Miracle-dz/UniFAR-FTUs huggingface dataset:
Mobile technologies and empowerment: Enhancing human development through participation and innovation.

Mobile technologies are opening new channels of communication between people and governments potentially offering greater access to public information and basic services to all. No other technology has been in the hands of so many people in so many countries in such a short period of time. In fact globally more people now have access to a mobile device than to justice or legal services. Recent estimates indicate that ICTs could be accessible to everyone by 2015 and bring internationally agreed development targets ever closer to achievement. Indeed we are witnessing a new wave of democratization of access to innovative ICT channels propelled by state-of-the-art technologies and diminishing barriers to entry. The Millennium Development Goals (MDGs) have set forth global commitments to foster human development across the world. One of the targets calls for making the benefits of ICTs available to all. If we subscribe to the latest figures on mobile usage and availability then we can argue that this particular target is achievable by 2015 if not before. But how does this relate to the other 17 MDG targets if at all and to all other Internationally Agreed Development Goals (IADGs)? The main objective of this primer is to provide UNDP programme staff and development partners and practitioners with a practical understanding of how mobile technologies can amplify development programming. By looking at basic concepts current trends and real life examples the primer intends to shed light on how development practitioners can harness the potential of mobile technologies to improve development outputs and outcomes at the country level.
Near-Optimal Sample Complexity Bounds for Constrained MDPs

In contrast to the advances in characterizing the sample complexity for solving Markov decision processes (MDPs), the optimal statistical complexity for solving constrained MDPs (CMDPs) remains unknown. We resolve this question by providing minimax upper and lower bounds on the sample complexity for learning near-optimal policies in a discounted CMDP with access to a generative model (simulator). In particular, we design a model-based algorithm that addresses two settings: (i) relaxed feasibility, where small constraint violations are allowed, and (ii) strict feasibility, where the output policy is required to satisfy the constraint. For (i), we prove that our algorithm returns an $\epsilon$-optimal policy with probability $1 - \delta$, by making $\tilde{O}\left(\frac{S A \log(1/\delta)}{(1 - \gamma)^3 \epsilon^2}\right)$ queries to the generative model, thus matching the sample-complexity for unconstrained MDPs. For (ii), we show that the algorithm's sample complexity is upper-bounded by $\tilde{O} \left(\frac{S A \, \log(1/\delta)}{(1 - \gamma)^5 \, \epsilon^2 \zeta^2} \right)$ where $\zeta$ is the problem-dependent Slater constant that characterizes the size of the feasible region. Finally, we prove a matching lower-bound for the strict feasibility setting, thus obtaining the first near minimax optimal bounds for discounted CMDPs. Our results show that learning CMDPs is as easy as MDPs when small constraint violations are allowed, but inherently more difficult when we demand zero constraint violation.
Multiparty Holomeetings: Toward a New Era of Low-Cost Volumetric Holographic Meetings in Virtual Reality

Fueled by advances in multi-party communications, increasingly mature immersive technologies being adopted, and the COVID-19 pandemic, a new wave of social virtual reality (VR) platforms have emerged to support socialization, interaction, and collaboration among multiple remote users who are integrated into shared virtual environments. Social VR aims to increase levels of (co-)presence and interaction quality by overcoming the limitations of 2D windowed representations in traditional multi-party video conferencing tools, although most existing solutions rely on 3D avatars to represent users. This article presents a social VR platform that supports real-time volumetric holographic representations of users that are based on point clouds captured by off-the-shelf RGB-D sensors, and it analyzes the platform’s potential for conducting interactive holomeeting scenarios (i.e., holoconferencing sessions for remote gathering and formal discussion). This work evaluates such a platform’s performance and readiness for conducting holomeetings with up to four users, and it provides insights into aspects of the user experience when using single-camera and low-cost capture systems in scenarios with both frontal and side viewpoints. Overall, the obtained results confirm the platform’s maturity and the potential of holographic communications for conducting interactive multi-party meetings, even when using low-cost systems and single-camera capture systems in scenarios where users are sitting or have a limited translational movement along the X, Y, and Z axes within the 3D virtual environment (commonly known as 3 Degrees of Freedom plus, 3DoF+).
The Impact Of Social Media In The Fight Against The Spread Of Coronavirus (Covid-19) Pandemic In Anambra State, Nigeria

