Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-reciprocity permits edge states and strong localization in stochastic topological systems

According to the celebrated bulk-boundary correspondence, topological invariants in the bulk yield a system response on the boundary. While this has been established in quantum and other classical systems, we demonstrate that this correspondence operates under different conditions in stochastic systems. Namely, despite sharing the same bulk topological invariant, stochastic systems only exhibit boundary responses in the presence of non-reciprocal (or non-Hermitian) transitions. Indeed, this response in stochastic systems grows dramatically with increasing non-reciprocity but plateaus in quantum systems – which we demonstrate for two models exhibiting localized probability and steady state currents respectively. For the latter, we propose a novel mechanism by which non-reciprocity engenders the steady-state edge current: it allows the spectrum to spread in complex space thereby decreasing edge-bulk hybridization. Our work highlights the necessity of non-reciprocal interactions in permitting localized dynamics in soft and living matter.
Intense anomalous high harmonics in graphene quantum dots caused by disorder or vacancies

This article aims to study the linear and nonlinear optical response of inversion symmetric graphene quantum dots (GQDs) in the presence of on-site disorder or vacancies. The presence of disorder or vacancy breaks the special inversion symmetry leading to the emergence of intense Hall-type anomalous harmonics. This phenomenon is attributed to the intrinsic time-reversal symmetry-breaking in quantum dots with a pseudo-relativistic Hamiltonian, even in the absence of an external magnetic field. We demonstrate that the effects induced by disorder or vacancy have a distinct impact on the optical response of GQDs. In the linear response, we observe significant Hall conductivity. In the presence of an intense laser field, we observe the radiation of strong anomalous odd and even-order harmonics already for relatively small levels of disorder or mono-vacancy. The both disorder and vacancy lift the degeneracy of states, thereby creating new channels for interband transitions and enhancing the emission of near-cutoff high-harmonic signals.
An Algorithm to Recover Shredded Random Matrices

Given some binary matrix $M$, suppose we are presented with the collection of its rows and columns in independent arbitrary orderings. From this information, are we able to recover the unique original orderings and matrix? We present an algorithm that identifies whether there is a unique ordering associated with a set of rows and columns, and outputs either the unique correct orderings for the rows and columns or the full collection of all valid orderings and valid matrices. We show that there is a constant $c>0$ such that the algorithm terminates in $O(n^2)$ time with high probability and in expectation for random $n \times n$ binary matrices with i.i. d. \ Bernoulli $(p)$ entries $(m_{ij})_{ij=1}^n$ such that $\frac{c\log^2(n)}{n(\log\log(n))^2} \leq p \leq \frac{1}{2}$.
Open Knowledge Base Canonicalization with Multi-task Unlearning

The construction of large open knowledge bases (OKBs) is integral to many applications in the field of mobile computing. Noun phrases and relational phrases in OKBs often suffer from redundancy and ambiguity, which calls for the investigation on OKB canonicalization. However, in order to meet the requirements of some privacy protection regulations and to ensure the timeliness of the data, the canonicalized OKB often needs to remove some sensitive information or outdated data. The machine unlearning in OKB canonicalization is an excellent solution to the above problem. Current solutions address OKB canonicalization by devising advanced clustering algorithms and using knowledge graph embedding (KGE) to further facilitate the canonicalization process. Effective schemes are urgently needed to fully synergise machine unlearning with clustering and KGE learning. To this end, we put forward a multi-task unlearning framework, namely MulCanon, to tackle machine unlearning problem in OKB canonicalization. Specifically, the noise characteristics in the diffusion model are utilized to achieve the effect of machine unlearning for data in OKB. MulCanon unifies the learning objectives of diffusion model, KGE and clustering algorithms, and adopts a two-step multi-task learning paradigm for training. A thorough experimental study on popular OKB canonicalization datasets validates that MulCanon achieves advanced machine unlearning effects.
Beyond IID weights: sparse and low-rank deep Neural Networks are also Gaussian Processes

