Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of the Optimal Dissolved CO2 Concentration and pH Combination for the Growth of Nitrifying Bacteria

Ammonium (NH4+) is a biological nutrient that is transformed in a wastewater treatment plant (WWTP) in a process called activated sludge. This is accomplished in an aerobic environment using microorganisms and inorganic carbon that convert the ammonium to nitrate (NO3-). This process is termed nitrification. Removal of ammonium is necessary due to its oxygen demand and toxicity to the environment. Nitrification is considered a slow process due to the slow growth rate of the nitrifying bacteria. Ammonia oxidizing bacteria (AOB) first covert the ammonium (NH4+) to nitrite (NO2-) followed by conversion to nitrate (NO3-) by nitrite oxidizing bacteria (NOB). These slow rates limit the treatment capacity of the WWTP. The initial hypothesis suggested that these slow rates were due to limited carbon in the aeration basin of a WWTP. A series of designed experiments and observational studies revealed substantial dissolved CO2 exists throughout a WWTP. Based on these findings, the central research focused on determining if an optimum dissolved CO2 concentration/ pH combination exists that maximizes nitrification. Experimentation conducted at a pH of 7.0 and varying concentrations of dissolved CO2 concentration revealed inhibition at low ( 30 mg/l) dissolved CO2 concentration levels. Further research found that optimum nitrification can be attained in a dissolved CO2 concentration range of 10 - 15 mg/l and a pH range of 7.5 - 8.0. A maximum specific growth rate of 1.05 - 1.15 days-1 was achieved. A partitioning of the sums of squares from these designed experiments found that pH accounts for approximately 83 percent of the sums of squares due to treatment with the dissolved CO2 concentration accounting for 17 percent. This suggests that pH is the dominant factor affecting nitrification when dissolved CO2 concentration is optimized. Analysis of the growth kinetics for two of the designed experiments was conducted. However, a set of parameters could not be found that described growth conditions for all operating conditions. Evaluating the results from these two experiments may suggest that a microbial population shift occurred between 16 and 19 mg/l of dissolved CO2 concentration. These dissolved CO2 concentrations represent pH values of 7.1 and 7.0, respectively, and were compared to experimentation conducted at a pH of 7.0. Though the pH difference is minor, in combination with the elevated dissolved CO2 concentration, a microbial shift was hypothesized. Microbial samples were collected from the designed experiment that optimized dissolved CO2 concentration (5, 10 and 15 mg/l) and pH (6.5, 7.0, 7.5 and 8.0). These samples were evaluated using Fluorescence in situ hybridizations (FISH) to determine the population density of common ammonium oxidizing bacteria (AOB) (Nitrosomonas and Nitrosospira) and nitrite oxidizing bacteria (NOB) Nitrobacter and Nitrospirae). The dominant AOB and NOB microbes were found to be Nitrosomonas and Nitrospirae. These results suggest that increased nitrification…
Xilobam: Effect of salt form on pharmaceutical properties

AbstractIn an effort to protect xilobam from the effects of high temperatures and high humidities without adversely affecting its dissolution from tablets, three arylsulfonic acid salts and the saccharin salt were prepared. All of the salts were determined to be more stable at 74% relative humidity and 70° than the free base.In spite of the large differences between the effect of high humidity at a high temperature on the salts versus the free base, the dissolution of the most stable salt (1-napsylate) was essentially equivalent to the dissolution of the free base when both were prepared as formulated tablets.
On the Intensities of Electron Diffraction Rings

The intensities of the rings formed when a beam of fast electrons is sent through a poly crystalline film were first studied by G. P. Thomson (1929) in the case of thin films of gold. The intensities were found to fit very well (Mott 1929) with those calculated theoretically on the assumption that the Laue theory held for scattering of electrons by the crystallites. Results also in accordance with the theory were obtained by Mark and Wierl (1930). A recent investigation by Ornstein and his collaborators (1938) on polycrystalline films of copper and silver have led to results diverging widely from the theoretical in the sense that if the (222) ring were fitted to the theoretical value the (111) ring, which is the strongest, had an intensity too low by a factor of two to three. It had also been pointed out by Kirchner (1932) that the amount of scattering by atoms of the heavy elements (Au, Ag) is so large that the Laue theory can hardly be expected to hold, and he discounted the significance of the agreement between theory and experiment.
FLEAM: A Federated Learning Empowered Architecture to Mitigate DDoS in Industrial IoT

