Miracle-dz/UniFAR-FTUs huggingface dataset:
Ten facts about land systems for sustainability

Land use is central to addressing sustainability issues, including biodiversity conservation, climate change, food security, poverty alleviation, and sustainable energy. In this paper, we synthesize knowledge accumulated in land system science, the integrated study of terrestrial social-ecological systems, into 10 hard truths that have strong, general, empirical support. These facts help to explain the challenges of achieving sustainability in land use and thus also point toward solutions. The 10 facts are as follows: 1) Meanings and values of land are socially constructed and contested; 2) land systems exhibit complex behaviors with abrupt, hard-to-predict changes; 3) irreversible changes and path dependence are common features of land systems; 4) some land uses have a small footprint but very large impacts; 5) drivers and impacts of land-use change are globally interconnected and spill over to distant locations; 6) humanity lives on a used planet where all land provides benefits to societies; 7) land-use change usually entails trade-offs between different benefits—"win–wins" are thus rare; 8) land tenure and land-use claims are often unclear, overlapping, and contested; 9) the benefits and burdens from land are unequally distributed; and 10) land users have multiple, sometimes conflicting, ideas of what social and environmental justice entails. The facts have implications for governance, but do not provide fixed answers. Instead they constitute a set of core principles which can guide scientists, policy makers, and practitioners toward meeting sustainability challenges in land use.
Self-Supervised Representation Learning for Speech Using Visual Grounding and Masked Language Modeling

In this paper, we describe our submissions to the ZeroSpeech 2021 Challenge and SUPERB benchmark. Our submissions are based on the recently proposed FaST-VGS model, which is a Transformer-based model that learns to associate raw speech waveforms with semantically related images, all without the use of any transcriptions of the speech. Additionally, we introduce a novel extension of this model, FaST-VGS+, which is learned in a multi-task fashion with a masked language modeling objective in addition to the visual grounding objective. On ZeroSpeech 2021, we show that our models perform competitively on the ABX task, outperform all other concurrent submissions on the Syntactic and Semantic tasks, and nearly match the best system on the Lexical task. On the SUPERB benchmark, we show that our models also achieve strong performance, in some cases even outperforming the popular wav2vec2.0 model.
On Continuous Integration / Continuous Delivery for Automated Deployment of Machine Learning Models using MLOps

In recent years, model deployment in machine learning is observed to be an interesting area of study. It can be seen as a process similar to the one established for traditional software development. Development and operations (DevOps) incorporating Continuous Integration and Continuous Delivery (CI/CD) have demonstrated to smooth out software advancement and speed up organizations. Nonetheless, employing CI/CD pipelines in an application that incorporates components of Machine Learning Operations (MLOps) has challenging issues, and pioneers in the field settle them with the utilization of exclusive tooling, frequently presented by cloud suppliers. This study gives a higher perspective on the machine learning lifecycle and the vital differences between DevOps and MLOps. We talk about tools and techniques to execute the CI/CD pipeline of machine learning frameworks in the MLOps approach. Subsequently, we deep dive into push and pull-based deployments in Github Operations (GitOps). Open exploration challenges are additionally distinguished and added that can direct future research.
Integration of a machine learning model into a decision support tool to predict absenteeism at work of prospective employees

Purpose - Inefficient hiring may result in lower productivity and higher training costs. Productivity losses caused by absenteeism at work cost U.S. employers billions of dollars each year. Also, employers typically spend a considerable amount of time managing employees who perform poorly. The purpose of this study is to develop a decision support tool to predict absenteeism among potential employees. Design/methodology/approach - We utilized a popular open-access dataset. In order to categorize absenteeism classes, the data have been preprocessed, and four methods of machine learning classification have been applied: Multinomial Logistic Regression (MLR), Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Random Forests (RF). We selected the best model, based on several validation scores, and compared its performance against the existing model; we then integrated the best model into our proposed web-based for hiring managers. Findings - A web-based decision tool allows hiring managers to make more informed decisions before hiring a potential employee, thus reducing time, financial loss and reducing the probability of economic insolvency. Originality/value - In this paper, we propose a model that is trained based on attributes that can be collected during the hiring process. Furthermore, hiring managers may lack experience in machine learning or do not have the time to spend developing machine learning algorithms. Thus, we propose a web-based interactive tool that can be used without prior knowledge of machine learning algorithms.
FSM: FBS Set Management, An energy efficient multi-drone 3D trajectory approach in cellular networks

