Miracle-dz/UniFAR-FTUs huggingface dataset:
Temperature and humidity effects on ferric sulfate stability and phase transformation

Abstract Evolution of concentrated Fe2(SO4)3 solution, a process including both evaporation of the solution and post-evaporation aging of the precipitates, was studied at 2 and 50 °C under controlled relative humidity (RH). At 50 °C and 42-47% RH, ferricopiapite [Fe4.67(SO4)6(OH)2⋅20H2O] and rhomboclase [(H5O2)Fe(SO4)2⋅2H2O] first crystallized from the starting solution, and then combined to form kornelite [Fe2(SO4)3⋅7.5H2O] at 42% RH or to paracoquimbite [Fe2(SO4)3⋅9H2O] at 46-47% RH. At 2 °C and 34-43% RH, initially crystallized ferricopiapite and rhomboclase appeared to be stable and did not proceed to form a single ferric sulfate hydrate phase over 385 days. At both 2 and 50 °C and RH ≤ 31%, an amorphous ferric sulfate formed. The amorphous ferric sulfate was preserved longer at low RH conditions, e.g., RH ≤ 11%, than higher RH, at which it slowly transformed to crystalline phases of rhomboclase and ferricopiapite, as observed at 31% RH and 50 °C. Combining the results from this study and those from our previous study at 25 °C, the ferric sulfate phase evolution at 2, 25, and 50 °C were mapped and compared. Temperature shows a strong effect on the evolution kinetics; low T may inhibit the evolution from reaching an equilibrium state. Also, an RH and T-controlled in situ X-ray diffraction (RH-T-XRD) method was used to study phase transitions of ferric sulfate hydrates at temperatures from 25 to 80 °C. A dehydration of paracoquimbite to ferric sulfate pentahydrate [Fe2(SO4)3·5H2O] was identified at 80 °C. The results are discussed with a previously constructed ferric sulfate RH-T phase diagram by Ackermann et al. (2009).
Language Model Evaluation Beyond Perplexity

We propose an alternate approach to quantifying how well language models learn natural language: we ask how well they match the statistical tendencies of natural language. To answer this question, we analyze whether text generated from language models exhibits the statistical tendencies present in the human-generated text on which they were trained. We provide a framework–paired with significance tests–for evaluating the fit of language models to these trends. We find that neural language models appear to learn only a subset of the tendencies considered, but align much more closely with empirical trends than proposed theoretical distributions (when present). Further, the fit to different distributions is highly-dependent on both model architecture and generation strategy. As concrete examples, text generated under the nucleus sampling scheme adheres more closely to the type–token relationship of natural language than text produced using standard ancestral sampling; text from LSTMs reflects the natural language distributions over length, stopwords, and symbols surprisingly well.
RAI-Net: Range-Adaptive LiDAR Point Cloud Frame Interpolation Network

LiDAR point cloud frame interpolation, which synthesizes the intermediate frame between the captured frames, has emerged as an important issue for many applications. Especially for reducing the amounts of point cloud transmission, it is by predicting the intermediate frame based on the reference frames to upsample data to high frame rate ones. However, due to high-dimensional and sparse characteristics of point clouds, it is more difficult to predict the intermediate frame for LiDAR point clouds than videos. In this paper, we propose a novel LiDAR point cloud frame interpolation method, which exploits range images (RIs) as an intermediate representation with CNNs to conduct the frame interpolation process. Considering the inherited characteristics of RIs differ from that of color images, we introduce spatially adaptive convolutions to extract range features adaptively, while a high-efficient flow estimation method is presented to generate optical flows. The proposed model then warps the input frames and range features, based on the optical flows to synthesize the interpolated frame. Extensive experiments on the KITTI dataset have clearly demonstrated that our method consistently achieves superior frame interpolation results with better perceptual quality to that of using state-of-the-art video frame interpolation methods. The proposed method could be integrated into any LiDAR point cloud compression systems for inter prediction.
Toward a secure crowdsourced location tracking system

