Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Communicate Using Counterfactual Reasoning

This paper introduces a new approach for multi-agent communication learning called multi-agent counterfactual communication (MACC) learning. Many real-world problems are currently tackled using multi-agent techniques. However, in many of these tasks the agents do not observe the full state of the environment but only a limited observation. This absence of knowledge about the full state makes completing the objectives significantly more complex or even impossible. The key to this problem lies in sharing observation information between agents or learning how to communicate the essential data. In this paper we present a novel multi-agent communication learning approach called MACC. It addresses the partial observability problem of the agents. MACC lets the agent learn the action policy and the communication policy simultaneously. We focus on decentralized Markov Decision Processes (Dec-MDP), where the agents have joint observability. This means that the full state of the environment can be determined using the observations of all agents. MACC uses counterfactual reasoning to train both the action and the communication policy. This allows the agents to anticipate on how other agents will react to certain messages and on how the environment will react to certain actions, allowing them to learn more effective policies. MACC uses actor-critic with a centralized critic and decentralized actors. The critic is used to calculate an advantage for both the action and communication policy. We demonstrate our method by applying it on the Simple Reference Particle environment of OpenAI and a MNIST game. Our results are compared with a communication and non-communication baseline. These experiments demonstrate that MACC is able to train agents for each of these problems with effective communication policies.
SimpleMKKM: Simple Multiple Kernel K-Means

We propose a simple yet effective multiple kernel clustering algorithm, termed simple multiple kernel k-means (SimpleMKKM). It extends the widely used supervised kernel alignment criterion to multi-kernel clustering. Our criterion is given by an intractable minimization-maximization problem in the kernel coefficient and clustering partition matrix. To optimize it, we equivalently rewrite the minimization-maximization formulation as a minimization of an optimal value function, prove its differenentiablity, and design a reduced gradient descent algorithm to decrease it. Furthermore, we prove that the resultant solution of SimpleMKKM is the global optimum. We theoretically analyze the performance of SimpleMKKM in terms of its clustering generalization error. After that, we develop extensive experiments to investigate the proposed SimpleMKKM from the perspectives of clustering accuracy, advantage on the formulation and optimization, variation of the learned consensus clustering matrix with iterations, clustering performance with varied number of samples and base kernels, analysis of the learned kernel weight, the running time and the global convergence. The experimental study demonstrates the effectiveness of the proposed SimpleMKKM by considerably and consistently outperforming state of the art multiple kernel clustering alternatives. In addition, the ablation study shows that the improved clustering performance is contributed by both the novel formulation and new optimization. Our work provides a more effective approach to integrate multi-view data for clustering, and this could trigger novel research on multiple kernel clustering. The source code and data for SimpleMKKM are available at https://github.com/xinwangliu/SimpleMKKMcodes/.
Quasi-Orthogonal Z-Complementary Pairs and Its Applications in Fully Polarimetric Radar Systems

One objective of this paper is to propose a novel class of sequence pairs, called "Quasi-orthogonal Z-complementary pairs (QOZCPs)", each depicting Z-complementary property for their aperiodic auto-correlation sums and also have a zero correlation zone when their aperiodic cross-correlation is considered. Construction of QOZCPs based on Successively Distributed Algorithms under Majorization Minimization (SDAMM) is presented. Another objective of this paper is to apply the proposed QOZCPs in fully polarimetric radar systems and analyse the corresponding ambiguity functions. It turns out that QOZCP waveforms are much more Doppler resilient than the known Golay complementary waveforms.
Land reclamation and restoration.

Land reclamation and restoration is designed to meet the increasing demands to manage and restore post-mining landscapes, brownfield and greenfield sites to the amenity, natural habitat and/or agricultural use engendered by UK Government, EU and International directives. Industry acknowledges the need for highly trained engineers and science-based professionals able to implement management strategies in response to these demands. Accredited by the Institute of Agricultural Engineers (IAgrE) and the Landscape Institute, this course aims to provide the knowledge and skills required to bridge the gap between degraded land and fully restored ecosystems. It provides the skills to assess, plan and implement strategies needed to restore, reclaim and remediate degraded land in the public and corporate sectors. Visits to relevant land reclamation sites form a major part of the programme. Students benefit from dedicated laboratories for the study of soil engineering, soil, plant and water interactions, and ecosystem behaviour. Our laboratories have specialist capabilities in soil chemistry and biology and we own land which is used for field trials and research investigations.
Introduction to Modern Methods for Classical and Quantum Fields in General Relativity

