Miracle-dz/UniFAR-FTUs huggingface dataset:
Exergy Analysis of Biomass Drying Based on Self-Heat Recuperation Technology and Its Application to Industry: a Simulation and Experimental Study

An energy-saving process for biomass drying was proposed to improve the overall energy efficiency by using self-heat recuperation technology for future industrial use. Energy analysis based on the simulation results showed that the primary energy consumption and CO2 emission in the proposed process can be decreased to 74% and 36% of that of the conventional heat recovery dryer, respectively. Moreover, exergy analysis was conducted to clarify exergy losses in the proposed drying process, and further energy-saving solutions were raised to minimize exergy loss based on exergy recuperation theory. The experimental results indicated that three times of the minimum fluidization velocity was a necessary condition for good fluidization. It was also confirmed that the proposed fluidized bed dryer for biomass drying achieves stable performance for more than 2 h in the experiments.
Long COVID following mild SARS-CoV-2 infection: characteristic T cell alterations and response to antihistamines

Long COVID is characterized by the emergence of multiple debilitating symptoms following SARS-CoV-2 infection. Its etiology is unclear and it often follows a mild acute illness. Anecdotal reports of gradual clinical responses to histamine receptor antagonists (HRAs) suggest a histamine-dependent mechanism that is distinct from anaphylaxis, possibly mediated by T cells, which are also regulated by histamine. T cell perturbations have been previously reported in post-viral syndromes, but the T cell landscape in patients who have recovered from mild COVID-19 and its relationship to both long COVID symptoms and any symptomatic response to HRA remain underexplored. We addressed these questions in an observational study of 65 individuals who had recovered from mild COVID-19. Participants were surveyed between 87 and 408 days after the onset of acute symptoms; none had required hospitalization, 16 had recovered uneventfully, and 49 had developed long COVID. Symptoms were quantified using a structured questionnaire and T cell subsets enumerated in a standard diagnostic assay. Patients with long-COVID had reduced CD4+ and CD8+ effector memory (EM) cell numbers and increased PD-1 (programmed cell death protein 1) expression on central memory (CM) cells, whereas the asymptomatic participants had reduced CD8+ EM cells only and increased CD28 expression on CM cells. 72% of patients with long COVID who received HRA reported clinical improvement, although T cell profiling did not clearly distinguish those who responded to HRA. This study demonstrates that T cell perturbations persist for several months after mild COVID-19 and are associated with long COVID symptoms.
Effect of Citrate Concentration on the Extraction Efficiency of Selected Micronutrients from Soil

ABSTRACT This study was carried out with the objective of evaluating the effect of citrate concentration on the extraction efficiency of some micronutrients from soil. Composite surface soil samples (0–20 cm) were collected from Eastern Harage Zone (Babile and Haramaya Districts), Wolaita Zone (Damot Sore, Boloso Bombe, Damot Pulasa and Humbo Districts) and Dire Dawa Administrative Council in purposive sampling. The treatments were arranged in completely randomized design (CRD) with three replications. A greenhouse pot experiment with soybean plant was conducted to determine the correlation between soil test methods and the selected micronutrients, such as iron (Fe), copper (Cu), manganese (Mn) and zinc (Zn) in the leaves of the plant. The results showed that, among the different citrate concentrations with strontium chloride (SrCl2) tested for the determination of available Fe, Mn and Zn, the highest correlation coefficients (r = 0.82, p < 0.05), (r = 0.96, p < 0.001) and (r = 0.98, p < 0.001) were found between the diethylenetriamine pentaacetic acid (DTPA) method and 0.02 M strontium chloride (SrCl2)-0.025 M citric acid extractant, respectively. Therefore, 0.02 M SrCl2-0.025 M citric acid extractant is considered to be the most effective for the determination of Fe, Mn and Zn in soils of the studied areas. Similarly, high correlation coefficients (r = 0.97, p < 0.001) were found between DTPA and 0.02 M SrCl2-0.05 M citric acid and (r = 0.88, p < 0.01) between DTPA and 0.02 M SrCl2-0.025 M citric acid extractants for the determination of available Cu from soils. Hence, the 0.02 M SrCl2-0.05 M citric acid extractant was shown to be the best for the determination of Cu in soils of the studied areas. However, considering the use of universal extractant, the 0.02 M SrCl2-0.025 M citric acid extractant could easily be adopted as a procedure for the determination of Fe, Cu, Mn and Zn for both agricultural and environmental purposes. The greenhouse experiment confirmed the result.
Harrodians and Kaleckians: A Suggested Reconciliation and Synthesis

