Miracle-dz/UniFAR-FTUs huggingface dataset:
Open Set Wireless Signal Classification: Augmenting Deep Learning with Expert Feature Classifiers

In shared spectrum with multiple radio access technologies, wireless standard classification is vital for applications such as dynamic spectrum access (DSA) and wideband spectrum monitoring. However, interfering signals and the presence of unknown classes of signals can diminish classification accuracy. To reduce interference, signals can be isolated in time, frequency, and space, but the isolation process adds distortion that reduces the accuracy of deep learning classifiers. We find that the distortion can be partially mitigated by augmenting the classifier training data with the signal isolation steps. To address unknown signals, we propose an open set hybrid classifier, which combines deep learning and expert feature classifiers to leverage the reliability and explainability of expert feature classifiers and the lower computational complexity of deep learning classifiers. The hybrid classifier reduces the computational complexity by 2 to 7 times on average compared to the expert feature classifiers, while achieving an accuracy of 95% at 15 dB SNR for known signal classes. The hybrid classifier manages to detect unknown classes at nearly 100% accuracy, due to the robustness of the expert feature classifiers.
Simplified Markerless Stride Detection Pipeline (sMaSDP) for Surface EMG Segmentation

To diagnose mobility impairments and select appropriate physiotherapy, gait assessment studies are often recommended. These studies are usually conducted in confined clinical settings, which may feel foreign to a subject and affect their motivation, coordination, and overall mobility. Conducting gait studies in unconstrained natural settings instead, such as the subject’s Activities of Daily Life (ADL), could provide a more accurate assessment. To appropriately diagnose gait deficiencies, muscle activity should be recorded in parallel with typical kinematic studies. To achieve this, Electromyography (EMG) and kinematic are collected synchronously. Our protocol sMaSDP introduces a simplified markerless gait event detection pipeline for the segmentation of EMG signals via Inertial Measurement Unit (IMU) data, based on a publicly available dataset. This methodology intends to provide a simple, detailed sequence of processing steps for gait event detection via IMU and EMG, and serves as tutorial for beginners in unconstrained gait assessment studies. In an unconstrained gait experiment, 10 healthy subjects walk through a course designed to mimic everyday walking, with their kinematic and EMG data recorded, for a total of 20 trials. Five different walking modalities, such as level walking, ramp up/down, and staircase up/down are included. By segmenting and filtering the data, we generate an algorithm that detects heel-strike events, using a single IMU, and isolates EMG activity of gait cycles. Applicable to different datasets, sMaSDP was tested in healthy gait and gait data of Parkinson’s Disease (PD) patients. Using sMaSDP, we extracted muscle activity in healthy walking and identified heel-strike events in PD patient data. The algorithm parameters, such as expected velocity and cadence, are adjustable and can further improve the detection accuracy, and our emphasis on the wearable technologies makes this solution ideal for ADL gait studies.
Polarization-insensitive widely tunable wavelength converter using a single semiconductor optical amplifier

Summary form only given.Wavelength conversion is needed to avoid wavelength blocking in wavelength-routed optical networks. Wavelength conversion using four-wave-mixing (FWM) in a semiconductor optical amplifier (SOA) has the advantages of both transparency and wide tunability but suffers from polarization dependence. In this paper, we report a novel technique which combines a novel polarization diversity configuration (requiring only one SOA) for polarization insensitive wavelength conversion and broadband orthogonal pumps (BOP) to achieve a wide tuning range.
Generalized Langevin equation with shear flow and its fluctuation-dissipation theorems derived from a Caldeira-Leggett Hamiltonian.