Low-energy Bluetooth devices have become ubiquitous and widely used for different applications. Among these, Bluetooth trackers are becoming popular as they allow users to track the location of their physical objects. To do so, Bluetooth trackers are often built-in within other commercial products connected to a larger crowdsourced tracking system. Such a system, however, can pose a threat to the security and privacy of the users, for instance, by revealing the location of a user's valuable object. In this paper, we introduce a set of security properties and investigate the state of commercial crowdsourced tracking systems, which present common design flaws that make them insecure. Leveraging the results of our investigation, we propose a new design for a secure crowdsourced tracking system (SECrow), which allows devices to leverage the benefits of the crowdsourced model without sacrificing security and privacy. Our preliminary evaluation shows that SECrow is a practical, secure, and effective crowdsourced tracking solution.
More Behind Your Electricity Bill: a Dual-DNN Approach to Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) is a well-known single-channel blind source separation problem that aims to decompose the household energy consumption into itemised energy usage of individual appliances. Recent investigations have shown that deep neural networks (DNNs) based approaches are promising for the NILM task. Nevertheless, they normally ignore the inherent properties of appliance operations in the network design, potentially leading to implausible results. We are thus motivated to develop the dual Deep Neural Networks (dual-DNN), which aims to i) take advantage of DNNs’ learning capability of latent features and ii) empower the DNN architecture with identification ability of universal properties. Specifically in the design of dual-DNN, we adopt one subnetwork to measure power ratings of different appliances’ operation states, and the other subnetwork to identify the running states of target appliances. The final result is then obtained by multiplying these two network outputs. Compared with the state-of-the-art NILM methods, our dual-DNN approach demonstrates a 21.67% performance improvement in average on two public benchmark datasets.
Comprehensive Nonlinear Optimal Energy Management Scheme for Electrified Powertrains: Application and Implementation

In this paper we present a benchmark solution with higher number of continuous and discrete states and control levers using validated powertrain component models, where DP fails due to exponential rise in the computation time. The problem involves 13 states and 4 control levers, with complex interactions between multiple subsystems. Some of these variables are discrete while some are continuous. Some have slow dynamics while some have fast dynamics. A novel three step PS3 algorithm [1] which is presented in our prequel paper is used to obtain a near-optimal solution. PS3 algorithm makes use of pseudo spectral method for accurate state estimations. We present three scenarios where only fuel is minimized, only emissions are minimized and, lastly a combination of both fuel and emissions are minimized. All three cases are analyzed for their performance and computation time. The optimal compromise between fuel consumption and emissions are analyzed using a Pareto-front study. This large-scale powertrain optimization problem is solved for a P2 parallel hybrid architecture on a class 6 Pick-up & Delivery truck.
Wavelength Dependence of Activity-induced Photometric Variations for Young Cool Stars in Hyades

We investigate photometric variations due to stellar activity that induce systematic radial-velocity errors (so-called “jitter”) for the four targets in the Hyades open cluster observed by the K2 mission (EPIC 210721261, EPIC 210923016, EPIC 247122957, and EPIC 247783757). Applying Gaussian process regressions to the K2 light curves and the near-infrared (NIR) light curves observed with the IRSF 1.4 m telescope, we derive the wavelength dependences of the photometric signals due to stellar activity. To estimate the temporal variations in the photometric variability amplitudes between the two observation periods of K2 and IRSF, separated by more than 2 yr, we analyze a number of K2 targets in Hyades that have also been observed in Campaigns 4 and 13 and find a representative variation rate over 2 yr of 38% ± 71%. Taking this temporal variation into account, we constrain projected sizes and temperature contrast properties of the starspots in the stellar photosphere to be approximately 10% and 0.95%, respectively. These starspot properties can induce relatively large differences in the variability amplitude over different observational passbands, and we find that radial-velocity jitter may be more suppressed in the NIR than previously expected. Our result supports profits of ongoing exoplanet search projects that are attempting to detect or confirm young planets in open clusters via radial-velocity measurements in the NIR.
Optimal Algorithms for Multiwinner Elections and the Chamberlin-Courant Rule