Due to resource constraints and working surroundings, many IIoT nodes are easily hacked and turn into zombies from which to launch attacks. It is challenging to detect such networked zombies rooted behind the Internet for any individual defender. In this article, we combine federated learning (FL) and fog/edge computing to combat malicious codes. Our protocol trains a global optimized model based on distributed datasets of collaborators while removing the data and communication constraints. The FL-based detection protocol maximizes the values of distributed data samples, resulting in an accurate model timely. On top of the protocol, we place mitigation intelligence in a distributed and collaborative manner. Our approach improves accuracy, eliminates mitigation time, and enlarges attackers’ expense within a defense alliance. Comprehensive evaluations confirm that the cost incurred is 2.7 times larger, the mitigation response time is 72% lower, and the accuracy is 47% higher on average. Besides, the protocol evaluation shows the detection accuracy is approximately 98% in the FL, which is almost the same as centralized training.
Underactuated Motion Planning and Control for Jumping With Wheeled-Bipedal Robots

This letter studies jumping for wheeled-bipedal robots, a motion that takes full advantage of the benefits from the hybrid wheeled and legged design features. A comprehensive hierarchical scheme for motion planning and control of jumping with wheeled-bipedal robots is developed. Underactuation of the wheeled-bipedal dynamics is the main difficulty to be addressed, especially in the planning problem. To tackle this issue, a novel wheeled-spring-loaded inverted pendulum (W-SLIP) model is proposed to characterize the essential dynamics of wheeled-bipedal robots during jumping. Relying on a differential-flatness-like property of the W-SLIP model, a tractable quadratic programming based solution is devised for planning jumping motions for wheeled-bipedal robots. Combined with a kinematic planning scheme accounting for the flight phase motion, a complete planning scheme for the W-SLIP model is developed. To enable accurate tracking of the planned trajectories, a linear quadratic regulator based wheel controller and a task-space whole-body controller for the other joints are blended through disturbance observers. The overall planning and control scheme is validated using V-REP simulations of a prototype wheeled-bipedal robot.
Magneto-stimulation limits in medical imaging applications with rapid field dynamics

Objective. The goal of this work is to extend previous peripheral nerve stimulation (PNS) studies to scenarios relevant to magnetic particle imaging (MPI) and low-field magnetic resonance imaging (MRI), where field dynamics can evolve at kilo-hertz frequencies. Approach. We have constructed an apparatus for PNS threshold determination on a subject’s limb, capable of narrow and broad-band magnetic stimulation with pulse characteristic times down to 40 μs. Main result. From a first set of measurements on 51 volunteers, we conclude that the PNS dependence on pulse frequency/rise-time is compatible with traditional stimulation models where nervous responses are characterized by a rheobase and a chronaxie. Additionally, we have extended pulse length studies to these fast timescales and confirm thresholds increase significantly as trains transition from tens to a few pulses. We also look at the influence of field spatial distribution on PNS effects, and find that thresholds are higher in an approximately linearly inhomogeneous field (relevant to MRI) than in a rather homogeneous distribution (as in MPI). Significance. PNS constrains the clinical performance of MRI and MPI systems. Extensive magneto-stimulation studies have been carried out recently in the field of MPI, where typical operation frequencies range from single to tens of kilo-hertz. However, PNS literature is scarce for MRI in this fast regime, relevant to small (low inductance) dedicated MRI setups, and where the resonant character of MPI coils prevents studies of broad-band stimulation pulses. This work advances in this direction.
Reinforcement Learning Agents for Ubisoft's Roller Champions

