Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient computation of the volume of a polytope in high-dimensions using Piecewise Deterministic Markov Processes

Computing the volume of a polytope in high dimensions is computationally challenging but has wide applications. Current state-of-the-art algorithms to compute such volumes rely on efficient sampling of a Gaussian distribution restricted to the polytope, using e.g. Hamiltonian Monte Carlo. We present a new sampling strategy that uses a Piecewise Deterministic Markov Process. Like Hamiltonian Monte Carlo, this new method involves simulating trajectories of a non-reversible process and inherits similar good mixing properties. However, importantly, the process can be simulated more easily due to its piecewise linear trajectories - and this leads to a reduction of the computational cost by a factor of the dimension of the space. Our experiments indicate that our method is numerically robust and is one order of magnitude faster (or better) than existing methods using Hamiltonian Monte Carlo. On a single core processor, we report computational time of a few minutes up to dimension 500.
Edge coherence in multiplex networks

This paper introduces a nonparametric framework for the setting where multiple networks are observed on the same set of nodes, also known as multiplex networks. Our objective is to provide a simple parameterization which explicitly captures linear dependence between the different layers of networks. For non-Euclidean observations, such as shapes and graphs, the notion of “linear” must be deﬁned appropriately. Taking inspiration from the representation of stochastic processes and the analogy of the multivariate spectral representation of a stochastic process with joint exchangeability of Bernoulli arrays, we introduce the notion of edge coherence as a measure of linear dependence in the graph limit space. Edge coherence is deﬁned for pairs of edges from any two network layers and is the key novel parameter. We illustrate the utility of our approach by eliciting simple models such as a correlated stochastic blockmodel and a correlated inhomogeneous graph limit model.
TAP equations for orthogonally invariant spin glasses at high temperature

We study the high-temperature regime of a mean-ﬁeld spin glass model whose couplings matrix is orthogonally invariant in law. The magnetization of this model is conjectured to satisfy a system of TAP equations, originally derived by Parisi and Potters [PP95] using a diagrammatic expansion of the Gibbs free energy. We prove that this TAP description is correct in an L 2 sense, in a regime of suﬃciently high temperature. Our approach develops a novel geometric argument for proving the convergence of an Approximate Message Passing (AMP) algorithm to the magnetization vector, which is applicable in models without i.i. d. couplings. This convergence is shown via a conditional second moment analysis of the free energy restricted to a thin band around the output of the AMP algorithm, in a system of many “orthogonal” replicas.
Volatility forecasting with machine learning and intraday commonality

We apply machine learning models to forecast intraday realized volatility (RV), by exploiting commonality in intraday volatility via pooling stock data together, and by incorporating a proxy for the market volatility. Neural networks dominate linear regressions and tree-based models in terms of performance, due to their ability to uncover and model complex latent interactions among variables. Our findings remain robust when we apply trained models to new stocks that have not been included in the training set, thus providing new empirical evidence for a universal volatility mechanism among stocks. Finally, we propose a new approach to forecasting 1-day-ahead RVs using past intraday RVs as predictors, and highlight interesting time-of-day effects that aid the forecasting mechanism. The results demonstrate that the proposed methodology yields superior out-of-sample forecasts over a strong set of traditional baselines that only rely on past daily RVs.
Masked prediction tasks: a parameter identifiability view

The vast majority of work in self-supervised learning, both theoretical and empirical (though mostly the latter), have largely focused on recovering good features for downstream tasks, with the definition of"good"often being intricately tied to the downstream task itself. This lens is undoubtedly very interesting, but suffers from the problem that there isn't a"canonical"set of downstream tasks to focus on -- in practice, this problem is usually resolved by competing on the benchmark dataset du jour. In this paper, we present an alternative lens: one of parameter identifiability. More precisely, we consider data coming from a parametric probabilistic model, and train a self-supervised learning predictor with a suitably chosen parametric form. Then, we ask whether we can read off the ground truth parameters of the probabilistic model from the optimal predictor. We focus on the widely used self-supervised learning method of predicting masked tokens, which is popular for both natural languages and visual data. While incarnations of this approach have already been successfully used for simpler probabilistic models (e.g. learning fully-observed undirected graphical models), we focus instead on latent-variable models capturing sequential structures -- namely Hidden Markov Models with both discrete and conditionally Gaussian observations. We show that there is a rich landscape of possibilities, out of which some prediction tasks yield identifiability, while others do not. Our results, borne of a theoretical grounding of self-supervised learning, could thus potentially beneficially inform practice. Moreover, we uncover close connections with uniqueness of tensor rank decompositions -- a widely used tool in studying identifiability through the lens of the method of moments.
Unsupervised and supervised learning of interacting topological phases from single-particle correlation functions

