Miracle-dz/UniFAR-FTUs huggingface dataset:
Single Image Deraining Network with Rain Embedding Consistency and Layered LSTM

Single image deraining is typically addressed as residual learning to predict the rain layer from an input rainy image. For this purpose, an encoder-decoder network draws wide attention, where the encoder is required to encode a high-quality rain embedding which determines the performance of the subsequent decoding stage to reconstruct the rain layer. However, most of existing studies ignore the significance of rain embedding quality, thus leading to limited performance with over/under-deraining. In this paper, with our observation of the high rain layer reconstruction performance by an rain-to-rain autoencoder, we introduce the idea of "Rain Embedding Consistency" by regarding the encoded embedding by the autoencoder as an ideal rain embedding and aim at enhancing the deraining performance by improving the consistency between the ideal rain embedding and the rain embedding derived by the encoder of the deraining network. To achieve this, a Rain Embedding Loss is applied to directly supervise the encoding process, with a Rectified Local Contrast Normalization (RLCN) as the guide that effectively extracts the candidate rain pixels. We also propose Layered LSTM for recurrent deraining and fine-grained encoder feature refinement considering different scales. Qualitative and quantitative experiments demonstrate that our proposed method outperforms previous state-of-the-art methods particularly on a real-world dataset. Our source code is available at http://www.ok. sc.e. titech.ac. jp/res/SIR/.
Spatio-Temporal Urban Knowledge Graph Enabled Mobility Prediction

With the rapid development of the mobile communication technology, mobile trajectories of humans are massively collected by Internet service providers (ISPs) and application service providers (ASPs). On the other hand, the rising paradigm of knowledge graph (KG) provides us a promising solution to extract structured "knowledge" from massive trajectory data. In this paper, we focus on modeling users' spatio-temporal mobility patterns based on knowledge graph techniques, and predicting users' future movement based on the "knowledge" extracted from multiple sources in a cohesive manner. Specifically, we propose a new type of knowledge graph, i.e., spatio-temporal urban knowledge graph (STKG), where mobility trajectories, category information of venues, and temporal information are jointly modeled by the facts with different relation types in STKG. The mobility prediction problem is converted to the knowledge graph completion problem in STKG. Further, a complex embedding model with elaborately designed scoring functions is proposed to measure the plausibility of facts in STKG to solve the knowledge graph completion problem, which considers temporal dynamics of the mobility patterns and utilizes PoI categories as the auxiliary information and background knowledge. Extensive evaluations confirm the high accuracy of our model in predicting users' mobility, i.e., improving the accuracy by 5.04% compared with the state-of-the-art algorithms. In addition, PoI categories as the background knowledge and auxiliary information are confirmed to be helpful by improving the performance by 3.85% in terms of accuracy. Additionally, experiments show that our proposed method is time-efficient by reducing the computational time by over 43.12% compared with existing methods.
Recurrent Neural Networks for Learning Long-term Temporal Dependencies with Reanalysis of Time Scale Representation

Recurrent neural networks with a gating mechanism such as an LSTM or GRU are powerful tools to model sequential data. In the mechanism, a forget gate, which was introduced to control information flow in a hidden state in the RNN, has recently been re-interpreted as a representative of the time scale of the state, i.e., a measure how long the RNN retains information on inputs. On the basis of this interpretation, several parameter initialization methods to exploit prior knowledge on temporal dependencies in data have been proposed to improve learn-ability. However, the interpretation relies on various unrealistic assumptions, such as that there are no inputs after a certain time point. In this work, we reconsider this interpretation of the forget gate in a more realistic setting. We first generalize the existing theory on gated RNNs so that we can consider the case where inputs are successively given. We then argue that the interpretation of a forget gate as a temporal representation is valid when the gradient of loss with respect to the state decreases exponentially as time goes back. We empirically demonstrate that existing RNNs satisfy this gradient condition at the initial training phase on several tasks, which is in good agreement with previous initialization methods. On the basis of this finding, we propose an approach to construct new RNNs that can represent a longer time scale than conventional models, which will improve the learnability for long-term sequential data. We verify the effectiveness of our method by experiments with real-world datasets.
Energy and water use at a WSUD subdivision in Brisbane, Australia

Abstract This paper reports on the water and energy balances of a small ecosensitive subdivision in Brisbane, Australia. The 22-lot development is designed around individual and communal rainwater tanks, on-site greywater reuse, off-peak sewer pump outs, and energy and water efficient house design and appliances. Results to date are limited by time and occupied house constraints, but they clearly identified the importance of rainwater and greywater in substituting for potable water (up to 190 L/hh/year). However, this water self sufficiency came at the cost of specific energy use well in excess of that from centralised water and sewerage systems. Suggestions are made on how to offset these energy losses, and include greenhouse gas efficient hot water system and grid connected photovoltaics (PVs). Future monitoring will include the social attitudes of residents towards sustainable urban practices and composition of the various water streams (e.g. rain, greywater, sewage and storm flow)
Sequence learning, prediction, and replay in networks of spiking neurons