The Kaleckian and Harrodian approaches to growth frequently arrive at antagonistic<br>positions with respect to key issues in heterodox macrodynamics, including the treatment <br>of the rate of capacity utilization and the very nature of the long-run growth<br>process. Nevertheless, this paper is devoted to exploring the possibilities for reconciling <br>and even synthesizing these traditions. It does so by directly addressing three key<br>areas of dispute: the Keynesian stability condition; Harrodian instability; and the question <br>as to whether long-run growth is best conceived in terms of a stable steady state<br>or `corridor instability'. It is shown that reconciliation and even synthesis is possible<br>- the latter producing a `corridor within a corridor' conception of the growth process<br>in which the economy switches between Kaleckian and Harrodian dynamics depending<br>on the extent to which the rate of capacity utilization departs from its normal rate.
Pairs Trading via Unsupervised Learning

This paper develops a pairs trading strategy via unsupervised learning. Unlike conventional pairs trading strategies that identify pairs based on return time series, we identify pairs by incorporating firm characteristics as well as price information. Firm characteristics are revealed to provide important information for pair identification and significantly improve the performance of the pairs trading strategy. Applied to the US stock market from January 1980 to December 2020, the long-short portfolio constructed via the agglomerative clustering earns a statistically significant annualized mean return of 24.8% and a Sharpe ratio of 2.69. The strategy remains profitable after accounting for transaction costs and removing stocks below 20% NYSE-size quantile. A host of robustness tests confirm that the results are not driven by data snooping.
Spectroscopic Observations of Obscured Populations in the Inner Galaxy: 2MASS-GC02, Terzan 4, and the 200 km s−1 stellar peak* * Based on observations taken by APOGEE.

The interpretation of potentially new and already known stellar structures located at low latitudes is hindered by the presence of dense gas and dust, as observations toward these sight lines are limited. We have identified Apache Point Observatory Galaxy Evolution Experiment (APOGEE) stars belonging to the low-latitude globular clusters 2MASS-GC02 and Terzan 4, presenting the first chemical element abundances of stars residing in these poorly studied clusters. As expected, the signature of multiple populations coexisting in these metal-rich clusters is evident. We redetermine the radial velocity of 2MASS-GC02 to be −87 ± 7 km s−1, finding that this cluster’s heliocentric radial velocity is offset by more than 150 km s−1 from the literature value. We investigate a potentially new low-latitude stellar structure and a kiloparsec-scale nuclear disk (or ring) that has been put forward to explain a high-velocity (V GSR ∼ 200 km s−1) peak reported in several Galactic bulge fields based on the APOGEE commissioning observations. New radial velocities of field stars at (l, b) = (−6°,0 °) are presented and combined with the APOGEE observations at negative longitudes to carry out this search. Unfortunately no prominent −200 km s−1 peak at negative longitudes along the plane of the Milky Way are apparent, as predicted for the signature of a nuclear feature. The distances and Gaia EDR3 proper motions of the high-V GSR stars do not support the current models of stars on bar-supporting orbits as an explanation of the +200 km s−1 peak.
Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms

The stochastic contextual bandit problem, which models the trade-off between exploration and exploitation, has many real applications, including recommender systems, online advertising and clinical trials. As many other machine learning algorithms, contextual bandit algorithms often have one or more hyper-parameters. As an example, in most optimal stochastic contextual bandit algorithms, there is an unknown exploration parameter which controls the trade-off between exploration and exploitation. A proper choice of the hyper-parameters is essential for contextual bandit algorithms to perform well. However, it is infeasible to use offline tuning methods to select hyper-parameters in contextual bandit environment since there is no pre-collected dataset and the decisions have to be made in real time. To tackle this problem, we first propose a two-layer bandit structure for auto tuning the exploration parameter and further generalize it to the Syndicated Bandits framework which can learn multiple hyper-parameters dynamically in contextual bandit environment. We derive the regret bounds of our proposed Syndicated Bandits framework and show it can avoid its regret dependent exponentially in the number of hyper-parameters to be tuned. Moreover, it achieves optimal regret bounds under certain scenarios. Syndicated Bandits framework is general enough to handle the tuning tasks in many popular contextual bandit algorithms, such as LinUCB, LinTS, UCB-GLM, etc. Experiments on both synthetic and real datasets validate the effectiveness of our proposed framework.
Proxy-Normalizing Activations to Match Batch Normalization while Removing Batch Dependence

We investigate the reasons for the performance degradation incurred with batch-independent normalization. We ﬁnd that the prototypical techniques of layer normalization and instance normalization both induce the appearance of failure modes in the neural network’s pre-activations: (i) layer normalization induces a collapse towards channel-wise constant functions; (ii) instance normalization induces a lack of variability in instance statistics, symptomatic of an alteration of the expressivity. To alleviate failure mode (i) without aggravating failure mode (ii), we introduce the technique “Proxy Normalization” that normalizes post-activations using a proxy distribution. When combined with layer normalization or group normalization, this batch-independent normalization emulates batch normalization’s behavior and consistently matches or exceeds its performance.
Reveal of Vision Transformers Robustness against Adversarial Attacks

The major part of the vanilla vision transformer (ViT) is the attention block that brings the power of mimicking the global context of the input image. For better performance, ViT needs large-scale training data. To overcome this data hunger limitation, many ViT-based networks, or hybrid-ViT, have been proposed to include local context during the training. The robustness of ViTs and its variants against adversarial attacks has not been widely investigated in the literature like CNNs. This work studies the robustness of ViT variants 1) against different Lp-based adversarial attacks in comparison with CNNs, 2) under adversarial examples (AEs) after applying preprocessing defense methods and 3) under the adaptive attacks using expectation over transformation (EOT) framework. To that end, we run a set of experiments on 1000 images from ImageNet-1k and then provide an analysis that reveals that vanilla ViT or hybrid-ViT are more robust than CNNs. For instance, we found that 1) Vanilla ViTs or hybrid-ViTs are more robust than CNNs under Lp-based attacks and under adaptive attacks. 2) Unlike hybrid-ViTs, Vanilla ViTs are not responding to preprocessing defenses that mainly reduce the high frequency components. Furthermore, feature maps, attention maps, and Grad-CAM visualization jointly with image quality measures, and perturbations' energy spectrum are provided for an insight understanding of attention-based models.
A Stochastic Model for Block Segmentation of Images Based on the Quadtree and the Bayes Code for It

In information theory, lossless compression of general data is based on an explicit assumption of a stochastic generative model on target data. However, in lossless image compression, researchers have mainly focused on the coding procedure that outputs the coded sequence from the input image, and the assumption of the stochastic generative model is implicit. In these studies, there is a difficulty in discussing the difference between the expected code length and the entropy of the stochastic generative model. We solve this difficulty for a class of images, in which they have non-stationarity among segments. In this paper, we propose a novel stochastic generative model of images by redefining the implicit stochastic generative model in a previous coding procedure. Our model is based on the quadtree so that it effectively represents the variable block size segmentation of images. Then, we construct the Bayes code optimal for the proposed stochastic generative model. It requires the summation of all possible quadtrees weighted by their posterior. In general, its computational cost increases exponentially for the image size. However, we introduce an efficient algorithm to calculate it in the polynomial order of the image size without loss of optimality. As a result, the derived algorithm has a better average coding rate than that of JBIG.
Integrating Auxiliary Information in Self-supervised Learning

