Reviewerly/exHarmony huggingface dataset:
The research on influence of gossypol-based composition on paraffin sediment

The effects of the composition of herbal origin, prepared at the ratio of gossypol resin:MARZA-1=3:1 (MARZA-1 is a reagent of surfactant organic origin with a halogen atom and three triple bonds in the molecule), on the paraffin sediment in high-paraffin oil and the kinetics of sediment formation was investigated under laboratory condition. An oil sample produced from Narimanov deposit was used for the experiment. It was found that the most of asphaltene-resin-paraffin sediment (ARPS) emerged at the temperature of 200C and formed 28.7%. However, at the same temperature, the amount of sediment in the oil with the addition of 700 g/t composition was reduces by 4 times and the protective effect of the reagent was 76%. As the oil temperature rises, the protective activity of the composition decreased. Thus, it was 57%, 39%, and 6.5% at 300C, 400C, and 500C, respectively. The rate of paraffin sediment formation was reduced by 1.75 times, 2.3 times and 3.5 times when the temperature was increased from 200C to 300C, 400C, and 500C, respectively. The highest rate of sediment formation in all temperature intervals under study is mainly observed during the first five minutes of the process. As the duration of the process increases, the rate decreases by 3–4 times depending on the temperature. The composition significantly reduces the rate of sediment formation by retaining the paraffin hydrocarbon crystals dependent on the oil volume. As compared with the reagent-free oil, the rate is reduced by an average of 2.3 times in the temperature interval of 20–300C. However, the rate of sediment formation in reagent-free and reagent-added oil is reduced by 1.5 times at 40–500C. The group composition of asphaltene-resin-paraffin sediments varies depending on the accumulation of oil sediments. The amount of paraffin hydrocarbons increases sharply in the sediment obtained from the original oil during the first 5–15 minutes, and practically does not vary during subsequent periods. On the other hand, paraffin hydrocarbons/asphaltene-resin ratio in the original oil increases from 1.2 to 2.2 over time, indicating that the sediment contains paraffin and paraffin hydrocarbons play a crucial role in the sediment formation process. However, after the application of the reagent, the amount of paraffin hydrocarbons in the ARPS decreases by 8–13%, depending on the time, as compared with the original oil.
Binarizing Split Learning for Data Privacy Enhancement and Computation Reduction

Split learning (SL) enables data privacy preservation by allowing clients to collaboratively train a deep learning model with the server without sharing raw data. However, SL still has limitations such as potential data privacy leakage and high computation at clients. In this study, we propose to binarize the SL local layers for faster computation (up to 17.5 times less forward-propagation time in both training and inference phases on mobile devices) and reduced memory usage (up to 32 times less memory and bandwidth requirements). More importantly, the binarized SL (B-SL) model can reduce privacy leakage from SL smashed data with merely a small degradation in model accuracy. To further enhance the privacy preservation, we also propose two novel approaches: 1) training with additional local leak loss and 2) applying differential privacy, which could be integrated separately or concurrently into the B-SL model. Experimental results with different datasets have affirmed the advantages of the B-SL models compared with several benchmark models. The effectiveness of B-SL models against feature-space hijacking attack (FSHA) is also illustrated. Our results have demonstrated B-SL models are promising for lightweight IoT/mobile applications with high privacy-preservation requirements such as mobile healthcare applications.
Secure multi-party quantum computation based on triply-even quantum error-correcting codes

In this paper we suggest secure multi-party quantum computation (MPQC) protocol based on a sub-class of Chalderbank-Shor-Steane (CSS) quantum error-correcting codes (QECCs), i.e., triply-even CSS QECCs. In order to achieve universal quantum computation (UQC) in the MPQC protocol based on triply-even CSS QECCs, in addition to the set of trivially implemented transversal quantum gates, one needs to implement a non-transversal $H$ gate, which can be accomplished by the gate teleportation technique with the logical ``plus'' state as an ancillary quantum state. In comparison, to achieve UQC in the previously suggested MPQC protocol based on another sub-class of CSS QECCs, i.e., self-dual CSS QECCs, instead of non-transversal $H$ gate one needs to implement a non-transversal $T$ gate. It also can be realized by the gate teleportation technique but requires additional verification of the logical ``magic'' state, which is used as an ancillary quantum state. Verification of the logical ``magic'' state suggested in the previous study not only requires extra workspace for the implementation but also employs a non-transversal $\mathrm{C}\text{-}XP^\dag$ gate, which may lead to a failure of the entire MPQC protocol. By replacing self-dual CSS QECCs with triply-even CSS QECCs, for which $T$ gate becomes transversal, we avoid verification of the logical ``magic'' state. Consequently, our demand for workspace per quantum node is reduced from $n^2 + 4n$ qubits in the previous suggestion to $n^2 + 3n$ qubits in our current suggestion, where $n$ is the number of quantum nodes participating in the MPQC protocol.
Theoretical Error Performance Analysis for Variational Quantum Circuit Based Functional Regression