The recent advances in machine learning algorithms have boosted the application of these techniques to the field of condensed matter physics, in order e.g. to classify the phases of matter at equilibrium or to predict the real-time dynamics of a large class of physical models. Typically in these works, a machine learning algorithm is trained and tested on data coming from the same physical model. Here we demonstrate that unsupervised and supervised machine learning techniques are able to predict phases of a non-exactly solvable model when trained on data of a solvable model. In particular, we employ a training set made by single-particle correlation functions of a non-interacting quantum wire and by using principal component analysis, k-means clustering, t-distributed stochastic neighbor embedding and convolutional neural networks we reconstruct the phase diagram of an interacting superconductor. We show that both the principal component analysis and the convolutional neural networks trained on the data of the non-interacting model can identify the topological phases of the interacting model. Our findings indicate that non-trivial phases of matter emerging from the presence of interactions can be identified by means of unsupervised and supervised techniques applied to data of non-interacting systems.
Pinpointing Anomaly Events in Logs from Stability Testing – N-Grams vs. Deep-Learning

As stability testing execution logs can be very long, software engineers need help in locating anomalous events. We develop and evaluate two models for scoring individual log-events for anomalousness, namely an N-Gram model and a Deep Learning model with LSTM (Long short-term memory). Both are trained on normal log sequences only. We evaluate the models with long log sequences of Android stability testing in our company case and with short log sequences from HDFS (Hadoop Distributed File System) public dataset. We evaluate next event prediction accuracy and computational efficiency. The LSTM model is more accurate in stability testing logs (0.848 vs 0.865), whereas in HDFS logs the N-Gram is slightly more accurate (0.904 vs 0.900). The N-Gram model has far superior computational efficiency compared to the Deep model (4 to 13 seconds vs 16 minutes to nearly 4 hours), making it the preferred choice for our case company. Scoring individual log events for anomalousness seems like a good aid for root cause analysis of failing test cases, and our case company plans to add it to its online services. Despite the recent surge in using deep learning in software system anomaly detection, we found limited benefits in doing so. However, future work should consider whether our finding holds with different LSTM-model hyper-parameters, other datasets, and with other deep-learning approaches that promise better accuracy and computational efficiency than LSTM based models.
Indirect Optimal Control of Advection-Diffusion Fields through Robotic Swarms

In this paper, we consider the problem of optimally guiding a large-scale swarm of underwater vehicles that is tasked with the indirect control of an advection-diffusion environmental field. The microscopic vehicle dynamics are governed by a stochastic differential equation with drift. The drift terms model the self-propelled velocity of the vehicle and the velocity field of the currents. In the mean-field setting, the macroscopic vehicle dynamics are governed by a Kolmogorov forward equation in the form of a linear parabolic advection-diffusion equation. The environmental field is governed by an advection-diffusion equation in which the advection term is defined by the fluid velocity field. The vehicles are equipped with on-board actuators that enable the swarm to act as a distributed source in the environmental field, modulated by a scalar control parameter that determines the local source intensity. In this setting, we formulate an optimal control problem to compute the vehicle velocity and actuator intensity fields that drive the environmental field to a desired distribution within a specified amount of time. In other words, we design optimal vector and scalar actuation fields to indirectly control the environmental field through a distributed source, produced by the swarm. After proving an existence result for the solution of the optimal control problem, we discretize and solve the problem using the Finite Element Method (FEM). The FEM discretization naturally provides an operator that represents the bilinear way in which the controls enter into the dynamics of the vehicle swarm and the environmental field. Finally, we show through numerical simulations the effectiveness of our control strategy in regulating the environmental field to zero or to a desired distribution in the presence of a double-gyre flow field.
Slice-reducible conformal Killing tensors, photon surfaces, and shadows

Kirill Kobialko, Igor Bogush, and Dmitri Gal’tsov Faculty of Physics, Moscow State University, 119899, Moscow, Russia Abstract We generalize our recent method for constructing Killing tensors of the second rank to conformal Killing tensors. The method is intended for foliated spacetimes of arbitrary dimension m, which have a set of conformal Killing vectors. It applies to foliations of a more general structure than in previous literature. The basic idea is to start with reducible Killing tensors in slices constructed from a set of conformal Killing vectors and the induced metric, and then lift them to the whole manifold. Integrability conditions are derived that ensure this, and a constructive lifting procedure is presented. The resulting conformal Killing tensor may be irreducible. It is shown that subdomains of foliation slices suitable for the method are fundamental photon surfaces if some additional photon region inequality is satisfied. Thus our procedure also opens the way to obtain a simple general analytical expression for the boundary of the gravitational shadow. We apply this technique to electrovacuum, and N = 2, 4, 8 supergravity black holes, providing a new easy way to establish the existence of exact and conformal Killing tensors.
SGPT: GPT Sentence Embeddings for Semantic Search

