Reviewerly/exHarmony huggingface dataset:
Recent Advances and Future Perspectives in the Use of Machine Learning and Mathematical Models in Nephrology

We reviewed some of the latest advancements in the use of mathematical models in nephrology. We looked over 2 distinct categories of mathematical models that are widely used in biological research and pointed out some of their strengths and weaknesses when applied to health care, especially in the context of nephrology. A mechanistic dynamical system allows the representation of causal relations among the system variables but with a more complex and longer development/implementation phase. Artificial intelligence/machine learning provides predictive tools that allow identifying correlative patterns in large data sets, but they are usually harder-to-interpret black boxes. Chronic kidney disease (CKD), a major worldwide health problem, generates copious quantities of data that can be leveraged by choice of the appropriate model; also, there is a large number of dialysis parameters that need to be determined at every treatment session that can benefit from predictive mechanistic models. Following important steps in the use of mathematical methods in medical science might be in the intersection of seemingly antagonistic frameworks, by leveraging the strength of each to provide better care.
Natural Language Processing in Nephrology

Unstructured data in the electronic health records contain essential patient information. Natural language processing (NLP), teaching a computer to read, allows us to tap into these data without needing the time and effort of manual chart abstraction. The core first step for all NLP algorithms is preprocessing the text to identify the core words that differentiate the text while filtering out the noise. Traditional NLP uses a rule-based approach, applying grammatical rules to infer meaning from the text. Newer NLP approaches use machine learning/deep learning which can infer meaning without explicitly being programmed. NLP use in nephrology research has focused on identifying distinct disease processes, such as CKD, and extraction of patient-oriented outcomes such as symptoms with high sensitivity. NLP can identify patient features from clinical text associated with acute kidney injury and progression of CKD. Lastly, inclusion of features extracted using NLP improved the performance of risk-prediction models compared to models that only use structured data. Implementation of NLP algorithms has been slow, partially hindered by the lack of external validation of NLP algorithms. However, NLP allows for extraction of key patient characteristics from free text, an infrequently used resource in nephrology.
Deterministic Preparation of a Hyper‐Entangled Three‐Photon Asymmetric <i>W</i> State Assisted by the Single‐Sided QD‐Cavity System

Abstract Hyper‐entangled states that encode qubits in multiple degrees of freedom of a quantum system can provide a decisive advantage in quantum information processing, such as improving the capacity of communication and speeding up the computation. Assisted by quantum dots in single‐sided optical microcavities, nonlinear interaction of optical circular polarizations is induced and a deterministic scheme for preparing a three‐photon hyper‐entangled asymmetric W state with polarization degree of freedom and spatial degree of freedom is proposed. The preparing process is composed of three steps: the preparation of a three‐photon polarization entangled asymmetric W state; the preparation of a three‐photon spatial entangled asymmetric W state; the preparation of a three‐photon hyper‐entangled asymmetric W state. It can be generalized to generate a three‐photon general W state according to practical requirement.
Research on the Classification of Policy Instruments Based on BERT Model

Currently, policy instruments are classified mainly by means of manual encoding and checking, which is highly subjective and inefficient, which greatly hinders the development of policy research. The research tries to apply the automatic classification algorithm based on BERT (Bidirectional Encoder Representation from Transformer) to the policy instruments to improve the efficiency and accuracy of policy instruments classification. An entrepreneurship policy instrument classification model was established on the basis of the pretraining language model to realize the automatic classification of entrepreneurship policy instruments. The research showed that through optimization and improvement of the model, the F1 value was 0.86 on the test set, indicating a good classification effect; through the comparative experiment, it was further proved that the classification effect of this model was far superior to other three commonly used text classification models. The model greatly improves the efficiency and objectivity of policy instrument classification and provides a new idea for investigating entrepreneurship policies and more generalized policy instruments.
MobiDepth

Real-time depth estimation is critical for the increasingly popular augmented reality and virtual reality applications on mobile devices. Yet existing solutions are insufficient as they require expensive depth sensors or motion of the device, or have a high latency. We propose MobiDepth, a real-time depth estimation system using the widely-available on-device dual cameras. While binocular depth estimation is a mature technique, it is challenging to realize the technique on commodity mobile devices due to the different focal lengths and unsynchronized frame flows of the on-device dual cameras and the heavy stereo-matching algorithm.
Continuous variable measurement device independent quantum conferencing with postselection

