an average improvement of approximately 5\% in terms of node classification. Furthermore, GraphAdapter can also adapt to other language models, including RoBERTa, GPT-2. The promising results demonstrate that GNNs can serve as effective adapters for LLMs in TAG modeling.
Dynamic Pricing and Learning with Long-term Reference Effects

We consider a dynamic pricing problem where customer response to the current price is impacted by the customer price expectation, aka reference price. We study a simple and novel reference price mechanism where reference price is the average of the past prices offered by the seller. As opposed to the more commonly studied exponential smoothing mechanism, in our reference price mechanism the prices offered by seller have a longer term effect on the future customer expectations. We show that under this mechanism, a markdown policy is near-optimal irrespective of the parameters of the model. This matches the common intuition that a seller may be better off by starting with a higher price and then decreasing it, as the customers feel like they are getting bargains on items that are ordinarily more expensive. For linear demand models, we also provide a detailed characterization of the near-optimal markdown policy along with an efficient way of computing it. We then consider a more challenging dynamic pricing and learning problem, where the demand model parameters are apriori unknown, and the seller needs to learn them online from the customers' responses to the offered prices while simultaneously optimizing revenue. The objective is to minimize regret, i.e., the $T$-round revenue loss compared to a clairvoyant optimal policy. This task essentially amounts to learning a non-stationary optimal policy in a time-variant Markov Decision Process (MDP). For linear demand models, we provide an efficient learning algorithm with an optimal $\tilde{O}(\sqrt{T})$ regret upper bound.
Advancing Large Language Models to Capture Varied Speaking Styles and Respond Properly in Spoken Conversations

In spoken dialogue, even if two current turns are the same sentence, their responses might still differ when they are spoken in different styles. The spoken styles, containing paralinguistic and prosodic information, mark the most significant difference between text and speech modality. When using text-only LLMs to model spoken dialogue, text-only LLMs cannot give different responses based on the speaking style of the current turn. In this paper, we focus on enabling LLMs to listen to the speaking styles and respond properly. Our goal is to teach the LLM that"even if the sentences are identical if they are spoken in different styles, their corresponding responses might be different". Since there is no suitable dataset for achieving this goal, we collect a speech-to-speech dataset, StyleTalk, with the following desired characteristics: when two current speeches have the same content but are spoken in different styles, their responses will be different. To teach LLMs to understand and respond properly to the speaking styles, we propose the Spoken-LLM framework that can model the linguistic content and the speaking styles. We train Spoken-LLM using the StyleTalk dataset and devise a two-stage training pipeline to help the Spoken-LLM better learn the speaking styles. Based on extensive experiments, we show that Spoken-LLM outperforms text-only baselines and prior speech LLMs methods.
APT-MMF: An advanced persistent threat actor attribution method based on multimodal and multilevel feature fusion

Threat actor attribution is a crucial defense strategy for combating advanced persistent threats (APTs). Cyber threat intelligence (CTI), which involves analyzing multisource heterogeneous data from APTs, plays an important role in APT actor attribution. The current attribution methods extract features from different CTI perspectives and employ machine learning models to classify CTI reports according to their threat actors. However, these methods usually extract only one kind of feature and ignore heterogeneous information, especially the attributes and relations of indicators of compromise (IOCs), which form the core of CTI. To address these problems, we propose an APT actor attribution method based on multimodal and multilevel feature fusion (APT-MMF). First, we leverage a heterogeneous attributed graph to characterize APT reports and their IOC information. Then, we extract and fuse multimodal features, including attribute type features, natural language text features and topological relationship features, to construct comprehensive node representations. Furthermore, we design multilevel heterogeneous graph attention networks to learn the deep hidden features of APT report nodes; these networks integrate IOC type-level, metapath-based neighbor node-level, and metapath semantic-level attention. Utilizing multisource threat intelligence, we construct a heterogeneous attributed graph dataset for verification purposes. The experimental results show that our method not only outperforms the existing methods but also demonstrates its good interpretability for attribution analysis tasks.
Tensor Completion with BMD Factor Nuclear Norm Minimization

This paper is concerned with the problem of recovering third-order tensor data from limited samples. A recently proposed tensor decomposition (BMD) method has been shown to efficiently compress third-order spatiotemporal data. Using the BMD, we formulate a slicewise nuclear norm penalized algorithm to recover a third-order tensor from limited observed samples. We develop an efficient alternating direction method of multipliers (ADMM) scheme to solve the resulting minimization problem. Experimental results on real data show our method to give reconstruction comparable to those of HaLRTC (Liu et al., IEEE Trans Ptrn Anal Mchn Int, 2012), a well-known tensor completion method, in about the same number of iterations. However, our method has the advantage of smaller subproblems and higher parallelizability per iteration.
Skipper-in-CMOS: Non-Destructive Readout with Sub-Electron Noise Performance for Pixel Detectors

