Miracle-dz/UniFAR-FTUs huggingface dataset:
Entanglement growth from squeezing on the MPS manifold

Finding suitable characterizations of quantum chaos is a major challenge in many-body physics, with a central difficulty posed by the linearity of the Schr\"odinger equation. A possible solution for recovering non-linearity is to project the dynamics onto some variational manifold. The classical chaos induced via this procedure may be used as a signature of quantum chaos in the full Hilbert space. Here, we demonstrate analytically a previously heuristic connection between the Lyapunov spectrum from projection onto the matrix product state (MPS) manifold and the growth of entanglement. This growth occurs by squeezing a localized distribution on the variational manifold. The process qualitatively resembles the Cardy-Calabrese picture, where local perturbations to a moving MPS reference are interpreted as bosonic quasi-particles. Taking careful account of the number of distinct channels for these processes recovers the connection to the Lyapunov spectrum. Our results rigorously establish the physical significance of the projected Lyapunov spectrum, suggesting it as an alternative method of characterizing chaos in quantum many-body systems, one that is manifestly linked to classical chaos.
Theoretical Analysis of Explicit Averaging and Novel Sign Averaging in Comparison-Based Search

In black-box optimization, noise in the objective function is inevitable. Noise disrupts the ranking of candidate solutions in comparison-based optimization, possibly deteriorating the search performance compared with a noiseless scenario. Explicit averaging takes the sample average of noisy objective function values and is widely used as a simple and versatile noise-handling technique. Although it is suitable for various applications, it is ineffective if the mean is not finite. We theoretically reveal that explicit averaging has a negative effect on the estimation of ground-truth rankings when assuming stably distributed noise without a finite mean. Alternatively, sign averaging is proposed as a simple but robust noise-handling technique. We theoretically prove that the sign averaging estimates the order of the medians of the noisy objective function values of a pair of points with arbitrarily high probability as the number of samples increases. Its advantages over explicit averaging and its robustness are also confirmed through numerical experiments.
Perceptual-oriented Learned Image Compression with Dynamic Kernel

In this paper, we extend our prior research named DKIC and propose the perceptual-oriented learned image compression method, PO-DKIC. Specifically, DKIC adopts a dynamic kernel-based dynamic residual block group to enhance the transform coding and an asymmetric space-channel context entropy model to facilitate the estimation of gaussian parameters. Based on DKIC, PO-DKIC introduces PatchGAN and LPIPS loss to enhance visual quality. Furthermore, to maximize the overall perceptual quality under a rate constraint, we formulate this challenge into a constrained programming problem and use the Linear Integer Programming method for resolution. The experiments demonstrate that our proposed method can generate realistic images with richer textures and finer details when compared to state-of-the-art image compression techniques.
Minimum Covariance Determinant: Spectral Embedding and Subset Size Determination

This paper introduces several enhancements to the minimum covariance determinant method of outlier detection and robust estimation of means and covariances. We leverage the principal component transform to achieve dimension reduction and ultimately better analyses. Our best subset selection algorithm strategically combines statistical depth and concentration steps. To ascertain the appropriate subset size and number of principal components, we introduce a bootstrap procedure that estimates the instability of the best subset algorithm. The parameter combination exhibiting minimal instability proves ideal for the purposes of outlier detection and robust estimation. Rigorous benchmarking against prominent MCD variants showcases our approach's superior statistical performance and computational speed in high dimensions. Application to a fruit spectra data set and a cancer genomics data set illustrates our claims.
Algorithmically Curated Lies: How Search Engines Handle Misinformation about US Biolabs in Ukraine

The growing volume of online content prompts the need for adopting algorithmic systems of information curation. These systems range from web search engines to recommender systems and are integral for helping users stay informed about important societal developments. However, unlike journalistic editing the algorithmic information curation systems (AICSs) are known to be subject to different forms of malperformance which make them vulnerable to possible manipulation. The risk of manipulation is particularly prominent in the case when AICSs have to deal with information about false claims that underpin propaganda campaigns of authoritarian regimes. Using as a case study of the Russian disinformation campaign concerning the US biolabs in Ukraine, we investigate how one of the most commonly used forms of AICSs - i.e. web search engines - curate misinformation-related content. For this aim, we conduct virtual agent-based algorithm audits of Google, Bing, and Yandex search outputs in June 2022. Our findings highlight the troubling performance of search engines. Even though some search engines, like Google, were less likely to return misinformation results, across all languages and locations, the three search engines still mentioned or promoted a considerable share of false content (33% on Google; 44% on Bing, and 70% on Yandex). We also find significant disparities in misinformation exposure based on the language of search, with all search engines presenting a higher number of false stories in Russian. Location matters as well with users from Germany being more likely to be exposed to search results promoting false information. These observations stress the possibility of AICSs being vulnerable to manipulation, in particular in the case of the unfolding propaganda campaigns, and underline the importance of monitoring performance of these systems to prevent it.
O(1) Insertion for Random Walk d-ary Cuckoo Hashing up to the Load Threshold