Decoder transformers have continued increasing in scale reaching hundreds of billions of parameters. Due to their scale the same decoder sets state-of-the-art results on various language tasks via prompting or fine-tuning. Yet, these large foundation models remain unusable for the related fields of semantic search and sentence embeddings. This prevents possibly new state-of-the-art results and forces organizations to train and maintain separate models. To this end, we propose SGPT to use decoders for sentence embeddings and semantic search via prompting or fine-tuning. At 5.8 billion parameters SGPT improves on the previously best sentence embeddings by a margin of 7% and outperforms a concurrent method with 175 billion parameters as measured on the BEIR search benchmark. Code, models and result files are freely available at https://github.com/Muennighoff/sgpt.
Caplet Pricing in Affine Models for Risk-Free Rates

Risk-free rates (RFRs) play a central role in the reform of interest rate benchmarks. We study a model for RFRs driven by a general affine process. In this context, under minimal assumptions, we derive explicit valuation formulas for forward-looking and backward-looking caplets/floorlets, term-basis caplets as well as 1-month and 3-month RFR futures contracts.
Fixed point sets and the fundamental group II: Euler characteristics

<jats:p>For a finite group <jats:inline-formula> <jats:alternatives> <jats:tex-math>$G$</jats:tex-math> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S0308210523000756_inline3. png" /> </jats:alternatives> </jats:inline-formula> of not prime power order, Oliver showed that the obstruction for a finite CW-complex <jats:inline-formula> <jats:alternatives> <jats:tex-math>$F$</jats:tex-math> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S0308210523000756_inline4. png" /> </jats:alternatives> </jats:inline-formula> to be the fixed point set of a contractible finite <jats:inline-formula> <jats:alternatives> <jats:tex-math>$G$</jats:tex-math> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S0308210523000756_inline5. png" /> </jats:alternatives> </jats:inline-formula>-CW-complex is determined by the Euler characteristic <jats:inline-formula> <jats:alternatives> <jats:tex-math>$\chi (F)$</jats:tex-math> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S0308210523000756_inline6. png" /> </jats:alternatives> </jats:inline-formula>. (He also has similar results for compact Lie group actions.) We show that the analogous problem for <jats:inline-formula> <jats:alternatives> <jats:tex-math>$F$</jats:tex-math> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S0308210523000756_inline7. png" /> </jats:alternatives> </jats:inline-formula> to be the fixed point set of a finite <jats:inline-formula> <jats:alternatives> <jats:tex-math>$G$</jats:tex-math> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S0308210523000756_inline8. png" /> </jats:alternatives> </jats:inline-formula>-CW-complex of some given homotopy type is still determined by the Euler characteristic. Using trace maps on <jats:inline-formula> <jats:alternatives> <jats:tex-math>$K_0$</jats:tex-math> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S0308210523000756_inline9. png" /> </jats:alternatives> </jats:inline-formula> [2, 7, 18], we also see that there are interesting roles for the fundamental group and the component structure of the fixed point set. </jats:p>
Impact of rural transportation networks on farmers’ income in Ilaje Local Government Area of Ondo State, Nigeria

Abstract This study examined how the nature of rural transportation networks affects the income of local farmers. The study involved arable crop farmers in the Ilaje local government area of Ondo State, Nigeria. A structured interview was used to gather information from a total of 120 farmers in rural communities across the local government using a two-stage random sampling technique. Descriptive statistics and inferential statistics such as chi-square and Pearson Product Moment Correlation (PPMC) were used to analyse the data. The study revealed that farmers’ poor nature of transportation networks has a negative impact on their income. Gender (χ2 = 6.472) and marital status (χ2 = 9.745) positively influenced income generated by farmers at p < 0.05. Additionally, there was a moderate and significant correlation between transportation systems used (r = 0.705, p < 0.05), the perceived impact of rural transport (r = 0.267, p < 0.05), and the farmers’ income on agricultural activities. The results have shown that the serious constraints faced by the farmers are high cost of transportation which has a great influence on their generated income. The study allows to conclude that the nature of transportation networks available in the communities under study has a significant impact on the level of income to be realised by farmers. The construction, development, and provision of more rural transport networks would not only boost the standard of living and reduce transport costs of the rural poor but could also be a solution for the prolonged deplorable and worsening situation of rural roads in the country.
Theory convergence in emotion science is timely and realistic