The infinitely wide neural network has been proven a useful and manageable mathematical model that enables the understanding of many phenomena appearing in deep learning. One example is the convergence of random deep networks to Gaussian processes that allows a rigorous analysis of the way the choice of activation function and network weights impacts the training dynamics. In this paper, we extend the seminal proof of Matthews et al. (2018) to a larger class of initial weight distributions (which we call PSEUDO-IID), including the established cases of IID and orthogonal weights, as well as the emerging low-rank and structured sparse settings celebrated for their computational speed-up benefits. We show that fully-connected and convolutional networks initialized with PSEUDO-IID distributions are all effectively equivalent up to their variance. Using our results, one can identify the Edge-of-Chaos for a broader class of neural networks and tune them at criticality in order to enhance their training. Moreover, they enable the posterior distribution of Bayesian Neural Networks to be tractable across these various initialization schemes.
A Comprehensive Python Library for Deep Learning-Based Event Detection in Multivariate Time Series Data

Event detection in time series data is crucial in various domains, including finance, healthcare, cybersecurity, and science. Accurately identifying events in time series data is vital for making informed decisions, detecting anomalies, and predicting future trends. Despite extensive research exploring diverse methods for event detection in time series, with deep learning approaches being among the most advanced, there is still room for improvement and innovation in this field. In this paper, we present a new deep learning supervised method for detecting events in multivariate time series data. Our method combines four distinct novelties compared to existing deep-learning supervised methods. Firstly, it is based on regression instead of binary classification. Secondly, it does not require labeled datasets where each point is labeled; instead, it only requires reference events defined as time points or intervals of time. Thirdly, it is designed to be robust by using a stacked ensemble learning meta-model that combines deep learning models, ranging from classic feed-forward neural networks (FFNs) to state-of-the-art architectures like transformers. This ensemble approach can mitigate individual model weaknesses and biases, resulting in more robust predictions. Finally, to facilitate practical implementation, we have developed a Python package to accompany our proposed method. The package, called eventdetector-ts, can be installed through the Python Package Index (PyPI). In this paper, we present our method and provide a comprehensive guide on the usage of the package. We showcase its versatility and effectiveness through different real-world use cases from natural language processing (NLP) to financial security domains.
PartRePer-MPI: Combining Fault Tolerance and Performance for MPI Applications

As we have entered Exascale computing, the faults in high-performance systems are expected to increase considerably. To compensate for a higher failure rate, the standard checkpoint/restart technique would need to create checkpoints at a much higher frequency resulting in an excessive amount of overhead which would not be sustainable for many scientific applications. Replication allows for fast recovery from failures by simply dropping the failed processes and using their replicas to continue the regular operation of the application. In this paper, we have implemented PartRePer-MPI, a novel fault-tolerant MPI library that adopts partial replication of some of the launched MPI processes in order to provide resilience from failures. The novelty of our work is that it combines both fault tolerance, due to the use of the User Level Failure Mitigation (ULFM) framework in the Open MPI library, and high performance, due to the use of communication protocols in the native MPI library that is generally fine-tuned for specific HPC platforms. We have implemented efficient and parallel communication strategies with computational and replica processes, and our library can seamlessly provide fault tolerance support to an existing MPI application. Our experiments using seven NAS Parallel Benchmarks and two scientific applications show that the failure-free overheads in PartRePer-MPI when compared to the baseline MVAPICH2, are only up to 6.4% for the NAS parallel benchmarks and up to 9.7% for the scientific applications.
From port-based teleportation to Frobenius reciprocity theorem: partially reduced irreducible representations and their applications

In this paper, we present a connection of two concepts as induced representation and partially reduced irreducible representations (PRIR) appear in the context of port-based teleportation protocols. Namely, for a given finite group $G$ with arbitrary subgroup $H$, we consider a particular case of matrix irreducible representations, whose restriction to the subgroup $H$, as a matrix representation of $H$, is completely reduced to diagonal block form with an irreducible representation of $H$ in the blocks. The basic properties of such representations are given. Then as an application of this concept, we show that the spectrum of the port-based teleportation operator is connected in a very simple way with the spectrum of the corresponding Jucys-Murphy operator for symmetric groups $S(m-1)\subset S(m)$ - basic objects from the point of view of representation theory of the symmetric group. This shows a deep connection between the central object describing properties of deterministic PBT schemes and objects appearing naturally in the abstract representation theory of the symmetric group. As an additional but not trivial result, we give also purely matrix proof of the Frobenius reciprocity theorem for characters.
Constraints on Coasting Cosmological Models from Gravitational-wave Standard Sirens