The last few decades have seen major developments in asymptotic analysis in the framework of general relativity, with the emergence of methods that, until recently, were not applied to curved Lorentzian geometries. This has led notably to the proof of the stability of the Kerr–de Sitter spacetime by P. Hintz and A. Vasy [17]. An essential feature of many recent works in the field is the use of dispersive estimates; they are at the core of most stability results and are also crucial for the construction of quantum states in quantum field theory, domains that have a priori little in common. Such estimates are in general obtained through geometric energy estimates (also referred to as vector field methods) or via microlocal/spectral analysis. In our minds, the two approaches should be regarded as complementary, and this is a message we hope this volume will convey succesfully. More generally than dispersive estimates, asymptotic analysis is concerned with establishing scattering-type results. Another fundamental example of such results is asymptotic completeness, which, in many cases, can be translated in terms of conformal geometry as the well-posedness of a characteristic Cauchy problem (Goursat problem) at null infinity. This has been used to develop alternative approaches to scattering theory via conformal compactifications (see for instance F. G. Friedlander [11] and L. Mason and J.-P. Nicolas [22]). The presence of symmetries in the geometrical background can be a tremendous help in proving scattering results, dispersive estimates in particular. What we mean by symmetry is generally the existence of an isometry associated with the flow of a Killing vector field, though there exists a more subtle type of symmetry, described sometimes as hidden, corresponding to the presence of Killing spinors for instance. Recently, the vector field method has been adapted to take such generalized symmetries into account by L. Andersson and P. Blue in [2]. This volume compiles notes from the eight-hour mini-courses given at the summer school on asymptotic analysis in general relativity, held at the Institut
Optimal shape of a column with clamped-elastically supported ends positioned on elastic foundation

We determine optimal shape of an elastic column positioned on elastic foundation of Winkler type. The Euler-Bernoulli model of beam is considered. The column is loaded by a compressive force and has one clamped end and the other elastically supported end. In deriving the optimality conditions, the Pontryagin’s principle was used. The optimality conditions for the case of bimodal optimization are derived. Optimal cross-sectional area is obtained from the solution of a non-linear boundary value problem. A first integral (Hamiltonian) is used to monitor accuracy of integration. This system is solved by using standard Math CAD procedure. New numerical results are obtained.
The calculations of thermophysical properties of molybdenum plasma

The thermophysical properties (pressure, internal energy, conductivity, thermal conductivity, and thermal power) of molybdenum plasma at temperatures 10–60 kK and densities less than 2 g/cm3 have been calculated. The corresponding model has been developed on the basis of the chemical approach and relaxation time approximation. The approximations used in our model are valid under these conditions. A comparison with available experimental data on conductivity has shown good agreement in this area.
DIFER: Differentiable Automated Feature Engineering

Feature engineering, a crucial step of machine learning, aims to construct useful features from 4 raw data to improve model performance. In recent years, great efforts have been devoted 5 to Automated Feature Engineering (AutoFE) to replace expensive human labor. However, 6 all existing methods treat AutoFE as an optimization problem over a discrete feature space, 7 leading to the problems of feature explosion and computational inefficiency. Unlike previous 8 work, we perform AutoFE in a continuous vector space and propose a differentiable method 9 called DIFER in this paper. Specifically, we first propose an evolutionary framework to 10 search for better features iteratively. In each feature evolution step, we introduce a feature 11 optimizer based on the encoder-predictor-decoder, which maps features into the continuous 12 vector space via the encoder, optimizes the embedding along the gradient direction induced 13 by the predictor, and recovers better features from the optimized embedding by the decoder. 14 Extensive experiments on classification and regression datasets demonstrate that DIFER 15 can significantly outperform the state-of-the-art AutoFE method in terms of both model 16 performance and computational efficiency. The implementation of DIFER is avaialable on 17 https://anonymous.4open. science/r/DIFER-3FBC/ .
Current and emerging quantitative magnetic resonance imaging methods for assessing and predicting the response of breast cancer to neoadjuvant therapy

: Reliable early assessment of breast cancer response to neoadjuvant therapy (NAT) would provide considerable benefit to patient care and ongoing research efforts, and demand for accurate and noninvasive early-response biomarkers is likely to increase. Response assessment techniques derived from quantitative magnetic resonance imaging (MRI) hold great potential for integration into treatment algorithms and clinical trials. Quantitative MRI techniques already available for assessing breast cancer response to neoadjuvant therapy include lesion size measurement, dynamic contrast-enhanced MRI, diffusion-weighted MRI, and proton magnetic resonance spectroscopy. Emerging yet promising techniques include magnetization transfer MRI, chemical exchange saturation transfer MRI, magnetic resonance elastography, and hyperpolarized MR. Translating and incorporating these techniques into the clinical setting will require close attention to statistical validation methods,
A global gridded dataset for cloud vertical structure from combined CloudSat and CALIPSO observations