We consider the algorithmic question of choosing a subset of candidates of a given size k from a set of m candidates, with knowledge of voters' ordinal rankings over all candidates. We consider the well-known and classic scoring rule for achieving diverse representation: the Chamberlin-Courant (CC) or 1-Borda rule, where the score of a committee is the average over the voters, of the rank of the best candidate in the committee for that voter; and its generalization to the average of the top s best candidates, called the s-Borda rule. Our first result is an improved analysis of the natural and well-studied greedy heuristic. We show that greedy achieves a (1 - 2/k+1)-approximation to the maximization (or satisfaction) version of CC rule, and a (1 - 2s/k+1)-approximation to the s-Borda score. This significantly improves the existing submodularity-based analysis of the greedy algorithm that only shows a (1-1/e)-approximation. Our result also improves on the best known approximation algorithm for this problem. We achieve this result by showing that the average dissatisfaction score for the greedy algorithm is at most 2 m+1/k+1 for the CC rule, and at most 2s2 m+1/k+1 for s-Borda. We show these dissatisfaction score bounds are tight up to constants, and even the constant factor of 2 in the case of the CC rule is almost tight. For the dissatisfaction (or minimization) version of the problem, it is known that the average dissatisfaction score of the best committee cannot be approximated in polynomial time to within any constant factor when s is a constant (under standard computational complexity assumptions). As our next result, we strengthen this to show that the score of m+1/k+1 can be viewed as an optimal benchmark for the CC rule, in the sense that it is essentially the best achievable score of any polynomial-time algorithm even when the optimal score is a polynomial factor smaller. We show that another well-studied algorithm for this problem, called the Banzhaf rule, attains this benchmark. We finally show that for the s-Borda rule, when the optimal value is small, these algorithms can be improved by a factor of ~Ømega(√s) via LP rounding. Our upper and lower bounds are a significant improvement over previous results, and taken together, not only enable us to perform a finer comparison of greedy algorithms for these problems, but also provide analytic justification for using such algorithms in practice.
Adversarial Defense for Automatic Speaker Veriﬁcation by Self-Supervised Learning

—Previous works have shown that automatic speaker veriﬁcation (ASV) is seriously vulnerable to malicious spooﬁng attacks, such as replay, synthetic speech, and recently emerged adversarial attacks. Great efforts have been dedicated to defending ASV against replay and synthetic speech; however, only a few approaches have been explored to deal with adversarial attacks. All the existing approaches to tackle adversarial attacks for ASV require the knowledge for adversarial samples generation, but it is impractical for defenders to know the exact attack algorithms that are applied by the in-the-wild attackers. This work is among the ﬁrst to perform adversarial defense for ASV without knowing the speciﬁc attack algorithms. Inspired by self-supervised learning models (SSLMs) that possess the merits of alleviating the superﬁcial noise in the inputs and reconstructing clean samples from the interrupted ones, this work regards adversarial perturbations as one kind of noise and conducts adversarial defense for ASV by SSLMs. Speciﬁcally, we propose to perform adversarial defense from two perspectives: 1) adversarial perturbation puriﬁcation and 2) adversarial perturbation detection. The puriﬁcation module aims to alleviate the adversarial perturbations in the samples and pull the contaminated adversarial inputs back towards the decision boundary. Experimental results show that our proposed puriﬁcation module effectively counters adversarial attacks and outperforms traditional ﬁlters from both alleviating the adversarial noise and maintaining the performance of genuine samples. The detection module aims to detect adversarial samples from genuine ones based on the statistical properties of ASV scores derived by ASV integrating with different number of SSLMs. Experimental results show that our detection module effectively shields the ASV by detecting adversarial samples with an accuracy of around 80%. Moreover, since there is no common metric for evaluating the adversarial defense performance for ASV, this work also formalizes evaluation metrics for adversarial defense considering both puriﬁcation and detection based approaches into account. We sincerely encourage future works to benchmark their approaches based on the proposed evaluation framework.
Scalar Field Comparison with Topological Descriptors: Properties and Applications for Scientific Visualization

