Miracle-dz/UniFAR-FTUs huggingface dataset:
External Validity: Four Models of Improving Student Achievement

Randomized controlled trials in lower-income countries have demonstrated ways to increase learning, in specific settings. This study uses a large-scale, nationwide RCT in Ghana to show the external validity of four school-based interventions inspired by other RCTs. Even though the government implemented the programs within existing systems, student learning increased across all four models, more so for female than male students, and many gains persisted one year after the program ended. Three of the four interventions had a similar cost effectiveness. The intervention that directly targeted classroom teachers increased the likelihood that teachers were engaged with students.
A new blockchain proposal supporting multi-stage proof-of-work

We introduce a new variant of decentralised, trustless, permissionless blockchain. The main novelty is that the proof-of-work for mining a block is divided into multiple stages. An appropriate linkage structure is deﬁned so that it becomes possible to simultaneously work on various stages of diﬀerent blocks. The overall eﬀect is an improvement in the transaction processing rate and the time for conﬁrming a transaction. These are achieved without compromising on security. The division of the proof-of-work into several stages also divides the block reward into an equal number of stage rewards. Once a block gets onto the blockchain, the miner which successfully completed a particular stage can claim the reward for that stage. This ensures a more equitable distribution of the reward for successfully mining a block.
SCREENING RHIZOBACTERIA CONTAINING ACC-DEAMINASE FOR GROWTH PROMOTION OF WHEAT UNDER WATER STRESS

Due to climate changes globally, rainfall has become more erratic causing water shortages in the rainfed regions, resulting in reasonable yield reduction in cereal crops including wheat. Plant growth is inhibited due to higher levels of ethylene production in the rhizosphere under water stress conditions. Rhizobacteria containing ACC-deaminase can facilitate plant growth to overcome these harmful effects. Isolation of rhizobacteria containing ACC-deaminase from the rhizosphere of wheat grown in different districts of Chakwal, Attock and Rawalpindi, were screened for growth promotion of wheat seedlings under axenic conditions. The results of the laboratory experiments conducted on wheat revealed that some rhizobacterial isolates increased root and shoot growth as compared to the uninoculated control. The selected strains were again tested in soil to assess their effectiveness for improving growth of wheat seedlings under water stress soil conditions. The results revealed that inoculation with selected isolates increased the root length (upto 54.6%), shoot length (up to 80.2%), dry root weight (up to 54.2%) and dry shoot weight (up to 95.4%) of wheat seedlings grown at different water levels (i.e., 60, 45, 30 and 15% water holding capacity, WHC) over uninoculated control.Isolated strains belonging to the genus Serratia and Aerococcus were found to be most effective strains in improving the growth of wheat seedlings in water stress conditions.The results clearly indicate that rhizobacteria isolated from rainfed regions were effective in promoting growth of wheat seedlings under axenic conditions.
Levels of ambition: 1. carbon intensity of the ship to decline through implementation of further phases of the energy efficiency design index (EEDI) for new ships 2. carbon intensity of international shipping to decline

According to the latest IMO MEPC 72 meeting held in April 2018, IMO adopted the Initial IMO Strategy on reduction of GHG emission from ships. The Initial Strategy is included in the annex to resolution MEPC.304(72). Section 3 of the Initial Strategy sets forth its levels of ambition and guiding principles. International shipping was estimated to have contributed to about 2.2% of the global emissions of carbon dioxide (CO2) in 2012 (IMO, 2014). Although it is the most energy efficient mode of mass, according a recent report by European Parliament (2015), the CO2 emission shares in global CO2 emissions may rise substantially to 17% for maritime transport by 2050, if action to combat climate change is further postponed.
Effect of Different Diets on the Behaviour of Slow-growing Broiler Genotype