Sequence learning, prediction and replay have been proposed to constitute the universal computations performed by the neocortex. The Hierarchical Temporal Memory (HTM) algorithm realizes these forms of computation. It learns sequences in an unsupervised and continuous manner using local learning rules, permits a context specific prediction of future sequence elements, and generates mismatch signals in case the predictions are not met. While the HTM algorithm accounts for a number of biological features such as topographic receptive fields, nonlinear dendritic processing, and sparse connectivity, it is based on abstract discrete-time neuron and synapse dynamics, as well as on plasticity mechanisms that can only partly be related to known biological mechanisms. Here, we devise a continuous-time implementation of the temporal-memory (TM) component of the HTM algorithm, which is based on a recurrent network of spiking neurons with biophysically interpretable variables and parameters. The model learns high-order sequences by means of a structural Hebbian synaptic plasticity mechanism supplemented with a rate-based homeostatic control. In combination with nonlinear dendritic input integration and local inhibitory feedback, this type of plasticity leads to the dynamic self-organization of narrow sequence-specific subnetworks. These subnetworks provide the substrate for a faithful propagation of sparse, synchronous activity, and, thereby, for a robust, context specific prediction of future sequence elements as well as for the autonomous replay of previously learned sequences. By strengthening the link to biology, our implementation facilitates the evaluation of the TM hypothesis based on experimentally accessible quantities. The continuous-time implementation of the TM algorithm permits, in particular, an investigation of the role of sequence timing for sequence learning, prediction and replay. We demonstrate this aspect by studying the effect of the sequence speed on the sequence learning performance and on the speed of autonomous sequence replay.
Versatile Learned Video Compression

Learned video compression methods have demonstrated great promise in catching up with traditional video codecs in their rate-distortion (R-D) performance. However, existing learned video compression schemes are limited by the binding of the prediction mode and the fixed network framework. They are unable to support various inter prediction modes and thus inapplicable for various scenarios. In this paper, to break this limitation, we propose a versatile learned video compression (VLVC) framework that uses one model to support all possible prediction modes. Specifically, to realize versatile compression, we first build a motion compensation module that applies multiple 3D motion vector fields (i.e., voxel flows) for weighted trilinear warping in spatial-temporal space. The voxel flows convey the information of temporal reference position that helps to decouple inter prediction modes away from framework designing. Secondly, in case of multiple-reference-frame prediction, we apply a flow prediction module to predict accurate motion trajectories with unified polynomial functions. We show that the flow prediction module can largely reduce the transmission cost of voxel flows. Experimental results demonstrate that our proposed VLVC not only supports versatile compression in various settings, but also is the first end-to-end learned video compression method that outperforms the latest VVC/H.266 standard reference software in terms of MS-SSIM.
Macroscopic condensation of photon noise in sharply nonlinear dissipative systems

Nicholas Rivera, Jamison Sloan, Yannick Salamin, and Marin Soljačić Department of Physics, MIT, Cambridge, MA 02139, USA. Research Laboratory of Electronics, MIT, Cambridge, MA 02139, USA. Abstract Macroscopic non-Gaussian states of light are among the most highly-coveted “holy grails” in quantum science and technology. An important example is that of macroscopic number (Fock) states of light. Due to their exactly defined number of photons, they are of great interest in quantum metrology, spectroscopy, simulation, and information processing. However, the deterministic creation and stabilization of even approximate large-number Fock states remains thus far a long-standing open problem. Here, we introduce a mechanism that deterministically generates macroscopic sub-Poissonian states, including Fock states, at optical and infrared frequencies. The method takes advantage of the fact that a photon with a sharply increasing nonlinear loss will have its intensity noise condense as it decays. We reveal regimes where this phenomenon can lead to transient generation of Fock and sub-Poissonian states. We further show that these low noise states can be preserved in steady-state by building a laser with a sharp loss element, stabilizing the state with an equilibrium between gain and loss. We show two examples of these phenomena: one which produces a near-Fock state with 5,000 photons (with an uncertainty of < 1 photon), and one which generates a macroscopic sub-Poissonian state with 1012 photons, yet noise 99% below that of a coherent state of the same size. When realized, our results may enable many of the previously envisaged applications of number states and sub-Poissonian states for quantum algorithms, simulation, metrology, and spectroscopy. More generally, our results provide a new and general type of nonlinearity which could form the basis for many new optoelectronic devices which emit macroscopic non-Gaussian light.
Thermodynamic structure of a generic null surface and the zeroth law in scalar-tensor theory