Abstract The noisy intermediate-scale quantum (NISQ) devices enable the implementation of the variational quantum circuit (VQC) for quantum neural networks (QNN). Although the VQC-based QNN has succeeded in many machine learning tasks, the representation and generalization powers of VQC still require further investigation, particularly when the dimensionality reduction of classical inputs is concerned. In this work, we first put forth an end-to-end quantum neural network, namely, TTN-VQC, which consists of a quantum tensor network based on a tensor-train network (TTN) for dimensionality reduction and a VQC for functional regression. Then, we aim at the error performance analysis for the TTN-VQC in terms of representation and generalization powers. We also characterize the optimization properties of TTN-VQC by leveraging the Polyak-Lojasiewicz (PL) condition. Moreover, we conduct the experiments of functional regression on a handwritten digit classification dataset to justify our theoretical analysis.
Subsequence-based Graph Routing Network for Capturing Multiple Risk Propagation Processes

In finance, the risk of an entity depends not only on its historical information but also on the risk propagated by its related peers. Pilot studies rely on Graph Neural Networks (GNNs) to model this risk propagation, where each entity is treated as a node and represented by its time-series information. However, conventional GNNs are constrained by their unified messaging mechanism with an assumption that the risk of a given entity only propagates to its related peers with the same time lag and has the same effect, which is against the ground truth. In this study, we propose the subsequence-based graph routing network (S-GRN) for capturing the variant risk propagation processes among different time-series represented entities. In S-GRN, the messaging mechanism between each node pair is dynamically and independently selected from multiple messaging mechanisms based on the dependencies of variant subsequence patterns. The S-GRN is extensively evaluated on two synthetic tasks and three real-world datasets and demonstrates state-of-the-art performance.
A Novel Chinese Dialect TTS Frontend with Non-Autoregressive Neural Machine Translation

Chinese dialects are different variations of Chinese and can be considered as different languages in the same language family with Mandarin. Though they all use Chinese characters, the pronunciations, grammar and idioms can vary significantly, and even local speakers may find it hard to input correct written forms of dialect. Besides, using Mandarin text as text-to-speech inputs would generate speech with poor naturalness. In this paper, we propose a novel Chinese dialect TTS frontend with a translation module, which converts Mandarin text into dialectic expressions to improve the intelligibility and naturalness of synthesized speech. A non-autoregressive neural machine translation model with various tricks is proposed for the translation task. It is the first known work to incorporate translation with TTS frontend. Experiments on Cantonese show the proposed model improves 2.56 BLEU and TTS improves 0.27 MOS with Mandarin inputs.
A Report on Achieving Complete Regular-Expression Matching using Mealy Machines

While regexp matching is a powerful mechanism for finding patterns in data streams, regexp engines in general only find matches that do not overlap. Moreover, different forms of nondeterministic exploration, where symbols read are processed more than once, are often used, which can be costly in real-time matching. We present an algorithm that constructs from any regexp a Mealy machine that finds all matches and while reading each input symbol only once. The machine computed can also detect and distinguish different patterns or sub-patterns inside patterns. Additionally, we show how to compute a minimal Mealy machine via a variation of DFA minimization, by formalizing Mealy machines in terms of regular languages.
Skill Transfer for Temporally-Extended Task Specifications

Deploying robots in real-world domains, such as households and flexible manufacturing lines, requires the robots to be taskable on demand. Linear temporal logic (LTL) is a widely-used specification language with a compositional grammar that naturally induces commonalities across tasks. However, the majority of prior research on reinforcement learning with LTL specifications treats every new formula independently. We propose LTL-Transfer, a novel algorithm that enables subpolicy reuse across tasks by segmenting policies for training tasks into portable transition-centric skills capable of satisfying a wide array of unseen LTL specifications while respecting safety-critical constraints. Experiments in a Minecraft-inspired domain show that LTL-Transfer can satisfy over 90% of 500 unseen tasks after training on only 50 task specifications and never violating a safety constraint. We also deployed LTL-Transfer on a quadruped mobile manipulator in an analog household environment to demonstrate its ability to transfer to many fetch and delivery tasks in a zero-shot fashion.
Adversarial Counterfactual Environment Model Learning

