Miracle-dz/UniFAR-FTUs huggingface dataset:
Asynchronous Edge Learning using Cloned Knowledge Distillation

With the increasing demand for more and more data, the federated learning (FL) methods, which try to utilize highly distributed on-device local data in the training process, have been proposed.However, fledgling services provided by startup companies not only have limited number of clients, but also have minimal resources for constant communications between the server and multiple clients. In addition, in a real-world environment where the user pool changes dynamically, the FL system must be able to efficiently utilize rapid inflow and outflow of users, while at the same time experience minimal bottleneck due to network delays of multiple users. In this respect, we amend the federated learning scenario to a more flexible asynchronous edge learning. To solve the aforementioned learning problems, we propose an asynchronous model-based communication method with knowledge distillation. In particular, we dub our knowledge distillation scheme as "cloned distillation" and explain how it is different from other knowledge distillation method. In brief, we found that in knowledge distillation between the teacher and the student there exist two contesting traits in the student: to attend to the teacher's knowledge or to retain its own knowledge exclusive to the teacher. And in this edge learning scenario, the attending property should be amplified rather than the retaining property, because teachers are dispatched to the users to learn from them and recollected at the server to teach the core model. Our asynchronous edge learning method can elastically handle the dynamic inflow and outflow of users in a service with minimal communication cost, operate with essentially no bottleneck due to user delay, and protect user's privacy. Also we found that it is robust to users who behave abnormally or maliciously.
ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

GNNs and chemical fingerprints are the predominant approaches to representing molecules for property prediction. However, in NLP, transformers have become the de-facto standard for representation learning thanks to their strong downstream task transfer. In parallel, the software ecosystem around transformers is maturing rapidly, with libraries like HuggingFace and BertViz enabling streamlined training and introspection. In this work, we make one of the first attempts to systematically evaluate transformers on molecular property prediction tasks via our ChemBERTa model. ChemBERTa scales well with pretraining dataset size, offering competitive downstream performance on MoleculeNet and useful attention-based visualization modalities. Our results suggest that transformers offer a promising avenue of future work for molecular representation learning and property prediction. To facilitate these efforts, we release a curated dataset of 77M SMILES from PubChem suitable for large-scale self-supervised pretraining.
Feature Inference Attack on Model Predictions in Vertical Federated Learning

Federated learning (FL) is an emerging paradigm for facilitating multiple organizations’ data collaboration without revealing their private data to each other. Recently, vertical FL, where the participating organizations hold the same set of samples but with disjoint features and only one organization owns the labels, has received increased attention. This paper presents several feature inference attack methods to investigate the potential privacy leakages in the model prediction stage of vertical FL. The attack methods consider the most stringent setting that the adversary controls only the trained vertical FL model and the model predictions, relying on no background information of the attack target’s data distribution. We first propose two specific attacks on the logistic regression (LR) and decision tree (DT) models, according to individual prediction output. We further design a general attack method based on multiple prediction outputs accumulated by the adversary to handle complex models, such as neural networks (NN) and random forest (RF) models. Experimental evaluations demonstrate the effectiveness of the proposed attacks and highlight the need for designing private mechanisms to protect the prediction outputs in vertical FL.
BiST: Bi-directional Spatio-Temporal Reasoning for Video-Grounded Dialogues

Video-grounded dialogues are very challenging due to (i) the complexity of videos which contain both spatial and temporal variations, and (ii) the complexity of user utterances which query different segments and/or different objects in videos over multiple dialogue turns. However, existing approaches to video-grounded dialogues often focus on superficial temporal-level visual cues, but neglect more fine-grained spatial signals from videos. To address this drawback, we propose Bi-directional Spatio-Temporal Learning (BiST), a vision-language neural framework for high-resolution queries in videos based on textual cues. Specifically, our approach not only exploits both spatial and temporal-level information, but also learns dynamic information diffusion between the two feature spaces through spatial-to-temporal and temporal-to-spatial reasoning. The bidirectional strategy aims to tackle the evolving semantics of user queries in the dialogue setting. The retrieved visual cues are used as contextual information to construct relevant responses to the users. Our empirical results and comprehensive qualitative analysis show that BiST achieves competitive performance and generates reasonable responses on a large-scale AVSD benchmark. We also adapt our BiST models to the Video QA setting, and substantially outperform prior approaches on the TGIF-QA benchmark.
Knowledge, attitudes and preventive practices for human Papilloma virus infection among female sex workers in Lagos metropolis