Abstract. The vertical structure of clouds has a profound effect on the global energy budget, the global circulation, and the atmospheric hydrological cycle. The CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) missions have taken complementary, colocated observations of cloud vertical structure for over a decade. However, no globally gridded dataset is available to the public for the full length of this unique combined data record. Here we present the 3S-GEOPROF-COMB product (Bertrand et al. 2023, https://doi.org/10.5281/zenodo.8057791), a globally gridded (level 3S) community data product summarizing geometrical profiles (GEOPROF) of hydrometeor occurrence from combined (COMB) CloudSat and CALIPSO data. Our product is calculated from the latest release (R05) of per-orbit (level-2) combined cloud mask profiles. We process a set of cloud cover, vertical cloud fraction, and sampling variables at 2.5, 5, and 10° spatial resolutions and monthly and seasonal temporal resolutions. We address the 2011 reduction in CloudSat data collection with Daylight-Only Operations (DO-Op) mode by subsampling pre-2011 data to mimic DO-Op collection patterns, thereby allowing users to evaluate the impact of the reduced sampling on their analyses. We evaluate our data product against CloudSat-only and CALIPSO-only global-gridded data products as well as four comparable surface-based sites, underscoring the added value of the combined product. Interest in the product is anticipated for the study of cloud processes, cloud–climate interactions, and as a candidate baseline climate data record for comparison to follow-up satellite missions, among other uses.
miR-7 inhibits colorectal cancer cell proliferation and induces apoptosis by targeting XRCC2

Background: Analysis using publicly available algorithms predicts that X-ray repair complementing defective repair in Chinese hamster cells 2 (XRCC2), a key component in the homologous recombination repair pathway, is a potential target of micro-ribonucleic acid-7 (miR-7). Some studies have shown that both miR-7 and XRCC2 are associated with cancer development. For this purpose, we searched for the possible relationship between miR-7 and XRCC2 in the development of colorectal cancer (CRC). Methods: miR-7 expression was assessed in CRC specimens and cell lines using real-time polymerase chain reaction (PCR). Luciferase reporter assay was used to confirm the target asso-ciations. The effect of miR-7 on cell proliferation and apoptosis was confirmed in vitro by the methylthiazol tetrazolium (MTT) assay, colony formation assay, and flow cytometry. Gene and protein expression were examined using real time PCR and western blotting, respectively. Results: miR-7 was downregulated in CRC specimens and cell lines, and targeted the 3 ′ untranslated region of XRCC2. miR-7 overexpression reduced cyclin D1 expression and increased p21, caspase-3, and BAX expression, which subsequently inhibited CRC cell proliferation and induced CRC cell apoptosis. However, XRCC2 can repress the inhibitory effects of miR-7 on proliferation. Conclusion: Our findings suggest that miR-7 plays a protective role by inhibiting proliferation and increasing apoptosis of CRC cells. It may identify new targets for anti-cancer treatment. miR-7, micro-ribonucleic acid-7; NC, negative control; miR-7-in, micro-ribonucleic acid-7 inhibitor; NC-in, negative control inhibitor; miR-7-mut, micro-ribonucleic acid-7 mutation; XRCC2, X-ray repair complementing defective repair Chinese hamster
no More Rights Without Remedies an impact evaluation of the national crime Victim law institute’s Victims’ Rights clinics

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Concept Trending of Social Media Data Using Apriori Algorithm

—Topic trending is a popular research topic in recent years since, there are massive participations in social web sites, countless number of updates, news, opinions and product reviews are being constantly posted every day. The identification of popular topics discussed or posted on social media platforms is becoming more important as the new knowledge can be extracted from these findings. In this work, a novel method is proposed to extract popular topics from social media and determine the topic trending based on timeline using the Apriori algorithm. The approach uses Twitter’s tweets as the dataset. The data is then pre-processed by undergoing several processes that include stop words removal, stemming, tokenization and Term Frequency-Inverse Document Frequency ( TF − IDF ) weighting. The k -means clustering is then performed to cluster each data that consists of processed keywords and collected every day. The popular topics will be then extracted from the clusters and the topic trends will be determined based on the observed frequent patterns and correlation between keywords by using the association rules. The performance of the proposed method is evaluated based on the similarity of the results with the current trends obtained from the Twitter site. The result from the findings shows that the proposed method is able to produce more enriched trends that are similar to current initial trends.
Privacy-preserving Learning via Deep Net Pruning

Neural network pruning has demonstrated its success in signiﬁcantly improving the computational e � ciency of deep models while only introducing a small reduction on ﬁnal accuracy. In this paper, we explore an extra bonus of neural network pruning in terms of enhancing privacy. Speciﬁ-cally, we show a novel connection between magnitude-based pruning and adding di ↵ erentially private noise to intermediate layers under the over-parameterized regime. To the best of our knowledge, this is the ﬁrst work that bridges pruning with the theory of di ↵ erential privacy. The paper also presents experimental results by running the model inversion attack on two benchmark datasets, which supports the theoretical ﬁnding.
A Deep Decomposition Network for Image Processing: A Case Study for Visible and Infrared Image Fusion