This study examined the impact of social media in the fight against the spread of coronavirus (COVID-19) pandemic in Anambra state, Nigeria. The key objectives are to: find out if the numbers of social media users increased in Anambra state since the wake of coronavirus pandemic;find out if the social media is being utilised in the fight against the spread of coronavirus pandemic in Anambra state;find out how the social media is being utilised in the fight against the spread of coronavirus pandemic in Anambra state;and discover the impact of social media in the fight against the spread of coronavirus pandemic in Anambra State. It was anchored on Agenda Setting Theory, and the Technological Determinism Theory (TDT). The study was designed as a survey with close-ended questionnaire distributed to 400 respondents. The findings of this study revealed that usage and accessibility of social media increased in Anambra state because of coronavirus pandemic. It also revealed that the social media is being utilised by individuals, NGOs and government in the fight against the spread of coronavirus in Anambra state. The study also found that the social media is being utilised to gather and disseminate information, study, transact businesses, among other things. The finding also showed that the social media has positive impact in the fight against the spread of coronavirus in Anambra state. The study concluded that social media has much benefits than negative impact, and should be used to contain the spread of coronavirus. It, among other things, recommended training and empowerment of the citizens on effective utilisation of social media to create impact in the society.
Symmetric projected entangled-pair states analysis of a phase transition in coupled spin-
12
 ladders

Inﬁnite projected entangled-pair states (iPEPS) have been introduced to accurately describe many-body wave functions on two-dimensional lattices. In this context, two aspects are crucial: the systematic improvement of the Ansatz by the optimization of its building blocks, i.e., tensors characterized by bond dimension D , and the extrapolation scheme to reach the “thermodynamic” limit D → ∞ . Recent advances in variational optimization and scaling based on correlation lengths demonstrated the ability of iPEPS to capture the spontaneous breaking of a continuous symmetry in phases such as the antiferromagnetic (N´eel) phase with high ﬁdelity, in addition to valence-bond solids which are already well described by ﬁnite- D iPEPS. In contrast, systems in the vicinity of continuous quantum phase transitions still present a challenge for iPEPS, especially when non-abelian symmetries are involved. Here, we consider the iPEPS Ansatz to describe the continuous transition between the (gapless) antiferromagnet and the (gapped) paramagnet that exists in the S = 1 / 2 Heisenberg model on coupled two-leg ladders. In particular, we show how accurate iPEPS results can be obtained down to a narrow interval around criticality and analyze the scaling of the order parameter in the N´eel phase in a spatially anisotropic situation.
GLIPv2: Unifying Localization and Vision-Language Understanding

We present GLIPv2, a grounded VL understanding model, that serves both localization tasks (e.g., object detection, instance segmentation) and Vision-Language (VL) understanding tasks (e.g., VQA, image captioning). GLIPv2 elegantly unifies localization pre-training and Vision-Language Pre-training (VLP) with three pre-training tasks: phrase grounding as a VL reformulation of the detection task, region-word contrastive learning as a novel region-word level contrastive learning task, and the masked language modeling. This unification not only simplifies the previous multi-stage VLP procedure but also achieves mutual benefits between localization and understanding tasks. Experimental results show that a single GLIPv2 model (all model weights are shared) achieves near SoTA performance on various localization and understanding tasks. The model also shows (1) strong zero-shot and few-shot adaption performance on open-vocabulary object detection tasks and (2) superior grounding capability on VL understanding tasks. Code will be released at https://github.com/microsoft/GLIP.
MuseGAN: Symbolic-domain Music Generation and Accompaniment with Multi-track Sequential Generative Adversarial Networks

Generating music has a few notable differences from generating images and videos. First, music is an art of time, necessitating a temporal model. Second, music is usually composed of multiple instruments/tracks, with close interaction with one another. Each track has its own temporal dynamics, but collectively they unfold over time interdependently. Lastly, for symbolic domain music generation, the targeted output is sequences of discrete musical events, not continuous values. In this paper, we propose and study three generative adversarial networks (GANs) for symbolic-domain multi-track music generation, using a data set of 127,731 MIDI bars of pop/rock music. The three models, which differ in the underlying model assumption and accordingly the network architecture, are referred to as the jamming model, composer model, and hybrid model, respectively. We propose a few intra-track and inter-track objective metrics to examine and compare their generation result, in addition to a subjective evaluation. We show that our models can learn from the noisy MIDI files and generate coherent music of four bars right from scratch (i.e. without human inputs). We also propose extensions of our models to facilitate human-AI cooperative music creation: given the piano track composed by human we can generate four additional tracks in return to accompany it.
Identifying and estimating effects of sustained interventions under parallel trends assumptions