This paper presents to integrate the auxiliary information (e.g., additional attributes for data such as the hashtags for Instagram images) in the self-supervised learning process. We first observe that the auxiliary information may bring us useful information about data structures: for instance, the Instagram images with the same hashtags can be semantically similar. Hence, to leverage the structural information from the auxiliary information, we present to construct data clusters according to the auxiliary information. Then, we introduce the Clustering InfoNCE (Cl-InfoNCE) objective that learns similar representations for augmented variants of data from the same cluster and dissimilar representations for data from different clusters. Our approach contributes as follows: 1) Comparing to conventional self-supervised representations, the auxiliary-information-infused self-supervised representations bring the performance closer to the supervised representations; 2) The presented Cl-InfoNCE can also work with unsupervised constructed clusters (e.g., k-means clusters) and outperform strong clustering-based self-supervised learning approaches, such as the Prototypical Contrastive Learning (PCL) method; 3) We show that Cl-InfoNCE may be a better approach to leverage the data clustering information, by comparing it to the baseline approach - learning to predict the clustering assignments with cross-entropy loss. For analysis, we connect the goodness of the learned representations with the statistical relationships: i) the mutual information between the labels and the clusters and ii) the conditional entropy of the clusters given the labels.
Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression

Modeling a system's temporal behaviour in reaction to external stimuli is a fundamental problem in many areas. Pure Machine Learning (ML) approaches often fail in the small sample regime and cannot provide actionable insights beyond predictions. A promising modification has been to incorporate expert domain knowledge into ML models. The application we consider is predicting the progression of disease under medications, where a plethora of domain knowledge is available from pharmacology. Pharmacological models describe the dynamics of carefully-chosen medically meaningful variables in terms of systems of Ordinary Differential Equations (ODEs). However, these models only describe a limited collection of variables, and these variables are often not observable in clinical environments. To close this gap, we propose the latent hybridisation model (LHM) that integrates a system of expert-designed ODEs with machine-learned Neural ODEs to fully describe the dynamics of the system and to link the expert and latent variables to observable quantities. We evaluated LHM on synthetic data as well as real-world intensive care data of COVID-19 patients. LHM consistently outperforms previous works, especially when few training samples are available such as at the beginning of the pandemic.
Differentially Private Multi-Armed Bandits in the Shuffle Model

We give an $(\varepsilon,\delta)$-differentially private algorithm for the multi-armed bandit (MAB) problem in the shuffle model with a distribution-dependent regret of $O\left(\left(\sum_{a\in [k]:\Delta_a>0}\frac{\log T}{\Delta_a}\right)+\frac{k\sqrt{\log\frac{1}{\delta}}\log T}{\varepsilon}\right)$, and a distribution-independent regret of $O\left(\sqrt{kT\log T}+\frac{k\sqrt{\log\frac{1}{\delta}}\log T}{\varepsilon}\right)$, where $T$ is the number of rounds, $\Delta_a$ is the suboptimality gap of the arm $a$, and $k$ is the total number of arms. Our upper bound almost matches the regret of the best known algorithms for the centralized model, and significantly outperforms the best known algorithm in the local model.
Tunneling-related electron spin relaxation in self-assembled quantum-dot molecules

We study theoretically spin relaxation during phonon-assisted tunneling of a single electron in self-assembled InAs/GaAs quantum-dot molecules formed by vertically stacked dots. We find that the spin-flip tunneling rate may be as high as 1% of the spin-conserving one. By studying the dependence of spin relaxation rate on external fields, we show that the process is active at a considerable rate even without the magnetic field, and scales with the latter differently than the relaxation in a Zeeman doublet. Utilizing a multiband k· p theory, we selectively investigate the impact of various spin-mixing terms in the electron energy and carrier-phonon interaction Hamiltonians. As a result, we identify the main contribution to come from the Dresselhaus spinorbit interaction, which is responsible for the zero-field effect. At magnetic fields above ∼15 T, this is surpassed by other contributions due to the structural shear strain. We also study the impact of the sample morphology and determine that the misalignment of the dots may enhance relaxation rate by over an order of magnitude. Finally, via virtual tunneling at nonzero temperature, the process in question also affects stationary electrons in tunnel-coupled structures and provides a Zeeman-doublet spin relaxation channel even without the magnetic field.
Transformer in Convolutional Neural Networks