In topological data analysis and visualization, topological descriptors such as persistence diagrams, merge trees, contour trees, Reeb graphs, and Morse–Smale complexes play an essential role in capturing the shape of scalar field data. We present a state‐of‐the‐art report on scalar field comparison using topological descriptors. We provide a taxonomy of existing approaches based on visualization tasks associated with three categories of data: single fields, time‐varying fields, and ensembles. These tasks include symmetry detection, periodicity detection, key event/feature detection, feature tracking, clustering, and structure statistics. Our main contributions include the formulation of a set of desirable mathematical and computational properties of comparative measures, and the classification of visualization tasks and applications that are enabled by these measures.
CLASSIFYING TORSION PAIRS OF NOETHERIAN ALGEBRAS

For a commutative Noetherian ring R and a module-finite R-algebra Λ, we study the set torsΛ (respectively, torf Λ) of torsion (respectively, torsionfree) classes of the category of finitely generated Λ-modules. We construct a bijection from torf Λ to ∏ p torf(Λ⊗R κ(p)), and an embedding Φ from torsΛ to TR(Λ) := ∏ p tors(Λ⊗R κ(p)), where p runs all prime ideals of R. When Λ = R, these give classifications of torsionfree classes, torsion classes and Serre subcategories of modR due to Takahashi, Stanley-Wang and Gabriel. To give a description of ImΦ, we introduce the notion of compatible elements in TR(Λ), and prove that all elements in ImΦ are compatible. We give a sufficient condition on (R,Λ) such that all compatible elements belong to ImΦ (we call (R,Λ) compatible in this case). For example, if R is semi-local and dimR ≤ 1, then (R,Λ) is compatible. We also give a sufficient condition in terms of silting Λ-modules. As an application, for a Dynkin quiver Q, (R,RQ) is compatible and we have a poset isomorphism torsRQ ≃ Homposet(SpecR,CQ) for the Cambrian lattice CQ of Q.
Tracé alternant detector for grading hypoxic-ischemic encephalopathy in neonatal EEG

Electroencephalography (EEG) is an important clinical tool to capture sleep-wake cycling. It can also be used for grading injury, known as hypoxic-ischaemic encephalophathy (HIE), caused by lack of oxygen or blood to the brain during birth. Trace alternant (TA) is a distinctive component of normal quiet sleep which consists of alternating periods of high-voltage activity (bursts) separated by lower-voltage activity (inter-bursts). This study presents an automated method to grade the severity of injury in HIE, using an automated method to first detect TA activity. The TA detector uses the output of an existing method to detect inter-bursts. Features are extracted from a processed output and then combined in a support vector machine (SVM). Next, we develop an HIE grading system using the TA detector by combining different features from the temporal organisation of the detected TA mask, again using an SVM. Training and testing for both models use a leave-one-baby-out cross-validation procedure, with model hyper-parameters selected from nested cross validations. The TA detector, tested on EEG from 71 healthy term neonates, has an accuracy of 79.1 % (Cohen's κ=0.55). The HIE grading system, tested on EEG from 54 term neonates in intensive care, has an accuracy of 81.5% (κ=0.74). These results validate how detecting the presence or absence of TA can be used to quantify the grade of HIE injury in neonatal EEG and open up the possibility of a clinically-meaningful grading system.
High Resolution Infrared Spectroscopic Analysis Of Human Colorectal Pathology

3 ACKNOWLEDGEMENTS . 4 LIST OF ABBREVIATIONS . 8 LIST OF FIGURES . 9 LIST OF TABLES . 13 LIST OF APPENDIX A FIGURES . 14 CHAPTER 1 . 15 THE COLORECTUM AND COLORECTAL CANCER . 15 1.1 The Colon and Rectum . 15 1.1.1 Anatomy . 15 1.1.2 Blood supply . 16 1.1.3 Histology . 17 1.2 Colorectal cancer in the UK . 19 1.3 National Bowel Cancer Screening . 20 1.3. 1 The National Bowel Cancer Screening Programme . 21 1.4 Management of Colorectal Cancer . 23 1.4. 1 Management of Early Polyp cancers . 25 1.5 Colorectal cancer . 26 1.5. 1 Pathogenesis of Colorectal Cancer . 27 1.6 Colorectal polyps . 29 1.6. 1 Hyperplastic and other serrated polyps: . 30 1.6. 2 Adenomatous polyp: . 30 1.6. 3 Mixed polyps: . 32 1.6. 4 Carcinomatous Polyps: . 32 1.7 Epithelial Misplacement . 34 1.7. 1 Epithelial Misplacement in colonic polyps: . 35 1.8 Clinical Diagnostic Need . 36 CHAPTER 2 . 38 VIBRATIONAL SPECTROSCOPY . 38 2.1 Biophotonics . 38 2.2 What is light? 38 2.3 Biological Diagnostics . 41 2.4 Overview of Wide-field Imaging Techniques . 43 2.4. 1 High definition white light endoscopy . 43 2.4. 2 Chromo-endoscopy . 43
Momentum via Primal Averaging: Theoretical Insights and Learning Rate Schedules for Non-Convex Optimization