We provide a first-principles derivation of the Langevin equation with shear flow and its corresponding fluctuation-dissipation theorems. Shear flow of simple fluids has been widely investigated by numerical simulations. Most studies postulate a Markovian Langevin equation with a simple shear drag term in the manner of Stokes. However, this choice has never been justified from first principles. We start from a particle-bath system described by a classical Caldeira-Leggett Hamiltonian modified by adding a term proportional to the strain-rate tensor according to Hoover's DOLLS method, and we derive a generalized Langevin equation for the sheared system. We then compute, analytically, the noise time-correlation functions in different regimes. Based on the intensity of the shear rate, we can distinguish between close-to-equilibrium and far-from-equilibrium states. According to the results presented here, the standard, simple, and Markovian form of the Langevin equation with shear flow postulated in the literature is valid only in the limit of extremely weak shear rates compared to the effective vibrational temperature of the bath. For even marginally higher shear rates, the (generalized) Langevin equation is strongly non-Markovian, and nontrivial fluctuation-dissipation theorems are derived.
Feature-based SpMV Performance Analysis on Contemporary Devices

The SpMV kernel is characterized by high performance variation per input matrix and computing platform. While GPUs were considered State-of-the-Art for SpMV, with the emergence of advanced multicore CPUs and low-power FPGA accelerators, we need to revisit its performance and energy efficiency. This paper provides a high-level SpMV performance analysis based on structural features of matrices related to common bottlenecks of memory-bandwidth intensity, low ILP, load imbalance and memory latency overheads. Towards this, we create a wide artificial matrix dataset that spans these features and study the performance of different storage formats in nine modern HPC platforms; five CPUs, three GPUs and an FPGA. After validating our proposed methodology using real-world matrices, we analyze our extensive experimental results and draw key insights on the competitiveness of different target architectures for SpMV and the impact of each feature/bottleneck on its performance.
Non-Stationary Bandits with Knapsack Problems with Advice

We consider a non-stationary Bandits with Knapsack problem. The outcome distribution at each time is scaled by a non-stationary quantity that signiﬁes changing demand volumes. Instead of studying settings with limited non-stationarity, we investigate how online predictions on the total demand volume Q allows us to improve our performance guarantees. We show that, without any prediction, any online algorithm incurs a linear-in-T regret. In contrast, with online predictions on Q , we propose an online algorithm that judiciously incorporates the predictions, and achieve regret bounds that depends on the accuracy of the predictions. These bounds are shown to be tight in settings when prediction accuracy improves across time. Our theoretical results are corroborated by our numerical ﬁndings.
Large time behaviour of a conservation law regularised by a Riesz-Feller operator: the sub-critical case

We study the large time behaviour of the solutions of a nonlocal regularisation of a scalar conservation law. This regularisation is given by a fractional derivative of order 1 + α , with α ∈ (0, 1), which is a Riesz-Feller operator. The nonlinear flux is given by the locally Lipschitz function ∣ u ∣^ q −1 u / q for q > 1. We show that in the sub-critical case, 1 < q < 1 + α , the large time behaviour is governed by the unique entropy solution of the scalar conservation law. Our proof adapts the proofs of the analogous results for the local case (where the regularisation is the Laplacian) and, more closely, the ones for the regularisation given by the fractional Laplacian with order larger than one, see L. I. Ignat and D. Stan (2018). The main difference is that our operator is not symmetric and its Fourier symbol is not real. We can also adapt the proof and obtain similar results for general Riesz-Feller operators.
Thermal stability of hairy black holes

We discuss thermodynamical stability for hairy black hole spacetimes, viewed as defects in the thermodynamical parameter space, taking into account the backreaction of a secondary hair onto the spacetime geometry, which is modified non trivially. We derive, in a model independent way, the conditions for the hairy black hole with the secondary hair to reach a stable thermal equilibrium with the heat bath. Specifically, if the scalar hair, induced by interactions of the matter fields with quadratic-curvature corrections, produces an inner horizon in the deformed geometry, a thermodynamically stable configuration will be reached with the black hole becoming extremal in its final stage. We also attempt to make some conjectures concerning the implications of this thermal stability for the existence of a minimum length in a quantum space time.
a near-optimal upper bound for the layer number of the grid

The number of steps required to exhaust a point set by iteratively removing the vertices of its convex hull is called the layer number of the point set. This article presents a short proof that the layer number of the grid { 1 , 2 , . , n } d is at most 14 dn 2 + 1 , signiﬁcantly improving the best known upper bound. We also prove a lower bound of 12 d ( n − 1) + 1 , which shows that the layer number of the grid is linear in d .
Diffusionless relaxation of half-skyrmion liquid, hexatic, and crystalline states in a chiral molecular crystal

