Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Efficient Hyperparameter Tuning for Policy Gradients

The performance of policy gradient methods is sensitive to hyperparameter settings that must be tuned for any new application. Widely used grid search methods for tuning hyperparameters are sample inefficient and computationally expensive. More advanced methods like Population Based Training that learn optimal schedules for hyperparameters instead of fixed settings can yield better results, but are also sample inefficient and computationally expensive. In this paper, we propose Hyperparameter Optimisation on the Fly (HOOF), a gradient-free algorithm that requires no more than one training run to automatically adapt the hyperparameter that affect the policy update directly through the gradient. The main idea is to use existing trajectories sampled by the policy gradient method to optimise a one-step improvement objective, yielding a sample and computationally efficient algorithm that is easy to implement. Our experimental results across multiple domains and algorithms show that using HOOF to learn these hyperparameter schedules leads to faster learning with improved performance.
CaRe: Open Knowledge Graph Embeddings

Open Information Extraction (OpenIE) methods are effective at extracting (noun phrase, relation phrase, noun phrase) triples from text, e.g., (Barack Obama, took birth in, Honolulu). Organization of such triples in the form of a graph with noun phrases (NPs) as nodes and relation phrases (RPs) as edges results in the construction of Open Knowledge Graphs (OpenKGs). In order to use such OpenKGs in downstream tasks, it is often desirable to learn embeddings of the NPs and RPs present in the graph. Even though several Knowledge Graph (KG) embedding methods have been recently proposed, all of those methods have targeted Ontological KGs, as opposed to OpenKGs. Straightforward application of existing Ontological KG embedding methods to OpenKGs is challenging, as unlike Ontological KGs, OpenKGs are not canonicalized, i.e., a real-world entity may be represented using multiple nodes in the OpenKG, with each node corresponding to a different NP referring to the entity. For example, nodes with labels Barack Obama, Obama, and President Obama may refer to the same real-world entity Barack Obama. Even though canonicalization of OpenKGs has received some attention lately, output of such methods has not been used to improve OpenKG embed- dings. We fill this gap in the paper and propose Canonicalization-infused Representations (CaRe) for OpenKGs. Through extensive experiments, we observe that CaRe enables existing models to adapt to the challenges in OpenKGs and achieve substantial improvements for the link prediction task.
A Simple Data Cube Representation for Efficient Computing and Updating

—This paper presents a simple approach to represent data cubes that allows efficient computing, querying and updating. The representation is based on (i) a recursive construction of the power set of the scheme of the fact table and (ii) a prefix tree structure for the compact storage of cuboids. The experimental results on large real datasets show that the approach is efficient in run time, storage space, and for incremental update.
Serialization in Object-Oriented Programming Languages

This chapter depicts the process of converting object state into a format that can be transmitted or stored in currently used object-oriented programming languages. This process is called serialization (marshaling); the opposite is called deserialization (unmarshalling) processes. It is a low-level technique, and several technical issues should be considered like endianness, size of memory representation, representation of numbers, object references, recursive object connections and others. In this chapter we discuss these issues and give them solutions. We also include a short review of tools currently used, and we showed that meeting all requirements is not possible. Finally, we presented a new C++ library that supports forward compatibility.
Extracting Clinical Features From Dictated Ambulatory Consult Notes Using a Commercially Available Natural Language Processing Tool: Pilot, Retrospective, Cross-Sectional Validation Study

Background The increasing adoption of electronic health records (EHRs) in clinical practice holds the promise of improving care and advancing research by serving as a rich source of data, but most EHRs allow clinicians to enter data in a text format without much structure. Natural language processing (NLP) may reduce reliance on manual abstraction of these text data by extracting clinical features directly from unstructured clinical digital text data and converting them into structured data. Objective This study aimed to assess the performance of a commercially available NLP tool for extracting clinical features from free-text consult notes. Methods We conducted a pilot, retrospective, cross-sectional study of the accuracy of NLP from dictated consult notes from our tuberculosis clinic with manual chart abstraction as the reference standard. Consult notes for 130 patients were extracted and processed using NLP. We extracted 15 clinical features from these consult notes and grouped them a priori into categories of simple, moderate, and complex for analysis. Results For the primary outcome of overall accuracy, NLP performed best for features classified as simple, achieving an overall accuracy of 96% (95% CI 94.3-97.6). Performance was slightly lower for features of moderate clinical and linguistic complexity at 93% (95% CI 91.1-94.4), and lowest for complex features at 91% (95% CI 87.3-93.1). Conclusions The findings of this study support the use of NLP for extracting clinical features from dictated consult notes in the setting of a tuberculosis clinic. Further research is needed to fully establish the validity of NLP for this and other purposes.
Open Relation Extraction: Relational Knowledge Transfer from Supervised Data to Unsupervised Data

