Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Resource Allocation and Configuration Design for STAR-RIS-Enhanced Wireless-Powered MEC

In this paper, a novel concept called simultaneously transmitting and reflecting RIS (STAR-RIS) is introduced into the wireless-powered mobile edge computing (MEC) systems to improve the efficiency of energy transfer and task offloading. Compared with traditional reflecting-only RIS, STAR-RIS extends the half-space coverage to full-space coverage by simultaneously transmitting and reflecting incident signals, and also provides new degrees-of-freedom (DoFs) for manipulating signal propagation. We aim to maximize the total computation rate of all users, where the energy transfer time, transmit power and CPU frequencies of users, and the configuration design of STAR-RIS are jointly optimized. Considering the characteristics of STAR-RIS, three operating protocols, namely energy splitting (ES), mode switching (MS), and time splitting (TS) are studied, respectively. For the ES protocol, based on the penalty method, successive convex approximation (SCA), and the linear search method, an iterative algorithm is proposed to solve the formulated non-convex problem. Then, the proposed algorithm for ES protocol is extended to solve the MS and TS problems. Simulation results illustrate that the STAR-RIS outperforms traditional reflecting/transmitting-only RIS. More importantly, the TS protocol can achieve the largest computation rate among the three operating protocols of STAR-RIS.
Image-Specific Information Suppression and Implicit Local Alignment for Text-based Person Search

Text-based person search (TBPS) is a challenging task that aims to search pedestrian images with the same identity from an image gallery given a query text. In recent years, TBPS has made remarkable progress, and state-of-the-art (SOTA) methods achieve superior performance by learning local fine-grained correspondence between images and texts. However, most existing methods rely on explicitly generated local parts to model fine-grained correspondence between modalities, which is unreliable due to the lack of contextual information or the potential introduction of noise. Moreover, the existing methods seldom consider the information inequality problem between modalities caused by image-specific information. To address these limitations, we propose an efficient joint multilevel alignment network (MANet) for TBPS, which can learn aligned image/text feature representations between modalities at multiple levels, and realize fast and effective person search. Specifically, we first design an image-specific information suppression (ISS) module, which suppresses image background and environmental factors by relation-guided localization (RGL) and channel attention filtration (CAF), respectively. This module effectively alleviates the information inequality problem and realizes the alignment of information volume between images and texts. Second, we propose an implicit local alignment (ILA) module to adaptively aggregate all pixel/word features of image/text to a set of modality-shared semantic topic centers and implicitly learn the local fine-grained correspondence between modalities without additional supervision and cross-modal interactions. Also, a global alignment (GA) is introduced as a supplement to the local perspective. The cooperation of global and local alignment modules enables better semantic alignment between modalities. Extensive experiments on multiple databases demonstrate the effectiveness and superiority of our MANet.
Value functional and optimal feedback control in linear-quadratic optimal control problem for fractional-order system

In this paper, a finite-horizon optimal control problem involving a dynamical system described by a linear Caputo fractional differential equation and a quadratic cost functional is considered. An explicit formula for the value functional is given, which includes a solution of a certain Fredholm integral equation. A step-by-step feedback control procedure for constructing $\varepsilon$-optimal controls with any accuracy $\varepsilon>0$ is proposed. The basis for obtaining these results is the study of a solution of the associated Hamilton-Jacobi-Bellman equation with so-called fractional coinvariant derivatives.
Anomaly Detection using Contrastive Normalizing Flows

Detecting test data deviating from training data is a central problem for safe and robust machine learning. Likelihoods learned by a generative model, e.g., a normalizing ﬂow via standard log-likelihood training, perform poorly as an anomaly score. We propose to use an unlabelled auxiliary dataset and a probabilistic outlier score for anomaly detection. We use a self-supervised feature extractor trained on the auxiliary dataset and train a normalizing ﬂow on the extracted features by maximizing the likelihood on in-distribution data and minimizing the likelihood on the auxiliary dataset. We show that this is equivalent to learning the normalized positive difference between the in-distribution and the auxiliary feature density. We conduct experiments on benchmark datasets and show a robust improvement compared to likelihood, likelihood ratio methods and state-of-the-art anomaly detection methods.
Identifying Latent Causal Content for Multi-Source Domain Adaptation