In recent years, Reinforcement Learning (RL) has seen increasing popularity in research and popular culture. However, skepticism still surrounds the practicality of RL in modern video game development. In this paper, we demonstrate by example that RL can be a great tool for Artificial Intelligence (AI) design in modern, non-trivial video games. We present our RL system for Ubisoft's Roller Champions, a 3v3 Competitive Multiplayer Sports Game played on an oval-shaped skating arena. Our system is designed to keep up with agile, fast-paced development, taking 1--4 days to train a new model following gameplay changes. The AIs are adapted for various game modes, including a 2v2 mode, a Training with Bots mode, in addition to the Classic game mode where they replace players who have disconnected. We observe that the AIs develop sophisticated co-ordinated strategies, and can aid in balancing the game as an added bonus. Please see the accompanying video at this https URL (password: rollerRWRL2020) for examples.
One-pot catalytic conversion of microalgae (Chlorococcum sp.) into 5-hydroxymethylfurfural over the commercial H-ZSM-5 zeolite

Herein, we report a one-pot approach to produce HMF from aquatic microalgae (Chlorococcum sp.) with a yield up to 48.0% under mild reaction conditions (200 °C, 2 h) over the commercial cheap H-ZSM-5 catalyst. Conversion of microalgae to HMF involved three steps: (1) degradation of microalgae to carbohydrates; (2) hydrolysis of polysaccharides to glucose and mannose; (3) their isomerization to fructose on Lewis acid sites and its further dehydration to HMF over Bronsted acid sites. Proteins and lipids in microalgal cells play an important role in stabilizing HMF in water. Ball-milling pretreatment or addition of another organic solvent enhanced the productivity of HMF from microalgae. Besides, this cheap H-ZSM-5 catalyst also demonstrated excellent stability, and a slight loss of its activity can be easily recovered by simple calcination treatment.
Exploring Deep Neural Networks and Transfer Learning for Analyzing Emotions in Tweets

In this paper, we present an experiment on using deep learning and transfer learning techniques for emotion analysis in tweets and suggest a method to interpret our deep learning models. The proposed approach for emotion analysis combines a Long Short Term Memory (LSTM) network with a Convolutional Neural Network (CNN). Then we extend this approach for emotion intensity prediction using transfer learning technique. Furthermore, we propose a technique to visualize the importance of each word in a tweet to get a better understanding of the model. Experimentally, we show in our analysis that the proposed models outperform the state-of-the-art in emotion classification while maintaining competitive results in predicting emotion intensity.
Objectness-Guided Open Set Visual Search and Closed Set Detection

Searching for small objects in large images is currently challenging for deep learning systems, but is a task with numerous applications including remote sensing and medical imaging. Thorough scanning of very large images is computationally expensive, particularly at resolutions sufficient to capture small objects. The smaller an object of interest, the more likely it is to be obscured by clutter or otherwise deemed insignificant. We examine these issues in the context of two complementary problems: closed-set object detection and open-set target search. First, we present a method for predicting pixel-level objectness from a low resolution gist image, which we then use to select regions for subsequent evaluation at high resolution. This approach has the benefit of not being fixed to a predetermined grid, allowing fewer costly high-resolution glimpses than existing methods. Second, we propose a novel strategy for open-set visual search that seeks to find all objects in an image of the same class as a given target reference image. We interpret both detection problems through a probabilistic, Bayesian lens, whereby the objectness maps produced by our method serve as priors in a maximum-a-posteriori approach to the detection step. We evaluate the end-to-end performance of both the combination of our patch selection strategy with this target search approach and the combination of our patch selection strategy with standard object detection methods. Both our patch selection and target search approaches are seen to significantly outperform baseline strategies.
Exploring Facial Expressions and Affective Domains for Parkinson Detection