Open relation extraction (OpenRE) aims to extract relational facts from the open-domain corpus. To this end, it discovers relation patterns between named entities and then clusters those semantically equivalent patterns into a united relation cluster. Most OpenRE methods typically confine themselves to unsupervised paradigms, without taking advantage of existing relational facts in knowledge bases (KBs) and their high-quality labeled instances. To address this issue, we propose Relational Siamese Networks (RSNs) to learn similarity metrics of relations from labeled data of pre-defined relations, and then transfer the relational knowledge to identify novel relations in unlabeled data. Experiment results on two real-world datasets show that our framework can achieve significant improvements as compared with other state-of-the-art methods. Our code is available at https://github.com/thunlp/RSN.
Egocentric Action Anticipation by Disentangling Encoding and Inference

Egocentric action anticipation consists in predicting future actions from videos collected by means of a wearable camera. Action anticipation methods should be able to continuously 1) summarize the past and 2) predict possible future actions. We observe that action anticipation benefits from explicitly disentangling the two tasks. To this aim, we introduce a learning architecture which makes use of a "rolling" LSTM to continuously summarize the past and an "unrolling" LSTM to anticipate future actions at multiple temporal scales. The model includes a spatial and a temporal branch which process RGB images and optical flow fields independently. The predictions performed by the two branches are fused using a novel modality attention mechanism which leverages the complementary nature of the modalities. Experiments on the EPIC-KITCHENS dataset show that the proposed method surpasses the state-of-the-art by +4.02% and +6.39% when considering Top-1 and Top-5 accuracy respectively. Please see the project webpage at http://iplab.dmi. unict.it/rulstm/.
Understanding and Improving Layer Normalization

Layer normalization (LayerNorm) is a technique to normalize the distributions of intermediate layers. It enables smoother gradients, faster training, and better generalization accuracy. However, it is still unclear where the effectiveness stems from. In this paper, our main contribution is to take a step further in understanding LayerNorm. Many of previous studies believe that the success of LayerNorm comes from forward normalization. Unlike them, we find that the derivatives of the mean and variance are more important than forward normalization by re-centering and re-scaling backward gradients. Furthermore, we find that the parameters of LayerNorm, including the bias and gain, increase the risk of over-fitting and do not work in most cases. Experiments show that a simple version of LayerNorm (LayerNorm-simple) without the bias and gain outperforms LayerNorm on four datasets. It obtains the state-of-the-art performance on En-Vi machine translation. To address the over-fitting problem, we propose a new normalization method, Adaptive Normalization (AdaNorm), by replacing the bias and gain with a new transformation function. Experiments show that AdaNorm demonstrates better results than LayerNorm on seven out of eight datasets.
Persuasive System Design Principles that Impact the Usability of Smartphone Applications

While the market for Smartphone applications (apps) is competitive, app loyalty is a constant concern for developers and vendors. Only one in five smartphone apps are downloaded more than 1,000 times; and for every four downloaded applications, at least one has never been used (Hoehle et al. 2015). The success of globally popular apps, such as WhatsApp, will be undermined if user loyalty cannot be sustained. With competitors (e.g. Line, WeChat, and Snap Chat) attempting to imitate the persuasive design of WhatsApp, investigating the factors that influence user loyalty to software applications across cultures is a paramount issue for designers. Such factors can reduce the cost, effort, and Abstract
The Effectiveness of a Computer-Assisted, Cognitive-Behavior Program for Treating Anxiety Symptoms in Children with Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a complex and multifaceted, neurodevelopmental disorder that severely impacts children and families across a variety of settings. Prevalence rates of ASD are continuing to increase rapidly, with sizeable intervention and treatment costs placed on families and society. Further complicating the matter, many individuals with ASD also evidence co-occurring anxiety symptoms or disorders and tend to be at a higher risk for developing such problems when compared to other clinical populations or typically developing children. This study investigated the effectiveness of a computer-assisted CBT program, Camp Cope-A-Lot (CCAL), in regards to reducing anxiety symptoms in four participants diagnosed with ASD. Data from quantitative measures revealed inconsistent results, yet informal, qualitative feedback from parents as well as information from the researcher’s observations and progress notes appeared more promising for use of the CCAL intervention for this population of children.
A Structurally Regularized Convolutional Neural Network for Image Classification Using Wavelet-Based Subband Decomposition

