Reviewerly/exHarmony huggingface dataset:
Diffusion-based Image Translation using Disentangled Style and Content Representation

Diffusion-based image translation guided by semantic texts or a single target image has enabled flexible style transfer which is not limited to the specific domains. Unfortunately, due to the stochastic nature of diffusion models, it is often difficult to maintain the original content of the image during the reverse diffusion. To address this, here we present a novel diffusion-based unsupervised image translation method using disentangled style and content representation. Specifically, inspired by the splicing Vision Transformer, we extract intermediate keys of multihead self attention layer from ViT model and used them as the content preservation loss. Then, an image guided style transfer is performed by matching the [CLS] classification token from the denoised samples and target image, whereas additional CLIP loss is used for the text-driven style transfer. To further accelerate the semantic change during the reverse diffusion, we also propose a novel semantic divergence loss and resampling strategy. Our experimental results show that the proposed method outperforms state-of-the-art baseline models in both text-guided and image-guided translation tasks.
SpeechLM: Enhanced Speech Pre-Training with Unpaired Textual Data

How to boost speech pre-training with textual data is an unsolved problem due to the fact that speech and text are very different modalities with distinct characteristics. In this paper, we propose a cross-modal Speech and Language Model (SpeechLM) to explicitly align speech and text pre-training with a pre-defined unified discrete representation. Specifically, we introduce two alternative discrete tokenizers to bridge the speech and text modalities, including phoneme-unit and hidden-unit tokenizers, which can be trained using a small amount of paired speech-text data. Based on the trained tokenizers, we convert the unlabeled speech and text data into tokens of phoneme units or hidden units. The pre-training objective is designed to unify the speech and the text into the same discrete semantic space with a unified Transformer network. We evaluate SpeechLM on various spoken language processing tasks including speech recognition, speech translation, and universal representation evaluation framework SUPERB, demonstrating significant improvements on content-related tasks. Code and models are available at https://aka.ms/SpeechLM.
Linear Convergence for Natural Policy Gradient with Log-linear Policy Parametrization

We analyze the convergence rate of the unregularized natural policy gradient algorithm with log-linear policy parametrizations in infinite-horizon discounted Markov decision processes. In the deterministic case, when the Q-value is known and can be approximated by a linear combination of a known feature function up to a bias error, we show that a geometrically-increasing step size yields a linear convergence rate towards an optimal policy. We then consider the sample-based case, when the best representation of the Q- value function among linear combinations of a known feature function is known up to an estimation error. In this setting, we show that the algorithm enjoys the same linear guarantees as in the deterministic case up to an error term that depends on the estimation error, the bias error, and the condition number of the feature covariance matrix. Our results build upon the general framework of policy mirror descent and extend previous findings for the softmax tabular parametrization to the log-linear policy class.
Exploiting Instance-based Mixed Sampling via Auxiliary Source Domain Supervision for Domain-adaptive Action Detection

We propose a novel domain adaptive action detection approach and a new adaptation protocol that leverages the recent advancements in image-level unsupervised domain adaptation (UDA) techniques and handle vagaries of instance-level video data. Self-training combined with cross-domain mixed sampling has shown remarkable performance gain in semantic segmentation in UDA (unsupervised domain adaptation) context. Motivated by this fact, we propose an approach for human action detection in videos that transfers knowledge from the source domain (annotated dataset) to the target domain (unannotated dataset) using mixed sampling and pseudo-label-based selftraining. The existing UDA techniques follow a ClassMix algorithm for semantic segmentation. However, simply adopting ClassMix for action detection does not work, mainly because these are two entirely different problems, i.e., pixel-label classification vs. instance-label detection. To tackle this, we propose a novel action instance mixed sampling technique that combines information across domains based on action instances instead of action classes. Moreover, we propose a new UDA training protocol that addresses the long-tail sample distribution and domain shift problem by using supervision from an auxiliary source domain (ASD). For the ASD, we propose a new action detection dataset with dense frame-level annotations. We name our proposed framework as domain-adaptive action instance mixing (DA-AIM). We demonstrate that DA-AIM consistently outperforms prior works on challenging domain adaptation benchmarks. The source code is available at https://github.com/wwwfan628/DA-AIM.
Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature Interactions

