Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Continuous Time Sequences with Intermittent Observations using Marked Temporal Point Processes

A large fraction of data generated via human activities such as online purchases, health records, spatial mobility, etc. can be represented as a sequence of events over a continuous-time. Learning deep learning models over these continuous-time event sequences is a non-trivial task as it involves modeling the ever-increasing event timestamps, inter-event time gaps, event types, and the influences between different events within and across different sequences. In recent years, neural enhancements to marked temporal point processes (MTPP) have emerged as a powerful framework to model the underlying generative mechanism of asynchronous events localized in continuous time. However, most existing models and inference methods in the MTPP framework consider only the complete observation scenario i.e., the event sequence being modeled is completely observed with no missing events – an ideal setting that is rarely applicable in real-world applications. A recent line of work which considers missing events while training MTPP utilizes supervised learning techniques that require additional knowledge of missing or observed label for each event in a sequence, which further restricts its practicability as in several scenarios the details of missing events is not known a priori. In this work, we provide a novel unsupervised model and inference method for learning MTPP in presence of event sequences with missing events. Specifically, we first model the generative processes of observed events and missing events using two MTPP, where the missing events are represented as latent random variables. Then, we devise an unsupervised training method that jointly learns both the MTPP by means of variational inference. Such a formulation can effectively impute the missing data among the observed events, which in turn enhances its predictive prowess, and can identify the optimal position of missing events in a sequence. Experiments with eight real-world datasets show that IMTPP outperforms the state-of-the-art MTPP frameworks for event prediction and missing data imputation, and provides stable optimization.
EMVGAN: Emotion-Aware Music-Video Common Representation Learning via Generative Adversarial Networks

Music can enhance our emotional reactions to videos and images, while videos and images can enrich our emotional response to music. Cross-modality retrieval technology can be used to recommend appropriate music for a given video and vice versa. However, the heterogeneity gap caused by the inconsistent distribution between different data modalities complicates learning the common representation space from different modalities. Accordingly, we propose an emotion-aware music-video cross-modal generative adversarial network (EMVGAN) model to build an affective common embedding space to bridge the heterogeneity gap among different data modalities. The evaluation results revealed that the proposed EMVGAN model can learn affective common representations with convincing performance while outperforming other existing models. Furthermore, the satisfactory performance of the proposed network encouraged us to undertake the music-video bidirectional retrieval task.
Cache-coherent accelerators for persistent memory crash consistency

Building persistent memory (PM) data structures is difficult because crashes interrupt operations, leaving data structures in an inconsistent state. Solving this requires augmenting code that modifies PM state to ensure that interrupted operations can be completed or undone. Today, this is done using careful, hand-crafted code, a compiler pass, or page faults. We propose a new, easy way to transform volatile data structure code to work with PM that uses a cache-coherent accelerator to do this augmentation, and we show that it may outperform existing approaches for building PM structures.
High-efficiency single-photon generation via large-scale active time multiplexing

Time multiplexing with a low-loss adjustable optical delay line enables largely enhanced emissions of heralded single photons. Deterministic generation of single- and multiphoton states is a key requirement for large-scale optical quantum information and communication applications. While heralded single-photon sources (HSPSs) using nonlinear optical processes have enabled proof-of-principle demonstrations in this area of research, they are not scalable as their probabilistic nature severely limits their generation efficiency. We overcome this limitation by demonstrating a substantial improvement in HSPS efficiency via large-scale time multiplexing. Using an ultra-low loss, adjustable optical delay to multiplex 40 conventional HSPS photon generation processes into each operation cycle, we have observed a factor of 9.7(5) enhancement in efficiency, yielding a 66.7(24)% probability of collecting a single photon with high indistinguishability (90%) into a single-mode fiber per cycle. We also experimentally investigate the trade-off between a high single-photon probability and unwanted multiphoton emission. Upgrading our time-multiplexed source with state-of-the-art HSPS and single-photon detector technologies will enable the generation of >30 coincident photons with unprecedented efficiency.
Domain Adaptational Text Steganalysis Based on Transductive Learning

Traditional text steganalysis methods rely on a large amount of labeled data. At the same time, the test data should be independent and identically distributed with the training data. However, in practice, a large number of text types make it difficult to satisfy the i.i. d condition between the training set and the test set, which leads to the problem of domain mismatch and significantly reduces the detection performance. In this paper, we draw on the ideas of domain adaptation and transductive learning to design a novel text steganalysis method. In this method, we design a distributed adaptation layer and adopt three loss functions to achieve domain adaptation, so that the model can learn the domain-invariant text features. The experimental results show that the method has better steganalysis performance in the case of domain mismatch.
Parallelism Network with Partial-aware and Cross-correlated Transformer for Vehicle Re-identification