We present the first test of coasting cosmological models with gravitational-wave (GW) standard sirens observed in the first three observing runs of the LIGO–Virgo–KAGRA detector network. We apply the statistical galaxy catalog method adapted to coasting cosmologies and infer constraints on the H 0 Hubble constant for the three fixed values of the curvature parameter k=−1,0,+1 in H02c−2 units. The maximum posteriors and 68.3% highest density intervals we obtained from a combined analysis of 46 dark siren detections and a single bright siren detection are H0=68.1−5.6+8.5,67.5−5.2+8.3,67.1−5.8+6.6kms−1Mpc−1 , respectively. All our constraints on H 0 are consistent within 1σ with the H 0 measured with the differential age method, which provides a constraint on H 0 in coasting cosmologies independently from k. Our results constrain all cosmological models with a(t) ∝ t linear expansion in the luminosity distance and redshift range of the 47 LIGO–Virgo detections, i.e., d L ≲ 5Gpc and z ≲ 0.8, which practically include all (both strictly linear and quasi-linear) models in the coasting model family. As we have found, the coasting models and the Lambda cold dark matter (or ΛCDM) model fit equally well to the applied set of GW detections.
Learning Independent Program and Architecture Representations for Generalizable Performance Modeling

This paper proposes PerfVec, a novel deep learning-based performance modeling framework that learns high-dimensional, independent/orthogonal program and microarchitecture representations. Once learned, a program representation can be used to predict its performance on any microarchitecture, and likewise, a microarchitecture representation can be applied in the performance prediction of any program. Additionally, PerfVec yields a foundation model that captures the performance essence of instructions, which can be directly used by developers in numerous performance modeling related tasks without incurring its training cost. The evaluation demonstrates that PerfVec is more general, efficient, and accurate than previous approaches.
High-resolution sequence stratigraphy of a Late Jurassic mixed carbonate-siliciclastic ramp, Lower Saxony Basin, Northwestern Germany [Hochauflösende Sequenzstratigrafie einer oberjurassischen, gemischt karbonatisch-siliziklastischen Rampe, Niedersächsisches Becken, NW-Deutschland]