Multi-source domain adaptation (MSDA) learns to predict the labels in target domain data, under the setting that data from multiple source domains are labelled and data from the target domain are unlabelled. Most methods for this task focus on learning invariant representations across domains. However, their success relies heavily on the assumption that the label distribution remains consistent across domains, which may not hold in general real-world problems. In this paper, we propose a new and more flexible assumption, termed \textit{latent covariate shift}, where a latent content variable $\mathbf{z}_c$ and a latent style variable $\mathbf{z}_s$ are introduced in the generative process, with the marginal distribution of $\mathbf{z}_c$ changing across domains and the conditional distribution of the label given $\mathbf{z}_c$ remaining invariant across domains. We show that although (completely) identifying the proposed latent causal model is challenging, the latent content variable can be identified up to scaling by using its dependence with labels from source domains, together with the identifiability conditions of nonlinear ICA. This motivates us to propose a novel method for MSDA, which learns the invariant label distribution conditional on the latent content variable, instead of learning invariant representations. Empirical evaluation on simulation and real data demonstrates the effectiveness of the proposed method.
Discrete Monitored Barrier Options: by Finite Difference Schemes

The paper is devoted to pricing options characterized by discontinuities in the terminal condition. Finite difference schemes are examined to highlight how discontinuities can generate numerical drawbacks such as spurious oscillations. The proposed schemes are free of spurious oscillations and satisfy both the positivity requirement and maximum principle, as demanded for the financial and diffusive solution to the original Black-Scholes partial differential equation.
ZEUS: An Experimental Toolkit for Evaluating Congestion Control Algorithms in 5G Environments

As global cellular networks converge to 5G, one question lingers: Are we ready for the 5G challenge? A growing concern surrounds how well do existing congestion control algorithms perform in diverse 5G networks. Given that 5G networks are not yet widely deployed, assessing the performance of existing congestion control algorithms in realistic 5G settings presents several challenges. Moreover, existing network simulation and emulation environments are also not ideally suited to address the unique challenges of 5G network environments. Therefore, building a simple and easily accessible platform becomes crucial to allow testing and comparison of congestion control algorithms under different testing conditions. This paper makes two main contributions. First, we present Zeus, an open-source testbed that emulates 5G channels to evaluate congestion control algorithms in a repeatable and reproducible manner. Second, we assess and characterize ten of today's state-of-the-art congestion control algorithms under different 5G environments and show the difficulties of these solutions to achieve high performance under highly varying 5G channel conditions. In addition, we also utilize the recently proposed harm metric to characterize the detriment done by such algorithms to TCP Cubic cross traffic in 5G environments.
Spaces of states of the two-dimensional $O(n)$ and Potts models

We determine the spaces of states of the two-dimensional O(n)O(n) and QQ-state Potts models with generic parameters n,Q\in \mathbb{C}n,Q∈ℂ as representations of their known symmetry algebras. While the relevant representations of the conformal algebra were recently worked out, it remained to determine the action of the global symmetry groups: the orthogonal group for the O(n)O(n) model, and the symmetric group S_QSQ for the QQ-state Potts model. We do this by two independent methods. First we compute the twisted torus partition functions of the models at criticality. The twist in question is the insertion of a group element along one cycle of the torus: this breaks modular invariance, but allows the partition function to have a unique decomposition into characters of irreducible representations of the global symmetry group. Our second method reduces the problem to determining branching rules of certain diagram algebras. For the O(n)O(n) model, we decompose representations of the Brauer algebra into representations of its unoriented Jones-Temperley-Lieb subalgebra. For the QQ-state Potts model, we decompose representations of the partition algebra into representations of the appropriate subalgebra. We find explicit expressions for these decompositions as sums over certain sets of diagrams, and over standard Young tableaux. We check that both methods agree in many cases. Moreover, our spaces of states are consistent with recent bootstrap results on four-point functions of the corresponding CFTs.
On Temporal Isolation Assessment in Virtualized Railway Signaling as a Service Systems

Railway signaling systems provide numerous critical functions at different safety level, to correctly implement the entire transport ecosystem. Today, we are witnessing the increasing use of the cloud and virtualization technologies in such mixed-criticality systems, with the main goal of reducing costs, improving reliability, while providing orchestration capabilities. Unfortunately, virtualization includes several issues for assessing temporal isolation, which is critical for safety-related standards like EN50128. In this short paper, we envision leveraging the real-time flavor of a general-purpose hypervisor, like Xen, to build the Railway Signaling as a Service (RSaaS) systems of the future. We provide a preliminary background, highlighting the need for a systematic evaluation of the temporal isolation to demonstrate the feasibility of using general-purpose hypervisors in the safety-critical context for certification purposes.
Debiasing Word Embeddings with Nonlinear Geometry

Debiasing word embeddings has been largely limited to individual and independent social categories. However, real-world corpora typically present multiple social categories that possibly correlate or intersect with each other. For instance, “hair weaves” is stereotypically associated with African American females, but neither African American nor females alone. Therefore, this work studies biases associated with multiple social categories: joint biases induced by the union of different categories and intersectional biases that do not overlap with the biases of the constituent categories. We first empirically observe that individual biases intersect non-trivially (i.e., over a one-dimensional subspace). Drawing from the intersectional theory in social science and the linguistic theory, we then construct an intersectional subspace to debias for multiple social categories using the nonlinear geometry of individual biases. Empirical evaluations corroborate the efficacy of our approach.
Call and Response: A Time-resolved Study of Chromospheric Evaporation in a Large Solar Flare