Many research questions in public health and medicine concern sustained interventions in populations defined by substantive priorities. Existing methods to answer such questions typically require a measured covariate set sufficient to control confounding, which can be questionable in observational studies. Differences‐in‐differences rely instead on the parallel trends assumption, allowing for some types of time‐invariant unmeasured confounding. However, most existing difference‐in‐differences implementations are limited to point treatments in restricted subpopulations. We derive identification results for population effects of sustained treatments under parallel trends assumptions. In particular, in settings where all individuals begin follow‐up with exposure status consistent with the treatment plan of interest but may deviate at later times, a version of Robins' g‐formula identifies the intervention‐specific mean under stable unit treatment value assumption, positivity, and parallel trends. We develop consistent asymptotically normal estimators based on inverse‐probability weighting, outcome regression, and a double robust estimator based on targeted maximum likelihood. Simulation studies confirm theoretical results and support the use of the proposed estimators at realistic sample sizes. As an example, the methods are used to estimate the effect of a hypothetical federal stay‐at‐home order on all‐cause mortality during the COVID‐19 pandemic in spring 2020 in the United States.
Robust Adaptive Beamforming via Worst-Case SINR Maximization With Nonconvex Uncertainty Sets

This paper considers a formulation of the robust adaptive beamforming (RAB) problem based on worst-case signal-to-interference-plus-noise ratio (SINR) maximization with a nonconvex uncertainty set for the steering vectors. The uncertainty set consists of a similarity constraint and a (nonconvex) double-sided ball constraint. The worst-case SINR maximization problem is turned into a quadratic matrix inequality (QMI) problem using the strong duality of semidefinite programming. Then a linear matrix inequality (LMI) relaxation for the QMI problem is proposed, with an additional valid linear constraint. Necessary and sufficient conditions for the tightened LMI relaxation problem to have a rank-one solution are established. When the tightened LMI relaxation problem still has a high-rank solution, the LMI relaxation problem is further restricted to become a bilinear matrix inequality (BLMI) problem. We then propose an iterative algorithm to solve the BLMI problem that finds an optimal/suboptimal solution for the original RAB problem by solving the BLMI formulations. To validate our results, simulation examples are presented to demonstrate the improved array output SINR of the proposed robust beamformer.
Predicting Corporate Risk by Jointly Modeling Company Networks and Dialogues in Earnings Conference Calls

Earnings conference calls are significant information events for volatility forecasting, which is essential for financial risk management and asset pricing. Although some recent volatility forecasting models have utilized the textual content of conference calls, the dialogue structures of conference calls and company relationships are almost ignored in extant literature. To bridge this gap, we propose a new model called Temporal Virtual Graph Neural Network (TVGNN) for volatility forecasting by jointly modeling conference call dialogues and company networks. Our model differs from existing models in several important ways. First, we propose to exploit more dialogue structures by encoding position, utterance, speaker role, and Q\&A segments. Second, we propose to encode the market states for volatility forecasting by extending the Gated Recurrent Units (GRU). Third, we propose a new method for constructing temporal company networks in which the messages can only flow from temporally preceding to successive nodes, and extend the Graph Attention Networks (GAT) for modeling company relationships. We collect conference call transcripts of S\&P500 companies from 2008 to 2019, and construct a dataset of conference call dialogues with additional information on dialogue structures and company networks. Empirical results on our dataset demonstrate the superiority of our model over competitive baselines for volatility forecasting. We also conduct supplementary analyses to examine the effectiveness of our model's key components and interpretability.
Exploring and Exploiting Hubness Priors for High-Quality GAN Latent Sampling

Despite the extensive studies on Generative Adversarial Networks (GANs), how to reliably sample high-quality images from their latent spaces remains an under-explored topic. In this paper, we propose a novel GAN latent sampling method by exploring and exploiting the hubness priors of GAN latent distributions. Our key insight is that the high dimensionality of the GAN latent space will inevitably lead to the emergence of hub latents that usually have much larger sampling densities than other latents in the latent space. As a result, these hub latents are better trained and thus contribute more to the synthesis of high-quality images. Unlike the a posteriori “cherry-picking”, our method is highly efﬁcient as it is an a priori method that identiﬁes high-quality latents before the synthesis of images. Furthermore, we show that the well-known but purely empirical truncation trick is a naive approximation of the central clustering effect of hub latents, which not only uncovers the rationale of the truncation trick, but also indicates the superiority and fundamentality of our method. Extensive experimental results demonstrate the effectiveness of the proposed method. Our code is available at: https://github. com/Byronliang8/HubnessGANSampling.
On Connections between Opacity and Security in Linear Systems

Opacity and attack detectability are important properties for any system as they allow the states to remain private and malicious attacks to be detected, respectively. In this paper, we show that a fundamental trade-off exists between these properties for a linear dynamical system, in the sense that if an opaque system is subjected to attacks, all attacks cannot be detected. We first characterize the opacity conditions for the system in terms of its weakly unobservable subspace (WUS) and show that the number of opaque states is proportional to the size of the WUS. Further, we establish conditions under which increasing the opaque sets also increases the set of undetectable attacks. This highlights a fundamental trade-off between security and privacy. We demonstrate application of our results on a remotely controlled automotive system.
Confident Sinkhorn Allocation for Pseudo-Labeling