Parkinson's Disease (PD) is a neurological disorder that affects facial movements and non-verbal communication. Patients with PD present a reduction in facial movements called hypomimia which is evaluated in item 3.2 of the MDS-UPDRS-III scale. In this work, we propose to use facial expression analysis from face images based on affective domains to improve PD detection. We propose different domain adaptation techniques to exploit the latest advances in face recognition and Face Action Unit (FAU) detection. The principal contributions of this work are: (1) a novel framework to exploit deep face architectures to model hypomimia in PD patients; (2) we experimentally compare PD detection based on single images vs. image sequences while the patients are evoked various face expressions; (3) we explore different domain adaptation techniques to exploit existing models initially trained either for Face Recognition or to detect FAUs for the automatic discrimination between PD patients and healthy subjects; and (4) a new approach to use triplet-loss learning to improve hypomimia modeling and PD detection. The results on real face images from PD patients show that we are able to properly model evoked emotions using image sequences (neutral, onset-transition, apex, offset-transition, and neutral) with accuracy improvements up to 5.5% (from 72.9% to 78.4%) with respect to single-image PD detection. We also show that our proposed affective-domain adaptation provides improvements in PD detection up to 8.9% (from 78.4% to 87.3% detection accuracy).
Anomalous Hall resistivity and possible topological Hall effect in the 
EuAl4
 antiferromagnet

We report the observation of anomalous Hall resistivity in single crystals of ${\mathrm{EuAl}}_{4}$, a centrosymmetric tetragonal compound, which exhibits coexisting antiferromagnetic (AFM) and charge-density-wave (CDW) orders with onset at ${T}_{\mathrm{N}}\ensuremath{\sim}15.6\phantom{\rule{0.16em}{0ex}}\mathrm{K}$ and ${T}_{\mathrm{CDW}}\ensuremath{\sim}140\phantom{\rule{0.16em}{0ex}}\mathrm{K}$, respectively. In the AFM state, when the magnetic field is applied along the $c$-axis direction, ${\mathrm{EuAl}}_{4}$ undergoes a series of metamagnetic transitions. Within this field range, we observe a clear humplike anomaly in the Hall resistivity, representing part of the anomalous Hall resistivity. By considering different scenarios, we conclude that such a humplike feature is most likely a manifestation of the topological Hall effect, normally occurring in noncentrosymmetric materials known to host nontrivial topological spin textures. In view of this, ${\mathrm{EuAl}}_{4}$ would represent a rare case where the topological Hall effect not only arises in a centrosymmetric structure, but it also coexists with CDW order.
Neural Dynamic Mode Decomposition for End-to-End Modeling of Nonlinear Dynamics

Koopman spectral analysis has attracted attention for understanding nonlinear dynamical systems by which we can analyze nonlinear dynamics with a linear regime by lifting observations using a nonlinear function. For analysis, we need to find an appropriate lift function. Although several methods have been proposed for estimating a lift function based on neural networks, the existing methods train neural networks without spectral analysis. In this paper, we propose neural dynamic mode decomposition, in which neural networks are trained such that the forecast error is minimized when the dynamics is modeled based on spectral decomposition in the lifted space. With our proposed method, the forecast error is backpropagated through the neural networks and the spectral decomposition, enabling end-to-end learning of Koopman spectral analysis. When information is available on the frequencies or the growth rates of the dynamics, the proposed method can exploit it as regularizers for training. We also propose an extension of our approach when observations are influenced by exogenous control time-series. Our experiments demonstrate the effectiveness of our proposed method in terms of eigenvalue estimation and forecast performance.
Electronic band gap of Si/SiO2 quantum wells: Comparison of ab initio calculations and photoluminescence measurements

We investigate the influence of layer thicknesses and interface modifications on the fundamental electronic gap of Si/SiO2 multilayers by a combined ab initio calculation and photoluminescence (PL) analysis. For the band gap calculations different Si/SiO2 interface models are studied. Experimentally investigated multiple quantum wells are prepared by remote plasma-enhanced chemical vapor deposition and rapid thermal annealing. The well-width dependence of the band gap obtained from PL measurements is much weaker than found in previous studies. This sublinear variation is in accordance with simulated electronic band gaps for hydrogen-free Si/SiO2 interfaces. The presence of hydrogen at the interfaces enforces the confinement effect for the band gap. Materials involved: nanocrystalline silicon, amorphous silica, β-cristobalite silica, and Si/SiO2 interface.
Classification of Tactile Perception and Attention on Natural Textures from EEG Signals