Vehicle re-identification (ReID) aims to identify a specific vehicle in the dataset captured by non-overlapping cameras, which plays a great significant role in the development of intelligent transportation systems. Even though CNN-based model achieves impressive performance for the ReID task, its Gaussian distribution of effective receptive fields has limitations in capturing the long-term dependence between features. Moreover, it is crucial to capture fine-grained features and the relationship between features as much as possible from vehicle images. To address those problems, we propose a partial-aware and cross-correlated transformer model (PCTM), which adopts the parallelism network extracting discriminant features to optimize the feature representation for vehicle ReID. PCTM includes a cross-correlation transformer branch that fuses the features extracted based on the transformer module and feature guidance module, which guides the network to capture the long-term dependence of key features. In this way, the feature guidance module promotes the transformer-based features to focus on the vehicle itself and avoid the interference of excessive background for feature extraction. Moreover, PCTM introduced a partial-aware structure in the second branch to explore fine-grained information from vehicle images for capturing local differences from different vehicles. Furthermore, we conducted experiments on 2 vehicle datasets to verify the performance of PCTM.
ScalaRAID: optimizing linux software RAID system for next-generation storage

RAID has been widely adopted to enhance the performance, capacity, and reliability of the existing storage systems. However, we observe that the Linux software RAID (mdraid) suffers from its poor implementation of the lock mechanism. To address this, we propose ScalaRAID, which refines the role domain of locks and designs a new data structure to prevent different threads from preempting the RAID resources. By doing so, ScalaRAID can maximize the thread-level parallelism and reduce the time consumption of I/O request handling. Our evaluation results reveal that ScalaRAID can improve throughput by 89.4% while decreasing 99.99th percentile latency by 85.4% compared to mdraid.
Mobile Emotion Recognition via Multiple Physiological Signals using Convolution-augmented Transformer

Recognising and monitoring emotional states play a crucial role in mental health and well-being management. Importantly, with the widespread adoption of smart mobile and wearable devices, it has become easier to collect long-term and granular emotion-related physiological data passively, continuously, and remotely. This creates new opportunities to help individuals manage their emotions and well-being in a less intrusive manner using off-the-shelf low-cost devices. Pervasive emotion recognition based on physiological signals is, however, still challenging due to the difficulty to efficiently extract high-order correlations between physiological signals and users' emotional states. In this paper, we propose a novel end-to-end emotion recognition system based on a convolution-augmented transformer architecture. Specifically, it can recognise users' emotions on the dimensions of arousal and valence by learning both the global and local fine-grained associations and dependencies within and across multimodal physiological data (including blood volume pulse, electrodermal activity, heart rate, and skin temperature). We extensively evaluated the performance of our model using the K-EmoCon dataset, which is acquired in naturalistic conversations using off-the-shelf devices and contains spontaneous emotion data. Our results demonstrate that our approach outperforms the baselines and achieves state-of-the-art or competitive performance. We also demonstrate the effectiveness and generalizability of our system on another affective dataset which used affect inducement and commercial physiological sensors.
MuLER: Multiplet-Loss for Emotion Recognition

With the rise of human-machine interactions, it has become necessary for machines to better understand humans in order to respond appropriately. Hence, in order to increase communication and interaction, it would be ideal for machines to automatically detect human emotions. Speech Emotion Recognition (SER) has been a focus of a lot of studies in the past few years. However, they can be considered poor in accuracy and must be improved. In our work, we propose a new loss function that aims to encode speeches instead of classifying them directly as the majority of the existing models do. The encoding will be done in a way that utterances with the same labels would have similar encodings. The encoded speeches were tested on two datasets and we managed to get 88.19% accuracy with the RAVDESS (Ryerson Audiovisual Database of Emotional Speech and Song) dataset and 91.66% accuracy with the RML (Ryerson Multimedia Research Lab) dataset.
BlindSpot: Watermarking Through Fairness

With the increasing development of machine learning models in daily businesses, a strong need for intellectual property protection arised. For this purpose, current works suggest to leverage backdoor techniques to embed a watermark into the model, by overfitting to a set of particularly crafted and secret input-output pairs called triggers. By sending verification queries containing triggers, the model owner can analyse the behavior of any suspect model on the queries to claim its ownership. However, when it comes to scenarios where frequent monitoring is needed, the computational overhead of these verification queries in terms of volume demonstrates that backdoor-based watermarking appears to be too sensitive to outlier detection attacks and cannot guarantee the secrecy of the triggers. To solve this issue, we introduce BlindSpot, to watermark machine learning models through fairness. Our trigger-less approach is compatible with a high number of verification queries while being robust to outlier detection attacks. We show on Fashion-MNIST and CIFAR-10 datasets that BlindSpot is efficiently watermarking models while robust to outlier detection attacks, at a performance cost on the accuracy of 2%.
Formal verification of multiplier circuits using computer algebra