We propose a convolutional neural network (CNN) architecture for image classification based on subband decomposition of the image using wavelets. The proposed architecture decomposes the input image spectra into multiple critically sampled subbands, extracts features using a single CNN per sub-band, and finally, performs classification by combining the extracted features using a fully connected layer. Processing each of the subbands by an individual CNN, thereby limiting the learning scope of each CNN to a single subband, imposes a form of structural regularization. This provides better generalization capability as seen by the presented results. The proposed architecture achieves best-in-class performance in terms of total multiply-add-accumulator operations and nearly best-in-class performance in terms of total parameters required, yet it maintains competitive classification performance. We also show the proposed architecture is more robust than the regular full-band CNN to noise caused by weight-and-bias quantization and input quantization.
Deep Multimodal Multilinear Fusion with High-order Polynomial Pooling

Tensor-based multimodal fusion techniques have exhibited great predictive performance. However, one limitation is that existing approaches only consider bilinear or trilinear pooling, which fails to unleash the complete expressive power of multilinear fusion with restricted orders of interactions. More importantly, simply fusing features all at once ignores the complex local intercorrelations, leading to the deterioration of prediction. In this work, we first propose a polynomial tensor pooling (PTP) block for integrating multimodal features by considering high-order moments, followed by a tensorized fully connected layer. Treating PTP as a building block, we further establish a hierarchical polynomial fusion network (HPFN) to recursively transmit local correlations into global ones. By stacking multiple PTPs, the expressivity capacity of HPFN enjoys an exponential growth w.r. t. the number of layers, which is shown by the equivalence to a very deep convolutional arithmetic circuits. Various experiments demonstrate that it can achieve the state-of-the-art performance.
DEEP CONVOLUTIONAL NEURAL NETWORKS FOR SENTIMENT ANALYSIS OF
CULTURAL HERITAGE

Abstract. The promotion of Cultural Heritage (CH) goods has become a major challenges over the last years. CH goods promote economic development, notably through cultural and creative industries and tourism. Thus, an effective planning of archaeological, cultural, artistic and architectural sites within the territory make CH goods easily accessible. A way of adding value to these services is making them capable of providing, using new technologies, a more immersive and stimulating fruition of information. In this light, an effective contribution can be provided by sentiment analysis. The sentiment related to a monument can be used for its evaluation considering that if it is positive, it influences its public image by increasing its value. This work introduces an approach to estimate the sentiment of Social Media pictures CH related. The sentiment of a picture is identified by an especially trained Deep Convolutional Neural Network (DCNN); aftewards, we compared the performance of three DCNNs: VGG16, ResNet and InceptionResNet. It is interesting to observe how these three different architectures are able to correctly evaluate the sentiment of an image referred to a ancient monument, historical buildings, archaeological sites, museum objects, and more. Our approach has been applied to a newly collected dataset of pictures from Instagram, which shows CH goods included in the UNESCO list of World Heritage properties.
Intracardiac signal processing for mapping and characterising cardiac arrhythmias

Cardiovascular diseases are the principal cause of death worldwide, especially in the US and Europe. Some of these diseases manifest as arrhythmic events, i.e., alterations of the normal sinus rhythm of the heart. Arrhythmia treatment varies depending on the type of arrhythmia and patient characteristics. Anti-arrhythmic drug therapy is often the first choice for arrhythmia management, although catheter ablation is becoming a first line therapy specially in those cases when the disease is on its first states, anti-arrhythmic drug therapy is ineffective or the arrhythmia focus is identifiable. Catheter ablation is a minimally invasive clinical procedure that deploys small catheters in the cardiac chambers. Those catheters are equipped with electrodes that sense the local electrical activity of the heart, known as electrograms (EGM), during arrhythmia or sinus rhythm. Based on the characteristics of this electrical activity, radio frequency energy is delivered at those sites responsible of the clinical arrhythmia. The aim of this thesis is to investigate signal processing techniques of invasive EGM signals in order to provide useful tools for helping in the decision process during ablation procedures and electrophysiological studies. In chapter 2, an automatic EGM delineator based on the wavelet transform of the bipolar electrogram (b-EGM) signal envelope is introduced and validated. It is motivated by the need of an operator-independent algorithm to measure local activation times (LATs) and to build ventricular activation maps in electroanatomical mapping (EAM) systems. The accuracy of the method is first validated against manual annotations performed by two experts, showing errors in the same order as the annotations performed manually during the interventions in the electrophysiology lab. A second study assesses the ability of the method to generate activation maps in order to identify the area of interest for ablation and to determine the site of origin (SOO) in patients with idiopathic outflow tract ventricular arrhythmias (OTVAs). Automatic activation maps were compared with manual maps created during the procedure, showing similar accuracy in identifying the earliest activation area for ablation purposes and a slightly superior performance in the SOO identification of idiopathic OTVAs. Therefore, these results suggest that the proposed automatic b-EGM delineator may potentially help to reduce mapping acquisition time, especially when multi-electrode catheters are used, to reduce operator variability and to increase ablation outcomes. In chapter 3, a novel spatiotemporal approach to resolve high-density cardiac activation during atrial fibrillation (AF) using multi-electrode array (MEA) sensors is
Quadrature Index Modulation Based Multiple Access Scheme for 5G and Beyond