The random walk $d$-ary cuckoo hashing algorithm was defined by Fotakis, Pagh, Sanders, and Spirakis to generalize and improve upon the standard cuckoo hashing algorithm of Pagh and Rodler. Random walk $d$-ary cuckoo hashing has low space overhead, guaranteed fast access, and fast in practice insertion time. In this paper, we give a theoretical insertion time bound for this algorithm. More precisely, for every $d\ge 3$ hashes, let $c_d^*$ be the sharp threshold for the load factor at which a valid assignment of $cm$ objects to a hash table of size $m$ likely exists. We show that for any $d\ge 4$ hashes and load factor $c<c_d^*$, the expectation of the random walk insertion time is $O(1)$, that is, a constant depending only on $d$ and $c$ but not $m$.
Sensitivity of two-mode SRF cavity to generic electromagnetic interactions of ultralight dark matter

The ultralight dark matter (ULDM) such as axion or wavelike scalar plays as a plausible DM candidate. Recently, the possible non-standard ULDM couplings draw much attention. In this work we investigate the detection of electromagnetic couplings in a few benchmark models of ULDM. For illustration, we consider the generic axion electrodynamics including CP violating coupling as well as the newly proposed axion electromagnetodynamics. The superconducting radio frequency (SRF) cavity with two-mode has more advantages than the traditional cavity approach with static background field. We utilize the two-mode SRF cavity to probe the generic couplings of ULDM with frequency lower than GHz. The choices of the transverse electromagnetic modes are explicitly specified for the detection. We show the sensitivity of the SRF cavity to the axion couplings in the above frameworks.
Optical phase encoding in pulsed approach to reservoircomputing

The exploitation of the full structure of multimode light fields enables compelling capabilities in many fields including classical and quantum information science. We exploit data-encoding on the optical phase of the pulses of a femtosecond laser source for a photonic implementation of a reservoir computing protocol. Rather than intensity detection, data-reading is done via homodyne detection that accesses combinations of amplitude and phase of the field. Numerical and experimental results on NARMA tasks and laser dynamic predictions are shown. We discuss perspectives for quantum enhanced protocols.
Learning to navigate efficiently and precisely in real environments

In the context of autonomous navigation of terrestrial robots, the creation of realistic models for agent dynamics and sensing is a widespread habit in the robotics literature and in commercial applications, where they are used for model based control and/or for localization and mapping. The more recent Embodied AI literature, on the other hand, focuses on modular or end-to-end agents trained in simulators like Habitat or AI-Thor, where the emphasis is put on photo-realistic rendering and scene diversity, but high-fidelity robot motion is assigned a less privileged role. The resulting sim2real gap significantly impacts transfer of the trained models to real robotic platforms. In this work we explore end-to-end training of agents in simulation in settings which minimize the sim2real gap both, in sensing and in actuation. Our agent directly predicts (discretized) velocity commands, which are maintained through closed-loop control in the real robot. The behavior of the real robot (including the underlying low-level controller) is identified and simulated in a modified Habitat simulator. Noise models for odometry and localization further contribute in lowering the sim2real gap. We evaluate on real navigation scenarios, explore different localization and point goal calculation methods and report significant gains in performance and robustness compared to prior work.
EVALUASI KINERJA PROTOKOL ROUTING DSDV TERHADAP PENGARUH MALICIOUS NODE PADA MANET MENGGUNAKAN NETWORK SIMULATOR 2 (NS-2)

