Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Switching State Space Model (DS$^3$M) for Nonlinear Time Series Forecasting with Regime Switching

Modern time series data often display complex nonlinear dependencies along with irregular regime-switching behaviors. These features present technical challenges in modeling, inference, and in offering insightful understanding into the underlying stochastic phenomena. To tackle these challenges, we introduce a novel modeling framework known as the Deep Switching State Space Model (DS$^3$M). This framework is engineered to make accurate forecasts for such time series while adeptly identifying the irregular regimes hidden within the dynamics. These identifications not only have significant economic ramifications but also contribute to a deeper understanding of the underlying phenomena. In DS$^3$M, the architecture employs discrete latent variables to represent regimes and continuous latent variables to account for random driving factors. By melding a Recurrent Neural Network (RNN) with a nonlinear Switching State Space Model (SSSM), we manage to capture the nonlinear dependencies and irregular regime-switching behaviors, governed by a Markov chain and parameterized using multilayer perceptrons. We validate the effectiveness and regime identification capabilities of DS$^3$M through short- and long-term forecasting tests on a wide array of simulated and real-world datasets, spanning sectors such as healthcare, economics, traffic, meteorology, and energy. Experimental results reveal that DS$^3$M outperforms several state-of-the-art models in terms of forecasting accuracy, while providing meaningful regime identifications.
Addressing Inquiries about History: An Efficient and Practical Framework for Evaluating Open-domain Chatbot Consistency

A good open-domain chatbot should avoid presenting contradictory responses about facts or opinions in a conversational session, known as its consistency capacity. However, evaluating the consistency capacity of a chatbot is still challenging. Employing human judges to interact with chatbots on purpose to check their capacities is costly and low-efficient, and difficult to get rid of subjective bias. In this paper, we propose the Addressing Inquiries about History (AIH), an efficient and practical framework for the consistency evaluation. At the conversation stage, AIH attempts to address appropriate inquiries about the dialogue history to induce the chatbot to redeclare the historical facts or opinions. We carry out the conversation between chatbots, which is more efficient than the human-bot interaction and can also alleviate the subjective bias. In this way, we manage to rapidly obtain a dialog session that contains responses with high contradiction possibilities. At the contradiction recognition stage, we can either employ human judges or a natural language inference (NLI) model to recognize whether the answers to the inquiries are contradictory with history. Finally, we are able to rank chatbots according to the contradiction statistics. Experiments on open-domain chatbots show that our approach can efficiently and reliably assess the consistency capacity of chatbots and achieve a high ranking correlation with the human evaluation. We release the framework and hope to help improve the consistency capacity of chatbots. \footnote{\url{https://github.com/ictnlp/AIH}}
Erhöhung des Gehaltes an konjugierter Linolsäure im Broilerfleisch durch Fütterung