Black-box models, such as deep neural networks, exhibit superior predictive performances, but understanding their behavior is notoriously difficult. Many explainable artificial intelligence methods have been proposed to reveal the decision-making processes of black box models. However, their applications in high-stakes domains remain limited. Recently proposed neural additive models (NAM) have achieved state-of-the-art interpretable machine learning. NAM can provide straightforward interpretations with slight performance sacrifices compared with multi-layer perceptron. However, NAM can only model 1$^{\text{st}}$-order feature interactions; thus, it cannot capture the co-relationships between input features. To overcome this problem, we propose a novel interpretable machine learning method called higher-order neural additive models (HONAM) and a feature interaction method for high interpretability. HONAM can model arbitrary orders of feature interactions. Therefore, it can provide the high predictive performance and interpretability that high-stakes domains need. In addition, we propose a novel hidden unit to effectively learn sharp-shape functions. We conducted experiments using various real-world datasets to examine the effectiveness of HONAM. Furthermore, we demonstrate that HONAM can achieve fair AI with a slight performance sacrifice. The source code for HONAM is publicly available.
Improving the Efficiency of Payments Systems Using Quantum Computing

High-value payment systems (HVPSs) are typically liquidity-intensive as the payment requests are indivisible and settled on a gross basis. Finding the right order in which payments should be processed to maximize the liquidity efficiency of these systems is an $NP$-hard combinatorial optimization problem, which quantum algorithms may be able to tackle at meaningful scales. We developed an algorithm and ran it on a hybrid quantum annealing solver to find an ordering of payments that reduced the amount of system liquidity necessary without substantially increasing payment delays. Despite the limitations in size and speed of today's quantum computers, our algorithm provided quantifiable efficiency improvements when applied to the Canadian HVPS using a 30-day sample of transaction data. By reordering each batch of 70 payments as they entered the queue, we achieved an average of C\$240 million in daily liquidity savings, with a settlement delay of approximately 90 seconds. For a few days in the sample, the liquidity savings exceeded C\$1 billion. This algorithm could be incorporated as a centralized preprocessor into existing HVPS without entailing a fundamental change to their risk management models.
PyPose: A Library for Robot Learning with Physics-based Optimization

Deep learning has had remarkable success in robotic perception, but its data-centric nature suffers when it comes to generalizing to ever-changing environments. By contrast, physics-based optimization generalizes better, but it does not perform as well in complicated tasks due to the lack of high-level semantic information and reliance on manual parametric tuning. To take advantage of these two complementary worlds, we present PyPose: a robotics-oriented, PyTorch-based library that combines deep perceptual models with physics-based optimization. PyPose's architecture is tidy and well-organized, it has an imperative style interface and is efficient and user-friendly, making it easy to integrate into real-world robotic applications. Besides, it supports parallel computing of any order gradients of Lie groups and Lie algebras and $2^{\text{nd}}$-order optimizers, such as trust region methods. Experiments show that PyPose achieves more than $10\times$ speedup in computation compared to the state-of-the-art libraries. To boost future research, we provide concrete examples for several fields of robot learning, including SLAM, planning, control, and inertial navigation.
Medical Image Understanding with Pretrained Vision Language Models: A Comprehensive Study

The large-scale pre-trained vision language models (VLM) have shown remarkable domain transfer capability on natural images. However, it remains unknown whether this capability can also apply to the medical image domain. This paper thoroughly studies the knowledge transferability of pre-trained VLMs to the medical domain, where we show that well-designed medical prompts are the key to elicit knowledge from pre-trained VLMs. We demonstrate that by prompting with expressive attributes that are shared between domains, the VLM can carry the knowledge across domains and improve its generalization. This mechanism empowers VLMs to recognize novel objects with fewer or without image samples. Furthermore, to avoid the laborious manual designing process, we develop three approaches for automatic generation of medical prompts, which can inject expert-level medical knowledge and image-specific information into the prompts for fine-grained grounding. We conduct extensive experiments on thirteen different medical datasets across various modalities, showing that our well-designed prompts greatly improve the zero-shot performance compared to the default prompts, and our fine-tuned models surpass the supervised models by a significant margin.
Theoretical analysis of quantum key distribution systems when integrated with a DWDM optical transport network

A theoretical research and numerical simulation of the noise influence caused by spontaneous Raman scattering, four-wave mixing, and linear channel crosstalk on the performance of QKD systems was conducted. Three types of QKD systems were considered: coherent one-way (COW) QKD protocol, subcarrier-wave (SCW) QKD system, and continuous-variable (CV) QKD integrated with classical DWDM channels. We calculate the secure key generation rate for the systems mentioned addressing different channel allocation schemes (i.e., configurations). A uniform DWDM grid is considered with quantum channel located in C-band and O-band (at 1310 nm) of a telecommunication window. The systems' performance is analyzed in terms of the maximal achievable distance values. Configurations for the further analysis and investigation are chosen optimally, i.e., their maximal achievable distances are the best.
Suppressing quantum circuit errors due to system variability

