Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying the computational capability of a nanomagnetic reservoir computing platform with emergent magnetisation dynamics

Devices based on arrays of interconnected magnetic nano-rings with emergent magnetization dynamics have recently been proposed for use in reservoir computing applications, but for them to be computationally useful it must be possible to optimise their dynamical responses. Here, we use a phenomenological model to demonstrate that such reservoirs can be optimised for classification tasks by tuning hyperparameters that control the scaling and input-rate of data into the system using rotating magnetic fields. We use task-independent metrics to assess the rings’ computational capabilities at each set of these hyperparameters and show how these metrics correlate directly to performance in spoken and written digit recognition tasks. We then show that these metrics, and performance in tasks, can be further improved by expanding the reservoir’s output to include multiple, concurrent measures of the ring arrays’ magnetic states.
Schema matching using Gaussian mixture models with Wasserstein distance

Gaussian mixture models find their place as a powerful tool, mostly in the clustering problem, but with proper preparation also in feature extraction, pattern recognition, image segmentation and in general machine learning. When faced with the problem of schema matching, different mixture models computed on different pieces of data can maintain crucial information about the structure of the dataset. In order to measure or compare results from mixture models, the Wasserstein distance can be very useful, however it is not easy to calculate for mixture distributions. In this paper we derive one of possible approximations for the Wasserstein distance between Gaussian mixture models and reduce it to linear problem. Furthermore, application examples concerning real world data are shown.
FastTrees: Parallel Latent Tree-Induction for Faster Sequence Encoding

Inducing latent tree structures from sequential data is an emerging trend in the NLP research landscape today, largely popularized by recent methods such as Gumbel LSTM and Ordered Neurons (ON-LSTM). This paper proposes FASTTREES, a new general purpose neural module for fast sequence encoding. Unlike most previous works that consider recurrence to be necessary for tree induction, our work explores the notion of parallel tree induction, i.e., imbuing our model with hierarchical inductive biases in a parallelizable, non-autoregressive fashion. To this end, our proposed FASTTREES achieves competitive or superior performance to ON-LSTM on four well-established sequence modeling tasks, i.e., language modeling, logical inference, sentiment analysis and natural language inference. Moreover, we show that the FASTTREES module can be applied to enhance Transformer models, achieving performance gains on three sequence transduction tasks (machine translation, subject-verb agreement and mathematical language understanding), paving the way for modular tree induction modules. Overall, we outperform existing state-of-the-art models on logical inference tasks by +4% and mathematical language understanding by +8%.
Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation

The nonuniform quantization strategy for compressing neural networks usually achieves better performance than its counterpart, i.e., uniform strategy, due to its superior representational capacity. However, many nonuniform quantization methods overlook the complicated projection process in implementing the nonuniformly quantized weights/activations, which incurs non-negligible time and space overhead in hardware deployment. In this study, we propose Nonuniform-to-Uniform Quantization (N2UQ), a method that can maintain the strong representation ability of nonuniform methods while being hardware-friendly and efficient as the uniform quantization for model inference. We achieve this through learning the flexible inequidistant input thresholds to better fit the underlying distribution while quantizing these real-valued inputs into equidistant output levels. To train the quantized network with learnable input thresholds, we introduce a generalized straight-through estimator (G-STE) for intractable backward derivative calculation w.r. t. threshold parameters. Additionally, we consider entropy preserving regularization to further reduce information loss in weight quantization. Even under this adverse constraint of imposing uniformly quantized weights and activations, our N2UQ outperforms state-of-the-art nonuniform quantization methods by 0.5 ~ 1.7% on ImageNet, demonstrating the contribution of N2UQ design. Code and models are available at: https://github.com/liuzechun/Nonuniform-to-Uniform-Quantization.
Solving classification problems using Traceless Genetic Programming

Traceless Genetic Programming (TGP) is a new Genetic Programming (GP) that may be used for solving difficult real-world problems. The main difference between TGP and other GP techniques is that TGP does not explicitly store the evolved computer programs. In this paper, TGP is used for solving real-world classification problems taken from PROBEN1. Numerical experiments show that TGP performs similar and sometimes even better than other GP techniques for the considered test problems.
FashionSearchNet-v2: Learning Attribute Representations with Localization for Image Retrieval with Attribute Manipulation