This paper presents a high-resolution sequence stratigraphic analysis of Late Jurassic storm-dominated, shallowwater mixed carbonate-siliciclastic ramp deposits (Korallenoolith Formation) of the Lower Saxony Basin. Detailed mapping and logging of eight sections along a 45 km W–E transect (Wiehengebirge, Wesergebirge, Süntel) were carried out to document the lateral and vertical facies architecture. Considerable thickness variations of the Korallenoolith Formation along this transect indicate synsedimentary extensional tectonics. New data from microfacies, sedimentary structures, gamma ray signals, carbonate contents and δ13C and δ18O stable isotopes were integrated to characterise and defi ne depositional sequences. The periodic variations of the data series were evaluated by spectral analysis. Six medium-scale depositional sequences (3–28 m thick) have been recognized, built-up by 1–6 m thick small-scale sequences. Medium-scale sequences probably represent 400 Ka Milankovitch cycles and can be correlated along the 45 km W–E transect. They form larger-scale regressive-transgressive sequence sets, bounded by a major unconformity. This unconformity probably corresponds to the R8b/T9 sequence boundary. However, the sparse biostratigraphic markers prevent a precise correlation with the sea-level chart after Hardenbol et al. (1998). Kurzfassung: Die fl achmarinen, gemischt karbonatisch-siliziklastischen Ablagerungen der Korallenoolith-Formation (Oberjura) im Niedersächsischen Becken werden sequenzstratigrafi sch interpretiert. Anhand detaillierter Aufnahmen von acht Profi len (Wiehengebirge, Wesergebirge, Süntel) wurde die laterale und vertikale Faziesarchitektur rekonstruiert. Entlang eines 45 km langen W–E-Profi ls sind Mächtigkeitsschwankungen des Korallenoolith erkennbar, die auf synsedimentäre extensionale Tektonik zurückgeführt werden. Neue Daten, basierend auf Mikrofazies, Sedimentstrukturen, Gamma-Ray-Messungen, Karbonatgehalt und Isotopenwerte (δ13C und δ18O), dienen der Defi nition und Charakterisierung von Ablagerungssequenzen. Periodische Variationen werden mittels Spektralanalyse bestimmt. Sechs „medium-scale“-Sequenzen (3–28 m mächtig) werden von „small-scale“-Sequenzen (1–6 m mächtig) aufgebaut. Die „medium-scale“-Sequenzen können entlang eines W–E Profi ls korreliert werden und repräsentieren vermutlich 400 000 a Milankovitch-Zyklen. Die „medium-scale“-Sequenzen bauen Sequenzsets übergeordneter Ablagerungssequenzen auf. Die Diskordanz zwischen dem regressiven und dem transgressiven Zyklus dieser übergeordneten Ablagerungssequenzen korreliert wahrscheinlich mit der R8b/T9-Sequenzgrenze. Dennoch verhindert das Fehlen von Leitfossilien eine genaue biostratigrafi sche Einstufung und somit eine präzise Korrelation mit der Meeresspiegelkurve von Hardenbol et al. (1998).
AirFL-Mem: Improving Communication-Learning Trade-Off by Long-Term Memory

Addressing the communication bottleneck inherent in federated learning (FL), over-the-air FL (AirFL) has emerged as a promising solution, which is, however, hampered by deep fading conditions. In this paper, we propose AirFL-Mem, a novel scheme designed to mitigate the impact of deep fading by implementing a \emph{long-term} memory mechanism. Convergence bounds are provided that account for long-term memory, as well as for existing AirFL variants with short-term memory, for general non-convex objectives. The theory demonstrates that AirFL-Mem exhibits the same convergence rate of federated averaging (FedAvg) with ideal communication, while the performance of existing schemes is generally limited by error floors. The theoretical results are also leveraged to propose a novel convex optimization strategy for the truncation threshold used for power control in the presence of Rayleigh fading channels. Experimental results validate the analysis, confirming the advantages of a long-term memory mechanism for the mitigation of deep fading.
SparseDFF: Sparse-View Feature Distillation for One-Shot Dexterous Manipulation

Humans demonstrate remarkable skill in transferring manipulation abilities across objects of varying shapes, poses, and appearances, a capability rooted in their understanding of semantic correspondences between different instances. To equip robots with a similar high-level comprehension, we present SparseDFF, a novel DFF for 3D scenes utilizing large 2D vision models to extract semantic features from sparse RGBD images, a domain where research is limited despite its relevance to many tasks with fixed-camera setups. SparseDFF generates view-consistent 3D DFFs, enabling efficient one-shot learning of dexterous manipulations by mapping image features to a 3D point cloud. Central to SparseDFF is a feature refinement network, optimized with a contrastive loss between views and a point-pruning mechanism for feature continuity. This facilitates the minimization of feature discrepancies w.r. t. end-effector parameters, bridging demonstrations and target manipulations. Validated in real-world scenarios with a dexterous hand, SparseDFF proves effective in manipulating both rigid and deformable objects, demonstrating significant generalization capabilities across object and scene variations.
Contextual Bandits for Evaluating and Improving Inventory Control Policies