—Nowadays, Unmanned Aerial Vehicles (UAVs) have been signiﬁcantly improved, and one of their most important applications is to provide temporary coverage for cellular users. Terrestrial Base Station cannot service all users due to disasters or events such as ground BS breakdowns, bad weather conditions, natural disasters, transmission errors, etc. The UAV can be sent to the target location and establishes the necessary communication links without requiring any predetermined infrastructure and covers that area. Finding the optimal location and the appropriate number (DBS) of drone-BS in this area is a challenge. In this paper, the optimal location and optimal number of DBSs are distributed in the current state of the users and the subsequent user states determined by the prediction. Finally, the DBS transition is optimized from the current state to the predicted future locations. The simulation results show that the proposed method can provide acceptable coverage on the network.
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities

Accurate real-time traffic forecast is critical for intelligent transportation systems (ITS) and it serves as the cornerstone of various smart mobility applications. Though this research area is dominated by deep learning, recent studies indicate that the accuracy improvement by developing new model structures is becoming marginal. Instead, we envision that the improvement can be achieved by transferring the ''forecasting-related knowledge" across cities with different data distributions and network topologies. To this end, this paper aims to propose a novel transferable traffic forecasting framework: Domain Adversarial Spatial-Temporal Network (DASTNet). DASTNet is pre-trained on multiple source networks and fine-tuned with the target network's traffic data. Specifically, we leverage the graph representation learning and adversarial domain adaptation techniques to learn the domain-invariant node embeddings, which are further incorporated to model the temporal traffic data. To the best of our knowledge, we are the first to employ adversarial multi-domain adaptation for network-wide traffic forecasting problems. DASTNet consistently outperforms all state-of-the-art baseline methods on three benchmark datasets. The trained DASTNet is applied to Hong Kong's new traffic detectors, and accurate traffic predictions can be delivered immediately (within one day) when the detector is available. Overall, this study suggests an alternative to enhance the traffic forecasting methods and provides practical implications for cities lacking historical traffic data. Source codes of DASTNet are available at https://github.com/YihongT/DASTNet.
Attacking c-MARL More Effectively: A Data Driven Approach

In recent years, a proliferation of methods were developed for cooperative multi-agent reinforcement learning (c-MARL). However, the robustness of c-MARL agents against adversarial attacks has been rarely explored. In this paper, we propose to evaluate the robustness of c-MARL agents via a model-based approach, named c-MBA. Our proposed formulation can craft much stronger adversarial state perturbations of c-MARL agents to lower total team rewards than existing model-free approaches. In addition, we propose the first victim-agent selection strategy and the first data-driven approach to define targeted failure states where each of them allows us to develop even stronger adversarial attack without the expert knowledge to the underlying environment. Our numerical experiments on two representative MARL benchmarks illustrate the advantage of our approach over other baselines: our model-based attack consistently outperforms other baselines in all tested environments.
Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters

This paper introduces a novel and generic framework to solve the flagship task of supervised labeled graph prediction by leveraging Optimal Transport tools. We formulate the problem as regression with the Fused Gromov-Wasserstein (FGW) loss and propose a predictive model relying on a FGW barycenter whose weights depend on inputs. First we introduce a non-parametric estimator based on kernel ridge regression for which theoretical results such as consistency and excess risk bound are proved. Next we propose an interpretable parametric model where the barycenter weights are modeled with a neural network and the graphs on which the FGW barycenter is calculated are additionally learned. Numerical experiments show the strength of the method and its ability to interpolate in the labeled graph space on simulated data and on a difficult metabolic identification problem where it can reach very good performance with very little engineering.
Positive-Definite Matrices over Finite Fields

The study of positive-definite matrices has focused on Hermitian matrices, that is, square matrices with complex (or real) entries that are equal to their own conjugate transposes. In the classical setting, positive-definite matrices enjoy a multitude of equivalent definitions and properties. In this paper, we investigate when a square, symmetric matrix with entries coming from a finite field can be called “positivedefinite” and discuss which of the classical equivalences and implications carry over.
On learning Whittle index policy for restless bandits with scalable regret

Reinforcement learning is an attractive approach to learn good resource allocation and scheduling policies based on data when the system model is unknown. However, the cumulative regret of most RL algorithms scales as $\tilde O(\mathsf{S} \sqrt{\mathsf{A} T})$, where $\mathsf{S}$ is the size of the state space, $\mathsf{A}$ is the size of the action space, $T$ is the horizon, and the $\tilde{O}(\cdot)$ notation hides logarithmic terms. Due to the linear dependence on the size of the state space, these regret bounds are prohibitively large for resource allocation and scheduling problems. In this paper, we present a model-based RL algorithm for such problems which has scalable regret. In particular, we consider a restless bandit model, and propose a Thompson-sampling based learning algorithm which is tuned to the underlying structure of the model. We present two characterizations of the regret of the proposed algorithm with respect to the Whittle index policy. First, we show that for a restless bandit with $n$ arms and at most $m$ activations at each time, the regret scales either as $\tilde{O}(mn\sqrt{T})$ or $\tilde{O}(n^2 \sqrt{T})$ depending on the reward model. Second, under an additional technical assumption, we show that the regret scales as $\tilde{O}(n^{1.5} \sqrt{T})$ or $\tilde{O}(\max\{m\sqrt{n}, n\} \sqrt{T})$. We present numerical examples to illustrate the salient features of the algorithm.
Decision boundaries and convex hulls in the feature space that deep learning functions learn from images