MANET merupakan teknologi telekomunikasi yang dikembangkan untuk memberikan kemudahan bagi pengguna dalam berkomunikasi. MANET terbentuk dari beberapa node yang bergerak bebas dan tidak bergantung pada infrastruktur tetap. Kelemahan utama MANET adalah masalah keamanannya. Node-node secara bebas masuk dan keluar dalam jaringan hal ini lah yang menyebabkan MANET rentan terhadap serangan. Salah satu contoh serangan dalam MANET adalah serangan malicious node. Pada penelitian menganalisis evaluasi kinerja DSDV pada MANET terhadap serangan malicious node. Beberapa parameter yang digunakan untuk mengukur kinerjanya antara lain throughput, delay total dan PDR. Perancangan MANET dilakukan menggunakan software NS2. Hasil simulasi menunjukkan serangan malicious node mengakibatkan penurunan nilai throughput, delay, dan PDR disetiap kondisi. Penurunan nilai throughput terbesar terjadi pada kondisi jaringan yang mengalami perubahan luas dimensi, yaitu jaringan 100 node dimensi 1000x1000 m 2 turun 100 Kbps dari kondisi normalnya. Sementara penurunan nilai delay total terbesar terjadi pada kondisi jaringan yang mengalamai perubahan kecepatan gerak node, yaitu pada jaringan 75 node saat kecepatan gerak node-nya 1,75 m/s turun 30,53 ms dari kondisi normalnya. Serangan malicious node memberikan efek paling besar untuk nilai PDR pada kondisi jaringan yang mengalami perubahan luas dimensi, yaitu jaringan 50 node dimensi 1000x1000 m 2 turun 0,76% dari kondisi normalnya.
The New Zealand Probabilistic Tsunami Hazard Model: development and implementation of a methodology for estimating tsunami hazard nationwide

Abstract For New Zealand, a country straddling the Pacific ‘Ring of Fire’, effective mitigation of the risks posed by tsunamis is an urgent priority. Mitigation measures include evacuation mapping, land-use planning and engineering of tsunami resilient buildings and infrastructure; but for these to be effective, a quantitative estimate of the tsunami hazard is needed. For this purpose we present the New Zealand Probabilistic Tsunami Hazard Model (NZPTHM). The model uses a Monte Carlo method for sampling from the geophysical parameters that constrain the magnitude–frequency distributions of the earthquake sources that can cause tsunamis affecting New Zealand. The sampled parameters are used to construct synthetic catalogues of the source events and the subsequent tsunami heights. Processing of these synthetic catalogues produces hazard curves, describing maximum tsunami height as a function of return period, which include ‘error bars’ (confidence intervals) as determined by the Monte Carlo model. Most practical mitigation measures require inundation modelling, and for this purpose we propose using de-aggregation, a process by which a small set of scenarios can be extracted from the NZPTHM for the purpose of detailed inundation modelling.
Who Changed the Destiny of Rural Students, and How?: Unpacking ICT-Mediated Remote Education in Rural China

The proliferation of Information and Communication Technologies (ICTs) has shown great promise in addressing educational challenges facing rural areas. However, the complex rural context poses significant challenges to the effective utilization of these technologies. This paper examines the empirical integration of live-streaming-based remote classrooms (LSRC) through a qualitative study in rural China. Our findings suggest that while LSRC enables rural students equal access to high-quality educational resources, its practical integration faces numerous challenges. In particular, we emphasize the crucial role of local teachers in addressing these challenges, ultimately achieving the desired improvement of students' learning outcomes. We also examine the impact of LSRC on the original rural education ecosystem. Building upon our findings, we call for a reconsideration of interaction paradigms and evaluation systems of ICT-mediated rural education, emphasizing the significance of rural teachers. We conclude by discussing the implications for future ICT-mediated technology interventions in rural settings.
Anomalous localization in spin-chain with tilted interactions

The localization properties of a disorder-free spin chain with inhomogeneous interactions are studied. In particular, we consider interaction strength growing linearly along the chain for systems with different interaction ranges. Using exact diagonalization we find the participation ratio of all eigenstates which allows us to quantify the localization volume in the Hilbert space. Surprisingly the localization volume changes nonmonotonically with the interaction range. The model for the infinite interaction range resembles the Schwinger model of lattice gauge theory in staggered formalism. The model studied may be implemented in state-of-the-art cold atomic devices and could reveal hidden features in disorder-free confinement phenomena.
The Past’s Affect on Development: Modernity and a Market

This article analyzes the redevelopment of the Gujari Bazaar -a traditional weekly market in Ahmedabad, India - by the city’s most ambitious development initiative, the Sabarmati Riverfront Development Project. As neo-liberal efforts to “reclaim” (cf. Smith 1990; Chatterjee 2004) the urban centers of many cities in the global south continue, activities such as these - and the spaces they occupy - come under increasing threat. Moreover, sites of cultural heritage and historical value face interesting tensions as efforts to modernize cities move to either displace them, in order to make way for sites fitting the interests of global finance capital, or preserve them as tourist attractions, sometimes contorting them for an envisaged tourist desire.
pix2gestalt: Amodal Segmentation by Synthesizing Wholes

We introduce pix2gestalt, a framework for zero-shot amodal segmentation, which learns to estimate the shape and appearance of whole objects that are only partially visible behind occlusions. By capitalizing on large-scale diffusion models and transferring their representations to this task, we learn a conditional diffusion model for reconstructing whole objects in challenging zero-shot cases, including examples that break natural and physical priors, such as art. As training data, we use a synthetically curated dataset containing occluded objects paired with their whole counterparts. Experiments show that our approach outperforms supervised baselines on established benchmarks. Our model can furthermore be used to significantly improve the performance of existing object recognition and 3D reconstruction methods in the presence of occlusions.
Assumptions and Bounds in the Instrumental Variable Model