The Skipper-in-CMOS image sensor integrates the non-destructive readout capability of Skipper Charge Coupled Devices (Skipper-CCDs) with the high conversion gain of a pinned photodiode in a CMOS imaging process, while taking advantage of in-pixel signal processing. This allows both single photon counting as well as high frame rate readout through highly parallel processing. The first results obtained from a 15 x 15 um^2 pixel cell of a Skipper-in-CMOS sensor fabricated in Tower Semiconductor's commercial 180 nm CMOS Image Sensor process are presented. Measurements confirm the expected reduction of the readout noise with the number of samples down to deep sub-electron noise of 0.15rms e-, demonstrating the charge transfer operation from the pinned photodiode and the single photon counting operation when the sensor is exposed to light. The article also discusses new testing strategies employed for its operation and characterization.
Triglobal resolvent-analysis-based control of separated flows around low-aspect-ratio wings

We perform direct numerical simulations (DNS) of actively controlled laminar separated wakes around low-aspect-ratio wings with two primary goals: (i) reducing the size of the separation bubble and (ii) attenuating the wing tip vortex. Instead of preventing separation, we aim to modify the three-dimensional ($3$-D) dynamics to exploit wake vortices for aerodynamic enhancements. A direct wake modification is considered using optimal harmonic forcing modes from triglobal resolvent analysis. For this study, we consider wings at angles of attack of $14^\circ$ and $22^\circ$, taper ratios $0.27$ and $1$, and leading edge sweep angles of $0^\circ$ and $30^\circ$, at a mean-chord-based Reynolds number of $600$. The wakes behind these wings exhibit $3$-D reversed-flow bubble and large-scale vortical structures. For swept and tapered wings, the diversity of wake vortices increases substantially, posing a challenge for flow control. To achieve the first control objective, a root-based actuation at the shedding frequency reduces the reversed-flow bubble size and capitalizes on wake vortices to significantly enhance the aerodynamic performance of the wing. For tapered and swept wings, actuation modifies the stalled flow, increasing the root contribution to lift. For the goal of controlling the tip vortex, we demonstrate the effectiveness of actuation with high-frequency perturbations near the tip. This study shows how insights from resolvent analysis for unsteady actuation lead to the global modification of $3$-D separated wakes and improved aerodynamics of wings.
Almost-Tight Bounds on Preserving Cuts in Classes of Submodular Hypergraphs

Recently, a number of variants of the notion of cut-preserving hypergraph sparsification have been studied in the literature. These variants include directed hypergraph sparsification, submodular hypergraph sparsification, general notions of approximation including spectral approximations, and more general notions like sketching that can answer cut queries using more general data structures than just sparsifiers. In this work, we provide reductions between these different variants of hypergraph sparsification and establish new upper and lower bounds on the space complexity of preserving their cuts. At a high level, our results use the same general principle, namely, by showing that cuts in one class of hypergraphs can be simulated by cuts in a simpler class of hypergraphs, we can leverage sparsification results for the simpler class of hypergraphs.
Understanding Difference-in-differences methods to evaluate policy effects with staggered adoption: an application to Medicaid and HIV

While a randomized control trial is considered the gold standard for estimating causal treatment effects, there are many research settings in which randomization is infeasible or unethical. In such cases, researchers rely on analytical methods for observational data to explore causal relationships. Difference-in-differences (DID) is one such method that, most commonly, estimates a difference in some mean outcome in a group before and after the implementation of an intervention or policy and compares this with a control group followed over the same time (i.e., a group that did not implement the intervention or policy). Although DID modeling approaches have been gaining popularity in public health research, the majority of these approaches and their extensions are developed and shared within the economics literature. While extensions of DID modeling approaches may be straightforward to apply to observational data in any field, the complexities and assumptions involved in newer approaches are often misunderstood. In this paper, we focus on recent extensions of the DID method and their relationships to linear models in the setting of staggered treatment adoption over multiple years. We detail the identification and estimation of the average treatment effect among the treated using potential outcomes notation, highlighting the assumptions necessary to produce valid estimates. These concepts are described within the context of Medicaid expansion and retention in care among people living with HIV (PWH) in the United States. While each DID approach is potentially valid, understanding their different assumptions and choosing an appropriate method can have important implications for policy-makers, funders, and public health as a whole.
Density and unitarity of the Burau representation from a non-semisimple TQFT