Die konjugierten Linolsäuren (CLA) sind interessant geworden, nachdem ihre antikarzinogene Wirkung bekannt wurde (IP und SCIMECA, 1997; WONG et al., 1997; CESANO et al., 1998). Als erste wiesen PARIZA und HARGRAVES (1985) nach, dass ein von Rindfleisch stammender Extrakt eine antikarzinogene Wirkung besitzt. Später bewiesen HA et al. (1987), dass die antikarzinogene Wirkung den CLA-Isomeren zuzuschreiben ist. Ein Zusatz von CLA zum Futter verminderte die LDL-Konzentration im Blutplasma und verhinderte die Entstehung von Arterienverkalkung bei Kaninchen (LEE et al., 1994) und bei Silberhamstern (NICOLOSI et al., 1997), die mit einer atherogenen Futterration gefüttert wurden. CLA besitzt eine deutliche antioxidative Kapazität (HA et al., 1990; IP et al., 1991). In einigen Versuchen mit Broilern wurde bei den Tieren, die eine CLA-Ergänzung bekamen, eine Verbesserung der oxidativen Stabilität beobachtet (BÖLÜKBASI und ERHAHN, 2007; ZHANG et al., 2008). CLA beeinflusst die Aktivität des Immunsystems dadurch, dass sie die Blastogenese, die cytotoxische Aktivität der Lymphozyten sowie die Aktivität der makrophagen Zellen steigert (MICHAL et al., 1992; WONG et al., 1997). In Broiler-Versuchen wurde auch festgestellt, dass CLA die Antikörperbildung steigert (TAKAHASHI et al., 2003; ZHANG et al., 2005). Ferner spielt CLA eine wichtige Rolle im intermediären Stoffwechsel. Anabole Prozesse werden gefördert und katabole gehemmt. CLA fördert den Proteineinbau in das Muskelgewebe, behindert die Entwicklung des Fettgewebes in der Jugendphase und hemmt den Lipideinbau in das schon vorhandene Fettgewebe (PARIZA et al., 2000; SEBEDIO, 2001; BADINGA, 2001). In Versuchen mit Schweinen (DUGAN et al., 1997) und mit Mäusen (PARK et al., 1997) wurde auch bestätigt, dass die Ergänzung des Futters mit CLA die Ablagerung des Fettes im Organismus vermindert. Bei Broilern wurde eine lineare Abnahme des Fettes in der Bauchhöhle bei den Tieren beobachtet, die eine CLA-Ergänzung bekamen (ZANINI et al., 2006; SUKSOMBAT et al., 2007). Diese Wirkung erklären PARK et al. (1997) damit, dass CLA die Mobilisierung der Fettsäuren im Fettgewebe verringert. Bei den Lebensmitteln enthält Milch die höchsten Gehalte an CLA (0,2-2,0 g CLA/100 g Fett). Im Fleisch der Monogastriden wurde nur ein Zehntel des CLA-Gehaltes ge-
A Residual Network based Deep Learning Model for Detection of COVID-19 from Cough Sounds

The present work proposes a deep-learning-based approach for the classification of COVID-19 coughs from non-COVID-19 coughs and that can be used as a low-resource-based tool for early detection of the onset of such respiratory diseases. The proposed system uses the ResNet-50 architecture, a popularly known Convolutional Neural Network (CNN) for image recognition tasks, fed with the log-Mel spectrums of the audio data to discriminate between the two types of coughs. For the training and validation of the proposed deep learning model, this work utilizes the Track-1 dataset provided by the DiCOVA Challenge 2021 organizers. Additionally, to increase the number of COVID-positive samples and to enhance variability in the training data, it has also utilized a large open-source database of COVID-19 coughs collected by the EPFL CoughVid team. Our developed model has achieved an average validation AUC of 98.88%. Also, applying this model on the Blind Test Set released by the DiCOVA Challenge, the system has achieved a Test AUC of 75.91%, Test Specificity of 62.50%, and Test Sensitivity of 80.49%. Consequently, this submission has secured 16th position in the DiCOVA Challenge 2021 leader-board.
A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few Samples

We propose an iterative algorithm for low-rank matrix completion that can be interpreted as an iteratively reweighted least squares (IRLS) algorithm, a saddle-escaping smoothing Newton method or a variable metric proximal gradient method applied to a non-convex rank surrogate. It combines the favorable data-efficiency of previous IRLS approaches with an improved scalability by several orders of magnitude. We establish the first local convergence guarantee from a minimal number of samples for that class of algorithms, showing that the method attains a local quadratic convergence rate. Furthermore, we show that the linear systems to be solved are well-conditioned even for very ill-conditioned ground truth matrices. We provide extensive experiments, indicating that unlike many state-of-the-art approaches, our method is able to complete very ill-conditioned matrices with a condition number of up to $10^{10}$ from few samples, while being competitive in its scalability.
Electronic band crossing in sliding bilayer graphene: Tight-binding calculations and symmetry group representation analysis