Brain-computer interface (BCI) allows people who have lost their motor skills to control robot limbs based on electroencephalography (EEG). Most BCIs are guided only by visual feedback and do not have somatosensory feedback, which is an important component of normal motor behavior. The sense of touch is a very crucial sensory modality, especially in object recognition and manipulation. When manipulating an object, the brain uses empirical information about the tactile properties of the object. In addition, the primary somatosensory cortex is not only involved in processing the sense of touch in our body but also responds to visible contact with other people or inanimate objects. Based on these findings, we conducted a preliminary experiment to confirm the possibility of a novel paradigm called touch imagery. A haptic imagery experiment was conducted on four objects, and through neurophysiological analysis, a comparison analysis was performed with the brain waves of the actual tactile sense. Also, we obtained the average classification performance of 68.07(±4.0)% and 57.55(±1.83)% with basic machine learning algorithms.
Pair-view Unsupervised Graph Representation Learning

Low-dimension graph embeddings have proved extremely useful in various downstream tasks in large graphs, e.g., link-related content recommendation and node classification tasks, etc. Most existing embedding approaches take nodes as the basic unit for information aggregation, e.g., node perception fields in GNN or con-textual nodes in random walks. The main drawback raised by such node-view is its lack of support for expressing the compound relationships between nodes, which results in the loss of a certain degree of graph information during embedding. To this end, this paper pro-poses PairE(Pair Embedding), a solution to use "pair", a higher level unit than a "node" as the core for graph embeddings. Accordingly, a multi-self-supervised auto-encoder is designed to fulfill two pretext tasks, to reconstruct the feature distribution for respective pairs and their surrounding context. PairE has three major advantages: 1) Informative, embedding beyond node-view are capable to preserve richer information of the graph; 2) Simple, the solutions provided by PairE are time-saving, storage-efficient, and require the fewer hyper-parameters; 3) High adaptability, with the introduced translator operator to map pair embeddings to the node embeddings, PairE can be effectively used in both the link-based and the node-based graph analysis. Experiment results show that PairE consistently outperforms the state of baselines in all four downstream tasks, especially with significant edges in the link-prediction and multi-label node classification tasks.
Ultrabright Multiplexed Energy-Time-Entangled Photon Generation from Lithium Niobate on Insulator Chip

High-flux entangled photon source is the key resource for quantum optical study and application. Here it is realized in a lithium niobate on isolator (LNOI) chip, with 2.79*10^11 Hz/mW photon pair rate and 1.53*10^9 Hz/nm/mW spectral brightness. These data are boosted by over two orders of magnitude compared to existing technologies. A 130-nm broad bandwidth is engineered for 8-channel multiplexed energy-time entanglement. Harnessed by high-extinction frequency correlation and Franson interferences up to 99.17% visibility, such energy-time entanglement multiplexing further enhances high-flux data rate, and warrants broad applications in quantum information processing on a chip.
Recent advances in heterogeneous electrocatalysts for the hydrogen evolution reaction

The hydrogen evolution reaction plays a decisive role in a range of electrochemical and photoelectrochemical devices. It requires efficient and robust electrocatalysts to lower the reaction overpotential and minimize energy consumption. Over the last decade, we have witnessed a rapid rise in new electrocatalysts, particularly those based on non-precious metals. Some of them approach the activity of precious metal benchmarks. Here, we present a comprehensive overview of the recent developments of heterogeneous electrocatalysts for the hydrogen evolution reaction. Detailed discussion is organized from precious metals to non-precious metal compounds including alloys, chalcogenides, carbides, nitrides, borides and phosphides, and finally to metal-free materials. Emphasis is placed on the challenges facing these electrocatalysts and solutions for further improving their performance. We conclude with a perspective on the development of future HER electrocatalysts.