Abstract Ipek, A. , Karabulut, A. , Sahan, U. , Canbolat, O. and Yilmaz-Dikmen, B. 2009. Effect of different diets on the behaviour of slow-growing broiler genotype. J. Appl. Anim. Res., 35: 87–90. A total of 420 male day-old-chicks of a slow-growing genetic line (Hubbard ISA Red JA) were used as the trial material. The treatment groups were: Dilute-AL (energy and protein diluted diet fed ad libitum), Dilute-R (restricted energy and protein diluted diet), High-AL (high energy and protein diet fed ad libitum) and High-R (restricted high energy and protein diet). Restricted birds showed a greater percentage of time spot pecking, preening and in feather maintenance and less time resting than birds fed ad libitum but the differences were relatively small.
Limits of Three-Dimensional Resolution and Dose for Aberration-Corrected Electron Tomography

Aberration-corrected electron microscopy can resolve the smallest atomic bond-lengths in nature. However, the high-convergence angles that enable spectacular resolution in 2D have unknown 3D resolution limits for all but the smallest objects ($< \sim$8nm). We show aberration-corrected electron tomography offers new limits for 3D imaging by measuring several focal planes at each specimen tilt. We present a theoretical foundation for aberration-corrected electron tomography by establishing analytic descriptions for resolution, sampling, object size, and dose---with direct analogy to the Crowther-Klug criterion. Remarkably, aberration-corrected scanning transmission electron tomography can measure complete 3D specimen structure of unbounded object sizes up to a specified cutoff resolution. This breaks the established Crowther limit when tilt increments are twice the convergence angle or smaller. Unprecedented 3D resolution is achievable across large objects. Atomic 3D imaging (1$\unicode{xC5}$) is allowed across extended objects larger than depth-of-focus (e.g. $>$ 20nm) using available microscopes and modest specimen tilting ($<$ 3$^\circ$). Furthermore, aberration-corrected tomography follows the rule of dose-fractionation where a specified total dose can be divided among tilts and defoci.
Dark energy loopholes some time after GW170817

We revisit the constraints on scalar tensor theories of modified gravity following the purge of GW170817. We pay particular attention to dynamical loopholes where the anomalous speed of propagation of the gravitational wave can vanish on-shell, when we impose the dynamical field equations. By working in the effective field theory formalism we are able to improve on previous analyses, scanning a much wider class of theories, including Beyond Horndeski and DHOST . Furthermore, the formalism is well adapted to consider the effect of inhomogeneous perturbations, where we explicitly take into account the fact that the galactic overdensities are pressureless to leading order.
On a Conjecture for a Hypergraph Edge Coloring Problem

Let $H =(\mathcal{M} \cup \mathcal{J} ,E \cup \mathcal{E})$ be a hypergraph with two hypervertices $\mathcal{G}_1$ and $\mathcal{G}_2$ where $\mathcal{M} =\mathcal{G}_{1} \cup \mathcal{G}_{2}$ and $\mathcal{G}_{1} \cap \mathcal{G}_{2} =\varnothing $. An edge $\{h ,j\} \in E$ in a bi-partite multigraph graph $(\mathcal{M} \cup \mathcal{J} ,E)$ has an integer multiplicity $b_{j h}$, and a hyperedge $\{\mathcal{G}_{\ell } ,j\} \in \mathcal{E}$, $\ell=1,2$, has an integer multiplicity $a_{j \ell }$. It has been conjectured in [5] that $\chi \prime (H) =\lceil \chi \prime _{f} (H)\rceil $, where $\chi \prime (H)$ and $\chi \prime _{f} (H)$ are the edge chromatic number of $H$ and the fractional edge chromatic number of $H$ respectively. Motivation to study this hyperedge coloring conjecture comes from the University timetabling, and open shop scheduling with multiprocessors. We prove this conjecture in this paper.
The effects of the foliar fungicide pyrazophos on beneficial arthropods in barley fields