A good model for action-effect prediction, named environment model, is important to achieve sample-efficient decision-making policy learning in many domains like robot control, recommender systems, and patients' treatment selection. We can take unlimited trials with such a model to identify the appropriate actions so that the costs of queries in the real world can be saved. It requires the model to handle unseen data correctly, also called counterfactual data. However, standard data fitting techniques do not automatically achieve such generalization ability and commonly result in unreliable models. In this work, we introduce counterfactual-query risk minimization (CQRM) in model learning for generalizing to a counterfactual dataset queried by a specific target policy. Since the target policies can be various and unknown in policy learning, we propose an adversarial CQRM objective in which the model learns on counterfactual data queried by adversarial policies, and finally derive a tractable solution GALILEO. We also discover that adversarial CQRM is closely related to the adversarial model learning, explaining the effectiveness of the latter. We apply GALILEO in synthetic tasks and a real-world application. The results show that GALILEO makes accurate predictions on counterfactual data and thus significantly improves policies in real-world testing.
A Causal Research Pipeline and Tutorial for Psychologists and Social Scientists

Causality is a fundamental part of the scientific endeavour to understand the world. Unfortunately, causality is still taboo in much of psychology and social science. Motivated by a growing number of recommendations for the importance of adopting causal approaches to research, we reformulate the typical approach to research in psychology to harmonize inevitably causal theories with the rest of the research pipeline. We present a new process which begins with the incorporation of techniques from the confluence of causal discovery and machine learning for the development, validation, and transparent formal specification of theories. We then present methods for reducing the complexity of the fully specified theoretical model into the fundamental submodel relevant to a given target hypothesis. From here, we establish whether or not the quantity of interest is estimable from the data, and if so, propose the use of semi-parametric machine learning methods for the estimation of causal effects. The overall goal is the presentation of a new research pipeline which can (a) facilitate scientific inquiry compatible with the desire to test causal theories (b) encourage transparent representation of our theories as unambiguous mathematical objects, (c) to tie our statistical models to specific attributes of the theory, thus reducing under-specification problems frequently resulting from the theory-to-model gap, and (d) to yield results and estimates which are causally meaningful and reproducible. The process is demonstrated through didactic examples with real-world data, and we conclude with a summary and discussion of limitations.
Syntactic Inductive Biases for Deep Learning Methods

In this thesis, we try to build a connection between the two schools by introducing syntactic inductive biases for deep learning models. We propose two families of inductive biases, one for constituency structure and another one for dependency structure. The constituency inductive bias encourages deep learning models to use different units (or neurons) to separately process long-term and short-term information. This separation provides a way for deep learning models to build the latent hierarchical representations from sequential inputs, that a higher-level representation is composed of and can be decomposed into a series of lower-level representations. For example, without knowing the ground-truth structure, our proposed model learns to process logical expression through composing representations of variables and operators into representations of expressions according to its syntactic structure. On the other hand, the dependency inductive bias encourages models to find the latent relations between entities in the input sequence. For natural language, the latent relations are usually modeled as a directed dependency graph, where a word has exactly one parent node and zero or several children nodes. After applying this constraint to a Transformer-like model, we find the model is capable of inducing directed graphs that are close to human expert annotations, and it also outperforms the standard transformer model on different tasks. We believe that these experimental results demonstrate an interesting alternative for the future development of deep learning models.
Sort by Structure: Language Model Ranking as Dependency Probing

Making an informed choice of pre-trained language model (LM) is critical for performance, yet environmentally costly, and as such widely underexplored. The field of Computer Vision has begun to tackle encoder ranking, with promising forays into Natural Language Processing, however they lack coverage of linguistic tasks such as structured prediction. We propose probing to rank LMs, specifically for parsing dependencies in a given language, by measuring the degree to which labeled trees are recoverable from an LM's contextualized embeddings. Across 46 typologically and architecturally diverse LM-language pairs, our probing approach predicts the best LM choice 79% of the time using orders of magnitude less compute than training a full parser. Within this study, we identify and analyze one recently proposed decoupled LM - RemBERT - and find it strikingly contains less inherent dependency information, but often yields the best parser after full fine-tuning. Without this outlier our approach identifies the best LM in 89% of cases.
Lightweight Conditional Model Extrapolation for Streaming Data under Class-Prior Shift

We introduce LIMES, a new method for learning with non-stationary streaming data, inspired by the recent success of meta-learning. The main idea is not to attempt to learn a single classifier that would have to work well across all occurring data distributions, nor many separate classifiers, but to exploit a hybrid strategy: we learn a single set of model parameters from which a specific classifier for any specific data distribution is derived via classifier adaptation. Assuming a multi-class classification setting with class-prior shift, the adaptation step can be performed analytically with only the classifier's bias terms being affected. Another contribution of our work is an extrapolation step that predicts suitable adaptation parameters for future time steps based on the previous data. In combination, we obtain a lightweight procedure for learning from streaming data with varying class distribution that adds no trainable parameters and almost no memory or computational overhead compared to training a single model. Experiments on a set of exemplary tasks using Twitter data show that LIMES achieves higher accuracy than alternative approaches, especially with respect to the relevant real-world metric of lowest within-day accuracy.
Explaining Image Classifiers Using Contrastive Counterfactuals in Generative Latent Spaces