Abstract Digital circuits are widely utilized in computers, because they provide models for various digital components and arithmetic operations. Arithmetic circuits are a subclass of digital circuits that are used to execute Boolean algebra. To avoid problems like the infamous Pentium FDIV bug, it is critical to ensure that arithmetic circuits are correct. Formal verification can be used to determine the correctness of a circuit with respect to a certain specification. However, arithmetic circuits, particularly integer multipliers, represent a challenge to current verification methodologies and, in reality, still necessitate a significant amount of manual labor. In my dissertation we examine and develop automated reasoning approaches based on computer algebra, where the word-level specification, modeled as a polynomial, is reduced by a Gröbner basis inferred by the gate-level representation of the circuit. We provide a precise formalization of this reasoning process, which includes soundness and completeness arguments and adds to the mathematical background in this field. On the practical side we present an unique incremental column-wise verification algorithm and preprocessing approaches based on variable elimination that simplify the inferred Gröbner basis. Furthermore, we provide an algebraic proof calculus in this thesis that allows obtaining certificates as a by-product of circuit verification in order to boost confidence in the outcomes of automated reasoning tools. These certificates can be efficiently verified with independent proof checking tools.
Feasibility Study for a Fuel Cell-Powered Unmanned Aerial Vehicle with a 75 Kg Payload

Abstract Among the possible electric powerplants currently driving low-payload UAVs (up to around 10 kg of payload), batteries offer certain clear benefits, but for medium-payload operation such as aerotaxis and heavy-cargo transportation UAVs, battery capacity requirements restrict their usage due to high weight and volume. In light of this situation, fuel cell (FC) systems (FCS) offer clear benefits over batteries for the medium-payload UAV segment (> 50 kg). Nevertheless, studies regarding the application of FCS powerplants to this UAV segment are limited and the in-flight performance has not been clearly analysed. In order to address this knowledge gap, a feasibility analysis of these particular applications powered by FCS is performed in this study. A validated FC stack model (40 kW of maximum power) was integrated into a balance of plant to conform an FCS. As a novelty, the management of the FCS was optimized to maximize the FCS efficiency at different altitudes up to 12500 ft, so that the operation always implies the lowest H2 consumption regardless of the altitude. In parallel, an UAV numerical model was developed based on the ATLANTE vehicle and characterized by calculating the aerodynamic coefficients through CFD simulations. Then, both models were integrated into a 0D-1D modelling platform together with an energy management strategy optimizer algorithm and a suitable propeller model. With the preliminary results obtained from the FCS and UAV models, it was possible to ascertain the range and endurance of the vehicle. As a result, it was concluded that the combination of both technologies could offer a range over 600 km and an endurance over 5 h. Finally, with the integrated UAV-FCS model, a flight profile describing a medium altitude, medium endurance mission was designed and used to analyse the viability of FC-powered UAV. The results showed how UAVs powered by FCS are viable for the considered aircraft segment, providing competitive values of specific range and endurance.
The Two-Phase Microchannel Flow Study of Chicken Blood on Lab-on-a-Chip

In previous study, we introduce problems in assembly the polydimethylsiloxane (PDMS) lab-on-a-chip (LOC) devices from literatures; a manual assembly by using acrylic plates and an adhered assembly by using a plasma cleaner. Since electrodes in the devices had been designed to provide a strong dielectrophoretic force by using sputtering and electroplating techniques, the thick electrodes were used. The PDMS LOC was also aimed to use with biological samples to study the different flow patterns of the samples prepared from chicken blood and to solve a PDMS hydrophobic problem from our previous study where the sample cannot flow through a channel of the adhered assembly LOC. Since the chicken blood contains of whole blood and plasma, the sample can be considered as a combination of particle and fluid parts or the two-phase flow. The channels on the PDMS LOC having dimensions of the order of microns can be considered as microchannels; Knudsen number is used to distinguish flow regimes. From the experiment study, the flow problem has still existed in the adhered method but the flow problem did not exist on the manual method with a new-acrylic-plate set. Slip flow occurred at the leak areas because interactions between the sample and walls became significant. Different flow patterns occurred in the channels of workable LOCs in different channel distances. No slip (continuum) flow occurred at the middle path where pressure drop was decreased. The hydrophobic problem was solved by using Bovine Serum Abumin solution to reduce PDMS hydrophobic behavior and this solution did not affect red blood cells because of none damaged cells.
Sugarcane Yield Response to Phosphorus Fertilizer as Related to Extractable Soil Phosphorus on Florida Mineral Soils