Particles in a crowded environment exhibit slow anomalous diffusion, as seen in biological systems, polymer solutions, glass-forming liquids and granular materials, and our daily experiences. In crystals, constituent particles do not diffuse, but skyrmions and half-skyrmions, spatially localised quasiparticles, exhibit normal diffusion in dilute conditions. However, the cooperative dynamics and diffusion of (half-)skyrmions in liquid, hexatic, and crystalline phases are elusive. Here we show that the fusion and fission of half-skyrmions, not their diffusion, are responsible for the primary structural relaxation in these condensed phases. The diffusion, which contributes to the secondary structural relaxation, is suppressed by cages formed by surrounding half-skyrmions, whereas enhanced by Mermin-Wagner fluctuation characteristic to two-dimensional systems, leading to subdiffusive motion. Large displacement of half-skyrmions is locally induced by fusion-fission and bond-breaking between adjacent half-skyrmions. Furthermore, the motion of half-skyrmions couples with transverse and longitudinal sound wave excitation differently. Our result provides a qualitative difference in dynamical properties between skyrmion gaseous and condensed phases, suggesting the efficient manipulation of high-density (half-)skyrmions in molecular crystals, liquid crystals, and magnets.
Concept Algebra for Text-Controlled Vision Models

This paper concerns the control of text-guided generative models, where a user provides a natural language prompt and the model generates samples based on this input. Prompting is intuitive, general, and ﬂexible. However, there are signiﬁcant limitations: prompting can fail in surprising ways, and it is often unclear how to ﬁnd a prompt that will elicit some desired target behavior. A core difﬁculty for developing methods to overcome these issues is that failures are know-it-when-you-see-it—it’s hard to ﬁx bugs if you can’t state precisely what the model should have done! In this paper, we introduce a formalization of “what the user intended” in terms of latent concepts implicit to the data generating process that the model was trained on. This formalization allows us to identify some fundamental limitations of prompting. We then use the formalism to develop concept algebra to overcome these limitations. Concept algebra is a way of directly manipulating the concepts expressed in the output through algebraic operations on a suitably deﬁned representation of input prompts. We give examples using concept algebra to overcome limitations of prompting, including concept transfer through arithmetic, and concept nulliﬁcation through projection. Code available at https://github.com/zihao12/concept-algebra .
Network Analysis of Verbal Communications in the Novel the Master and Margarita by M. A. Bulgakov

A network analysis of the structure of verbal communications in one of the most popular Russian novels of the Soviet era The Master and Margarita by Bulgakov has been carried out. The structure of the novel is complex (a story within a story). Moreover, the real-world-characters and the other-world-characters are interacting in the novel. This complex and unusual composition makes the novel especially attractive for a network analysis. In our study, only paired verbal communications (conversations) between explicitly present and acting characters have been taken into account. Based on a character pair verbal communication matrix, a graph has been constructed, the vertices of which are the characters of the novel, while the edges correspond to the connections between them. Taking only paired verbal communications into account leads to the result, that the character network can be described by an ordinary, rather than a directed graph. Since the activity of the conversations was out of our intended scope, the edges have been given no weights. The largest connected component of the graph consists of 76 characters. Centralities, such as degree, betweenness, closeness, eigenvector, and assortativity coefficient were computed to characterize the network. The structure of the communities in the network was also analysed. In addition to the obvious large communities-the characters from the Yershalaim part of the novel and the characters of the Moscow part-the analysis also revealed a fine structure in the Moscow component. Using the analysis of centralities, a group of main characters has been detected. The central characters of the novel are Koroviev, Margarita, Bezdomny, Woland, Behemoth, Azazello, Bosoi, Warenukha, Master, and Levi Matthew.
Deviation frequencies of Brownian path property approximations