Introduction risky and hard-to-reach populations like female sex workers (FSW) face a huge burden with sexually transmitted infections (STIs) among which is human Papilloma virus (HPV) infection. This study was conducted to evaluate the knowledge, attitudes and preventive practices for HPV infection among FSW in Lagos, Nigeria. Methods a descriptive cross-sectional study was carried out among 403 respondents. The sampling units were FSW in brothels in two urban communities of Lagos. A multistage sampling technique was used for selection of respondents. Pre-tested, validated questionnaire was used for data collection. Responses to knowledge, attitude and practice questions were scored graded as poor (<50%) and good (≥50%). Bivariate analysis were carried out using Chi-square, Fisher exact test and student t-test. Logistic regression model was used for multivariate analysis. P-value < 0.05 was considered statistically significant. Results the mean age of the respondents was 32.97 ± 8.43. Majority of the respondents were within the age range of 18-34 years (51.61%), christians (51.12%), single (42.93%) and had secondary education (52.61%). Among the respondents 51.61% had good knowledge, 97.27% had good attitude and 62.28% had good preventive practice. FSW belonging to the age group 35-51 or 52-68 years, were more likely to have a good knowledge compared to those between 18-34 years. FSW with no formal education or living with a relative are less likely to have a good knowledge, compared to those having primary education or living alone. FSW with traditional or other religious beliefs are less likely to have good preventive practices against HPV compared to christian religious belief. Having tertiary education or married makes a FSW less likely, while being widowed makes her more likely to have good preventive practice. FSW living with friends are more likely to be exposed to good preventive practices compared to those living alone. Conclusion there is a need for regular health education program on HPV for FSW in order to increase their awareness and encourage best preventive practices against HPV.
Investigating Cross-Domain Losses for Speech Enhancement

Recent years have seen a surge in the number of available frameworks for speech enhancement (SE) and recognition. Whether model-based or constructed via deep learning, these frameworks often rely in isolation on either time-domain signals or time-frequency (TF) representations of speech data. In this study, we investigate the advantages of each set of approaches by separately examining their impact on speech intelligibility and quality. Furthermore, we combine the fragmented benefits of time-domain and TF speech representations by introducing two new cross-domain SE frameworks. A quantitative comparative analysis against recent model-based and deep learning SE approaches is performed to illustrate the merit of the proposed frameworks.
CoRT: Complementary Rankings from Transformers

Many recent approaches towards neural information retrieval mitigate their computational costs by using a multi-stage ranking pipeline. In the first stage, a number of potentially relevant candidates are retrieved using an efficient retrieval model such as BM25. Although BM25 has proven decent performance as a first-stage ranker, it tends to miss relevant passages. In this context we propose CoRT, a simple neural first-stage ranking model that leverages contextual representations from pretrained language models such as BERT to complement term-based ranking functions while causing no significant delay at query time. Using the MS MARCO dataset, we show that CoRT significantly increases the candidate recall by complementing BM25 with missing candidates. Consequently, we find subsequent re-rankers achieve superior results with less candidates. We further demonstrate that passage retrieval using CoRT can be realized with surprisingly low latencies.
Lattice Boltzmann simulations of drying suspensions of soft particles

The ordering of particles in the drying process of a colloidal suspension is crucial in determining the properties of the resulting film. For example, microscopic inhomogeneities can lead to the formation of cracks and defects that can deteriorate the quality of the film considerably. This type of problem is inherently multiscale and here we study it numerically, using our recently developed method for the simulation of soft polymeric capsules in multicomponent fluids. We focus on the effect of the particle softness on the film microstructure during the drying phase and how it relates to the formation of defects. We quantify the order of the particles by measuring both the Voronoi entropy and the isotropic order parameter. Surprisingly, both observables exhibit a non-monotonic behaviour when the softness of the particles is increased. We further investigate the correlation between the interparticle interaction and the change in the microstructure during the evaporation phase. We observe that the rigid particles form chain-like structures that tend to scatter into small clusters when the particle softness is increased. This article is part of the theme issue ‘Progress in mesoscale methods for fluid dynamics simulation’.
Ultrafast modulation of covalency in GeTe driven by a ferroelectric soft mode

The general idea of using ultrashort light pulses to control ferroic order parameters has recently attracted attention as a means to achieve control over material properties on unprecedented time scales. Much of the challenge in such work is in understanding the mechanisms by which this control can be achieved, and in particular how observables can be connected to structural and electronic properties. Here we report on a combination of experimental and computational methods to study the electronic structure of the semiconducting ferroelectric GeTe when driven out of equilibrium by absorption of a femtosecond pulse of light. We observe coherent modulations of second harmonic generations on the order of 50%, which we attribute to a combination of atomic and electronic structure changes due to a coherently excited soft mode. Comparison of the observed experimental data with model calculations indicates that this effect is predominantly due to an ultrafast modulation of the covalency of the bonding between Ge and Te ions. This stands in contrast to previously held assumptions in other systems, indicating that care should be exercised in using indirect measurements of electronic structure to make strong conclusions about the magnitude of nuclear motions.
The Effect of Spectrogram Reconstruction on Automatic Music Transcription: An Alternative Approach to Improve Transcription Accuracy