Dirac points are found to emerge due to the crossing of bands in the electronic structure of bilayer graphene for configurations in which the alignment between two hexagonal lattices preserves the parallelism of the armchair/zigzag lines between two layers. On the base of electronic calculations using a tight-binding model for the $\pi$ bands it is shown that the crossing of the energy-band dispersion curves occurs in the vicinity of the corner points of the hexagonal Brillouin zone. Group representation theory analysis confirms the emergence of such Dirac points. It is demonstrated that the band crossings at generic $\mathbf{k}$ points are guaranteed by the compatibility relations between the symmetries of eigenstates at the high-symmetry $\mathbf{k}$ points in the Brillouin zone. The presence of Dirac points governs the geometrical properties of the energy surfaces, and thus the topological structure of the Fermi energy surface and the energy spectrum.
Teaching keyword spotters to spot new keywords with limited examples

Learning to recognize new keywords with just a few examples is essential for personalizing keyword spotting (KWS) models to a user's choice of keywords. However, modern KWS models are typically trained on large datasets and restricted to a small vocabulary of keywords, limiting their transferability to a broad range of unseen keywords. Towards easily customizable KWS models, we present KeySEM (Keyword Speech EMbedding), a speech embedding model pre-trained on the task of recognizing a large number of keywords. Speech representations offered by KeySEM are highly effective for learning new keywords from a limited number of examples. Comparisons with a diverse range of related work across several datasets show that our method achieves consistently superior performance with fewer training examples. Although KeySEM was pre-trained only on English utterances, the performance gains also extend to datasets from four other languages indicating that KeySEM learns useful representations well aligned with the task of keyword spotting. Finally, we demonstrate KeySEM's ability to learn new keywords sequentially without requiring to re-train on previously learned keywords. Our experimental observations suggest that KeySEM is well suited to on-device environments where post-deployment learning and ease of customization are often desirable.
Synchronization of chiral vortex nano-oscillators

The development of spintronic oscillators is driven by their potential applications in radio frequency telecommunication and neuromorphic computing. In this work, we propose a spintronic oscillator based on the chiral coupling in thin magnetic films with patterned anisotropy. With an in-plane magnetized disk imprinted on an out-of-plane magnetized slab, the oscillator takes a polar vortex-like magnetic structure in the disk stabilized by a strong Dzyaloshinskii–Moriya interaction. By means of micromagnetic simulations, we investigate its dynamic properties under applied spin current, and by placing an ensemble of oscillators in the near vicinity, we demonstrate their synchronization with different resonant frequencies. Finally, we show their potential application in neuromorphic computing using a network with six oscillators.
Subdivision-based Mesh Convolution Networks

Convolutionalneural networks (CNNs) have made great breakthroughs in two-dimensional (2D) computer vision. However, their irregular structure makes it hard to harness the potential of CNNs directly on meshes. A subdivision surface provides a hierarchical multi-resolution structure in which each face in a closed 2-manifold triangle mesh is exactly adjacent to three faces. Motivated by these two observations, this article presents SubdivNet, an innovative and versatile CNN framework for three-dimensional (3D) triangle meshes with Loop subdivision sequence connectivity. Making an analogy between mesh faces and pixels in a 2D image allows us to present a mesh convolution operator to aggregate local features from nearby faces. By exploiting face neighborhoods, this convolution can support standard 2D convolutional network concepts, e.g., variable kernel size, stride, and dilation. Based on the multi-resolution hierarchy, we make use of pooling layers that uniformly merge four faces into one and an upsampling method that splits one face into four. Thereby, many popular 2D CNN architectures can be easily adapted to process 3D meshes. Meshes with arbitrary connectivity can be remeshed to have Loop subdivision sequence connectivity via self-parameterization, making SubdivNet a general approach. Extensive evaluation and various applications demonstrate SubdivNet’s effectiveness and efficiency.
Symmetries and criticality of generalised van der Waals models

We consider a family of thermodynamic models such that the energy density can be expressed as an asymptotic expansion in the scale formal parameter and whose terms are suitable functions of the volume density. We examine the possibility to construct solutions for the Maxwell thermodynamic relations relying on their symmetry properties and deduce the critical properties implied in terms of the dynamics of coexistence curves in the space of thermodynamic variables.
Neural Architecture Search via Bregman Iterations