Semi-supervised learning is a critical tool in reducing machine learning's dependence on labeled data. It has been successfully applied to structured data, such as images and natural language, by exploiting the inherent spatial and semantic structure therein with pretrained models or data augmentation. These methods are not applicable, however, when the data does not have the appropriate structure, or invariances. Due to their simplicity, pseudo-labeling (PL) methods can be widely used without any domain assumptions. However, the greedy mechanism in PL is sensitive to a threshold and can perform poorly if wrong assignments are made due to overconfidence. This paper studies theoretically the role of uncertainty to pseudo-labeling and proposes Confident Sinkhorn Allocation (CSA), which identifies the best pseudo-label allocation via optimal transport to only samples with high confidence scores. CSA outperforms the current state-of-the-art in this practically important area of semi-supervised learning. Additionally, we propose to use the Integral Probability Metrics to extend and improve the existing PACBayes bound which relies on the Kullback-Leibler (KL) divergence, for ensemble models. Our code is publicly available at https://github.com/amzn/confident-sinkhorn-allocation.
Indirect-Instant Attention Optimization for Crowd Counting in Dense Scenes

One of appealing approaches to guiding learnable parameter optimization, such as feature maps, is global attention, which enlightens network intelligence at a fraction of the cost. However, its loss calculation process still falls short: 1)We can only produce one-dimensional 'pseudo labels' for attention, since the artificial threshold involved in the procedure is not robust; 2) The attention awaiting loss calculation is necessarily high-dimensional, and decreasing it by convolution will inevitably introduce additional learnable parameters, thus confusing the source of the loss. To this end, we devise a simple but efficient Indirect-Instant Attention Optimization (IIAO) module based on SoftMax-Attention , which transforms high-dimensional attention map into a one-dimensional feature map in the mathematical sense for loss calculation midway through the network, while automatically providing adaptive multi-scale fusion to feature pyramid module. The special transformation yields relatively coarse features and, originally, the predictive fallibility of regions varies by crowd density distribution, so we tailor the Regional Correlation Loss (RCLoss) to retrieve continuous error-prone regions and smooth spatial information . Extensive experiments have proven that our approach surpasses previous SOTA methods in many benchmark datasets.
MammoDL: Mammographic Breast Density Estimation using Federated Learning

: Assessing breast cancer risk from imaging remains a subjective process, in which radiologists employ simple computer aided detection (CAD) systems or qualitative visual assessment to estimate breast percent density (PD). Machine learning (ML) models have become the most promising way to quantify breast cancer risk for early, accurate, and equitable diagnoses, but training such models in medical research is often restricted to small, single-institution data. Since patient demographics and imaging characteristics may vary considerably across imaging sites, models trained on single-institution data tend not to generalize well. In response to this problem, MammoDL is proposed, an open-source software tool that leverages a U-Net architecture to accurately estimate breast PD and complexity from mammography. With the Open Federated Learning (OpenFL) library, this solution enables secure training on datasets across multiple institutions. MammoDL is a leaner, more flexible model than its predecessors, boasting improved generalization due to federation-enabled training on larger, more representative datasets. One-Sentence Summary: MammoDL uses the U-Net deep learning architecture to quantitatively assess breast tissue density and complexity from mammograms and enables a data privacy approach by using federated learning.
Better Teacher Better Student: Dynamic Prior Knowledge for Knowledge Distillation

Knowledge distillation (KD) has shown very promising capabilities in transferring learning representations from large models (teachers) to small models (students). However, as the capacity gap between students and teachers becomes larger, existing KD methods fail to achieve better results. Our work shows that the `prior knowledge' is vital to KD, especially when applying large teachers. Particularly, we propose the dynamic prior knowledge (DPK), which integrates part of teacher's features as the prior knowledge before the feature distillation. This means that our method also takes the teacher's feature as `input', not just `target'. Besides, we dynamically adjust the ratio of the prior knowledge during the training phase according to the feature gap, thus guiding the student in an appropriate difficulty. To evaluate the proposed method, we conduct extensive experiments on two image classification benchmarks (i.e. CIFAR100 and ImageNet) and an object detection benchmark (i.e. MS COCO. The results demonstrate the superiority of our method in performance under varying settings. Besides, our DPK makes the performance of the student model positively correlated with that of the teacher model, which means that we can further boost the accuracy of students by applying larger teachers. More importantly, DPK provides a fast solution in teacher model selection for any given model.