SUMMARY The insecticidal properties of the foliar fungicide pyrazophos were investigated in autumn- and spring-sown barley crops using several methods to estimate the density of non-target arthropods and by using manipulative experiments. In autumn-sown crops, pyrazophos significantly reduced densities of cereal aphid natural enemies relative to pre-treatment levels compared to areas of crop that were left unsprayed. These significant differences persisted in most groups for over 45 days after treatment. Groups most affected were aphid-specific predators, polyphagous predators, (Carabidae, Staphylinidae but not Araneae) and parasitoids. Insects known to be preferred food items of wild gamebird chicks and other non-target groups such as the Collembola were also significantly reduced in density on plots sprayed with pyrazophos, the effects again persisting for over 45 days. Densities of beneficial and non-target insects were also reduced in plots of spring barley sprayed with pyrazophos. However, the experimental design meant that levels of reductions were not significant for most arthropod groups. Removal of prey items (Drosophila pupae) placed in sprayed and unsprayed spring barley plots showed that the proportion predated was significantly lower on pyrazophos-treated plots compared to those remaining unsprayed, the difference persisting for at least 7 wk after treatment. Removal of cereal aphids from colonies introduced to sprayed and unsprayed plots was also significantly lower on plots treated with pyrazophos. Difficulties of data interpretation associated with experimental design are discussed together with the consequences of the use of broad-spectrum pesticides in I.P.M. programmes in cereals.
Diagnosis and Analysis of Celiac Disease and Environmental Enteropathy on Biopsy Images using Deep Learning Approaches

Celiac Disease (CD) and Environmental Enteropathy (EE) are common causes of malnutrition and adversely impact normal childhood development. Both conditions require a tissue biopsy for diagnosis and a major challenge of interpreting clinical biopsy images to differentiate between these gastrointestinal diseases is striking histopathologic overlap between them. In the current study, we propose four diagnosis techniques for these diseases and address their limitations and advantages. First, the diagnosis between CD, EE, and Normal biopsies is considered, but the main challenge with this diagnosis technique is the staining problem. The dataset used in this research is collected from different centers with different staining standards. To solve this problem, we use color balancing in order to train our model with a varying range of colors. Random Multimodel Deep Learning (RMDL) architecture has been used as another approach to mitigate the effects of the staining problem. RMDL combines different architectures and structures of deep learning and the final output of the model is based on the majority vote. CD is a chronic autoimmune disease that affects the small intestine genetically predisposed children and adults. Typically, CD rapidly progress from Marsh I to IIIa. Marsh III is sub-divided into IIIa (partial villus atrophy), Marsh IIIb (subtotal villous atrophy), and Marsh IIIc (total villus atrophy) to explain the spectrum of villus atrophy along with crypt hypertrophy and increased intraepithelial lymphocytes. In the second part of this study, we proposed two ways for diagnosing different stages of CD. Finally, in the third part of this study, these two steps are combined as Hierarchical Medical Image Classification (HMIC) to have a model to diagnose the disease data hierarchically.
Pure spinors and $D=11$ supergravity

In this Thesis we study first- and second-quantized approaches describing $D=11$ supergravity using pure spinor variables. We introduce the so-called $D=11$ pure spinor superparticle through BRST cohomology arguments starting from the semi-light-cone gauge $D=11$ Brink-Schwarz-like superparticle. After performing a light-cone gauge analysis of the pure spinor BRST cohomology at ghost number three, we find the linearized equations of motion of $D=11$ supergravity in $D=9$ superspace. In addition, we construct a BRST-closed, ghost number one vertex operator made out of worldline fields and $D=11$ supergravity superfields, and we run into an inconsistency when constructing a ghost number zero vertex operator satisfying a standard descent equation. We then introduce the non-minimal version of the $D=11$ pure spinor superparticle, in which a composite $b$-ghost can be constructed satisfying $\{Q,b\} = P^{2}$. However, its complicated expression makes it difficult to check its nilpotency. We show that introducing an $SO(1,10)$ fermionic vector $\bar{\Sigma}^{a}$ simplifies the form of the $b$-ghost considerably, which allows us to verify that $\{Q,b\} = P^{2}$ and $\{b,b\}=$ BRST-exact. Using this $b$-ghost we propose an alternative ghost number zero vertex operator satisfying a standard descent equation. However, its expression will depend on non-minimal pure spinor variables in a very complicated fashion. After discussing this first-quantized approach for $D=11$ supergravity, we move on to discussing the pure spinor master actions introduced by Cederwall for studying maximally supersymmetric gauge theories. We show that these actions indeed describe $D=10$ super-Yang-Mills, $D=10$ super-Born-Infeld and $D=11$ supergravity by extracting the equations of motion in ordinary superspace for each one of these theories.
Detection and Locating Cyber and Physical Stresses in Smart Grids using Graph Signal Processing