A continuous variable (CV), measurement device independent (MDI) quantum key distribution (QKD) protocol is analyzed, enabling three parties to connect for quantum conferencing. We utilise a generalised Bell detection at an untrusted relay and a postselection procedure, in which distant parties reconcile on the signs of the displacements of the quadratures of their prepared coherent states. We derive the rate of the protocol under a collective pure-loss attack, demonstrating improved rate-distance performance compared to the equivalent non-post-selected protocol. In the symmetric configuration in which all the parties lie the same distance from the relay, we find a positive key rate over 6 km. Such postselection techniques can be used to improve the rate of multi-party quantum conferencing protocols at longer distances at the cost of reduced performance at shorter distances.
Gaze estimation in videoconferencing settings

Screen-based communication increasingly replaces face-to-face interactions. Gaze information is important for nonverbal communication. Therefore, we investigate how humans (“receivers”) estimate gaze direction of others (“senders”) in videoconferencing-like settings, a major example of screen-based communication. In two online experiments, receivers estimated gaze targets – which were known to the experimenters – from images of senders. As in real videoconferencing settings, receivers had no information about the geometry of the senders' setup (camera position, etc.), but had to rely on the information available on screen. In Experiment 1, we found that gaze-target estimates were more closely related to the actual target positions in the horizontal than in the vertical direction, a bias toward the sender's head position, and some advantage of presenting the same sender in succession. For Experiment 2, we created a new database of sender images, in which the senders' head position in the image and gaze-target position were systematically varied. Additionally, images of natural scenes were presented whose content served as potential gaze targets. At large, we replicated the findings of Experiment 1, and found only little effect of image content on the estimates. As gaze is an important part of intuitive human-machine interaction, our findings bear relevance beyond videoconferencing. • Gaze estimation in videoconference settings is investigated in two online experiments. • Differences between horizontal and vertical estimates are found. • Some effect of repeating the same sender (person whose gaze is estimated) is observed. • There is little effect of content that is shared between the sender and receiver (person who estimates the gaze). • A database of over 6000 images of 8 senders looking at different gaze targets from different positions is made available.
A Lightweight Blockchain-based Public-Key Authenticated Encryption with Multi-Keyword Search for Cloud Computing

Cloud computing can provide users with sufficient computing resources, storage, and bandwidth to meet their needs. Data security and privacy protection are among the new threats faced by users. Searchable encryption is the combination of search technology and encryption technology. Searchable encryption can upload the user’s data to the cloud server after special encryption, and can realize the function of retrieving according to keywords. Comparatively to symmetric searchable encryption (SSE), public key searchable encryption (PEKS) simplifies key management greatly. However, most existing public key authenticated encryption with keyword search (PAEKS) schemes are based bilinear pairing, making them computationally expensive. Apart from this, complex retrieval requirements and the integrity of the results had not been considered. To address these problems, we propose a blockchain-based PAEKS schemes supporting multi-keyword queries and integrity verification. In addition, we provide security proofs for the PAEKS scheme under the decisional oracle Diffie-Hellman (DODH) assumption. This scheme a scheme that requires less storage and computational power than other schemes of the same kind.
Perturbation Effect

This paper provides evidence indicating that the most commonly used metric for validating feature attribution methods in eXplainable AI (XAI) is misleading when applied to time series data. To evaluate whether an XAI method attributes importance to relevant features, these are systematically perturbed while measuring the impact on the performance of the classifier. The assumption is that a drastic performance reduction with increasing perturbation of relevant features indicates that these are indeed relevant. We demonstrate empirically that this assumption is incomplete without considering low relevance features in the used metrics. We introduce a novel metric, the Perturbation Effect Size, and demonstrate how it complements existing metrics to offer a more faithful assessment of importance attribution. Finally, we contribute a comprehensive evaluation of attribution methods on time series data, considering the influence of perturbation methods and region size selection.
Interpretable Emotion Analysis Based on Knowledge Graph and OCC Model

Sentiment analysis or opinion mining has been significant for information extraction from the text. At the same time, emotion psychology also proposed many appraisal theories for emotional evaluations and concrete predictions. While sentiment analysis focuses on identifying the polarity, appraisal theories of emotion can define different emotions and view emotions as process rather than states. In real life, the mechanism of emotional generations and interactions is complicated. Only plausible polarity can't provide enough explanations for the emotional mechanism. Hence an explainable model is in demand during emotion inference and dynamical analysis. In this paper, an analysis framework is constructed for interpreting casual association based on the emotional logic. Knowledge graph is introduced into the appraisal theories for inferring the emotions and predicting the action tendency. The emotion knowledge graph levels: concept level and case level. The concept level can be built manually as an abstract based on the appraisal model of Ortony, Clore & Collins (OCC model). The inference and predictions can be implemented at this level. The case level includes entities, objects, events and cognitive relations between them that extract from the text through the modular functions. The elements in the case level can be linked to the abstract types in the concept level for the emotional inference. We test this emotional analysis framework on several datasets from the appraisal theory and the text of drama works. The results demonstrate that our framework can make better inferences on emotions and good interpretability for human beings.
A Mask-based Output Layer for Multi-level Hierarchical Classification