Despite their high accuracies, modern complex image classifiers cannot be trusted for sensitive tasks due to their unknown decision-making process and potential biases. Counterfactual explanations are very effective in providing transparency for these black-box algorithms. Nevertheless, generating counterfactuals that can have a consistent impact on classifier outputs and yet expose interpretable feature changes is a very challenging task. We introduce a novel method to generate causal and yet interpretable counterfactual explanations for image classifiers using pretrained generative models without any re-training or conditioning. The generative models in this technique are not bound to be trained on the same data as the target classifier. We use this framework to obtain contrastive and causal sufficiency and necessity scores as global explanations for black-box classifiers. On the task of face attribute classification, we show how different attributes influence the classifier output by providing both causal and contrastive feature attributions, and the corresponding counterfactual images.
Solution of DeBERTaV3 on CommonsenseQA

We report the performance of DeBERTaV3 on CommonsenseQA in this report. We simply formalize the answer selection as a text classification for DeBERTaV3. The strong natural language inference ability of DeBERTaV3 helps its single and ensemble model set the new (w/o external knowledge) state-of-the-art on CommonsenseQA.
Generate, Evaluate, and Select: A Dialogue System with a Response Evaluator for Diversity-Aware Response Generation

We aim to overcome the lack of diversity in responses of current dialogue systems and to develop a dialogue system that is engaging as a conversational partner.We propose a generator-evaluator model that evaluates multiple responses generated by a response generator and selects the best response by an evaluator.By generating multiple responses, we obtain diverse responses.We conduct human evaluations to compare the output of the proposed system with that of a baseline system.The results of the human evaluations showed that the proposed system's responses were often judged to be better than the baseline system's, and indicated the effectiveness of the proposed method.
Building Spatio-temporal Transformers for Egocentric 3D Pose Estimation

Egocentric 3D human pose estimation (HPE) from images is challenging due to severe self-occlusions and strong distortion introduced by the fish-eye view from the head mounted camera. Although existing works use intermediate heatmap-based representations to counter distortion with some success, addressing self-occlusion remains an open problem. In this work, we leverage information from past frames to guide our self-attention-based 3D HPE estimation procedure -- Ego-STAN. Specifically, we build a spatio-temporal Transformer model that attends to semantically rich convolutional neural network-based feature maps. We also propose feature map tokens: a new set of learnable parameters to attend to these feature maps. Finally, we demonstrate Ego-STAN's superior performance on the xR-EgoPose dataset where it achieves a 30.6% improvement on the overall mean per-joint position error, while leading to a 22% drop in parameters compared to the state-of-the-art.
Balanced Product of Experts for Long-Tailed Recognition

Many real-world recognition problems suffer from an imbalanced or long-tailed label distribution. Those distributions make representation learning more challenging due to limited generalization over the tail classes. If the test distribution differs from the training distribution, e.g. uniform versus long-tailed, the problem of the distribution shift needs to be addressed. To this aim, recent works have extended softmax cross-entropy using margin modifications, inspired by Bayes' theorem. In this paper, we generalize several approaches with a Balanced Product of Experts (BalPoE), which combines a family of models with different test-time target distributions to tackle the imbalance in the data. The proposed experts are trained in a single stage, either jointly or independently, and fused seamlessly into a BalPoE. We show that BalPoE is Fisher consistent for minimizing the balanced error and perform extensive experiments to validate the effectiveness of our approach. Finally, we investigate the effect of Mixup in this setting, discovering that regularization is a key ingredient for learning calibrated experts. Our experiments show that a regularized BalPoE can perform remarkably well in test accuracy and calibration metrics, leading to state-of-the-art results on CIFAR-100-LT, ImageNet-LT, and iNaturalist-2018 datasets. The code will be made publicly available upon paper acceptance.
Learn2Augment: Learning to Composite Videos for Data Augmentation in Action Recognition

We address the problem of data augmentation for video action recognition. Standard augmentation strategies in video are hand-designed and sample the space of possible augmented data points either at random, without knowing which augmented points will be better, or through heuristics. We propose to learn what makes a good video for action recognition and select only high-quality samples for augmentation. In particular, we choose video compositing of a foreground and a background video as the data augmentation process, which results in diverse and realistic new samples. We learn which pairs of videos to augment without having to actually composite them. This reduces the space of possible augmentations, which has two advantages: it saves computational cost and increases the accuracy of the final trained classifier, as the augmented pairs are of higher quality than average. We present experimental results on the entire spectrum of training settings: few-shot, semi-supervised and fully supervised. We observe consistent improvements across all of them over prior work and baselines on Kinetics, UCF101, HMDB51, and achieve a new state-of-the-art on settings with limited data. We see improvements of up to 8.6% in the semi-supervised setting.