The focus of this paper is on the problem of image retrieval with attribute manipulation. Our proposed work is able to manipulate the desired attributes of the query image while maintaining its other attributes. For example, the collar attribute of the query image can be changed from round to v-neck to retrieve similar images from a large dataset. A key challenge in e-commerce is that images have multiple attributes where users would like to manipulate and it is important to estimate discriminative feature representations for each of these attributes. The proposed FashionSearchNet-v2 architecture is able to learn attribute specific representations by leveraging on its weakly-supervised localization module, which ignores the unrelated features of attributes in the feature space, thus improving the similarity learning. The network is jointly trained with the combination of attribute classification and triplet ranking loss to estimate local representations. These local representations are then merged into a single global representation based on the instructed attribute manipulation where desired images can be retrieved with a distance metric. The proposed method also provides explainability for its retrieval process to help provide additional information on the attention of the network. Experiments performed on several datasets that are rich in terms of the number of attributes show that FashionSearchNet-v2 outperforms the other state-of-the-art attribute manipulation techniques. Different than our earlier work (FashionSearchNet), we propose several improvements in the learning procedure and show that the proposed FashionSearchNet-v2 can be generalized to different domains other than fashion.
Passive Indoor Localization with WiFi Fingerprints

This paper proposes passive WiFi indoor localization. Instead of using WiFi signals received by mobile devices as fingerprints, we use signals received by routers to locate the mobile carrier. Consequently, software installation on the mobile device is not required. To resolve the data insufficiency problem, flow control signals such as request to send (RTS) and clear to send (CTS) are utilized. In our model, received signal strength indicator (RSSI) and channel state information (CSI) are used as fingerprints for several algorithms, including deterministic, probabilistic and neural networks localization algorithms. We further investigated localization algorithms performance through extensive on-site experiments with various models of phones at hundreds of testing locations. We demonstrate that our passive scheme achieves an average localization error of 0.8 m when the phone is actively transmitting data frames and 1.5 m when it is not transmitting data frames.
Emergent Graphical Conventions in a Visual Communication Game

Humans communicate with graphical sketches apart from symbolic languages. Primarily focusing on the latter, recent studies of emergent communication overlook the sketches; they do not account for the evolution process through which symbolic sign systems emerge in the trade-off between iconicity and symbolicity. In this work, we take the very first step to model and simulate this process via two neural agents playing a visual communication game; the sender communicates with the receiver by sketching on a canvas. We devise a novel reinforcement learning method such that agents are evolved jointly towards successful communication and abstract graphical conventions. To inspect the emerged conventions, we define three fundamental properties -- iconicity, symbolicity, and semanticity -- and design evaluation methods accordingly. Our experimental results under different controls are consistent with the observation in studies of human graphical conventions. Of note, we find that evolved sketches can preserve the continuum of semantics under proper environmental pressures. More interestingly, co-evolved agents can switch between conventionalized and iconic communication based on their familiarity with referents. We hope the present research can pave the path for studying emergent communication with the modality of sketches.
Head and Body: Unified Detector and Graph Network for Person Search in Media

Person search in media has seen increasing potential in Internet applications, such as video clipping and character collection. This task is common but overlooked by previous person search works which focus on surveillance scenes. The media scenarios have some different challenges from surveillance scenes. For example, a person may change his clothes frequently. To alleviate this issue, this paper proposes a Unified Detector and Graph Network (UDGNet) for person search in media. UDGNet is the first person search framework to detect and re-identify the human body and head simultaneously. Specifically, it first builds two branches based on a unified network to detect the human body and head, then the detected body and head are used for re-identification. This dual-task approach can significantly enhance discriminative learning. To tackle the cloth-changing issue, UDGNet builds two graphs to explore reliable links among cloth-changing samples and utilizes a graph network to learn better embeddings. This design effectively enhances the robustness of person search to cloth-changing challenges. Besides, we demonstrate that UDGNet can be implemented with both anchor-based and anchor-free person search frameworks and further achieve performance improvement. This paper also contributes a large-scale dataset for Person Search in Media (PSM), which provides both body and head annotations. It is by far the largest dataset for person search in media. Experiments show that UDGNet improves the anchor-free model AlignPS by 12.1% in mAP. Meanwhile, it shows good generalization across surveillance and longterm scenarios. The dataset and code will be available at: https://github.com/shuxjweb/PSM.git.
Development of a control scheme of homodyne detection for extracting ponderomotive squeezing from a Michelson interferometer

We developed a control scheme of homodyne detection. To operate the homodyne detector as easy as possible, a simple Michelson interferometer is used. Here a motivation that the control scheme of the homodyne detection is developed is for our future experiment of extracting the ponderomotively squeezed vacuum fluctuations. To obtain the best signalto- noise ratio using the homodyne detection, the homodyne phase should be optimized. The optimization of the homodyne phase is performed by changing a phase of a local oscillator for the homodyne detection from a point at which a signal is maximized. In fact, in this experiment, using the developed control scheme, we locked the Michelson interferometer with the homodyne detector and changed the phase of the local oscillator for the homodyne detection. Then, we measured signals quantity changed by changing the phase of the local oscillator for the homodyne detection. Here we used the output from the homodyne detection as the signal.
How to select PDC bit for optimal drilling performance