We present a quantum circuit optimization technique that takes into account the variability in error rates that is inherent across present day noisy quantum computing platforms. This method can be run post qubit routing or post-compilation, and consists of computing isomorphic subgraphs to input circuits and scoring each using heuristic cost functions derived from system calibration data. Using an independent standard algorithmic test suite we show that it is possible to recover on average nearly 40% of missing fidelity using better qubit selection via efficient to compute cost functions. We demonstrate additional performance gains by considering qubit placement over multiple quantum processors. The overhead from these tools is minimal with respect to other compilation steps, such as qubit routing, as the number of qubits increases. As such, our method can be used to find qubit mappings for problems at the scale of quantum advantage and beyond.
Towards a Unified View of Affinity-Based Knowledge Distillation

Knowledge transfer between artificial neural networks has become an important topic in deep learning. Among the open questions are what kind of knowledge needs to be preserved for the transfer, and how it can be effectively achieved. Several recent work have shown good performance of distillation methods using relation-based knowledge. These algorithms are extremely attractive in that they are based on simple inter-sample similarities. Nevertheless, a proper metric of affinity and use of it in this context is far from well understood. In this paper, by explicitly modularising knowledge distillation into a framework of three components, i.e. affinity, normalisation, and loss, we give a unified treatment of these algorithms as well as study a number of unexplored combinations of the modules. With this framework we perform extensive evaluations of numerous distillation objectives for image classification, and obtain a few useful insights for effective design choices while demonstrating how relation-based knowledge distillation could achieve comparable performance to the state of the art in spite of the simplicity.
A simple analytical expression of quantum Fisher and Skew information and their dynamics under decoherence channels

In statistical estimation theory, it has been shown previously that the Wigner-Yanase skew information is bounded by the quantum Fisher information associated with the phase parameter. Besides, the quantum Cramér-Rao inequality is expressed in terms of skew information. Since these two fundamental quantities are based on the concept of quantum uncertainty, we derive here their analytical formulas for arbitrary two qubit $X$-states using the same analytical procedures. A comparison of these two informational quantifiers for two quasi-Werner states composed of two bipartite superposed coherent states is examined. Moreover, we investigated the decoherence effects on such quantities generated by the phase damping, depolarization and amplitude damping channels. We showed that decoherence strongly influences the quantum criteria during the evolution and these quantities exhibit similar dynamic behaviors. This current work is characterized by the fact that these two concepts play the same role and capture similar properties in quantum estimation protocols.
New Metric Formulas that Include Measurement Errors in Machine Learning for Natural Sciences

The application of machine learning to physics problems is widely found in the scientific literature. Both regression and classification problems are addressed by a large array of techniques that involve learning algorithms. Unfortunately, the measurement errors of the data used to train machine learning models are almost always neglected. This leads to estimations of the performance of the models (and thus their generalisation power) that is too optimistic since it is always assumed that the target variables (what one wants to predict) are correct. In physics, this is a dramatic deficiency as it can lead to the belief that theories or patterns exist where, in reality, they do not. This paper addresses this deficiency by deriving formulas for commonly used metrics (both for regression and classification problems) that take into account measurement errors of target variables. The new formulas give an estimation of the metrics which is always more pessimistic than what is obtained with the classical ones, not taking into account measurement errors. The formulas given here are of general validity, completely model-independent, and can be applied without limitations. Thus, with statistical confidence, one can analyze the existence of relationships when dealing with measurements with errors of any kind. The formulas have wide applicability outside physics and can be used in all problems where measurement errors are relevant to the conclusions of studies.
An Efficient Modular Exponentiation Proof Scheme

We present an efficient proof scheme for any instance of left-to-right modular exponentiation, used in many computational tests for primality. Specifically, we show that for any $(a,n,r,m)$ the correctness of a computation $a^n\equiv r\pmod m$ can be proven and verified with an overhead negligible compared to the computational cost of the exponentiation. Our work generalizes the Gerbicz-Pietrzak proof scheme used when $n$ is a power of $2$, and has been successfully implemented at PrimeGrid, doubling the efficiency of distributed searches for primes.
Extracting Entities and Relations in Analyst Stock Ratings News