Momentum methods are now used pervasively within the machine learning community for training non-convex models such as deep neural networks. Empirically, they out perform traditional stochastic gradient descent (SGD) approaches. In this work we develop a Lyapunov analysis of SGD with momentum (SGD+M), by utilizing a equivalent rewriting of the method known as the stochastic primal averaging (SPA) form. This analysis is much tighter than previous theory in the non-convex case, and due to this we are able to give precise insights into when SGD+M may out-perform SGD, and what hyper-parameter schedules will work and why.
Predicting Driver Intention Using Deep Neural Network

To improve driving safety and avoid car accidents, Advanced Driver Assistance Systems (ADAS) are given significant attention. Recent studies have focused on predicting driver intention as a key part of these systems. In this study, we proposed new framework in which 4 inputs are employed to anticipate diver maneuver using Brain4Cars dataset and the maneuver prediction is achieved from 5, 4, 3, 2, 1 seconds before the actual action occurs. We evaluated our framework in three scenarios: using only 1) inside view 2) outside view and 3) both inside and outside view. We divided the dataset into training, validation and test sets, also K-fold cross validation is utilized. Compared with state-of-the-art studies, our architecture is faster and achieved higher performance in second and third scenario. Accuracy, precision, recall and f1-score as evaluation metrics were utilized and the result of 82.41%, 82.28%, 82,42% and 82.24% for outside view and 98.90%, 98.96%, 98.90% and 98.88% for both inside and outside view were gained, respectively.
UVIP: Model-Free Approach to Evaluate Reinforcement Learning Algorithms

Policy evaluation is an important instrument for the comparison of different algorithms in Reinforcement Learning (RL). Yet even a precise knowledge of the value function $V^{\pi}$ corresponding to a policy $\pi$ does not provide reliable information on how far is the policy $\pi$ from the optimal one. We present a novel model-free upper value iteration procedure $({\sf UVIP})$ that allows us to estimate the suboptimality gap $V^{\star}(x) - V^{\pi}(x)$ from above and to construct confidence intervals for $V^\star$. Our approach relies on upper bounds to the solution of the Bellman optimality equation via martingale approach. We provide theoretical guarantees for ${\sf UVIP}$ under general assumptions and illustrate its performance on a number of benchmark RL problems.
Tactical Optimism and Pessimism for Deep Reinforcement Learning

In recent years, deep off-policy actor-critic algorithms have become a dominant approach to reinforcement learning for continuous control. One of the primary drivers of this improved performance is the use of pessimistic value updates to address function approximation errors, which previously led to disappointing performance. However, a direct consequence of pessimism is reduced exploration, running counter to theoretical support for the efficacy of optimism in the face of uncertainty. So which approach is best? In this work, we show that the most effective degree of optimism can vary both across tasks and over the course of learning. Inspired by this insight, we introduce a novel deep actor-critic framework, Tactical Optimistic and Pessimistic (TOP) estimation, which switches between optimistic and pessimistic value learning online. This is achieved by formulating the selection as a multi-arm bandit problem. We show in a series of continuous control tasks that TOP outperforms existing methods which rely on a fixed degree of optimism, setting a new state of the art in challenging pixel-based environments. Since our changes are simple to implement, we believe these insights can easily be incorporated into a multitude of off-policy algorithms.