The success of deep neural networks in image classification and learning can be partly attributed to the features they extract from images. It is often speculated about the properties of a low-dimensional manifold that models extract and learn from images. However, there is not sufficient understanding about this low-dimensional space based on theory or empirical evidence. For image classification models, their last hidden layer is the one where images of each class is separated from other classes and it also has the least number of features. Here, we develop methods and formulations to study that feature space for any model. We study the partitioning of the domain in feature space, identify regions guaranteed to have certain classifications, and investigate its implications for the pixel space. We observe that geometric arrangements of decision boundaries in feature space is significantly different compared to pixel space, providing insights about adversarial vulnerabilities, image morphing, extrapolation, ambiguity in classification, and the mathematical understanding of image classification models.
Streaming word problems

We study deterministic and randomized streaming algorithms for word problems of finitely generated groups. For finitely generated linear groups, metabelian groups and free solvable groups we show the existence of randomized streaming algorithms with logarithmic space complexity for their word problems. We also show that the class of finitely generated groups with a logspace randomized streaming algorithm for the word problem is closed under several group theoretical constructions: finite extensions, direct products, free products and wreath products by free abelian groups. We contrast these results with several lower bound. An example of a finitely presented group, where the word problem has only a linear space randomized streaming algorithm, is Thompson's group $F$.
Overleaf Example

In Machine Learning, a benchmark refers to an ensemble of datasets associated with one or multiple metrics together with a way to aggregate different systems performances. They are instrumental in (i) assessing the progress of new methods along different axes and (ii) selecting the best systems for practical use. This is particularly the case for NLP with the development of large pre-trained models (e.g. GPT, BERT) that are expected to generalize well on a variety of tasks. While the community mainly focused on developing new datasets and metrics, there has been little interest in the aggregation procedure, which is often reduced to a simple average over various performance measures. However, this procedure can be problematic when the metrics are on a different scale, which may lead to spurious conclusions. This paper proposes a new procedure to rank systems based on their performance across different tasks. Motivated by the social choice theory, the final system ordering is obtained through aggregating the rankings induced by each task and is theoretically grounded. We conduct extensive numerical experiments (on over 270k scores) to assess the soundness of our approach both on synthetic and real scores (e.g. GLUE, EXTREM, SEVAL, TAC, FLICKR). In particular, we show that our method yields different conclusions on state-of-the-art systems than the mean-aggregation procedure while being both more reliable and robust.
Study of UV bright sources in globular cluster NGC 4590 using Ultraviolet Imaging Telescope (UVIT) observations

We have studied ultraviolet (UV) bright sources in the Galactic globular cluster (GGC) NGC 4590 using Ultraviolet Imaging Telescope (UVIT) on-board the AstroSat satellite. Using UV-optical colour-magnitude diagrams (CMDs), we have identified and characterized the sources of different evolutionary stages i.e. blue horizontal branch stars (BHBs), extremely blue horizontal branch stars (EHBs), blue straggler stars (BSs), variable stars, etc. We estimated effective temperature (Teff), gravity (log (g)), luminosity (Lbol), and hence the radius (R) of these hot stars by fitting spectral energy distribution (SED) with the help of stellar atmosphere models. Two new far-UV (FUV) bright cluster member stars situated near the core of the cluster have been detected; one of them is an EHB star and the other one is either in its post-blue hook evolutionary phase or in white dwarf phase. The evolutionary status of all the hot stars, identified in the cluster, has been investigated by using various evolutionary models. We find the massive and younger BSs are concentrated at the center of the cluster whereas the older and less massive BSs are distributed though out the cluster. The BSs normalized radial distribution seems to be bi-modal with a minimum located at rmin = 4.3 rc. We calculated A+ parameter of the cluster which is obtained using cumulative normalized radial distribution of horizontal branch stars (HBs) and BSs. We measured this value up to half-mass radius of the cluster to be +0.13, which indicates that NGC 4590 is one of the youngest clusters among dynamically intermediate age GGCs with a dynamical age of 0.423 ± 0.096 Gyr.
Tomography of the environment of the COSMOS/AzTEC-3 submillimeter galaxy at z~5.3 revealed by Lyman_alpha and MUSE observations