We show that the equation of motion of scalar-tensor theory acquires thermodynamic identity when projected on a generic null surface. The relevant projection is given by Eabl k, where Eab = 8πT (m) ab represents the equation motion for gravitational field in presence of external matter, l is the generator of the null surface and k is the corresponding auxiliary null vector. Our analysis is done completely in a covariant way. Therefore all the thermodynamic quantities are in covariant form and hence can be used for any specific form of metric adapted to a null surface. We show this both in Einstein and Jordan frames and find that these two frames provide equivalent thermodynamic quantities. This is consistent with the previous findings for a Killing horizon. Also, a concrete proof of the zeroth law in scalar-tensor theory is provided when the null surface is defined by a Killing vector.
THE BENEFITS OF PREDICTING CHANGE IN COMPLEX PRODUCTS: APPLICATION AREAS OF A DSM-BASED PREDICTION TOOL

Most designers struggle to fully understand a complex product. Throughout the entire life cycle of a product engineers are frequently required to predict how changes could propagate. Currently, few methods or tools are available to support this process. This paper describes part of an ongoing research project that aims to support designers by (1) giving them a better understanding of the linkages between components within their product and (2) helping them to assess the risk of change propagation. The paper gives an overview of a DSM-based method of modelling products, discusses other potential applications of it within the design process and describes a developmental computer tool for visualising linkages and predicting change propagation.
Antifungal Properties of Selected Medicinal Plant Species Against Fusarium spp. – A Preliminary Study

Usage of synthetic fungicides has inevitably been one of the agricultural practices in combating crop pathogens and maintaining the quality of production. Although fungicides have been proven to be profoundly effective, excessive and frequent reliance on these synthetic fungicides have caused negative impacts to the environment and human health. Besides that, indiscriminate use of fungicides may lead to the development of resistant strains of pathogenic fungi. The need to find an alternative solution to synthetic fungicides has led to the interest in finding plant-based fungicides. This study aimed to test the antifungal properties of plant extracts from 13 different medicinal plant species towards plant pathogenic fungi. Absolute methanol was used as a solvent to extract the secondary metabolites from the different plant species. The effect of methanolic crude extract at different concentrations (500 g/ml, 250 g/ml and 100 g/ml), from different medicinal plant species, were tested on the growth of two Fusarium spp. , FsB and FsP. The assay showed that the methanolic crude extract from six plant species viz. Alpinia galanga, Annona muricata, Archidendron jiringa, Nephelium lappaceum, Polygonum minus and Artocarpus hybrid (Nanchem) had successfully inhibit the radial mycelial growth of either FsB or FsP, or both. The assay suggested that the six plant species have antifungal properties towards the crop pathogenic fungi tested. Keywords: antimicrobial, Fusarium, plant extracts, methanolic extracts, biofungicides
Automatic Steering Based on Roadway Markers: From Highway Driving to Precision Docking

Bus Rapid Transit (BRT) is an effective alternative for providing rail-like corridor transit service. An advanced BRT concept involves the use of automated buses to provide functions of a rail transit system. A vehicle under automatic steering control following a prescribed trajectory is operated like a train on a rail. A lateral position sensing that uses roadway markers, such as magnetic markers embedded under the roadway, as lateral reference is one of the promising approaches for a reliable sensing system. The BRT concept requires the steering control system to consistently perform all necessary steering functions from high speed driving to low speed precision docking. This paper describes a single steering controller that achieves all performance objectives. Various data collected during several public demonstrations are presented in this paper to illustrate the effectiveness of the approach. These data include the following automatic steering control scenarios: over 100 mph high-speed driving, high-g maneuvers, sharp curve following, and low speed precision docking.
A simple model for characteristic impedance of wide microstrip lines for flexible PCB

During the last decade, communication technologies of fast digital signals were developed. Popular digital communication interfaces like LVDS format or Hot Link, became very common. Transmission data speeds arrived to some hundreds of MHz and are approaching 1 GHz and transfer of data at such high speed between boards or systems demands a meticulous design of the traces and groundplane, as transmission lines with controlled characteristic impedance. Flat flexible printed circuit boards (PCBs) are the preferred media for data transfer among boards and between a motherboard and external connectors. The flexibility is achieved by producing thin conductors above thin layers of dielectric materials. Because of their dimensions, there is no reliable model for designing transmission lines with controlled impedance for a flexible PCB. This paper attempts to suggest such a model.
A hybrid generation system using variable speed wind turbines and diesel units

The paper presents a study of a power system consisting of wind turbines and diesel generation units. In the study, a direct drive modular permanent magnet generator is used for the wind turbine and a normal permanent magnet generator is used for the diesel generation unit. Both types of generation units are connected to the load via power electronic converters and are operated in variable speed mode. The variable speed operation of wind power conversion system maximizes the wind energy capture, while the variable speed diesel unit balances the system load demand with a reduced fuel consumption. The system configuration and principles are presented. The simulation models are developed for the system performance study. The operation schemes and the system characteristics are discussed.
How Much TCAM do we Need for Splitting Traffic?