Smart grids are large and complex cyber physical infrastructures that require real-time monitoring for ensuring the security and reliability of the system. Monitoring the smart grid involves analyzing continuous data-stream from various measurement devices deployed throughout the system, which are topologically distributed and structurally interrelated. In this paper, graph signal processing (GSP) has been used to represent and analyze the power grid measurement data. It is shown that GSP can enable various analyses for the power grid's structured data and dynamics of its interconnected components. Particularly, the effects of various cyber and physical stresses in the power grid are evaluated and discussed both in the vertex and the graph-frequency domains of the signals. Several techniques for detecting and locating cyber and physical stresses based on GSP techniques have been presented and their performances have been evaluated and compared. The presented study shows that GSP can be a promising approach for analyzing the power grid's data.
Learning to Infer 3D Object Models from Images

A crucial ability of human intelligence is to build up models of individual 3D objects from partial scene observations. Recent works have enabled unsupervised 3D representation learning at scene-level, yet learning to decompose the 3D scene into 3D objects and build their individual models from multi-object scene images remains elusive. In this paper, we propose a probabilistic generative model for learning to build modular and compositional 3D object models from observations of a multi-object scene. The proposed model can (i) infer the 3D object representations by learning to search and group object areas and also (ii) render from an arbitrary viewpoint not only individual objects but also the full scene by compositing the objects. The entire learning process is unsupervised and end-to-end. We also demonstrate that the learned representation permits object-wise manipulation and novel scene generation, and generalizes to various settings.
RTEX: A novel methodology for Ranking, Tagging, and Explanatory diagnostic captioning of radiography exams

This paper introduces RTEx, a novel methodology for a) ranking radiography exams based on their probability to contain an abnormality, b) generating abnormality tags for abnormal exams, and c) providing a diagnostic explanation in natural language for each abnormal exam. The task of ranking radiography exams is an important first step for practitioners who want to identify and prioritize those radiography exams that are more likely to contain abnormalities, for example, to avoid mistakes due to tiredness or to manage heavy workload (e.g., during a pandemic). We used two publicly available datasets to assess our methodology and demonstrate that for the task of ranking it outperforms its competitors in terms of NDCG@k. For each abnormal radiography exam RTEx generates a set of abnormality tags alongside an explanatory diagnostic text to explain the tags and guide the medical expert. Our tagging component outperforms two strong competitor methods in terms of F1. Moreover, the diagnostic captioning component of RTEx, which exploits the already extracted tags to constrain the captioning process, outperforms all competitors with respect to clinical precision and recall.
Brownian bees in the infinite swarm limit

The Brownian bees model is a branching particle system with spatial selection. It is a system of $N$ particles which move as independent Brownian motions in $\mathbb{R}^d$ and independently branch at rate 1, and, crucially, at each branching event, the particle which is the furthest away from the origin is removed to keep the population size constant. In the present work we prove that as $N \to \infty$ the behaviour of the particle system is well approximated by the solution of a free boundary problem (which is the subject of a companion paper), the hydrodynamic limit of the system. We then show that for this model the so-called selection principle holds, i.e. that as $N \to \infty$ the equilibrium density of the particle system converges to the steady state solution of the free boundary problem.
Towards Unified Dialogue System Evaluation: A Comprehensive Analysis of Current Evaluation Protocols

As conversational AI-based dialogue management has increasingly become a trending topic, the need for a standardized and reliable evaluation procedure grows even more pressing. The current state of affairs suggests various evaluation protocols to assess chat-oriented dialogue management systems, rendering it difficult to conduct fair comparative studies across different approaches and gain an insightful understanding of their values. To foster this research, a more robust evaluation protocol must be set in place. This paper presents a comprehensive synthesis of both automated and human evaluation methods on dialogue systems, identifying their shortcomings while accumulating evidence towards the most effective evaluation dimensions. A total of 20 papers from the last two years are surveyed to analyze three types of evaluation protocols: automated, static, and interactive. Finally, the evaluation dimensions used in these papers are compared against our expert evaluation on the system-user dialogue data collected from the Alexa Prize 2020.
A PDD Decoder for Binary Linear Codes With Neural Check Polytope Projection

