Miracle-dz/UniFAR-FTUs huggingface dataset:
Causal Deep Reinforcement Learning using Observational Data

Deep reinforcement learning (DRL) requires the collection of interventional data, which is sometimes expensive and even unethical in the real world, such as in the autonomous driving and the medical field. Offline reinforcement learning promises to alleviate this issue by exploiting the vast amount of observational data available in the real world. However, observational data may mislead the learning agent to undesirable outcomes if the behavior policy that generates the data depends on unobserved random variables (i.e., confounders). In this paper, we propose two deconfounding methods in DRL to address this problem. The methods first calculate the importance degree of different samples based on the causal inference technique, and then adjust the impact of different samples on the loss function by reweighting or resampling the offline dataset to ensure its unbiasedness. These deconfounding methods can be flexibly combined with existing model-free DRL algorithms such as soft actor-critic and deep Q-learning, provided that a weak condition can be satisfied by the loss functions of these algorithms. We prove the effectiveness of our deconfounding methods and validate them experimentally.
AWTE-BERT: Attending to Wordpiece Tokenization Explicitly on BERT for Joint Intent Classification and SlotFilling

Intent classiﬁcation and slot ﬁlling are two core tasks in natural language understanding (NLU). The interaction nature of the two tasks makes the joint models often outperform the single designs. One of the promising solutions, called BERT (Bidirectional Encoder Representations from Transformers), achieves the joint optimization of the two tasks. BERT adopts the wordpiece to tokenize each input token into multiple sub-tokens, which causes a mismatch between the tokens’ and the labels’ lengths. Previous methods utilize the hidden states corresponding to the ﬁrst sub-token as input to the classiﬁer, which limits performance improvement since some hidden semantic informations is discarded in the ﬁne-tune process. To address this issue, we propose a novel joint model based on BERT, which explicitly models the multiple sub-tokens features after wordpiece tokenization, thereby generating the context features that contribute to slot ﬁlling. Speciﬁcally, we encode the hidden states corresponding to multiple sub-tokens into a context vector via the attention mechanism. Then, we feed each context vector into the slot ﬁlling encoder, which preserves the integrity of the sentence. Experimental results demonstrate that our proposed model achieves signiﬁcant improvement on intent classiﬁcation accuracy, slot ﬁlling F1, and sentence-level semantic frame ac-∗
Practical Universal Noiseless Coding

Discrete data sources arising from practical problems are generally characterized by only partially known and varying statistics. This paper provides the development and analysis of some practical adaptive techniques for the efficient noiseless coding of a broad class of such data sources. Specifically, algorithms are developed for coding discrete memoryless sources which have a known symbol probability ordering but unknown probability values. A general applicability of these algorithms is obtained because most real world problems can be simply transformed into this form by appropriate preprocessing.
Charged black holes from a family of Born–Infeld-type electrodynamics models

We present a family of nonlinear electrodynamics models that are free of the infinite self-energy of the point charge. Each model is dependent on a dimensional nonlinearity parameter and is determined by the integer value of a dimensionless parameter [Formula: see text]. The Born–Infeld model is recovered when [Formula: see text]. Some of the characteristics of this family are studied. In addition, we study the solutions of electrically charged AdS black holes, which result from coupling these electrodynamic models to General Relativity. A Smarr formula consistent with the first law of thermodynamics is also obtained in an extended phase space.
A collocation method for the numerical Fourier analysis of quasi-periodic functions. I: Numerical tests and examples

The purpose of this paper is to develop a numerical procedure for the determination of frequencies and amplitudes of a quasi--periodic function, starting from equally-spaced samples of it on a finite time interval. It is based on a collocation method in frequency domain. Strategies for the choice of the collocation harmonics are discussed, in order to ensure good conditioning of the resulting system of equations. The accuracy and robustness of the procedure is checked with several examples. The paper is ended with two applications of its use as a dynamical indicator. The theoretical support for the method presented here is given in a companion paper [21].
Transfer RL via the Undo Maps Formalism

Transferring knowledge across domains is one of the most fundamental problems in machine learning, but doing so effectively in the context of reinforcement learning remains largely an open problem. Current methods make strong assumptions on the specifics of the task, often lack principled objectives, and -- crucially -- modify individual policies, which might be sub-optimal when the domains differ due to a drift in the state space, i.e., it is intrinsic to the environment and therefore affects every agent interacting with it. To address these drawbacks, we propose TvD: transfer via distribution matching, a framework to transfer knowledge across interactive domains. We approach the problem from a data-centric perspective, characterizing the discrepancy in environments by means of (potentially complex) transformation between their state spaces, and thus posing the problem of transfer as learning to undo this transformation. To accomplish this, we introduce a novel optimization objective based on an optimal transport distance between two distributions over trajectories -- those generated by an already-learned policy in the source domain and a learnable pushforward policy in the target domain. We show this objective leads to a policy update scheme reminiscent of imitation learning, and derive an efficient algorithm to implement it. Our experiments in simple gridworlds show that this method yields successful transfer learning across a wide range of environment transformations.
A Bayesian Approach to Reconstructing Interdependent Infrastructure Networks from Cascading Failures