ABSTRACT Relating phosphorus (P) fertilization to crop demand in sugarcane (Saccharum spp.) grown on mineral soils in southern Florida is critical for minimized environmental impact and sustained crop production. Previous P fertilizer recommendations date to 1974 and so revised recommendations are needed for sugarcane on these soils which account for 29% of the crop in Florida. Phosphorus rate studies were conducted at seven field locations (16 total site-crop years) with P rates of 0 to 73 kg P ha−1 at Site 1 and 0 to 61 kg P ha−1 at Sites 2–7 using small-plots (82 or 120 m2 plots) on four soil series representing Spodosols, Entisols, and Alfisols. Soil P extractions with Mehlich 3, 0.7 M ammonium acetate, and water were evaluated in relation to relative sucrose yield and leaf P concentrations. Mehlich 3 had stronger relationships with relative yield and leaf P and a Mehlich 3 P value of 80 g m−3 is proposed as a critical value above which no P fertilizer would be required. Without added P fertilizer, Mehlich 3-extractable P values decreased annually by an average of 20%. Results suggest maintaining 36.7 kg P ha−1 as the maximum P fertilizer rate for sugarcane in mineral soils. Abbreviations: EAA: Everglades Agricultural Area; BMPs: best management practices; DAP: diammonium phosphate; MCH: megagram (Mg) cane ha−1; MSH: megagram (Mg) sucrose ha−1; KSM: kg sucrose Mg−1 cane; M3P: Mehlich 3-extractable soil P; AmAcP: ammonium acetate-extractable soil P; Pw: water-extractable soil P
Analysis of the land suitability for paddy fields in Tanzania using a GIS-based analytical hierarchy process

ABSTRACT The importance of irrigation development is considered a key factor for food security and poverty reduction because it improves crop productivity, and ensures stable expansion of agricultural production. However, irrigation development requires understanding of the available resources including the suitability of the land for agriculture. In this study, the land suitability for paddy fields was evaluated within the United Republic of Tanzania mainland by integrating the geographic information system (GIS) and analytical hierarchy process (AHP). In this study, 11 criteria based on various sources (soil type, soil drainage, soil organic carbon, soil pH, soil depth, elevation, slope, land use, topographic wetness index, temperature, and precipitation) were used. These criteria were used within the GIS-based AHP to identify the most suitable land for sustainable paddy field cultivation considering the preservation of the natural environment of forests and protected areas by examining two scenarios: rainfed condition and irrigation priority. The former ten criteria were assumed to be constant in both scenarios and were assigned the same scores, while the latter criterion (precipitation) was assigned different scores for varying amounts to plan new irrigation projects. Unsuitable land represents 72.8% of the study area, reducing the potential agriculture land (PAL) appropriate for cultivation to 27.2%. In the rainfed condition scenario, the very high and high suitability classes represent 17.6% of the total land of the study area and 64.7% of the PAL. In the irrigation priority scenario, the same classes represent 21.4% of the total land of the study area and 78.6% of the PAL. Finally, the distribution of the land suitability for both scenarios was analyzed within eight administrative irrigation zones to determine the irrigation zone with the greatest potential for paddy field cultivation.
Typology of peach producers in Zacatecas, Mexico by attributes of fruit quality

Zacatecas is positioned among the five leading peach producing states in Mexico. The peach is creole, yellow, bonded bone, firm pulp, sexually propagated and cultivated in temporary in ≈ 81% of the cultivated surface; what, highlights the low competitiveness of this crop. However, due to the number of producers dedicated to this crop, it enhances its socioeconomic importance in relation to other fruit trees. If it is intended to understand the characteristics and problems of the production units (UP) that serve as an instrument to optimize the allocation of public resources, and propose strategies that improve the development of agriculture, it is necessary to understand the heterogeneity of producers considering their socio-economic characteristics within the UP. Therefore, this research sought to analyze and segment technically and typologically the peach producers of the state of Zacatecas. The data was collected through a survey of 204 peach producers in Zacatecas, Mexico. The results suggest four types of producers: 1) those who plant peach without objective; 2) those who plant peach at the backyard level; 3) producers where the peach is part of an agricultural production system; and 4) peach business producers. In this last category, ≈ 31% of the producers were found, which could explain, in part, the low competitiveness of the crop with other
Aug 15, 2021 Code Generation for Protocols from CPN models

template tree construc-tion. The abstract syntax tree (ATT) for a CPN module is an ordered tree that has nodes corresponding to explicit and inferred pragmatics. The ATT is derived from the CPN model based on the pragmatics. The ATT represents the control structure of the CPN independently from a speciﬁc programming lan-gage. The nodes of the ATT is bound to code templates which are then applied during code generation. When construct-ing the ATT