Index Modulation Multiple Access (IMMA) technique is proposed recently as a viable candidate for future wireless networks. The idea is to utilize the available orthogonal resources for multiple users as index constellation symbols and allow users to access all available resources without any need of scheduling algorithm. Yet, IMMA receiver applies brute–force search algorithm and suffers from immense computational complexity. Also, IMMA applies index modulation and allows each user to activate a single orthogonal resource at each transmission time. Hence, this letter aims at enhancing the overall performance of IMMA by allowing each user to activate two of the available orthogonal resources to transmit orthogonal data. As well, a novel low complexity receiver is proposed and shown to significantly reduce the computational complexity without any performance degradation. The new scheme is called Quadrature IMMMA (QIMMA) and shown to have superior performance, in terms of number of users and error probability, as compared to IMMA scheme. Analytical framework to compute the average bit error probability for the proposed QIMMA is derived and substantiated through Monte Carlo simulation results.
Self-Attention Enhanced CNNs and Collaborative Curriculum Learning for Distantly Supervised Relation Extraction

Distance supervision is widely used in relation extraction tasks, particularly when large-scale manual annotations are virtually impossible to conduct. Although Distantly Supervised Relation Extraction (DSRE) benefits from automatic labelling, it suffers from serious mislabelling issues, i.e. some or all of the instances for an entity pair (head and tail entities) do not express the labelled relation. In this paper, we propose a novel model that employs a collaborative curriculum learning framework to reduce the effects of mislabelled data. Specifically, we firstly propose an internal self-attention mechanism between the convolution operations in convolutional neural networks (CNNs) to learn a better sentence representation from the noisy inputs. Then we define two sentence selection models as two relation extractors in order to collaboratively learn and regularise each other under a curriculum scheme to alleviate noisy effects, where the curriculum could be constructed by conflicts or small loss. Finally, experiments are conducted on a widely-used public dataset and the results indicate that the proposed model significantly outperforms baselines including the state-of-the-art in terms of P@N and PR curve metrics, thus evidencing its capability of reducing noisy effects for DSRE.
The hippocampus and medial prefrontal cortex during flexible behavior

The solving of complex tasks requires the functions of more than one brain area and their interaction. Whilst spatial navigation and memory is dependent on the hippocampus, flexible behavior relies on the medial prefrontal cortex (mPFC). To further examine the roles of the hippocampus and mPFC, we recorded their neural activity during a task that depends on both of these brain regions. With tetrodes, we recorded the extracellular activity of dorsal hippocampal CA1 (HPC) and mPFC neurons in Long-Evans rats performing a rule-switching task on the plus-maze. The plus-maze task had a spatial component since it required navigation along one of the two start arms and at the maze center a choice between one of the two goal arms. Which goal contained a reward depended on the rule currently in place. After an uncued rule change the animal had to abandon the old strategy and switch to the new rule, testing cognitive flexibility. Investigating the coordination of activity between the HPC and mPFC allows determination during which task stages their interaction is required. Additionally, comparing neural activity patterns in these two brain regions allows delineation of the specialized functions of the HPC and mPFC in this task. We analyzed neural activity in the HPC and mPFC in terms of oscillatory interactions, rule coding and replay. We found that theta coherence between the HPC and mPFC is increased at the center and goals of the maze, both when the rule was stable or has changed. Similar results were found for locking of HPC and mPFC neurons to HPC theta oscillations. However, no differences in HPC-mPFC theta coordination were observed between the spatially- and cue-guided rule. Phase locking of HPC and mPFC neurons to HPC gamma oscillations was not modulated by maze position or rule type. We found that the HPC coded for the two different rules with cofiring relationships between cell pairs. However, we could not find conclusive evidence for rule coding in the mPFC. Spatially-selective firing in the mPFC generalized between the two start and two goal arms. With Bayesian positional decoding, we found that the mPFC reactivated non-local positions during awake immobility periods. Replay of these non-local positions could represent entire behavioral trajectories resembling trajectory replay of the HPC. Furthermore, mPFC trajectory-replay at the goal positively correlated with rule-switching performance. Finally, HPC and mPFC trajectory replay occurred independently of each other. These results show that the mPFC can replay ordered patterns of activity during awake immobility, possibly underlying its role in flexible behavior.