—Analyzing the behavior of complex interdependent networks requires complete information about the network topology and the interdependent links across networks. For many applications such as critical infrastructure systems, understanding network interdependencies is crucial to anticipate cascading failures and plan for disruptions. However, data on the topology of individual networks are often publicly unavailable due to privacy and security concerns. Additionally, interdependent links are often only revealed in the aftermath of a disruption as a result of cascading failures. We propose a scalable nonparametric Bayesian approach to reconstruct the topology of interdependent infrastructure networks from observations of cascading failures. Metropolis-Hastings algorithm coupled with the infrastructure-dependent proposal are employed to increase the efﬁciency of sampling possible graphs. Results of reconstructing a synthetic system of interdependent infrastructure networks demonstrate that the proposed approach outperforms existing methods in both accuracy and computational time. We further apply this approach to reconstruct the topology of one synthetic and two real-world systems of interdependent infrastructure networks, including gas-power-water networks in Shelby County, TN, USA, and an interdependent system of power-water networks in Italy, to demonstrate the general applicability of the approach.
Fast DFT Computation for Signals With Structured Support

Suppose an <inline-formula> <tex-math notation="LaTeX">$N-$ </tex-math></inline-formula>length signal has known frequency support of size <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>. Given access to samples of this signal, how fast can we compute the DFT? The answer to this question depends on the structure of the frequency support. We first identify some frequency supports for which (an ideal) <inline-formula> <tex-math notation="LaTeX">$O(k \log k)$ </tex-math></inline-formula> complexity can be achieved, which we refer to as homogeneous sets. We give a generalization of the radix-2 FFT that enables <inline-formula> <tex-math notation="LaTeX">$O(k\log k)$ </tex-math></inline-formula> computation of signals with homogeneous frequency support. We use homogeneous sets as building blocks to construct more complex support structures for which the complexity of <inline-formula> <tex-math notation="LaTeX">$O(k\log k)$ </tex-math></inline-formula> is achievable. Applying these ideas, we present an <inline-formula> <tex-math notation="LaTeX">$O(k\log ^{2}k)$ </tex-math></inline-formula> algorithm for computing the DFT of signals whose frequency support is additively structured. We also present partial converses.
Flow: Per-instance Personalized Federated Learning

Personalization in Federated Learning (FL) aims to modify a collaboratively trained global model according to each client. Current approaches to personalization in FL are at a coarse granularity, i.e. all the input instances of a client use the same personalized model. This ignores the fact that some instances are more accurately handled by the global model due to better generalizability. To address this challenge, this work proposes Flow, a fine-grained stateless personalized FL approach. Flow creates dynamic personalized models by learning a routing mechanism that determines whether an input instance prefers the local parameters or its global counterpart. Thus, Flow introduces per-instance routing in addition to leveraging per-client personalization to improve accuracies at each client. Further, Flow is stateless which makes it unnecessary for a client to retain its personalized state across FL rounds. This makes Flow practical for large-scale FL settings and friendly to newly joined clients. Evaluations on Stackoverflow, Reddit, and EMNIST datasets demonstrate the superiority in prediction accuracy of Flow over state-of-the-art non-personalized and only per-client personalized approaches to FL.
ReGrAt: Regularization in Graphs using Attention to handle class imbalance

Node classiﬁcation is an important task to solve in graph- based learning. Even though a lot of work has been done in this ﬁeld, imbalance is neglected. Real-world data is not per- fect, and is imbalanced in representations most of the times. Apart from text and images, data can be represented using graphs, and thus addressing the imbalance in graphs has be-come of paramount importance. In the context of node clas- siﬁcation, one class has less examples than others. Changing data composition is a popular way to address the imbalance in node classiﬁcation. This is done by resampling the data to balance the dataset. However, that can sometimes lead to loss of information or add noise in the dataset. Therefore, in this work, we implicitly solve the problem by changing the model loss. Speciﬁcally, we study how attention networks can help tackle imbalance. Moreover, we observe that using a regular- izer to assign larger weights to minority nodes helps to mitigate this imbalance. We achieve State of the Art results than the existing methods on several standard citation benchmark datasets.
Identifying a 3-vertex strongly biconnected directed subgraph with minimum number of edges

A strongly connected graph is strongly biconnected if after ignoring the direction of its edges we have an undirected graph with no articulation points. A 3-vertex strongly biconnected graph is a strongly biconnected digraph that has the property that deleting any two vertices in this graph leaves a strongly binconnected subgraph. Jaberi [11] presented approximation algorithms for minimum cardinality 2-vertex strongly biconnected directed subgraph problem. We will focus in this paper on polynomial time algorithms which we have implemented for producing spanning subgraphs that are 3-vertex strongly biconnected.
The Impact of Racial Distribution in Training Data on Face Recognition Bias: A Closer Look

Face recognition algorithms, when used in the real world, can be very useful, but they can also be dangerous when biased toward certain demographics. So, it is essential to understand how these algorithms are trained and what factors affect their accuracy and fairness to build better ones. In this study, we shed some light on the effect of racial distribution in the training data on the performance of face recognition models. We conduct 16 different experiments with varying racial distributions of faces in the training data. We analyze these trained models using accuracy metrics, clustering metrics, UMAP projections, face quality, and decision thresholds. We show that a uniform distribution of races in the training datasets alone does not guarantee bias-free face recognition algorithms and how factors like face image quality play a crucial role. We also study the correlation between the clustering metrics and bias to understand whether clustering is a good indicator of bias. Finally, we introduce a metric called racial gradation to study the inter and intra race correlation in facial features and how they affect the learning ability of the face recognition models. With this study, we try to bring more understanding to an essential element of face recognition training, the data. A better understanding of the impact of training data on the bias of face recognition algorithms will aid in creating better datasets and, in turn, better face recognition systems.
The warp of the Galactic stellar disk detected in IRAS source counts

About 90,000 IRAS point sources have been used as disk tracers in order to explore the possibility of warp in the Galactic stellar disk. The results imply that the Galactic stellar disk is warped at large radii in a way similar to the H-I layer, and that the warp is an important characteristic of the Galaxy as a whole. It is suggested that the warp may be a long-lasting phenomenon, possibly caused by asymmetries of the mass distribution in the outer regions of the Galactic dark halo. 56 refs.
Patterns in transitional shear turbulence. Part 1. Energy transfer and mean-flow interaction

Abstract Low Reynolds number turbulence in wall-bounded shear flows en route to laminar flow takes the form of spatially intermittent turbulent structures. In plane shear flows, these appear as a regular pattern of alternating turbulent and quasi-laminar flow. Both the physical and the spectral energy balance of a turbulent–laminar pattern in plane Couette flow are computed and compared to those of uniform turbulence. In the patterned state, the mean flow is strongly modulated and is fuelled by two mechanisms: primarily, the nonlinear self-interaction of the mean flow (via mean advection), and secondly, the extraction of energy from turbulent fluctuations (via negative spectral production, associated with an energy transfer from small to large scales). Negative production at large scales is also found in the uniformly turbulent state. Important features of the energy budgets are surveyed as a function of $Re$ through the transition between uniform turbulence and turbulent–laminar patterns.
Towards Better Document-level Relation Extraction via Iterative Inference

Document-level relation extraction (RE) aims to extract the relations between entities from the input document that usually containing many difficultly-predicted entity pairs whose relations can only be predicted through relational inference. Existing methods usually directly predict the relations of all entity pairs of input document in a one-pass manner, ignoring the fact that predictions of some entity pairs heavily depend on the predicted results of other pairs. To deal with this issue, in this paper, we propose a novel document-level RE model with iterative inference. Our model is mainly composed of two modules: 1) a base module expected to provide preliminary relation predictions on entity pairs; 2) an inference module introduced to refine these preliminary predictions by iteratively dealing with difficultly-predicted entity pairs depending on other pairs in an easy-to-hard manner. Unlike previous methods which only consider feature information of entity pairs, our inference module is equipped with two Extended Cross Attention units, allowing it to exploit both feature information and previous predictions of entity pairs during relational inference. Furthermore, we adopt a two-stage strategy to train our model. At the first stage, we only train our base module. During the second stage, we train the whole model, where contrastive learning is introduced to enhance the training of inference module. Experimental results on three commonly-used datasets show that our model consistently outperforms other competitive baselines.
P4Testgen: An Extensible Test Oracle For P4-16

We present P4Testgen, a test oracle for the P416 language. P4Testgen supports automatic test generation for any P4 target and is designed to be extensible to many P4 targets. It models the complete semantics of the target's packet-processing pipeline including the P4 language, architectures and externs, and target-specific extensions. To handle non-deterministic behaviors and complex externs (e.g., checksums and hash functions), P4Testgen uses taint tracking and concolic execution. It also provides path selection strategies that reduce the number of tests required to achieve full coverage. We have instantiated P4Testgen for the V1model, eBPF, PNA, and Tofino P4 architectures. Each extension required effort commensurate with the complexity of the target. We validated the tests generated by P4Testgen by running them across the entire P4C test suite as well as the programs supplied with the Tofino P4 Studio. Using the tool, we have also confirmed 25 bugs in mature, production toolchains for BMv2 and Tofino.
Elemental Abundances in M31: Individual and Coadded Spectroscopic [Fe/H] and [α/Fe] throughout the M31 Halo with SPLASH

We present spectroscopic chemical abundances of red giant branch stars in Andromeda (M31), using medium-resolution (R ∼ 6000) spectra obtained via the Spectroscopic and Photometric Landscape of Andromeda’s Stellar Halo survey. In addition to individual chemical abundances, we coadd low signal-to-noise ratio spectra of stars to obtain a high enough signal to measure average [Fe/H] and [α/Fe] abundances. We obtain individual and coadded measurements for [Fe/H] and [α/Fe] for M31 halo stars, covering a range of 9–180 kpc in projected radius from the center of M31. With these measurements, we greatly increase the number of outer halo (R proj > 50 kpc) M31 stars with spectroscopic [Fe/H] and [α/Fe], adding abundance measurements for 45 individual stars and 33 coadds from a pool of an additional 174 stars. We measure the spectroscopic metallicity ([Fe/H]) gradient, finding a negative radial gradient of −0.0084 ± 0.0008 for all stars in the halo, consistent with gradient measurements obtained using photometric metallicities. Using the first measurements of [α/Fe] for M31 halo stars covering a large range of projected radii, we find a positive gradient (+0.0027 ± 0.0005) in [α/Fe] as a function of projected radius. We also explore the distribution in [Fe/H]–[α/Fe] space as a function of projected radius for both individual and coadded measurements in the smooth halo, and compare these measurements to those stars potentially associated with substructure. These spectroscopic abundance distributions add to existing evidence that M31 has had an appreciably different formation and merger history compared to our own Galaxy.
Dynamic Equilibrium with Insider Information and General Uninformed Agent Utility

We study a continuous time economy where agents have asymmetric information. The informed agent (``$I$''), at time zero, receives a private signal about the risky assets' terminal payoff $\Psi(X_T)$, while the uninformed agent (``$U$'') has no private signal. $\Psi$ is an arbitrary payoff function, and $X$ follows a time-homogeneous diffusion. Crucially, we allow $U$ to have von Neumann-Morgenstern preferences with a general utility function on $(0,\infty)$ satisfying the standard conditions. This extends previous constructions of equilibria with asymmetric information used when all agents have exponential utilities and enables us to study the impact of $U$'s initial share endowment on equilibrium. To allow for $U$ to have general preferences, we introduce a new method to prove existence of a partial communication equilibrium (PCE), where at time $0$, $U$ receives a less-informative signal than $I$. In the single asset case, this signal is recoverable by viewing the equilibrium price process over an arbitrarily short period of time, and hence the PCE is a dynamic noisy rational expectations equilibrium. Lastly, when $U$ has power (constant relative risk aversion) utility, we identify the equilibrium price in the small and large risk aversion limits.
Automatically Extracting Information in Medical Dialogue: Expert System And Attention for Labelling

Medical dialogue information extraction is becoming an increasingly significant problem in modern medical care. It is difficult to extract key information from electronic medical records (EMRs) due to their large numbers. Previously, researchers proposed attention-based models for retrieving features from EMRs, but their limitations were reflected in their inability to recognize different categories in medical dialogues. In this paper, we propose a novel model, Expert System and Attention for Labelling (ESAL). We use mixture of experts and pre-trained BERT to retrieve the semantics of different categories, enabling the model to fuse the differences between them. In our experiment, ESAL was applied to a public dataset and the experimental results indicated that ESAL significantly improved the performance of Medical Information Classification.
High peak power continuum generation by high-order solitons at the mid-infrared wavelength in a photonic crystal fiber

A photonic crystal fiber (PCF) is fabricated from silica material purified by the complexing method of rectification and adsorption in our laboratory. The parameters of pump pulses and fiber length are optimized in the experiment, and the nonlinear optical process of the high-order soliton generation is studied. The high peak power continuums at the mid-infrared wavelength are generated by the overlap among the high-order soliton peaks. Specifically, when a PCF with a length of 60 cm is used, the conversion efficiency η and bandwidth B of the continuum generated can be up to 65% and 358 nm, and the corresponding peak power Ppeak-o and pulse width Wp are 144 kW and 30 fs. It is believed that the high peak power and broadband mid-infrared continuum can be used as short pulse sources, which will find important applications in biophotonics and ultrafast science and technology.