Traffic splitting is a required functionality in networks, for example for load balancing over paths or servers or by the source's access restrictions. The capacities of the servers (or the number of users with particular access restrictions) determine the sizes of the parts into which traffic should be split. A recent approach implements traffic splitting within the ternary content addressable memory (TCAM), which is often available in switches. It is important to reduce the amount of memory allocated for this task since TCAMs are power consuming and are often also required for other tasks such as classification and routing. Recent works suggested algorithms to compute a smallest implementation of a given partition in the longest prefix match (LPM) model. In this paper we analyze properties of such minimal representations and prove lower and upper bounds on their size. The upper bounds hold for general TCAMs, and we also prove an additional lower-bound for general TCAMs.
Clustreams: Data Plane Clustering

Clusteringis a basic machine learning task. In this task, a stream of input items needs to be grouped into clusters, such that all items classified into the same cluster are closer to each other than to items classified to other clusters. Each cluster is centered around a centroidpoint, which may either be given as a parameter, or must be learned during the process in the case of unsupervised online learning. This work studies the ability to perform clustering, e.g., for classifying network traffic, in programmable switches. Conducting such classification by the switches through which the traffic flows is potentially the most efficient approach. To that end, we develop Clustreams, a novel in-network clustering system designed to handle clustering in the data path. At the core of Clustreamsis a novel clustering algorithm that relies heavily on TCAM (Ternary Content Addressable Memory) match-action capabilities. This algorithm is realized for the Nvidia Spectrum-3 switch, and is limited to classification when the centroid points are known a-priori. The work includes accuracy measurements for the algorithms, as well as run-time performance measurements and analysis of the clustering algorithm on a Spectrum-3 switch. As shown in the measurements, Clustreamsobtains very high accuracy without any noticeable run-time impact on the switch' performance.
The mediating role of self-esteem in the relationship between childhood maltreatment and god image among Turkish Muslims

Childhood maltreatment is widespread in predominantly Muslim countries. However, the research investigating the impact of childhood maltreatment on the adult survivors’ religious and spiritual lives has mainly focused on Western Judeo-Christian samples. Considering cross-cultural differences in religious beliefs, in this study, we investigated the relationship between childhood maltreatment and god image, and the mediating role of self-esteem in a sample of Muslim Turkish adults. Eight hundred two participants completed Childhood Trauma Questionnaire–Short Form, God Perception Scale, and Self-Esteem Scale. Findings revealed that all childhood maltreatment subtypes predicted negative image of god and self-esteem mediated some of the negative effects of each maltreatment subtype on god image. The study also found that emotional neglect was the most prominent predictor of negative image of god, followed by emotional abuse. Based on attachment theory, we concluded that the emotional component of childhood maltreatment had more long-lasting adverse consequences in survivors’s relationship with god in Muslim adults.
The Sub-Daily Variability of Aerosol Loading and Associated Radiative Forcing Over the Indian Region

The sub-daily variability of aerosols affects the estimates of daily mean aerosol loading. However, large spatial scale estimates of their climate effects are mostly based on snapshots from low orbit satellites that may bias the mean estimate for daily, monthly, or annual timescales. In this study, an attempt is made to estimate the magnitude of such bias based on ground and satellite-based datasets. Using ground-based measurements, we show an apparent asymmetry (of the order of 10–50%) in the sub-daily variability of aerosol loading over the Indian region. For the first time, it is reported that this sub-daily variability has a spatial pattern with an increasing amplitude toward the east of the subcontinent. We also find this variability in aerosol loading is well-captured by the satellites but with a lower amplitude. Our study shows that such differences could alter the annual surface radiative forcing estimates by more than ∼15 W m−2 over this region. We find that NASA’s Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), a state-of-the-art model-based chemical reanalysis, is unable to capture these sub-daily variabilities. This implies that both model and satellite-based radiative forcing estimates for large spatial scales should improve aerosol sub-daily information/variabilities for obtaining reliable radiative forcing estimates.
When We Feel “Like-Minded” Is It Only in Our Minds?

Much of our time is spent communicating with other people, including family, friends, and teachers. When we talk with others, we are interested in understanding one another, and we can often sense when the communication is good. Scientists suggest that synchronization—a state in which things happen simultaneously—contributes to good communication. Research has found that synchronization in facial expressions, ways of speaking, body movements, pulse, breath, and even brain activity, are related to positive communication. In this article, we will describe the role of synchronization and discuss the possibility that when we feel we are “riding on the same wave” with the person we are talking to, we actually are on the same physical and mental wave—synchronized.