This case study proposes robustness quantifications of many classical sample path properties of Brownian motion in terms of the (mean) deviation frequencies along typical a.s. ~approximations. This includes L\'evy's construction of Brownian motion, the Kolmogorov-Chentsov (and the Kolmogorov-Totoki) continuity theorem, L\'evy's modulus of continuity, the Paley-Wiener-Zygmund theorem, the a.s. ~approximation of the quadratic variation as well as the laws of the iterated logarithm by Khinchin, Chung and Strassen, among others.
Finite projected entangled pair states for the Hubbard model

We adapt and optimize the projected-pair-entangled-state (PEPS) algorithm on finite lattices (fPEPS) for two-dimensional Hubbard models and apply the algorithm to the Hubbard model with nearest-neighbor hopping on a square lattice. In particular, we formulate the PEPS algorithm using projected entangled pair operators, incorporate SU(2) symmetry in all tensor indices, and optimize the PEPS using both iterative-diagonalization-based local bond optimization and gradient-based optimization of the PEPS. We discuss the performance and convergence of the algorithm for the Hubbard model on lattice sizes of up to 8x8 for PEPS states with U(1) symmetric bond dimensions of up to D = 8 and SU(2) symmetric bond dimensions of up to D = 6. Finally, we comment on the relative and overall efficiency of schemes for optimizing fPEPS.
Generalisations of Thompson's group V arising from purely infinite groupoids

We study a class of generalisations of Thompson's group $V$ arising naturally as topological full groups of purely infinite, minimal groupoids. In the process, we show that the derived subgroup of such a group is 2-generated whenever it is finitely generated and has no proper characters in full generality. We characterise this class of groupoids through a number of group-theoretic conditions on their full groups including vigor, the existence of suitable embeddings of $V$, and compressibility. We moreover give a complete abstract characterisation of those groups that arise as either topological full groups or derived subgroups of purely infinite, minimal groupoids. As an application, we describe all proper characters of the Brin-Higman-Thompson groups.
Layered State Discovery for Incremental Autonomous Exploration

We study the autonomous exploration (AX) problem proposed by Lim&Auer (2012). In this setting, the objective is to discover a set of $\epsilon$-optimal policies reaching a set $\mathcal{S}_L^{\rightarrow}$ of incrementally $L$-controllable states. We introduce a novel layered decomposition of the set of incrementally $L$-controllable states that is based on the iterative application of a state-expansion operator. We leverage these results to design Layered Autonomous Exploration (LAE), a novel algorithm for AX that attains a sample complexity of $\tilde{\mathcal{O}}(LS^{\rightarrow}_{L(1+\epsilon)}\Gamma_{L(1+\epsilon)} A \ln^{12}(S^{\rightarrow}_{L(1+\epsilon)})/\epsilon^2)$, where $S^{\rightarrow}_{L(1+\epsilon)}$ is the number of states that are incrementally $L(1+\epsilon)$-controllable, $A$ is the number of actions, and $\Gamma_{L(1+\epsilon)}$ is the branching factor of the transitions over such states. LAE improves over the algorithm of Tarbouriech et al. (2020a) by a factor of $L^2$ and it is the first algorithm for AX that works in a countably-infinite state space. Moreover, we show that, under a certain identifiability assumption, LAE achieves minimax-optimal sample complexity of $\tilde{\mathcal{O}}(LS^{\rightarrow}_{L}A\ln^{12}(S^{\rightarrow}_{L})/\epsilon^2)$, outperforming existing algorithms and matching for the first time the lower bound proved by Cai et al. (2022) up to logarithmic factors.
FFHR: Fully and Flexible Hyperbolic Representation for Knowledge Graph Completion