This paper proposes a novel mask-based output layer for multi-level hierarchical classification, addressing the limitations of existing methods which (i) often do not embed the taxonomy structure being used, (ii) use a complex backbone neural network with n disjoint output layers that do not constraint each other, (iii) may output predictions that are often inconsistent with the taxonomy in place, and (iv) have often a fixed value of n. Specifically, we propose a model agnostic output layer that embeds the taxonomy and that can be combined with any model. Our proposed output layer implements a top-down divide-and-conquer strategy through a masking mechanism to enforce that predictions comply with the embedded hierarchy structure. Focusing on image classification, we evaluate the performance of our proposed output layer on three different datasets, each with a three-level hierarchical structure. Experiments on these datasets show that our proposed mask-based output layer allows to improve several multi-level hierarchical classification models using various performance metrics.
MNCM: Multi-level Network Cascades Model for Multi-Task Learning

Recently, multi-task learning based on the deep neural network has been successfully applied in many recommender system scenarios. The prediction quality of current mainstream multi-task models often relies on the extent to which the relationships among tasks are extracted. Much of the prior research work has focused on two important tasks in recommender systems: predicting click-through rate (CTR) and post-click conversion rate (CVR), which rely on sequential user action pattern of impression → click → conversion. Therefore, there exists sequential dependence between CTR and CVR tasks. However, there is no satisfactory solution to explicitly model the sequential dependence among tasks without sacrificing the first task in terms of the design of the model network structure. In this paper, inspired by the Multi-task Network Cascades (MNC) and Adaptive Information Transfer Multi-task (AITM) frameworks, we propose a Multi-level Network Cascades Model (MNCM) based on the pattern of specific and shared experts separation. In MNCM, we introduce two types of information transfer modules: Task-Level Information Transfer Module (TITM) and Expert-Level Information Transfer Module (EITM), which can learn transferred information adaptively from task level and task-specific experts level, respectively, thereby fully capture sequential dependence among tasks. Compared with AITM, MNCM effectively avoids the problem of the first task in a task sequence becoming the sacrificial side of the seesaw phenomenon and contributes to mitigating potential conflicts among tasks. We conduct considerable experiments based on open-source large-scale recommendation datasets. The experimental results demonstrate that MNCM outperforms AITM and the mainstream baseline models in the mixture-experts-bottom pattern and probability-transfer pattern. In addition, we conduct an ablation study on the necessity of introducing two kinds of information transfer modules and verify the effectiveness of this pattern.
ExpertBert: Pretraining Expert Finding

Expert Finding is an important task in Community Question Answering (CQA) platforms, which could help route questions to potential expertise users to answer. The key is to model the question content and experts based on their historical answered questions accurately. Recently Pretrained Language Models (PLMs, e.g., Bert) have shown superior text modeling ability and have been used in expert finding preliminary. However, most PLMs-based models focus on the corpus or document granularity during pretraining, which is inconsistent with the downstream expert modeling and finding task. In this paper, we propose an expert-level pretraining language model named ExpertBert, aiming to model questions, experts as well as question-expert matching effectively in a pretraining manner. In our approach, we aggregate the historical answered questions of an expert as the expert-specific input.Besides, we integrate the target question into the input and design a label-augmented Masked Language Model (MLM) task to further capture the matching pattern between question and experts, which makes the pretraining objectives that more closely resemble the downstream expert finding task. Experimental results and detailed analysis on real-world CQA datasets demonstrate the effectiveness of our ExpertBert.
Measuring and Comparing the Consistency of IR Models for Query Pairs with Similar and Different Information Needs

A widespread use of supervised ranking models has necessitated an investigation on how consistent their outputs align with user expectations. While a match between the user expectations and system outputs can be sought at different levels of granularity, we study this alignment for search intent transformation across a pair of queries. Specifically, we propose a consistency metric, which for a given pair of queries - one reformulated from the other with at least one term in common, measures if the change in the set of the top-retrieved documents induced by this reformulation is as per a user's expectation. Our experiments led to a number of observations, such as DRMM (an early interaction based IR model) exhibits better alignment with set-level user expectations, whereas transformer-based neural models (e.g., MonoBERT) agree more consistently with the content and rank-based expectations of overlap.
Calibrated Conversion Rate Prediction via Knowledge Distillation under Delayed Feedback in Online Advertising