Linear Programming (LP) is an important decoding technique for binary linear codes. However, the advantages of LP decoding, such as low error floor and strong theoretical guarantee, etc., come at the cost of high computational complexity and poor performance at the low signal-to-noise ratio (SNR) region. In this letter, we adopt the penalty dual decomposition (PDD) framework and propose a PDD algorithm to address the fundamental polytope based maximum likelihood (ML) decoding problem. Furthermore, we propose to integrate machine learning techniques into the most time-consuming part of the PDD decoding algorithm, i.e., check polytope projection (CPP). Inspired by the fact that a multi-layer perception (MLP) can theoretically approximate any nonlinear mapping function, we present a specially designed neural CPP (NCPP) algorithm to decrease the decoding latency. Simulation results demonstrate the effectiveness of the proposed algorithms.
Transferring and Regularizing Prediction for Semantic Segmentation

Semantic segmentation often requires a large set of images with pixel-level annotations. In the view of extremely expensive expert labeling, recent research has shown that the models trained on photo-realistic synthetic data (e.g., computer games) with computer-generated annotations can be adapted to real images. Despite this progress, without constraining the prediction on real images, the models will easily overfit on synthetic data due to severe domain mismatch. In this paper, we novelly exploit the intrinsic properties of semantic segmentation to alleviate such problem for model transfer. Specifically, we present a Regularizer of Prediction Transfer (RPT) that imposes the intrinsic properties as constraints to regularize model transfer in an unsupervised fashion. These constraints include patch-level, cluster-level and context-level semantic prediction consistencies at different levels of image formation. As the transfer is label-free and data-driven, the robustness of prediction is addressed by selectively involving a subset of image regions for model regularization. Extensive experiments are conducted to verify the proposal of RPT on the transfer of models trained on GTA5 and SYNTHIA (synthetic data) to Cityscapes dataset (urban street scenes). RPT shows consistent improvements when injecting the constraints on several neural networks for semantic segmentation. More remarkably, when integrating RPT into the adversarial-based segmentation framework, we report to-date the best results: mIoU of 53.2%/51.7% when transferring from GTA5/SYNTHIA to Cityscapes, respectively.
DivNoising: Diversity Denoising with Fully Convolutional Variational Autoencoders

Deep Learning based methods have emerged as the indisputable leaders for virtually all image restoration tasks. Especially in the domain of microscopy images, various content-aware image restoration (CARE) approaches are now used to improve the interpretability of acquired data. But there are limitations to what can be restored in corrupted images, and any given method needs to make a sensible compromise between many possible clean signals when predicting a restored image. Here, we propose DivNoising -- a denoising approach based on fully-convolutional variational autoencoders, overcoming this problem by predicting a whole distribution of denoised images. Our method is unsupervised, requiring only noisy images and a description of the imaging noise, which can be measured or bootstrapped from noisy data. If desired, consensus predictions can be inferred from a set of DivNoising predictions, leading to competitive results with other unsupervised methods and, on occasion, even with the supervised state-of-the-art. DivNoising samples from the posterior enable a plethora of useful applications. We are (i) discussing how optical character recognition (OCR) applications could benefit from diverse predictions on ambiguous data, and (ii) show in detail how instance cell segmentation gains performance when using diverse DivNoising predictions.
On Decoding Fountain Codes with Erroneous Received Symbols

Motivated by the application of fountain codes in the DNA-based data storage systems, in this paper, we propose the basis-finding algorithm (BFA) for decoding fountain codes for an erasure-error channel where each received symbol has a fixed probability to be correct. The key idea of the BFA is to find a basis of the received symbols, and then use the most reliable basis elements to recover the source symbols with the inactivation decoding. Gaussian elimination can be used to find the basis and to identify the most reliable basis elements. For random fountain codes, we are able to derive some theoretical bounds for the frame error rate (FER) of the BFA, which reveal that the BFA can perform very well for decoding fountain codes for the considered channel.