We studied an X1.6 solar flare produced by NOAA Active Region 12602 on 2014 October 22. The entirety of this event was covered by RHESSI, IRIS, and Hinode/EIS, allowing analysis of the chromospheric response to a nonthermal electron driver. We derived the energy contained in nonthermal electrons via RHESSI spectral fitting and linked the time-dependent parameters of this call to the response in Doppler velocity, density, and nonthermal width across a broad temperature range. The total energy injected was 4.8 × 1030 erg and lasted 352 s. This energy drove explosive chromospheric evaporation, with a delineation in both Doppler and nonthermal velocities at the flow reversal temperature, between 1.35 and 1.82 MK. The time of peak electron injection (14:06 UT) corresponded to the time of highest velocities. At this time, we found 200 km s−1 blueshifts in the core of Fe xxiv, which is typically assumed to be at rest. Shortly before this time, the nonthermal electron population had the shallowest spectral index (≈6), corresponding to the peak nonthermal velocity in Si iv and Fe xxi. Nonthermal velocities in Fe xiv, formed near the flow reversal temperature, were low and not correlated with density or Doppler velocity. Nonthermal velocities in ions with similar temperatures were observed to increase and correlate with Doppler velocities, implying unresolved flows surrounding the flow reversal point. This study provides a comprehensive, time-resolved set of chromospheric diagnostics for a large X-class flare, along with a time-resolved energy injection profile, ideal for further modeling studies.
Probing the Extent of Vertical Mixing in Brown Dwarf Atmospheres with Disequilibrium Chemistry

Evidence of disequilibrium chemistry due to vertical mixing in the atmospheres of many T- and Y-dwarfs has been inferred due to enhanced mixing ratios of CO and reduced NH3. Atmospheric models of planets and brown dwarfs typically parameterize this vertical mixing phenomenon with the vertical eddy diffusion coefficient, K zz . While K zz can perhaps be approximated in the convective regions in the atmosphere with mixing length theory, in radiative regions, the strength of vertical mixing is uncertain by many orders of magnitude. With a new grid of self-consistent 1D model atmospheres from T eff of 400–1000 K, computed with a new radiative-convective equilibrium python code PICASO 3.0, we aim to assess how molecular abundances and corresponding spectra can be used as a probe of depth-dependent K zz . At a given surface gravity, we find nonmonotonic behavior in the CO abundance as a function of T eff, as chemical abundances are sometimes quenched in either of two potential atmospheric convective zones, or quenched in either of two possible radiative zones. The temperature structure and chemical quenching behavior also change with gravity. We compare our models with available near-infrared and M-band spectroscopy of several T- and Y-dwarfs and assess their atmospheric vertical mixing profiles. We also compare to color–magnitude diagrams and make predictions for James Webb Space Telescope spectra. This work yields new constraints, and points the way to significant future gains, in determining K zz , a fundamental atmospheric parameter in substellar atmospheres, with significant implications for chemistry and cloud modeling.
Identifying dominant industrial sectors in market states of the S&P 500 financial data

Understanding and forecasting changing market conditions in complex economic systems like the financial market is of great importance to various stakeholders such as financial institutions and regulatory agencies. Based on the finding that the dynamics of sector correlation matrices of the S&P 500 stock market can be described by a sequence of distinct states via a clustering algorithm, we try to identify the industrial sectors dominating the correlation structure of each state. For this purpose, we use a method from explainable artificial intelligence (XAI) on daily S&P 500 stock market data from 1992 to 2012 to assign relevance scores to every feature of each data point. To compare the significance of the features for the entire data set we develop an aggregation procedure and apply a Bayesian change point analysis to identify the most significant sector correlations. We show that the correlation matrix of each state is dominated only by a few sector correlations. Especially the energy and IT sector are identified as key factors in determining the state of the economy. Additionally we show that a reduced surrogate model, using only the eight sector correlations with the highest XAI-relevance, can replicate 90% of the cluster assignments. In general our findings imply an additional dimension reduction of the dynamics of the financial market.
Modeling Spatial Trajectories Using Coarse-Grained Smartphone Logs