We propose a novel strategy for Neural Architecture Search (NAS) based on Bregman iterations. Starting from a sparse neural network our gradient-based one-shot algorithm gradually adds relevant parameters in an inverse scale space manner. This allows the network to choose the best architecture in the search space which makes it well-designed for a given task, e.g., by adding neurons or skip connections. We demonstrate that using our approach one can unveil, for instance, residual autoencoders for denoising, deblurring, and classification tasks. Code is available at https://github.com/TimRoith/BregmanLearning.
Grounding ‘Grounding’ in NLP

The NLP community has seen substantial recent interest in grounding to facilitate interaction between language technologies and the world. However, as a community, we use the term broadly to reference any linking of text to data or non-textual modality. In contrast, Cognitive Science more formally defines"grounding"as the process of establishing what mutual information is required for successful communication between two interlocutors -- a definition which might implicitly capture the NLP usage but differs in intent and scope. We investigate the gap between these definitions and seek answers to the following questions: (1) What aspects of grounding are missing from NLP tasks? Here we present the dimensions of coordination, purviews and constraints. (2) How is the term"grounding"used in the current research? We study the trends in datasets, domains, and tasks introduced in recent NLP conferences. And finally, (3) How to advance our current definition to bridge the gap with Cognitive Science? We present ways to both create new tasks or repurpose existing ones to make advancements towards achieving a more complete sense of grounding.
Contracting Neural-Newton Solver

Recent advances in deep learning have set the focus on neural networks (NNs) that can successfully replace traditional numerical solvers in many applications, achieving impressive computing gains. One such application is time domain simulation, which is indispensable for the design, analysis and operation of many engineering systems. Simulating dynamical systems with implicit Newton-based solvers is a computationally heavy task, as it requires the solution of a parameterized system of differential and algebraic equations at each time step. A variety of NN-based methodologies have been shown to successfully approximate the dynamical trajectories computed by numerical time domain solvers at a fraction of the time. However, so far no previous NN-based model has explicitly captured the fact that any predicted point on the time domain trajectory also represents the ﬁxed point of the numerical solver itself. As we show, explicitly capturing this property can lead to signiﬁcantly increased NN accuracy and much smaller NN sizes. In this paper, we model the Newton solver at the heart of an implicit Runge-Kutta integrator as a contracting map iteratively seeking this ﬁxed point. Our primary contribution is to develop a recurrent NN simulation tool, termed the Contracting Neural-Newton Solver ( CoNNS ), which explicitly captures the contracting nature of these Newton iterations. To build CoNNS , we train a feedforward NN and mimic this contraction behavior by embedding a series of training constraints which guarantee the mapping provided by the NN satisﬁes the Banach ﬁxed-point theorem; thus, we are able to prove that successive passes through the NN are guaranteed to converge to a unique, ﬁxed point. Recent advances in NN training libraries (i.e., CVXPY Layers) allow us to embed our derived contraction constraints, which are posed as tractable semideﬁnite programs, directly into the NN training routine. We showcase the efﬁcacy of the method through simulation results, gathered from a suite of
Local Adaptivity in Federated Learning: Convergence and Consistency

The federated learning (FL) framework trains a machine learning model using decentralized data stored at edge client devices by periodically aggregating locally trained models. Popular optimization algorithms of FL use vanilla (stochastic) gradient descent for both local updates at clients and global updates at the aggregating server. Recently, adaptive optimization methods such as AdaGrad have been studied for server updates. However, the effect of using adaptive optimization methods for local updates at clients is not yet understood. We show in both theory and practice that while local adaptive methods can accelerate convergence, they can cause a non-vanishing solution bias, where the final converged solution may be different from the stationary point of the global objective function. We propose correction techniques to overcome this inconsistency and complement the local adaptive methods for FL. Extensive experiments on realistic federated training tasks show that the proposed algorithms can achieve faster convergence and higher test accuracy than the baselines without local adaptivity.
Hallucination In Object Detection — A Study In Visual Part VERIFICATION