—Image decomposition is a crucial subject in the ﬁeld of image processing. It can extract salient features from the source image. We propose a new image decomposition method based on convolutional neural network. This method can be applied to many image processing tasks. In this paper, we apply the image decomposition network to the image fusion task. We input infrared image and visible light image and decompose them into three high-frequency feature images and a low-frequency feature image respectively. The two sets of feature images are fused using a speciﬁc fusion strategy to obtain fusion feature images. Finally, the feature images are reconstructed to obtain the fused image. Compared with the state-of-the-art fusion methods, this method has achieved better performance in both subjective and objective evaluation.
Preparation and In Vitro/In Vivo Characterization of Polymeric Nanoparticles Containing Methotrexate to Improve Lymphatic Delivery

Methotrexate (MTX) is a folic acid antagonist used as an effective drug to treat various kinds of cancers. However, MTX has limited use in cancer chemotherapy due to its adverse effects such as poor bioavailability, low specificity, drug resistance, and dose-dependent side effects. To improve lymphatic delivery and reduce toxicity of MTX, MTX-loaded nanoparticles (NPs) were prepared in the present study. NPs were prepared with double emulsion solvent evaporation method using poly(lactide-co-glycolide) (PLGA). NPs were assessed for size, encapsulation efficiency, morphology, Fourier-transform infrared spectroscopy, X-ray diffraction, and thermal characterization. In vitro release profiles and cytotoxicity of these NPs were also evaluated. Prepared NPs and free MTX were administered orally or intravenously (5 mg/kg as MTX) to rats to evaluate their pharmacokinetic characteristics and lymphatic delivery effects. Mean particle size and encapsulation efficiency of NPs were 163.7 ± 10.25 nm and 93.3 ± 0.5%, respectively. Prepared NPs showed a sustained release profile of MTX in vitro and may be effective to cancer cells. Area under the blood concentration-time curve, total clearance, half-life, and lymphatic targeting efficiency were significantly different (p < 0.05) between prepared NPs and free MTX. These results demonstrate that MTX-loaded PLGA NPs are good candidates for targeted delivery of MTX to the lymphatic system.
The generating structure of spatial conformation dynamics

This thesis develops a new approximation method for the transfer operator of a momentum-averaged Langevin equation. This transfer operator can be used to detect metastable sets and thus occurs in classical molecular conformation dynamics. It is shown to possess a generator-like analytic structure despite not forming a time-semigroup. This is exploited to discretize the operator without the computationally expensive time-integration and momentum-averaging steps of established methods. The performance of the new method is evaluated both analytically and by numerical examples.
Barcelona GSE Working Paper Series Working Paper n o 727 Labor Market Heterogeneity and the Aggregate Matching Function

The matching function —a key building block in models of labor market frictions—implies that the job finding rate depends only on labor market tightness. We estimate such a matching function and find that the relation, although remarkably stable over 1967-2007, broke down spectacularly after 2007. We argue that labor market heterogeneities are not fully captured by the standard matching function, but that a generalized matching function that explicitly takes into account worker heterogeneity and market segmentation is fully consistent with the behavior of the job finding rate. The standard matching function can break down when, as in the Great Recession, the average characteristics of the unemployed change too much, or when dispersion in labor market conditions —the extent to which some labor markets fare worse than others—increases too much. JEL classifications: J6, E24, E32. ∗We thank Jan Eeckhout, Bruce Fallick, Shigeru Fujita, Jordi Gali, Robert Hall, John Haltiwanger, Bart Hobijn, Rob Valletta, Thijs van Rens, William Wascher, Yanos Zylberberg and seminar participants at the 2012 AEA Annual Meetings, the 2012 CEPR European Summer Symposium in International Macroeconomics (ESSIM), the CREI-CEPR Conference "Understanding Jobless Recoveries", the Chicago Fed, the New York Fed, the Norges Bank, and the San Francisco Fed. We thank Peter Chen for excellent research assistance. The views expressed here do not necessarily reflect those of the Federal Reserve Board or the Federal Reserve System. Any errors are our own.
p
 th moment consensus of multi‐agent systems with relative state‐dependent measurement noises and time delays

This study investigates the distributed consensus control problem of multi-agent systems impacted by the combined uncertainty of measurement noises and communication delays under directed fixed topology. Each agent can only obtain the state information of its own and neighbours, which is delayed and interfered by measurement noises. The intensity of the noise is considered as a function related to the relative state of the agent. Under general networks with directed fixed topology, a distributed consensus protocol is proposed to obtain the pth moment linear χ -consensus of the multi-agent system. The sufficient condition for the consensus of the multi-agent system is derived under the arbitrary upper bound of the time-delay, in which both relative state-dependent measurement noises and time-varying time delays are taken into account. The effectiveness of the proposed consensus protocol is shown by some simulations.