Prevailing calibration methods may fail to generalize well due to the pervasively delayed feedback issue in online advertising. That is, the labels of recent samples are more likely to be inaccurate because of the delayed feedback by users, while the old samples with complete feedback may suffer from the data shift compared to the recent ones. In this paper, we propose to calibrate conversion rate prediction models considering delayed feedback via the knowledge distillation technique. Specifically, we deploy a teacher model modeling by the samples with complete feedback to learn long-term conversion patterns and a student model modeling by the recent data to reduce the impact of data shift. We also devise a distillation loss to buoy the student model to learn from the teacher. Experimental results on two real-world advertising conversion rate prediction datasets demonstrate that our method can provide more calibrated predictions compared with the existing ones. We also exhibit that our method can be extended to different base models.
GCWSNet

We propose using "powered generalized min-max'' (pGMM) hashed (linearized) via the "generalized consistent weighted sampling'' (GCWS) for training (deep) neural networks (hence the name "GCWSNet''). The pGMM and several related kernels were proposed in 2017. We demonstrate that pGMM hashed by GCWS provide a numerically stable scheme for applying power transformation on the original data, regardless of the magnitude of p and the data. Our experiments show that GCWSNet often improves the accuracy. It is also evident that GCWSNet converges substantially faster, reaching reasonable accuracy with merely one epoch of the training process. This property is much desired because many applications, such as advertisement click-through rate (CTR) prediction models, or data streams (i.e., data seen only once), often train just one epoch. Another beneficial side effect is that the computations of the first layer of the neural networks become additions instead of multiplications because the input data become binary and highly sparse.
RelpNet

Node-based link prediction methods have occupied a dominant position in the graph link prediction task. These methods commonly aggregate node features from the subgraph to generate the potential link representation. However, in constructing subgraphs, these methods extract each node's local neighborhood from the target node pair separately without considering the correlation between them and the whole node pair. As a result, many nodes in the subgraph may have little contribution to predicting the potential edge. Aggregating these node features will reduce the model's accuracy and efficiency. In addition, these methods indirectly represent the potential link by the node embeddings in the subgraph. We argue that this formalism is not the best choice for link prediction. In this paper, we propose a relation-based link prediction neural network named RelpNet, which aggregates edge features along the structural interactions between two target nodes and directly represents their relationship. RelpNet first extracts paths between the target node pair as structural interactions, which have strong correlations with the whole node pair and fewer nodes and edges than node-based methods' subgraph. To aggregate edge embeddings along the links between edges, we propose transforming the paths into a line graph. Then, the Tree-LSTM model is adopted to transfer and aggregate the node embeddings in the line graph as a comprehensive representation of the target node pair. We evaluate RelpNet on 7 benchmark datasets against 15 popular and state-of-the-art approaches, and the results demonstrate its significant superiority and high training efficiency.
FedCDR

The cold-start problem, faced when providing recommendations to newly joined users with no historical interaction record existing in the platform, is one of the most critical problems that negatively impact the performance of a recommendation system. Fortunately, cross-domain recommendation~(CDR) is a promising approach for solving this problem, which can exploit the knowledge of these users from source domains to provide recommendations in the target domain. However, this method requires that the central server has the interaction behaviour data in both domains of all the users, which prevents users from participating due to privacy issues.
Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge

How can we accurately recommend actions for users to control their devices at home? Action recommendation for smart home has attracted increasing attention due to its potential impact on the markets of Internet of Things (IoT). However, designing an effective action recommender system is challenging because it requires handling context correlations, considering both queried contexts and previous histories of users, and dealing with capricious intentions in history. In this work, we propose SmartSense, an accurate action recommendation method for smart home. For individual action, SmartSense summarizes its device control and temporal contexts in a self-attentive manner, to reflect the importance of the correlation between them. SmartSense then summarizes sequences considering queried contexts in a query-attentive manner to extract the query-related patterns from the sequential actions. SmartSense also transfers the commonsense knowledge from routine data to better handle intentions in action sequences. As a result, SmartSense addresses all three main challenges of action recommendation for smart home, and achieves the state-of-the-art performance giving up to 9.8% higher [email protected] than the best competitor.