We tackle the low-efﬁciency ﬂaw of vision transformer caused by the high computational/space complexity in Multi-Head Self-Attention (MHSA). To this end, we propose the Hierarchical MHSA (H-MHSA), whose representation is computed in a hierarchical manner. Speciﬁcally, our H-MHSA ﬁrst learns feature relationships within small grids by viewing image patches as tokens. Then, small grids are merged into larger ones, within which feature relationship is learned by viewing each small grid at the preceding step as a token. This process is iterated to gradually reduce the number of tokens. The H-MHSA module is readily pluggable into any CNN architectures and amenable to training via backpropagation. We call this new backbone TransCNN, and it essentially inherits the advantages of both transformer and CNN. Experiments demonstrate that TransCNN achieves state-of-the-art accuracy for image recognition. Code and pretrained models are available at https://github.com/yun-liu/TransCNN . This technical report will keep updating by adding more experiments.
The Convexity and Concavity of Envelopes of the Minimum-Relative-Entropy Region for the DSBS

In this paper, we prove that for the doubly symmetric binary distribution, the lower increasing envelope and the upper envelope of the minimum-relative-entropy region are respectively convex and concave. We also prove that another function induced the minimum-relative-entropy region is concave. These two envelopes and this function were previously used to characterize the optimal exponents in strong small-set expansion problems and strong Brascamp– Lieb inequalities. The results in this paper, combined with the strong small-set expansion theorem derived by Yu, Anantharam, and Chen (2021), and the strong Brascamp–Lieb inequality derived by Yu (2021), confirm positively Ordentlich–Polyanskiy–Shayevitz’s conjecture on the strong small-set expansion (2019) and Polyanskiy’s conjecture on the strong Brascamp–Lieb inequality (2016). The proofs in this paper are based on the equivalence between the convexity of a function and the convexity of the set of minimizers of its Lagrangian dual. Index Terms Convexity, minimum-relative-entropy region, DSBS, strong small-set expansion conjecture, strong Brascamp– Lieb inequality conjecture
Fundamental differences in the radio properties of red and blue quasars: kiloparsec-scale structures revealed by e-MERLIN

Red quasi-stellar objects (QSOs) are a subset of the quasar population with colours consistent with reddening due to intervening dust. Recent work has demonstrated that red QSOs show special radio properties that fundamentally distinguish them from normal blue QSOs, specifically a higher incidence of low-power radio emission (1.4 GHz luminosities L1.4≈ 10 – 10 W Hz−1) that is physically compact when imaged by arcsecond-resolution radio surveys such as FIRST. In this work, we present e-MERLIN imaging of a set of intermediate-redshift (1.0 < z < 1.55), luminous (bolometric luminosities Lbol ≈ 10 – 10 erg s−1) red and normal QSOs carefully selected to have radio properties that span the range over which red QSOs show the most divergence from the general population. With an angular resolution ×25 better than FIRST, we resolve structures within the host galaxies of these QSOs (> 2 kpc). We report a statistically significant difference in the incidence of extended kpc-scale emission in red QSOs. From an analysis of the radio size distributions of the sample, we find that the excess radio emission in red QSOs can be attributed to structures that are confined to galaxy scales (< 10 kpc), while we confirm previous results that red and normal QSOs have similar incidences of radio jets and lobes on circumgalactic or larger scales (> 10 kpc). Our results indicate that the primary mechanism that generates the enhanced radio emission in red QSOs is not directly connected with the nuclear engine or accretion disc, but is likely to arise from extended components such as AGN-driven jets or winds.
Itihasa: A large-scale corpus for Sanskrit to English translation

This work introduces Itihasa, a large-scale translation dataset containing 93,000 pairs of Sanskrit shlokas and their English translations. The shlokas are extracted from two Indian epics viz., The Ramayana and The Mahabharata. We first describe the motivation behind the curation of such a dataset and follow up with empirical analysis to bring out its nuances. We then benchmark the performance of standard translation models on this corpus and show that even state-of-the-art transformer architectures perform poorly, emphasizing the complexity of the dataset.