Context. Submillimeter galaxies (SMGs) have been proposed as the progenitors of massive ellipticals in the local Universe. Mapping the neutral gas distribution and investigating the gas accretion toward the SMGs at high redshift can provide information on the way SMG environments can evolve into clusters at z = 0. Aims. In this work, we study the members of the protocluster around AzTEC-3, a submillimeter galaxy at z = 5.3. We use Lyα emission and its synergy with previous CO and [CII]158 μm observations. Methods. We analyzed the data from the Multi Unit Spectroscopic Explorer (MUSE) instrument in an area of 1.4x1. 4 arcmin2 around AzTEC-3 and derived information on the Lyα line in emission. We compared the Lyα profile of various regions of the environment with the zELDA radiative transfer model, revealing the neutral gas distribution and kinematics. Results. We identified ten Lyα emitting sources, including two regions with extended emission: one embedding AzTEC-3 and LBG3, which is a star-forming galaxy located 2” (12 kpc) north of the SMG and another toward LBG-1, which is a star-forming galaxy located 15′′ (90 kpc) to the southeast. The two regions extend for ∼ 27×38 kpc2 (∼ 170×240 ckpc2) and ∼ 20×20 kpc2 (∼ 125×125 ckpc2), respectively. The sources appear distributed in an elongated configuration of about 70′′ (430 kpc) in extent. The number of sources confirms the overdensity around AzTEC-3. We study the MUSE spectra of the AzTEC-3+LBG-3 system and LBG-1 in detail. For the AzTEC-3+LBG-3 system, the Lyα emission appears redshifted and more spatially extended than the [CII] line emission. Similarly, the Lyα line spectrum is broader in velocity than [CII] for LBG-1. In the former spectrum, the Lyα emission is elongated to the north of LBG-3 and to the south of AzTEC-3, where a faint Lyα emitting galaxy is also located. The elongated structures could resemble tidal features due to the interaction of the two galaxies with AzTEC-3. Also, we find a bridge of gas, revealed by the Lyα emission between AzTEC-3 and LBG-3. The Lyα emission toward LBG-1 embeds its three components. The HI kinematics support the idea of a merger of the three components. Conclusions. Given the availability of CO and [CII] observations from previous campaigns, and the Lyα information from our MUSE dataset, we find evidence of starburst-driven phenomena and interactions around AzTEC-3. The stellar mass of the galaxies of the overdensity and the Lyα luminosity of the HI nebula associated with AzTEC-3 imply a dark matter halo of ∼ 1012 M at z = 5.3. By comparing this with semi-analytical models, the dark matter halo mass indicates that the region could evolve into a cluster of 2× 1013 M by z = 2 and into a Fornax-type cluster at z = 0 with a typical mass of 2 × 1014 M .
Network Comparison Study of Deep Activation Feature Discriminability with Novel Objects

Feature extraction has always been a critical component of the computer vision field. More recently, state-of-the-art computer visions algorithms have incorporated Deep Neural Networks (DNN) in feature extracting roles, creating Deep Convolutional Activation Features (DeCAF). The transferability of DNN knowledge domains has enabled the wide use of pretrained DNN feature extraction for applications with novel object classes, especially those with limited training data. This study analyzes the general discriminability of novel object visual appearances encoded into the DeCAF space of six of the leading visual recognition DNN architectures. The results of this study characterize the Mahalanobis distances and cosine similarities between DeCAF object manifolds across two visual object tracking benchmark data sets. The backgrounds surrounding each object are also included as an object classes in the manifold analysis, providing a wider range of novel classes. This study found that different network architectures led to different network feature focuses that must to be considered in the network selection process. These results are generated from the VOT2015 and UAV123 benchmark data sets; however, the proposed methods can be applied to efficiently compare estimated network performance characteristics for any labeled visual data set.
CALM: Contrastive Aligned Audio-Language Multirate and Multimodal Representations

Deriving multimodal representations of audio and lexical inputs is a central problem in Natural Language Understanding (NLU). In this paper, we present Contrastive Aligned Audio-Language Multirate and Multimodal Representations (CALM), an approach for learning multimodal representations using contrastive and multirate information inherent in audio and lexical inputs. The proposed model aligns acoustic and lexical information in the input embedding space of a pretrained language-only contextual embedding model. By aligning audio representations to pretrained language representations and utilizing contrastive information between acoustic inputs, CALM is able to bootstrap audio embedding competitive with existing audio representation models in only a few hours of training time. Operationally, audio spectrograms are processed using linearized patches through a Spectral Transformer (SpecTran) which is trained using a Contrastive Audio-Language Pretraining objective to align audio and language from similar queries. Subsequently, the derived acoustic and lexical tokens representations are input into a multimodal transformer to incorporate utterance level context and derive the proposed CALM representations. We show that these pretrained embeddings can subsequently be used in multimodal supervised tasks and demonstrate the benefits of the proposed pretraining steps in terms of the alignment of the two embedding spaces and the multirate nature of the pretraining. Our system shows 10-25\% improvement over existing emotion recognition systems including state-of-the-art three-modality systems under various evaluation objectives.