Learning hyperbolic embeddings for knowledge graph (KG) has gained increasing attention due to its superiority in capturing hierarchies. However, some important operations in hyperbolic space still lack good definitions, making existing methods unable to fully leverage the merits of hyperbolic space. Specifically, they suffer from two main limitations: 1) existing Graph Convolutional Network (GCN) methods in hyperbolic space rely on tangent space approximation, which would incur approximation error in representation learning, and 2) due to the lack of inner product operation definition in hyperbolic space, existing methods can only measure the plausibility of facts (links) with hyperbolic distance, which is difficult to capture complex data patterns. In this work, we contribute: 1) a Full Poincar\'{e} Multi-relational GCN that achieves graph information propagation in hyperbolic space without requiring any approximation, and 2) a hyperbolic generalization of Euclidean inner product that is beneficial to capture both hierarchical and complex patterns. On this basis, we further develop a \textbf{F}ully and \textbf{F}lexible \textbf{H}yperbolic \textbf{R}epresentation framework (\textbf{FFHR}) that is able to transfer recent Euclidean-based advances to hyperbolic space. We demonstrate it by instantiating FFHR with four representative KGC methods. Extensive experiments on benchmark datasets validate the superiority of our FFHRs over their Euclidean counterparts as well as state-of-the-art hyperbolic embedding methods.
Simulation of CH3OH ice UV photolysis under laboratory conditions

Methanol is the most complex molecule securely identified in interstellar ices and is a key chemical species for understanding chemical complexity in astrophysical environments. Important aspects of the methanol ice photochemistry are still unclear such as the branching ratios and photo-dissociation cross-sections at different temperatures and irradiation fluxes. This work aims at a quantitative agreement between laboratory experiments and astrochemical modelling of the CH3OH ice UV photolysis. This work also allows us to better understand which processes govern the methanol ice photochemistry present in laboratory experiments. We use ProDiMo to simulate the conditions of laboratory measurements. The simulations start with simple chemistry consisting only of methanol ice and helium to mimic the residual gas in the experimental chamber. A surface chemical network enlarged by photo-dissociation reactions is used to study the chemical reactions within the ice. Additionally, different surface chemistry parameters (surface competition, tunnelling, thermal diffusion and reactive desorption) are adopted to check those that reproduce the experimental results. The chemical models with ProDiMo can reproduce the methanol ice destruction via UV photodissociation at temperatures of 20, 30, 50 and 70 K as observed in the experiments. We note that the results are sensitive to different branching ratios after photolysis and to the mechanisms of reactive desorption. In the simulations of a molecular cloud at 20 K, we observed an increase in the methanol gas abundance of one order of magnitude, with a similar decrease in the solid-phase abundance. Comprehensive astrochemical models provide new insights into laboratory experiments as the quantitative understanding of the processes that govern the reactions within the ice. Ultimately, those insights can help to better interpret astronomical observations.
On the (linear) convergence of Generalized Newton Inexact ADMM

This paper presents GeNI-ADMM, a framework for large-scale composite convex optimization, that facilitates theoretical analysis of both existing and new approximate ADMM schemes. GeNI-ADMM encompasses any ADMM algorithm that solves a first- or second-order approximation to the ADMM subproblem inexactly. GeNI-ADMM exhibits the usual $\mathcal O(1/t)$-convergence rate under standard hypotheses and converges linearly under additional hypotheses such as strong convexity. Further, the GeNI-ADMM framework provides explicit convergence rates for ADMM variants accelerated with randomized linear algebra, such as NysADMM and sketch-and-solve ADMM, resolving an important open question on the convergence of these methods. This analysis quantifies the benefit of improved approximations and can inspire the design of new ADMM variants with faster convergence.
Zero-shot Sim2Real Adaptation Across Environments

Simulation based learning often provides a cost-efficient recourse to reinforcement learning applications in robotics. However, simulators are generally incapable of accurately replicating real-world dynamics, and thus bridging the sim2real gap is an important problem in simulation based learning. Current solutions to bridge the sim2real gap involve hybrid simulators that are augmented with neural residual models. Unfortunately, they require a separate residual model for each individual environment configuration (i.e., a fixed setting of environment variables such as mass, friction etc.), and thus are not transferable to new environments quickly. To address this issue, we propose a Reverse Action Transformation (RAT) policy which learns to imitate simulated policies in the real-world. Once learnt from a single environment, RAT can then be deployed on top of a Universal Policy Network to achieve zero-shot adaptation to new environments. We empirically evaluate our approach in a set of continuous control tasks and observe its advantage as a few-shot and zero-shot learner over competing baselines.