Selecting bit when you have multiple bit vendors giving their proposal can be a challenge for the operator. To justify the bit selection can be hard for the drilling engineer in a multiple vendor situation. The purpose of this paper is to show a systematic approach on how to select PDC bit based on quantitative measure by using a simple scorecard. When the drilling organization has agreed on the overall drilling objective for the well a scorecard is used as decision criteria for selecting bits. To quantify the input for each bit a drilling simulator was used. The simulator can, based on a rock strength prognosis for a well, predict the rate of penetration and bit wear for each bit based on the bit design. For other criteria which are more difficult to obtain e.g. ability to create dog leg a qualitative ranking was used. In the two field examples shown from the North Sea the method has worked well to give a reliable and transparent bit selection method. Using a scorecard also reduced the ambiguity among the bit company representatives on how the selection process was done.
Complex-valued forecasting of the global solar irradiation

In this paper, a forecasting of the global solar irradiation in the complex-valued domain is proposed. A method to transform the meteorological data into complex values is developed and the Complex Valued Neural Network (CVNN) is used to model and forecast the daily and the hourly solar irradiation. The measured data of Tamanrasset city, Algeria (altitude: 1362 m; latitude: 22°48 N; longitude: 05°26 E) is used to validate the developed model. In the hourly solar irradiation case, the 24 h ahead will be forecasted using the combination of the past daily meteorological dataset. Several models are presented to test the feasibility and the performance of the CVNN for forecasting either daily or hourly solar irradiation for both multi input single output and multi input multi output strategies. Results obtained throughout this paper show that the CVNN technique is suitable for modeling and forecasting daily and hourly solar irradiation.
Recognition of Human Home Activities via Depth Silhouettes and ℜ Transformation for Smart Homes

This paper presents a novel human home activity recognition (HAR) system designed for smart homes that utilize depth silhouettes and ℜ transformation to continuously recognize the daily activities of the elderly and disabled in an indoor environment for better lifecare and e-healthcare services. Previously, ℜ transformation has been applied only on binary silhouettes that provide only the shape information of human activities. In this work, ℜ transformation was utilized on depth silhouettes such that the depth information of human body parts could be used in HAR in addition to the shape information. In ℜ transformation, 2D directional projection maps are computed via Radon transform, and then 1D feature profiles, which are translation and scaling invariants. Then, by applying the principle component analysis and linear discriminant analysis, the prominent activity features would be extracted. Finally, Hidden Markov Models would be used to train and recognize daily home activities. The results showed a mean recognition rate of 96.55% over ten typical home activities, whereas the same system utilizing binary silhouettes could achieve only 85.75%. The proposed methodology should be useful in designing and developing a compact HAR system that can be practically used in a smart environment including smart home for the care of the elderly, infirmed or disabled people.
Ambiguity function of quadriphase coded radar pulse

Taylor's quadriphase coding (J.W. Taylor, Jr. and H.J. Blinchikoff, ibid., vol.23, no.2, p.156-70, Mar. 1988) is investigated for nonzero Doppler shifts. While the zero-Doppler cut of the ambiguity function (i.e. the autocorrelation) strongly resembles that of the corresponding biphase code, the remaining ambiguity function differs considerably. The ambiguity function of quadriphase code 13 is typified by a diagonal ridge as found in linear FM signals. The ambiguity function of quadriphase code 28A resembles the three parallel ridges of Frank code 16. >
TripletGO: Integrating Transcript Expression Profiles with Protein Homology Inferences for High-Accuracy Gene Function Annotations

Gene Ontology (GO) has been widely used to annotate functions of genes and gene products. We proposed a new method (TripletGO) to deduce GO terms of protein-coding and non-coding genes, through the integration of four complementary pipelines built on transcript expression profiling, genetic sequence alignment, protein sequence alignment and naïve probability, respectively. TripletGO was tested on a large set of 5,754 genes from 8 species (human, mouse, arabidopsis, rat, fly, budding yeast, fission yeast, and nematoda) and 2,433 proteins with available expression data from the CAFA3 experiment and achieved function annotation accuracy significantly beyond the current state-of-the-art approaches. Detailed analyses show that the major advantage of TripletGO lies in the coupling of a new triplet-network based profiling method with the feature space mapping technique which can accurately recognize function patterns from transcript expressions. Meanwhile, the combination of multiple complementary models, especially those from transcript expression and protein-level alignments, improves the coverage and accuracy of the final GO annotation results. The standalone package and an online server of TripletGO are freely available at https://zhanglab.ccmb. med.umich. edu/TripletGO/.
Relationship between Lipase Activity and Biogas Rate in Anaerobic Digestion of Organic Fraction of Municipal Solid Waste