Abstract Massive volumes of finance-related data are created on the Internet daily,whether on question-answering forums, news articles, or stocks analysis sites. This data can be critical in the decision-making process fortargeting investments in the stock market. Our research paper aimsto extract information from such sources in order to utilize the volumes of data, which is impossible to process manually. In particular,analysts’ ratings on the stocks of well-known companies are considereddata of interest. Two subdomains of Information Extraction will be performed on the analysts’ ratings, Named Entity Recognition and RelationExtraction. The former is a technique for extracting entities from araw text, giving us insights into phrases that have a special meaning inthe domain of interest. However, apart from the actual positions andlabels of those phrases, it lacks the ability to explain the mutual relations between them, bringing up the necessity of the latter model, whichexplains the semantic relationships between entities and enriches theamount of information we can extract when stacked on top of the NamedEntity Recognition model. This study is based on the employment of different models for word embedding and different Deep Learning classification architectures for extracting the entities and predicting relationsbetween them. Furthermore, the multilingual abilities of a joint pipelineare being explored by combining English and German corpora. For bothsubtasks, we record state-of-the-art performances of 97.69% F1 score fornamed entity recognition and 89.70% F1 score for relation extraction.
Photo Story Usage to Improve Students’ Speaking Skill and Motivation

This study is aimed to find out the improvement of students` speaking skill by using photo story. This study used Classroom Action Research (CAR) to solve the students’ problem in English speaking. The researchers chose twenty students of the second-grade of MA Darul Aminin NW Aikmual as the participants of the study. Speaking test, interview, and recording were used to collect the data. The data were analyzed by using mix method, namely qualitative and quantitative analyses. The qualitative data were gained by analyzing the interview and observation, and the quantitative data were obtained from the students’ speaking score of pre-test and post-test. The results of the qualitative analysis indicated that the students’ score of post-test was higher than pre-test. Then, the results of the data analysis in quantitative indicated that there was significant different between using photo story and the speaking skill and motivation of students.
“Get Haircut” Application based on Mobile Android Using React Native Framework

Salon or Barbershop is one of the businesses that engaged in services. At this time, hair care is one of the appearance factors that are often considered by both men and women. This is further strengthened by the increase in the salon or barbershop business in Cirebon. Every time the number of customers at the salon or barbershop is increasing and of course it creates problems, such as, regarding reservations (Booking) and also incoming transaction reports. From these problems, we need a system that can handle these problems. Get Haircut Application is an android-based application built using the React Native framework with the PHP programming language that can make it easier for salons or barbershops to receive orders from customers online. In addition, this application is able to process queue number data, service data and related data. This application is made using the Rational Unified Process (RUP) development method.
Generalized Gradient Emphasis Learning for Off-Policy Evaluation and Control with Function Approximation

Abstract Emphatic temporal-difference (TD) learning (Sutton et al. 2016) is a pioneering off-policy reinforcement learning method involving the use of the followon trace. The recently proposed Gradient Emphasis Learning (GEM, Zhang et al. 2020) algorithm is used to fix the problems of unbounded variance and large emphasis approximation error introduced by the followon trace from the perspective of stochastic approximation. In this paper, we rethink GEM and introduce a novel generalized GEM(β) algorithm to learn the true emphasis. The key to the construction of the generalized GEM(β) algorithm is introducing a tunable hyper-parameter β that is not necessarily the same as the discount factor γ to the GEM operator. We then apply the emphasis estimated by the proposed GEM(β) algorithm to the value estimation gradient and the policy gradient, respectively, yielding the corresponding emphatic TD variant for off-policy evaluation and actor-critic algorithm for off-policy control. Finally, we demonstrate empirically the advantage of the proposed algorithms across a range of problems, for both off-policy evaluation and off-policy control, and for both linear and nonlinear function approximation.
Analysis of the Role of the Indonesian Christian Student Movement (GMKI) Medan Branch in Improving Cadre Information Literacy through the Literacy Movement

This study aims to analyze the role of the Medan branch of the Indonesian Christian Student Movement in increasing the information literacy of cadres through the literacy movement. Analyzing what factors are the drivers and barriers to the role of GMKI in increasing the information literacy of GMKI cadres through the literacy movement. The method of this research is descriptive research with a qualitative approach. Data collection techniques used interviews, observations and document studies. Based on the research, it was found that GMKI Medan Branch performs its role as a forum for learning, developing creativity to the application of knowledge through the provision of training programs through book review programs, book reviews, journalistic training, discussing topics that are developing in the community, and leadership training and others. The driving factor for GMKI's role in increasing cadre information literacy is the lack of literacy and communication culture that produces commitment. Meanwhile, the inhibiting factor for GMKI's role in increasing information literacy is the absence of a special program on the literacy movement. The conclusion shows that GMKI performs its role in increasing the information literacy of cadres even though it is in the basic stages. But this has answered one of the tasks of youth as agents of change.