Over the last century, emotion research has been beset by the problem of major disagreements with respect to the definition of the phenomenon and an abundance of different theories. Arguably, these divergences have had adverse effects on theory development, on the theoretical foundations of empirical research, and on knowledge accumulation in the study of emotion. Similar problems have been encountered in other areas of behavioural science. Increasingly, there have been calls to work towards some form of theory integration. In contrast, here an effort is made to show that a reasonable degree of theory convergence in the area of emotion science can be attained by adopting a design feature-based working definition of emotion and highlighting the basic agreement on the components of the dynamic emotion process. The aim is to invite constructive discussion on communalities and divergences between different theories and foster the development of more complementary theoretical frameworks to guide future empirical research.
The Beautiful Encounters

This paper introduces an art project called “The Beautiful Encounters” that resonates with the surrealist painter Rene Magritte's work and aims to introduce an interactive scenario as a case study that applies AI technology. The article's focus lies on the research process exploring methods to integrate the real-time image of the sky into an interactive artwork. The technical part explains how multiple technologies, like face tracking, its alignment, the AI model of face-parsing, shaders, and web technologies, are combined in the project. In the end, we discuss the importance of contextualizing AI-related artworks in relation to art history.
Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2

The AlphaFold Protein Structure Database contains predicted structures for millions of proteins. For the majority of human proteins that contain intrinsically disordered regions (IDRs), which do not adopt a stable structure, it is generally assumed these regions have low AlphaFold2 confidence scores that reflect low-confidence structural predictions. Here, we show that AlphaFold2 assigns confident structures to nearly 15% of human IDRs. By comparison to experimental NMR data for a subset of IDRs that are known to conditionally fold (i.e., upon binding or under other specific conditions), we find that AlphaFold2 often predicts the structure of the conditionally folded state. Based on databases of IDRs that are known to conditionally fold, we estimate that AlphaFold2 can identify conditionally folding IDRs at a precision as high as 88% at a 10% false positive rate, which is remarkable considering that conditionally folded IDR structures were minimally represented in its training data. We find that human disease mutations are nearly 5-fold enriched in conditionally folded IDRs over IDRs in general, and that up to 80% of IDRs in prokaryotes are predicted to conditionally fold, compared to less than 20% of eukaryotic IDRs. These results indicate that a large majority of IDRs in the proteomes of human and other eukaryotes function in the absence of conditional folding, but the regions that do acquire folds are more sensitive to mutations. We emphasize that the AlphaFold2 predictions do not reveal functionally relevant structural plasticity within IDRs and cannot offer realistic ensemble representations of conditionally folded IDRs. Significance Statement AlphaFold2 and other machine learning-based methods can accurately predict the structures of most proteins. However, nearly two-thirds of human proteins contain segments that are highly flexible and do not autonomously fold, otherwise known as intrinsically disordered regions (IDRs). In general, IDRs interconvert rapidly between a large number of different conformations, posing a significant problem for protein structure prediction methods that define one or a small number of stable conformations. Here, we found that AlphaFold2 can readily identify structures for a subset of IDRs that fold under certain conditions (conditional folding). We leverage AlphaFold2’s predictions of conditionally folded IDRs to quantify the extent of conditional folding across the tree of life, and to rationalize disease-causing mutations in IDRs. Classifications: Biological Sciences; Biophysics and Computational Biology
Protein compactness and interaction valency define the architecture of a biomolecular condensate across scales

Non-membrane-bound biomolecular condensates have been proposed to represent an important mode of subcellular organization in diverse biological settings. However, the fundamental principles governing the spatial organization and dynamics of condensates at the atomistic level remain unclear. The S. cerevisiae Lge1 protein is required for histone H2B ubiquitination and its N-terminal intrinsically disordered fragment (Lge11-80) undergoes robust phase separation. This study connects single- and multi-chain all-atom molecular dynamics simulations of Lge11-80 with the in vitro behavior of Lge11-80 condensates. Analysis of modelled protein-protein interactions elucidates the key determinants of Lge11-80 condensate formation and links configurational entropy, valency and compactness of proteins inside the condensates. A newly derived analytical formalism, related to colloid fractal cluster formation, describes condensate architecture across length scales as a function of protein valency and compactness. In particular, the formalism provides an atomistically resolved model of Lge11-80 condensates on the scale of hundreds of nanometers starting from individual protein conformers captured in simulations. The simulation-derived fractal dimensions of condensates of Lge11-80 and its mutants agree with their in vitro morphologies. The presented framework enables a multiscale description of biomolecular condensates and embeds their study in a wider context of colloid self-organization.