We study the density of the Burau representation from the perspective of a non-semisimple TQFT at a fourth root of unity. This gives a TQFT construction of Squier's Hermitian form on the Burau representation with possibly mixed signature. We prove that the image of the braid group in the space of possibly indefinite unitary representations is dense. We also argue for the potential applications of non-semisimple TQFTs toward topological quantum computation.
Sedimentation dynamics of triply-twisted M\"obius bands: Geometry versus topology

Chiral objects have intrigued scientists across several disciplines, including mathematics, crystallography, chemistry, and biology. A M\"obius band, an emblematic chiral structure, can be made by connecting the ends of a strip after applying an odd number of twists. Traditionally, the direction of the twist governs its rotational behaviour during sedimentation in a fluid. Here, we present experimental and computational investigations of triply-twisted M\"obius bands boasting threefold rotational symmetry that challenge this prevailing understanding. We explore three types of bands with different curvatures, each defined by its construction method. Experimental observations reveal that all three types of bands align axially and exhibit rotational motion during sedimentation. Surprisingly, for only one type of band the spinning direction (chiral hydrodynamic response) departs from expectations; it is not solely determined by the twist direction but changes with the aspect ratio of the band. Numerical simulations corroborate this observation, and an in-depth analysis of the resistance tensors of each type of band sheds light on the possible causes of this transition. We propose that modifications in fluid-induced drag, combined with inertial effects, underpin this phenomenon. Our study challenges existing knowledge of chiral object hydrodynamics, enriching our understanding of complex fluid dynamics. Moreover, it offers transformative potential across diverse fields, promising advancements in mixing, separation processes, and innovative passive swimmers.
Distance Recoloring

Coloring a graph is a well known problem and used in many different contexts. Here we want to assign $k \geq 1$ colors to each vertex of a graph $G$ such that each edge has two different colors at each endpoint. Such a vertex-coloring, if exists, is called a feasible coloring of $G$. \textsc{Distance Coloring} is an extension to the standard \textsc{Coloring} problem. Here we want to enforce that every pair of distinct vertices of distance less than or equal to $d$ have different colors, for integers $d \geq 1$ and $k \geq d+1$. Reconfiguration problems ask if two given configurations can be transformed into each other with certain rules. For example, the well-known \textsc{Coloring Reconfiguration} asks if there is a way to change one vertex's color at a time, starting from a feasible given coloring $\alpha$ of a graph $G$ to reach another feasible given coloring $\beta$ of $G$, such that all intermediate colorings are also feasible. In this paper, we study the reconfiguration of distance colorings on certain graph classes. We show that even for planar, bipartite, and $2$-degenerate graphs, reconfiguring distance colorings is $\mathsf{PSPACE}$-complete for $d \geq 2$ and $k = \Omega(d^2)$ via a reduction from the well-known \textsc{Sliding Tokens} problem. Additionally, we show that the problem on split graphs remains $\mathsf{PSPACE}$-complete when $d = 2$ and large $k$ but can be solved in polynomial time when $d \geq 3$ and $k \geq d+1$, and design a quadratic-time algorithm to solve the problem on paths for any $d \geq 2$ and $k \geq d+1$.
Antiferromagnetic Chern insulator with large charge gap in heavy transition-metal compounds

Despite the discovery of multiple intrinsic magnetic topological insulators in recent years the observation of Chern insulators is still restricted to very low temperatures due to the negligible charge gaps. Here, we uncover the potential of heavy transition-metal compounds for realizing a collinear antiferromagnetic Chern insulator (AFCI) with a charge gap as large as 300 meV. Our analysis relies on the Kane-Mele-Kondo model with a ferromagnetic Hund coupling $J_{\rm H}$ between the spins of itinerant electrons and the localized spins of size $S$. We show that a spin-orbit coupling $\lambda_{\rm SO} \gtrsim 0.03t$, where $t$ is the nearest-neighbor hopping element, is already large enough to stabilize an AFCI provided the alternating sublattice potential $\delta$ is in the range $\delta \approx SJ_{\rm H}$. We establish a remarkable increase in the charge gap upon increasing $\lambda_{\rm SO}$ in the AFCI phase. Using our results we explain the collinear AFCI recently found in monolayers of CrO and MoO with charge gaps of 1 and $50$ meV, respectively. In addition, we propose bilayers of heavy transition-metal oxides of perovskite structure as candidates to realize a room-temperature AFCI if grown along the $[111]$ direction and subjected to a perpendicular electric field.
Emulating the interstellar medium chemistry with neural operators