In this note we give proofs for results relating to the Instrumental Variable (IV) model with binary response $Y$ and binary treatment $X$, but with an instrument $Z$ with $K$ states. These results were originally stated in Richardson&Robins (2014),"ACE Bounds; SEMS with Equilibrium Conditions,"arXiv:1410.0470.
Inverse Molecular Design with Multi-Conditional Diffusion Guidance

Inverse molecular design with diffusion models holds great potential for advancements in material and drug discovery. Despite success in unconditional molecule generation, integrating multiple properties such as synthetic score and gas permeability as condition constraints into diffusion models remains unexplored. We introduce multi-conditional diffusion guidance. The proposed Transformer-based denoising model has a condition encoder that learns the representations of numerical and categorical conditions. The denoising model, consisting of a structure encoder-decoder, is trained for denoising under the representation of conditions. The diffusion process becomes graph-dependent to accurately estimate graph-related noise in molecules, unlike the previous models that focus solely on the marginal distributions of atoms or bonds. We extensively validate our model for multi-conditional polymer and small molecule generation. Results demonstrate our superiority across metrics from distribution learning to condition control for molecular properties. An inverse polymer design task for gas separation with feedback from domain experts further demonstrates its practical utility.
Dataset and Benchmark: Novel Sensors for Autonomous Vehicle Perception

Conventional cameras employed in autonomous vehicle (AV) systems support many perception tasks, but are challenged by low-light or high dynamic range scenes, adverse weather, and fast motion. Novel sensors, such as event and thermal cameras, offer capabilities with the potential to address these scenarios, but they remain to be fully exploited. This paper introduces the Novel Sensors for Autonomous Vehicle Perception (NSAVP) dataset to facilitate future research on this topic. The dataset was captured with a platform including stereo event, thermal, monochrome, and RGB cameras as well as a high precision navigation system providing ground truth poses. The data was collected by repeatedly driving two ~8 km routes and includes varied lighting conditions and opposing viewpoint perspectives. We provide benchmarking experiments on the task of place recognition to demonstrate challenges and opportunities for novel sensors to enhance critical AV perception tasks. To our knowledge, the NSAVP dataset is the first to include stereo thermal cameras together with stereo event and monochrome cameras. The dataset and supporting software suite is available at: https://umautobots.github. io/nsavp
Bayesian adaptive learning to latent variables via Variational Bayes and Maximum a Posteriori

In this work, we aim to establish a Bayesian adaptive learning framework by focusing on estimating latent variables in deep neural network (DNN) models. Latent variables indeed encode both transferable distributional information and structural relationships. Thus the distributions of the source latent variables (prior) can be combined with the knowledge learned from the target data (likelihood) to yield the distributions of the target latent variables (posterior) with the goal of addressing acoustic mismatches between training and testing conditions. The prior knowledge transfer is accomplished through Variational Bayes (VB). In addition, we also investigate Maximum a Posteriori (MAP) based Bayesian adaptation. Experimental results on device adaptation in acoustic scene classification show that our proposed approaches can obtain good improvements on target devices, and consistently outperforms other cut-edging algorithms.
Scalarization via utility functions in multi-objective optimization

We study a general scalarization approach via utility functions in multi-objective optimization. It consists of maximizing utility which is obtained from the objectives' bargaining with regard to a disagreement reference point. The theoretical framework for a broad class of utility functions from microeconomics is developed. For that, we associate a utility-dependent single-objective optimization problem with the given multi-objective optimization problem. We show that Pareto optimal points of the latter can be recovered by solving the former. In particular, Cobb-Douglas, Leontief, and CES utility functions are considered. We prove that any Pareto optimal point can be obtained as a solution of scalarization via one of the mentioned utility functions. Further, we propose a numerical scheme to solve utility-dependent single-objective optimization problems. Here, the main difficulty comes from the necessity to address constraints which are associated with a disagreement reference point. Our crucial observation is that the explicit treatment of these additional constraints may be avoided. This is the case if the Slater condition is satisfied and the utility function under consideration has the so-called barrier property. Under these assumptions, we prove the convergence of our scheme to Pareto optimal points. Numerical experiments on real-world financial datasets in a portfolio selection context confirm the efficiency of our scalarization approach via utility functions.
A Survey on Indoor Visible Light Positioning Systems: Fundamentals, Applications, and Challenges

The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and future research directions for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses.