Miracle-dz/UniFAR-FTUs huggingface dataset:
Randomized Policy Optimization for Optimal Stopping

Optimal stopping is the problem of determining when to stop a stochastic system in order to maximize reward, which is of practical importance in domains such as ﬁnance, operations management and healthcare. Existing methods for high-dimensional optimal stopping that are popular in practice produce deterministic linear policies – policies that deterministically stop based on the sign of a weighted sum of basis functions – but are not guaranteed to ﬁnd the optimal policy within this policy class given a ﬁxed basis function architecture. In this paper, we propose a new methodology for optimal stopping based on randomized linear policies, which choose to stop with a probability that is determined by a weighted sum of basis functions. We motivate these policies by establishing that under mild conditions, given a ﬁxed basis function architecture, optimizing over randomized linear policies is equivalent to optimizing over deterministic linear policies. We formulate the problem of learning randomized linear policies from data as a smooth non-convex sample average approximation (SAA) problem. We theoretically prove the almost sure convergence of our randomized policy SAA problem and establish bounds on the out-of-sample performance of randomized policies obtained from our SAA problem based on Rademacher complexity. We also show that the SAA problem is in general NP-Hard, and consequently develop a practical heuristic for solving our randomized policy problem. Through numerical experiments on a benchmark family of option pricing problem instances, we show that our approach can substantially outperform state-of-the-art methods.
Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion

Human behavior has the nature of indeterminacy, which requires the pedestrian trajectory prediction system to model the multi-modality of future motion states. Unlike existing stochastic trajectory prediction methods which usually use a latent variable to represent multi-modality, we explicitly simulate the process of human motion variation from indeterminate to determinate. In this paper, we present a new framework to formulate the trajectory prediction task as a reverse process of motion indeterminacy diffusion (MID), in which we progressively discard indeterminacy from all the walkable areas until reaching the desired trajectory. This process is learned with a parameterized Markov chain conditioned by the observed trajectories. We can adjust the length of the chain to control the degree of indeterminacy and balance the diversity and determinacy of the predictions. Specifically, we encode the history behavior information and the social interactions as a state embedding and devise a Transformer-based diffusion model to capture the temporal dependencies of trajectories. Extensive experiments on the human trajectory prediction benchmarks including the Stanford Drone and ETH/UCY datasets demonstrate the superiority of our method. Code is available at https://github.com/gutianpei/MID.
Sparse Federated Learning With Hierarchical Personalization Models

Federated learning (FL) can achieve privacy-safe and reliable collaborative training without collecting users’ private data. Its excellent privacy security potential promotes a wide range of federated learning (FL) applications in Internet of Things (IoT), wireless networks, mobile devices, autonomous vehicles, and cloud medical treatment. However, the FL method suffers from poor model performance on non-independent and identically distributed (non-i.i.d.) data and excessive traffic volume. To this end, we propose a personalized FL algorithm using a hierarchical proximal mapping based on the moreau envelop, named sparse federated learning with hierarchical personalized models (sFedHP), which significantly improves the acrlong GM performance facing diverse data. A continuously differentiable approximated $\ell _{1}$ -norm is also used as the sparse constraint to reduce the communication cost. Convergence analysis shows that sFedHP’s convergence rate is state-of-the-art with linear speedup and the sparse constraint only reduces the convergence rate to a small extent while significantly reducing the communication cost. Experimentally, we demonstrate the benefits of sFedHP compared with the federated averaging (FedAvg), hierarchical fedavg (HierFAVG), and personalized FL methods based on local customization, including FedAMP, FedProx, per- FedAvg, pFedMe, and pFedGP.
UKP-SQUARE: An Online Platform for Question Answering Research

Recent advances in NLP and information retrieval have given rise to a diverse set of question answering tasks that are of different formats (e.g., extractive, abstractive), require different model architectures (e.g., generative, discriminative), and setups (e.g., with or without retrieval). Despite having a large number of powerful, specialized QA pipelines (which we refer to as Skills) that consider a single domain, model or setup, there exists no framework where users can easily explore and compare such pipelines and can extend them according to their needs. To address this issue, we present UKP-SQuARE, an extensible online QA platform for researchers which allows users to query and analyze a large collection of modern Skills via a user-friendly web interface and integrated behavioural tests. In addition, QA researchers can develop, manage, and share their custom Skills using our microservices that support a wide range of models (Transformers, Adapters, ONNX), datastores and retrieval techniques (e.g., sparse and dense). UKP-SQuARE is available on https://square.ukp-lab.de
One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia

NLP research is impeded by a lack of resources and awareness of the challenges presented by underrepresented languages and dialects. Focusing on the languages spoken in Indonesia, the second most linguistically diverse and the fourth most populous nation of the world, we provide an overview of the current state of NLP research for Indonesia’s 700+ languages. We highlight challenges in Indonesian NLP and how these affect the performance of current NLP systems. Finally, we provide general recommendations to help develop NLP technology not only for languages of Indonesia but also other underrepresented languages.
ILoveEye: Eyeliner Makeup Guidance System with Eye Shape Features

Drawing eyeliner is not an easy task for whom lacks experience in eye makeup. Everyone has a unique pair of eyes, so they need to draw eyeliner in a style that suits their eyes. We proposed ILoveEye, an interactive system that supports eye-makeup novices to draw natural and suitable eyeliner. The proposed system analyzes the shape of the user's eyes and classifies the eye types from camera frame. The system can recommend the eyeliner style to the user based on the designed recommendation rules. Then, the system can generate the original patterns corresponding to the eyeliner style, and the user can draw the eyeliner while observing the real-time makeup guidance. The user evaluation experiments are conducted to verify that the proposed ILoveEye system can help some users to draw reasonable eyeliner based on the specific eye shapes and improve their eye makeup skills.
Plagiarism Detection in the Bengali Language: A Text Similarity-Based Approach

Plagiarism means taking another person's work and not giving any credit to them for it. Plagiarism is one of the most serious problems in academia and among researchers. Even though there are multiple tools available to detect plagiarism in a document but most of them are domain-specific and designed to work in English texts, but plagiarism is not limited to a single language only. Bengali is the most widely spoken language of Bangladesh and the second most spoken language in India with 300 million native speakers and 37 million second-language speakers. Plagiarism detection requires a large corpus for comparison. Bengali Literature has a history of 1300 years. Hence most Bengali Literature books are not yet digitalized properly. As there was no such corpus present for our purpose so we have collected Bengali Literature books from the National Digital Library of India and with a comprehensive methodology extracted texts from it and constructed our corpus. Our experimental results find out average accuracy between 72.10 % - 79.89 % in text extraction using OCR. Levenshtein Distance algorithm is used for determining Plagiarism. We have built a web application for end-user and successfully tested it for Plagiarism detection in Bengali texts. In future, we aim to construct a corpus with more books for more accurate detection.
Snowmass2021 White Paper: Collider Physics Opportunities of Extended Warped Extra-Dimensional Models

While the warped extra-dimensional models provide an attractive solution to both the gauge and the flavor hierarchy problems, the mass scale of new particles predicted by the minimal models would be beyond the reach of the LHC. Models of extended warped extra dimensions have been proposed to evade these issues and their collider implications have been investigated for the last decade. This white paper summarizes the recent developments in the context of collider phenomenology. The strategies and lessons are broad, and provide a template to extend the experimental program, to cover a wider class of signals in other new physics scenarios as well.
Speech-enhanced and Noise-aware Networks for Robust Speech Recognition

Compensation for channel mismatch and noise interference is essential for robust automatic speech recognition. Enhanced speech has been introduced into the multi-condition training of acoustic models to improve their generalization ability. In this paper, a noise-aware training framework based on two cascaded neural structures is proposed to jointly optimize speech enhancement and speech recognition. The feature enhancement module is composed of a multi-task autoencoder, where noisy speech is decomposed into clean speech and noise. By concatenating its enhanced, noise-aware, and noisy features for each frame, the acoustic-modeling module maps each feature-augmented frame into a triphone state by optimizing the lattice-free maximum mutual information and cross entropy between the predicted and actual state sequences. On top of the factorized time delay neural network (TDNN-F) and its convolutional variant (CNN-TDNNF), both with SpecAug, the two proposed systems achieve word error rate (WER) of 3.90% and 3.55%, respectively, on the Aurora-4 task. Compared with the best existing systems that use bigram and trigram language models for decoding, the proposed CNN-TDNNF-based system achieves a relative WER reduction of 15.20% and 33.53%, respectively. In addition, the proposed CNN-TDNNF-based system also outperforms the baseline CNN-TDNNF system on the AMI task.
Interpretation of Chest x-rays affected by bullets using deep transfer learning

: The potential of deep learning, especially in medical imaging, initiated astonishing results and improved the methodologies after every passing day. Deep learning in radiology provides the opportunity to classify, detect and segment different diseases automatically. In the proposed study, we worked on a non-trivial aspect of medical imaging where we classified and localized the X-Rays affected by bullets . We tested Images on different classification and localization models to get considerable accuracy. The replicated data set used in the study was replicated on different images of chest X-Rays. The proposed model worked not only on chest radiographs but other body organs X-rays like leg, abdomen, head, even the training dataset based on chest radiographs. Custom models have been used for classification and localization purposes after tuning parameters. Finally, the results of our findings manifested using different frameworks. This might assist the research enlightening towards this field. To the best of our knowledge, this is the first study on the detection and classification of radiographs affected by bullets using deep learning .
Immersive Visual Analysis of Cello Bow Movements

We propose a 3D immersive visualization environment for analyz-ing the right hand movements of a cello player. To achieve this, we track the position and orientation of the cello bow and record audio. As movements mostly occur in a shallow volume and the motion is therefore mostly two-dimensional, we use the third dimension to encode time. Our concept further explores various mappings from motion and audio data to spatial and other visual attributes. We work in close cooperation with a cellist and plan to evaluate our prototype through a user study with a group of cellists in the near future.
Modeling Attrition in Recommender Systems with Departing Bandits

Traditionally, when recommender systems are formalized as multi-armed bandits, the policy of the recommender system influences the rewards accrued, but not the length of interaction. However, in real-world systems, dissatisfied users may depart (and never come back). In this work, we propose a novel multi-armed bandit setup that captures such policy-dependent horizons. Our setup consists of a finite set of user types, and multiple arms with Bernoulli payoffs. Each (user type, arm) tuple corresponds to an (unknown) reward probability. Each user's type is initially unknown and can only be inferred through their response to recommendations. Moreover, if a user is dissatisfied with their recommendation, they might depart the system. We first address the case where all users share the same type, demonstrating that a recent UCB-based algorithm is optimal. We then move forward to the more challenging case, where users are divided among two types. While naive approaches cannot handle this setting, we provide an efficient learning algorithm that achieves O(sqrt(T)ln(T)) regret, where T is the number of users.
p-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets

We study the p -generalized probit regression model, which is a generalized linear model for binary responses. It extends the standard probit model by replacing its link function, the standard normal cdf, by a p -generalized normal distribution for p ∈ [1 , ∞ ). The p generalized normal distributions (Subbotin, 1923) are of special interest in statistical modeling because they ﬁt much more ﬂexibly to data. Their tail behavior can be controlled by choice of the parameter p , which inﬂuences the model’s sensitivity to outliers. Special cases include the Laplace, the Gaussian, and the uniform distributions. We further show how the maximum likelihood estimator for p -generalized probit regression can be approximated eﬃciently up to a factor of (1 + ε ) on large data by combining sketching techniques with importance subsampling to obtain a small data summary called coreset. model
EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition

Emotion recognition in conversation (ERC) aims to analyze the speaker’s state and identify their emotion in the conversation. Recent works in ERC focus on context modeling but ignore the representation of contextual emotional tendency. In order to extract multi-modal information and the emotional tendency of the utterance effectively, we propose a new structure named Emoformer to extract multi-modal emotion vectors from different modalities and fuse them with sentence vector to be an emotion capsule. Furthermore, we design an end-to-end ERC model called EmoCaps, which extracts emotion vectors through the Emoformer structure and obtain the emotion classification results from a context analysis model. Through the experiments with two benchmark datasets, our model shows better performance than the existing state-of-the-art models.
Probing Pre-Trained Language Models for Cross-Cultural Differences in Values

Language embeds information about social, cultural, and political values people hold. Prior work has explored potentially harmful social biases encoded in Pre-trained Language Models (PLMs). However, there has been no systematic study investigating how values embedded in these models vary across cultures.In this paper, we introduce probes to study which cross-cultural values are embedded in these models, and whether they align with existing theories and cross-cultural values surveys. We find that PLMs capture differences in values across cultures, but those only weakly align with established values surveys. We discuss implications of using mis-aligned models in cross-cultural settings, as well as ways of aligning PLMs with values surveys.
Phonon Chirality Induced by Vibronic-Orbital Coupling

The notion that phonons can carry pseudo-angular momentum has become popular in the last decade, with recent research eﬀorts highlighting phonon chirality, Berry curvature of phonon band structure, and the phonon Hall eﬀect. When a phonon is resonantly coupled to a crystal electric ﬁeld excitation, a so-called vibronic bound state forms. Here, we observe angular momentum transfer of ∆ J z = ± 1 ~ between phonons and an orbital state in a vibronic bound state of a candidate quantum spin liquid. This observation has profound implications for the engineering of phonon band structure topology through chiral quasiparticle interactions.
The X-Ray Luminosity Function Evolution of Quasars and the Correlation between the X-Ray and Ultraviolet Luminosities

We explore the evolution of the X-ray luminosity function of quasars and the intrinsic correlation between the X-ray and 2500 Å ultraviolet luminosities, utilizing techniques verified in previous works and a sample of over 4000 quasars detected with Chandra and XMM-Newton in the range 0 < z < 5. We find that quasars have undergone significantly less evolution with redshift in their total X-ray luminosity than in other wave bands. We then determine that the best-fit intrinsic power-law correlation between the X-ray and ultraviolet luminosities, of the form LX′∝(LUV′)γ , is γ = 0.28 ± 0.03, and we derive the luminosity function and density evolution in the X-ray band. We discuss the implications of these results for models of quasar systems.
Microstructure Surface Reconstruction from SEM Images: An Alternative to Digital Image Correlation (DIC)

We reconstruct a 3D model of the surface of a material undergoing fatigue testing and experiencing cracking. Specifically we reconstruct the surface depth (out of plane intrusions and extrusions) and lateral (in-plane) motion from multiple views of the sample at the end of the experiment, combined with a reverse optical flow propagation backwards in time that utilizes interim single view images. These measurements can be mapped to a material strain tensor which helps to understand material life and predict failure. This approach offers an alternative to the commonly used Digital Image Correlation (DIC) technique which relies on tracking a speckle pattern applied to the material surface. DIC only produces in-plane (2D) measurements whereas our approach is 3D and non-invasive (requires no pattern being applied to the material).
Encoding quantum bits in bound electronic states of a graphene nanotorus

We propose to use the quantum states of an electron trapped on the inner surface of a graphene nanotorus to realize as a new kind of physical quantum bit, which can be used to encode quantum information. Fundamental tasks for quantum information processing, such as the qubit initialization and the implementation of arbitrary single qubit gates, can be performed using external magnetic and electric ﬁelds. We also analyze the robustness of the device again systematic errors, which can be suppressed by a suitable choice of the external control ﬁelds. These ﬁndings open new prospects for the development an alternative platform for quantum computing, for which the scalability remains to be determined.
MKQ-BERT: Quantized BERT with 4-bits Weights and Activations

Recently, pre-trained Transformer based language models, such as BERT, have shown great superiority over the traditional methods in many Natural Language Processing (NLP) tasks. However, the computational cost for deploying these models is prohibitive on resource-restricted devices. One method to alleviate this computation overhead is to quantize the original model into fewer bits representation, and previous work has proved that we can at most quantize both weights and activations of BERT into 8-bits, without degrading its performance. In this work, we propose MKQ-BERT, which further improves the compression level and uses 4-bits for quantization. In MKQ-BERT, we propose a novel way for computing the gradient of the quantization scale, combined with an advanced distillation strategy. On the one hand, we prove that MKQ-BERT outperforms the existing BERT quantization methods for achieving a higher accuracy under the same compression level. On the other hand, we are the first work that successfully deploys the 4-bits BERT and achieves an end-to-end speedup for inference. Our results suggest that we could achieve 5.3x of bits reduction without degrading the model accuracy, and the inference speed of one int4 layer is 15x faster than a float32 layer in Transformer based model.
Efficient k-clique Listing with Set Intersection Speedup [Technical Report]

Listing all k-cliques is a fundamental problem in graph mining, with applications in finance, biology, and social network analysis. However, owing to the exponential growth of the search space as k increases, listing all k-cliques is algorithmically challenging. DDegree and DDegCol are the state-of-the-art algorithms that exploit ordering heuristics based on degree ordering and color ordering, respectively. Both DDegree and DDegCol induce high time and space overhead for set intersections cause they construct and maintain all induced subgraphs. Meanwhile, it is non-trivial to implement the data level parallelism to further accelerate on DDegree and DDegCol. In this paper, we propose two efficient algorithms SDegree and BitCol for k-clique listing. We mainly focus on accelerating the set intersections for k-clique listing. Both SDegree and BitCol exploit the data level parallelism for further acceleration with single instruction multiple data (SIMD) or vector instruction sets. Furthermore, we propose two preprocessing techniques Pre-Core and Pre-List, which run in linear time. The preprocessing techniques significantly reduce the size of the original graph and prevent exploring a large number of invalid nodes. In the theoretical analysis, our algorithms have a comparable time complexity and a slightly lower space complexity than the state-of-the-art algorithms. The comprehensive experiments reveal that our algorithms outperform the state-of-the-art algorithms by 3.75x for degree ordering and 5.67x for color ordering on average.
KM3NeT upper bounds of detection rates of solar neutrinos from annihilations of dark matter at the solar core

The Weakly Interacting Massive Particles (WIMPs) so far remain one of the most popular candidates for dark matter. If captured gravitationally inside the core of the Sun, these WIMPs may produce high-energy neutrinos as the end product in case they undergo self-annihilations at the solar core. In this work, we address the detectability of such neutrinos at the proposed KM3NeT detector. Upper bounds of the detection rate for such neutrinos at KM3NeT are computed for the case of a generic dark matter scenario and also when specific models for particle dark matter are chosen. In this work, upper bounds of muon event rates for different annihilating dark matter masses are computed for each of the cases of dark matter annihilation channels (e.g. [Formula: see text], [Formula: see text], [Formula: see text], etc.). These upper bounds are also computed by considering the dark matter scattering cross-section using upper bounds obtained from PandaX-4T direct dark matter search experiment.