Solutions to address the periodic review inventory control problem with nonstationary random demand, lost sales, and stochastic vendor lead times typically involve making strong assumptions on the dynamics for either approximation or simulation, and applying methods such as optimization, dynamic programming, or reinforcement learning. Therefore, it is important to analyze and evaluate any inventory control policy, in particular to see if there is room for improvement. We introduce the concept of an equilibrium policy, a desirable property of a policy that intuitively means that, in hindsight, changing only a small fraction of actions does not result in materially more reward. We provide a light-weight contextual bandit-based algorithm to evaluate and occasionally tweak policies, and show that this method achieves favorable guarantees, both theoretically and in empirical studies.
CATE Lasso: Conditional Average Treatment Effect Estimation with High-Dimensional Linear Regression

In causal inference about two treatments, Conditional Average Treatment Effects (CATEs) play an important role as a quantity representing an individualized causal effect, defined as a difference between the expected outcomes of the two treatments conditioned on covariates. This study assumes two linear regression models between a potential outcome and covariates of the two treatments and defines CATEs as a difference between the linear regression models. Then, we propose a method for consistently estimating CATEs even under high-dimensional and non-sparse parameters. In our study, we demonstrate that desirable theoretical properties, such as consistency, remain attainable even without assuming sparsity explicitly if we assume a weaker assumption called implicit sparsity originating from the definition of CATEs. In this assumption, we suppose that parameters of linear models in potential outcomes can be divided into treatment-specific and common parameters, where the treatment-specific parameters take difference values between each linear regression model, while the common parameters remain identical. Thus, in a difference between two linear regression models, the common parameters disappear, leaving only differences in the treatment-specific parameters. Consequently, the non-zero parameters in CATEs correspond to the differences in the treatment-specific parameters. Leveraging this assumption, we develop a Lasso regression method specialized for CATE estimation and present that the estimator is consistent. Finally, we confirm the soundness of the proposed method by simulation studies.
EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression Recognition

Facial Expression Recognition (FER) is a crucial task in affective computing, but its conventional focus on the seven basic emotions limits its applicability to the complex and expanding emotional spectrum. To address the issue of new and unseen emotions present in dynamic in-the-wild FER, we propose a novel vision-language model that utilises sample-level text descriptions (i.e. captions of the context, expressions or emotional cues) as natural language supervision, aiming to enhance the learning of rich latent representations, for zero-shot classification. To test this, we evaluate using zero-shot classification of the model trained on sample-level descriptions on four popular dynamic FER datasets. Our findings show that this approach yields significant improvements when compared to baseline methods. Specifically, for zero-shot video FER, we outperform CLIP by over 10\% in terms of Weighted Average Recall and 5\% in terms of Unweighted Average Recall on several datasets. Furthermore, we evaluate the representations obtained from the network trained using sample-level descriptions on the downstream task of mental health symptom estimation, achieving performance comparable or superior to state-of-the-art methods and strong agreement with human experts. Namely, we achieve a Pearson's Correlation Coefficient of up to 0.85 on schizophrenia symptom severity estimation, which is comparable to human experts' agreement. The code is publicly available at: https://github.com/NickyFot/EmoCLIP.
Stochastic Latent Transformer: Efficient Modelling of Stochastically Forced Zonal Jets

We present a novel probabilistic deep learning approach, the 'Stochastic Latent Transformer' (SLT), designed for the efficient reduced-order modelling of stochastic partial differential equations. Stochastically driven flow models are pertinent to a diverse range of natural phenomena, including jets on giant planets, ocean circulation, and the variability of midlatitude weather. However, much of the recent progress in deep learning has predominantly focused on deterministic systems. The SLT comprises a stochastically-forced transformer paired with a translation-equivariant autoencoder, trained towards the Continuous Ranked Probability Score. We showcase its effectiveness by applying it to a well-researched zonal jet system, where the interaction between stochastically forced eddies and the zonal mean flow results in a rich low-frequency variability. The SLT accurately reproduces system dynamics across various integration periods, validated through quantitative diagnostics that include spectral properties and the rate of transitions between distinct states. The SLT achieves a five-order-of-magnitude speedup in emulating the zonally-averaged flow compared to direct numerical simulations. This acceleration facilitates the cost-effective generation of large ensembles, enabling the exploration of statistical questions concerning the probabilities of spontaneous transition events.