We show that object detectors can hallucinate and detect missing objects; potentially even accurately localized at their expected, but non-existing, position. This is particularly problematic for applications that rely on visual part verification: detecting if an object part is present or absent. We show how popular object detectors hallucinate objects in a visual part verification task and introduce the first visual part verification dataset: DelftBikes 1, which has 10,000 bike photographs, with 22 densely annotated parts per image, where some parts may be missing. We explicitly annotated an extra object state label for each part to reflect if a part is missing or intact. We propose to evaluate visual part verification by relying on recall and compare popular object detectors on DelftBikes.1https://github.com/oskyhn/DelftBikes
The NewHorizon simulation – to bar or not to bar

We use the NewHorizon simulation to study the redshift evolution of bar properties and fractions within galaxies in the stellar masses range M⋆ = 107.25–1011.4$\, \rm {M}_\odot$ over the redshift range z = 0.25–1.3. We select disc galaxies using stellar kinematics as a proxy for galaxy morphology. We employ two different automated bar detection methods, coupled with visual inspection, resulting in observable bar fractions of fbar = 0.070$_{{-0.012}}^{{+0.018}}$ at z ∼ 1.3, decreasing to fbar = 0.011$_{{-0.003}}^{{+0.014}}$ at z ∼ 0.25. Only one galaxy is visually confirmed as strongly barred in our sample. This bar is hosted by the most massive disk and only survives from z = 1.3 down to z = 0.7. Such a low bar fraction, in particular amongst Milky Way-like progenitors, highlights a missing bars problem, shared by literally all cosmological simulations with spatial resolution <100 pc to date. The analysis of linear growth rates, rotation curves and derived summary statistics of the stellar, gas and dark matter components suggest that galaxies with stellar masses below 109.5 − 1010$\, \rm {M}_\odot$ in NewHorizon appear to be too dominated by dark matter relative to stellar content to form a bar, while more massive galaxies typically have formed large bulges that prevent bar persistence at low redshift. This investigation confirms that the evolution of the bar fraction puts stringent constraints on the assembly history of baryons and dark matter on to galaxies.
BERNOULLI ACTIONS OF TYPE III WITH PRESCRIBED ASSOCIATED FLOW

We prove that many, but not all, injective factors arise as crossed products by nonsingular Bernoulli actions of the group $\mathbb {Z}$ . We obtain this result by proving a completely general result on the ergodicity, type and Krieger’s associated flow for Bernoulli shifts with arbitrary base spaces. We prove that the associated flow must satisfy a structural property of infinite divisibility. Conversely, we prove that all almost periodic flows, as well as many other ergodic flows, do arise as associated flow of a weakly mixing Bernoulli action of any infinite amenable group. As a byproduct, we prove that all injective factors with almost periodic flow of weights are infinite tensor products of $2 \times 2$ matrices. Finally, we construct Poisson suspension actions with prescribed associated flow for any locally compact second countable group that does not have property (T).
On an inverse scattering transform for the nonlinear Sch\"odinger equation on the half-line

In this paper, we develop an inverse scattering transform for the integrable focusing nonlinear Schr\"odinger (NLS) equation on the half-line subject to a class of boundary conditions. The method is based on the notions of integrable boundary conditions and double-row monodromy matrix, developed by Sklyanin, which characterize initial-boundary value problems for NLS on an interval. It follows from Sklyanin's approach that a hierarchy of integrable boundary conditions can be derived based on a semi-discrete type Lax pair involving the time-part Lax matrix of NLS and a reflection matrix. The inverse scattering transform relies on the formulation of a scattering system for the double-row monodromy matrix characterizing an interval problem. The scattering system for the half-line problem for NLS is obtained by extending one endpoint of the interval to infinity. Then, we derive spectral and analytic properties of the scattering systems, and set up the inverse part using a Riemann-Hilbert formulation. We also show that our approach is equivalent to a nonlinear method of reflection by extending the initial-boundary value problem on the half-line to an initial value on the whole axis. Explicit examples of soliton solutions on the half-line are provided. Although we only consider the NLS model as a particular example, the method we present in this paper can be readily applied to a wide range of integrable PDEs on the half-line.