Every user carries their smartphones wherever they go – a crucial aspect ignored by the current models for spatial recommendations. In detail, the current approaches learn the points-of-interest (POI) preferences of a user via the standard spatial features, i.e. the POI coordinates and the social network, and thus ignore the features related to the smartphone usage of a user. Moreover, with growing privacy concerns, users refrain from sharing their exact geographical coordinates as well as their social media activity. In this paper, we present ReVAMP, a sequential POI recommendation approach that uses smartphone app-usage logs to identify the mobility preferences of a user. Our work aligns with the recent psychological studies of online urban users which show that their spatial mobility behavior is largely influenced by the activity of their smartphone apps. Specifically, our proposal of coarse-grained data refers to data logs collected in a privacy-conscious manner consisting only of (a) category of smartphone app-used and (b) category of check-in location. Thus, ReVAMP is not privy to precise geo-coordinates, social networks, and the specific app being used. Buoyed by the efficacy of self-attention models, ReVAMP learns the POI preferences of a user using two forms of positional encodings – absolute and relative – with each extracted from the inter-check-in dynamics in the check-in sequence of a user. Extensive experiments across two large-scale datasets from China show that ReVAMP outperforms the state-of-the-art sequential POI recommendation approaches and can be extended to app- and POI-category prediction.
Gridless 3D Recovery of Image Sources From Room Impulse Responses

Given a sound field generated by a sparse distribution of impulse image sources, can the continuous 3D positions and amplitudes of these sources be recovered from discrete, band-limited measurements of the field at a finite set of locations, e.g., a multichannel room impulse response? Borrowing from recent advances in super-resolution imaging, it is shown that this non-linear, non-convex inverse problem can be efficiently relaxed into a convex linear inverse problem over the space of Radon measures in $\mathbb {R}^{3}$. The new linear operator introduced here stems from the fundamental solution of the wave equation combined with the receivers' responses. An adaptation of the Sliding Frank-Wolfe algorithm is proposed to numerically solve the problem off-the-grid, i.e., in continuous 3D space. Idealized simulated experiments show that the approach can recover hundreds of image sources at a rate and accuracy that are not achievable by previous methods, using a compact microphone array and source placed at random in random-sized shoe-box rooms. The impact of noise, sampling rate and array diameter on these results is also examined.
Regret-free truth-telling voting rules

We study the ability of different classes of voting rules to induce agents to report their preferences truthfully, if agents want to avoid regret. First, we show that regret-free truth-telling is equivalent to strategy-proofness among tops-only rules. Then, we focus on three important families of (non-tops-only) voting methods: maxmin, scoring, and Condorcet consistent ones. We prove positive and negative results for both neutral and anonymous versions of maxmin and scoring rules. In several instances we provide necessary and sufficient conditions. We also show that Condorcet consistent rules that satisfy a mild monotonicity requirement are not regret-free truth-telling. Successive elimination rules fail to be regret-free truth-telling despite not satisfying the monotonicity condition. Lastly, we provide two characterizations for the case of three alternatives and two agents.
Star Formation Suppression by Tidal Removal of Cold Molecular Gas from an Intermediate-redshift Massive Post-starburst Galaxy

Observations and simulations have demonstrated that star formation in galaxies must be actively suppressed to prevent the formation of overly massive galaxies. Galactic outflows driven by stellar feedback or supermassive black hole accretion are often invoked to regulate the amount of cold molecular gas available for future star formation but may not be the only relevant quenching processes in all galaxies. We present the discovery of vast molecular tidal features extending up to 64 kpc outside of a massive z = 0.646 post-starburst galaxy that recently concluded its primary star-forming episode. The tidal tails contain (1.2 ± 0.1) × 1010 M ⊙ of molecular gas, 47% ± 5% of the total cold gas reservoir of the system. Both the scale and magnitude of the molecular tidal features are unprecedented compared to all known nearby or high-redshift merging systems. We infer that the cold gas was stripped from the host galaxies during the merger, which is most likely responsible for triggering the initial burst phase and the subsequent suppression of star formation. While only a single example, this result shows that galaxy mergers can regulate the cold gas contents in distant galaxies by directly removing a large fraction of the molecular gas fuel, and plausibly suppress star formation directly, a qualitatively different physical mechanism than feedback-driven outflows.
Long-term behaviour of space radiometers

Experience with the two types of radiometers in the experiment VIRGO on the ESA/NASA mission SOHO launched in December 1995 has revealed important information about the long-term behaviour of such radiometers in space. Exposure-dependent changes can be determined from comparison of radiometers with different periods of exposure to the sun, as was done for, for example, the ACRIM series. The physical mechanisms for the long-term changes are not yet well understood. However, with the available data a model is developed which describes the temporal behaviour quite well and allows some inference of the mechanisms involved. The direct comparison of the two types of radiometers, after they have been individually corrected for the exposure-dependent part, reveals changes that are independent of the exposure to solar radiation. From this comparison an upper limit for the uncertainty of the long-term changes can be deduced. The uncertainty of the composite TSI depends mainly on the uncertainty of the tracing of ACRIM II to I, which amounts, together with the uncertainty of the HF correction during the gap between the two ACRIM experiments, to ±62 ppm (i.e. 62×10−6). Together with the result from a comparison with the independent record of ERBE the long-term uncertainty for the whole record from 1978 to present is estimated to ±85 ppm.