Hydrolytic enzymes, which are very key enzymes in hydrolytic step of anaerobic digestion, have an important effect on substrate hydrolysis and biogas production. To research those effect, this paper have investigated the relationship between lipase activity and biogas rate with organic fraction of municipal solid waste (OFMSW) as substrate, at the mesophilic temperature and batch fermentation. The results showed that in the process of AD, the curve of lipase activity was similar as that of biogas rate. With the increase and drop of enzyme activity, biogas rate increased and dropped. When biogas rate was at its peak period, enzyme activity of lipase was at its peak period, too.
Bagaza Virus in Wild Birds, Portugal, 2021

Bagaza virus (BAGV) emerged in Spain, in 2010. Since then, BAGV was not reported in other European countries until September 2021, when BAGV was diagnosed by molecular methods in one corn bunting and several red-legged partridges, after abnormal mortality in Southern Portugal. Sequencing revealed high similarity with strains reported previously.
Staphylococcus aureus β-Toxin Exerts Anti-angiogenic Effects by Inhibiting Re-endothelialization and Neovessel Formation

Staphylococcus aureus is the causative agent of numerous severe human infections associated with significant morbidity and mortality worldwide. S. aureus often targets the vascular endothelium to interfere with proper host responses during invasive infections. In this study, we provide evidence that S. aureus β-toxin inhibits wound repair mechanisms in human endothelial cells by preventing cell proliferation and migration. These findings were confirmed in a rabbit aortic explant model where β-toxin impedes sprout formation. Decreased cell proliferation was accompanied by decreased production of the angiogenic proteins endothelin-1, IGFBP-3, thrombospondin-1, TIMP-1, and TIMP-4. Meanwhile, inhibited wound repair was marked by increased HGF secretion from endothelial cells, likely a marker of endothelial cell damage. Together, these findings establish a mechanistic role for β-toxin where it inhibits proper tissue repair processes that likely promote S. aureus infective niche.
Adaptor linked K63 di-ubiquitin activates Nedd4/Rsp5 E3 ligase

Nedd4/Rsp5 family E3 ligases mediate numerous cellular processes, many of which require the E3 ligase to interact with PY-motif containing adaptor proteins. Several Arrestin-Related Trafficking adaptors(ARTs) of Rsp5 were self-ubiquitinated for activation, but the regulation mechanism remains elusive. Remarkably, we demonstrate that Art1, Art4, and Art5 undergo K63-linked di-ubiquitination by Rsp5. This modification enhances the PM recruitment of Rsp5 by Art1 or Art5 upon substrate induction, required for cargo protein ubiquitination. In agreement with these observations, we find that di-ubiquitin strengthens the interaction between the Pombe orthologs of Rsp5 and Art1, Pub1 and Any1. Further, we discover that the HECT domain exosite protects the K63-linked di-ubiquitin on the adaptors from cleavage by the deubiquitination enzyme Ubp2. Strikingly, loss of this protection results in the loss of K63-linked di-ubiquitin from the adaptors and diverts the adaptors for K48-linked poly-ubiquitination and proteasome-mediated degradation. Together, our study uncovers a novel ubiquitination modification implemented by Rsp5 adaptor proteins, underscoring the regulatory mechanism of how adaptor proteins control the recruitment and activity of Rsp5 for the turnover of membrane proteins.
A new replication medium enables a rapid identification of φx-174 virus by synchronous fluorescence of Tryptophan

Development of rapid methods for identification of bacteriophages based on their intrinsic fluorescence is challenging. Pure bacteriophages may be detected based on the strong fluorescence of the amino acid Tryptophan that exist in their proteins. Nevertheless, Tryptophan is a molecule that also exist in high quantities in the bacterial hosts and their cultivation media. In this work, we show that simple separation of the bacteriophage φx-174 from its E.coli host (grown on standard cultivation medium) by filtration is not sufficient for its identification based on the intrinsic fluorescence of its Tryptophan content. This is mostly because of the tryptophan residues that derive from the cultivation medium. We fabricate a new cultivation medium that does not have any significant fluorescence overlap with Tryptophan. By utilization of this new cultivation medium, we can identify φx-174 based on the spectral fingerprint of its intrinsic Tryptophan content by synchronous fluorescence measurements.
Life Strategies in Placozoa

Placozoans are essential reference species for understanding the origins and evolution of the animal organization. However, little is known about their life strategies in natural habitats. Here, by establishing long-term culturing for four species of Trichoplax and Hoilungia, we extend our knowledge about feeding and reproductive adaptations relevant to their ecology and immune mechanisms. Three modes of population growth depended upon feeding sources, including induction of social behaviors and different reproductive strategies. In addition to fission, representatives of all haplotypes produced ‘swarmers,’ which could be formed from the lower epithelium (with greater cell- type diversity) as a separate asexual reproduction stage. In aging culture, we reported the formation of specialized structures (‘spheres’) from the upper cell layer as a part of the innate immune defense response with the involvement of fiber cells. Finally, we showed that regeneration could be a part of the adaptive reproductive strategies in placozoans and a unique model for regenerative biology in general.