The study of galaxy formation and evolution critically depends on our understanding of the complex photo-chemical processes that govern the evolution and thermodynamics of the interstellar medium (ISM). In a computational sense, resolving the chemistry is among the weightiest tasks in cosmological and astrophysical simulations. Astrophysical simulations can include photo-chemical models that allow for a wide range of densities ($n$), abundances of different species ($n_i/n$) and temperature ($T$), and plausible evolution scenarios of the ISM under the action of a radiation field ($ F $) with different spectral shapes and intensities. The evolution of such a non-equilibrium photo-chemical network relies on implicit, precise, computationally costly, ordinary differential equations (ODE) solvers. Here, we aim to substitute such procedural solvers with fast, pre-trained emulators based on neural operators. We emulated a non-equilibrium chemical network up to H$_2$ formation (9 species, 52 reactions) by adopting the DeepONet formalism, namely: by splitting the ODE solver operator that maps the initial conditions and time evolution into a tensor product of two neural networks (named branch and trunk). We used $ KROME $ to generate a training set, spanning $-2 K < 0$, and adopting an incident radiation field, $ F $ sampled in ten energy bins with a continuity prior. We separately trained the solver for $T$ and each $n_i$ for $ GPUhrs$. Compared with the reference solutions obtained by $ KROME $ for single-zone models, the typical precision obtained is of the order of $10^ $, that is, it is 10 times better when using a training that is 40 times less costly, with respect to previous emulators that only considered a fixed $ F $. DeepONet also performs well for $T$ and $n_i$ outside the range of the training sample. Furthermore, the emulator aptly reproduces the ion and temperature profiles of photo dissociation regions as well; namely, by giving errors that are comparable to the typical difference between various photo-ionization codes. The present model achieves a speed-up of a factor of $128 with respect to stiff ODE solvers. Our neural emulator represents a significant leap forward in the modelling of ISM chemistry, offering a good balance of precision, versatility, and computational efficiency. Nevertheless, further work is required to address the challenges represented by the extrapolation beyond the training time domain and the removal of potential outliers.
Two Formulas for the Number of Lines on Complex Projective Hypersurfaces

Two formulas for the classical number $C_n$ of lines on a generic hypersurface of degree $2n-3$ in $\mathbb{CP}^n$ are obtained which differ from the formulas by Dominici, Harris, Libgober, and van der Waerden-Zagier. We review the splitting principle computation by Harris obtaining a similar general closed-form formula in terms of the Catalan numbers and elementary symmetric polynomials. This in turn yields $C_n$ as a linear difference recursion relation of unbounded order. Thus, for the sequence of certain linear combinations of $C_n$, a simple generating function is found. Then, a result from random algebraic geometry by Basu, Lerario, Lundberg, and Peterson, that expresses these classical enumerative invariants as proportional to the Bombieri norm of particular polynomial determinants, yields another combinatorial expansion in terms of certain set compositions and block labeling counting. As an example, we compute this combinatorial interpretation for the cases of 27 lines on a cubic surface and 2875 lines on a quintic threefold. As an application, we reobtain the parity and asymptotic upper bound of the sequence. In an appendix, we generalize the splitting principle calculation to obtain a formula for the number of lines on a generic complete intersection.
Creating a Fine Grained Entity Type Taxonomy Using LLMs

In this study, we investigate the potential of GPT-4 and its advanced iteration, GPT-4 Turbo, in autonomously developing a detailed entity type taxonomy. Our objective is to construct a comprehensive taxonomy, starting from a broad classification of entity types - including objects, time, locations, organizations, events, actions, and subjects - similar to existing manually curated taxonomies. This classification is then progressively refined through iterative prompting techniques, leveraging GPT-4's internal knowledge base. The result is an extensive taxonomy comprising over 5000 nuanced entity types, which demonstrates remarkable quality upon subjective evaluation. We employed a straightforward yet effective prompting strategy, enabling the taxonomy to be dynamically expanded. The practical applications of this detailed taxonomy are diverse and significant. It facilitates the creation of new, more intricate branches through pattern-based combinations and notably enhances information extraction tasks, such as relation extraction and event argument extraction. Our methodology not only introduces an innovative approach to taxonomy creation but also opens new avenues for applying such taxonomies in various computational linguistics and AI-related fields.