Most of the state-of-the-art automatic music transcription (AMT) models break down the main transcription task into sub-tasks such as onset prediction and offset prediction and train them with onset and offset labels. These predictions are then concatenated together and used as the input to train another model with the pitch labels to obtain the final transcription. We attempt to use only the pitch labels (together with spectrogram reconstruction loss) and explore how far this model can go without introducing supervised sub-tasks. In this paper, we do not aim at achieving state-of-the-art transcription accuracy, instead, we explore the effect that spectrogram reconstruction has on our AMT model. Our proposed model consists of two U-nets: the first U-net transcribes the spectrogram into a posteriorgram, and a second U-net transforms the posteriorgram back into a spectrogram. A reconstruction loss is applied between the original spectrogram and the reconstructed spectrogram to constrain the second U-net to focus only on reconstruction. We train our model on three different datasets: MAPS, MAESTRO, and MusicNet. Our experiments show that adding the reconstruction loss can generally improve the note-level transcription accuracy when compared to the same model without the reconstruction part. Moreover, it can also boost the frame-level precision to be higher than the state-of-the-art models. The feature maps learned by our U-net contain gridlike structures (not present in the baseline model) which implies that with the presence of the reconstruction loss, the model is probably trying to count along both the time and frequency axis, resulting in a higher note-level transcription accuracy.
ICFHR 2020 Competition on Image Retrieval for Historical Handwritten Fragments

This competition succeeds upon a line of competitions for writer and style analysis of historical document images. In particular, we investigate the performance of large-scale retrieval of historical document fragments in terms of style and writer identification. The analysis of historic fragments is a difficult challenge commonly solved by trained humanists. In comparison to previous competitions, we make the results more meaningful by addressing the issue of sample granularity and moving from writer to page fragment retrieval. The two approaches, style and author identification, provide information on what kind of information each method makes better use of and indirectly contribute to the interpretability of the participating method. Therefore, we created a large dataset consisting of more than 120 000 fragments. Although the most teams submitted methods based on convolutional neural networks, the winning entry achieves an mAP below 40 %.
First Degree Cohomology of Specht Modules for Two Part Partitions and p-ary Designs

Using purely combinatorial methods we calculate the first degree cohomology of Specht modules indexed by two part partitions over fields of characteristic $p\ge 3$. These combinatorial methods give an explicit description of all of the non-split extensions ofthe Specht module, $S^\lambda$, by the trivial module and is related to the construction of universal $p$-ary designs of constant block size. We also solve the problem of the existence of such designs and show their uniqueness, up to similarity, unless $\lambda$ is pointed, in which case we decompose the design in to two parts, which are unique up to similarity.
Effect of Drying and Formulation of Conidia on Virulence of the Entomofungal Pathogen Nomuraea rileyi (F) Samson

Drying Nomuraea rileyi conidia along with the solid substrate in an air stream gave a virulent product that gave high mortality of Spodoptera litura larvae in a laboratory bioassay whereas drying over silica gel or calcium chloride resulted in loss of efficacy. Bioassays with oil formulations of spores dried in an air stream resulted in comparable mortality of third instar S. litura (Fabr.) larvae with that of unformulated conidia. The oil formulation with Triton-X-100 caused significant reduction of S. litura populations feeding on groundnut and castor in field experiments. Natural parasitisation levels of two larval parasitoids on S. litura were similar in the fungus treatments and untreated controls.
Automotive Radar Interference Mitigation with Unfolded Robust PCA based on Residual Overcomplete Auto-Encoder Blocks

In autonomous driving, radar systems play an important role in detecting targets such as other vehicles on the road. Radars mounted on different cars can interfere with each other, degrading the detection performance. Deep learning methods for automotive radar interference mitigation can successfully estimate the amplitude of targets, but fail to recover the phase of the respective targets. In this paper, we propose an efficient and effective technique based on unfolded robust Principal Component Analysis (RPCA) that is able to estimate both amplitude and phase in the presence of interference. Our contribution consists in introducing residual overcomplete auto-encoder (ROC-AE) blocks into the recurrent architecture of unfolded RPCA, which results in a deeper model that significantly outperforms unfolded RPCA as well as other deep learning models. We also show that our approach achieves a faster processing time compared to state-of-the-art fully convolutional networks, thus being a suitable candidate to be deployed on devices embedded on vehicles.
Resonant enhancement of three-body loss between strongly interacting photons

Rydberg polaritons provide an example of a rare type of system where three-body interactions can be as strong or even stronger than two-body interactions. The three-body interactions can be either dispersive or dissipative, with both types possibly giving rise to exotic, strongly-interacting, and topological phases of matter. Despite past theoretical and experimental studies of the regime with dispersive interaction, the dissipative regime is still mostly unexplored. Using a renormalization group technique to solve the three-body Schrodinger equation, we show how the shape and strength of dissipative three-body forces can be universally enhanced for Rydberg polaritons. We demonstrate how these interactions relate to the transmission through a single-mode cavity, which can be used as a probe of the three-body physics in current experiments.
PROP: Pre-training with Representative Words Prediction for Ad-hoc Retrieval

Recently pre-trained language representation models such as BERT have shown great success when fine-tuned on downstream tasks including information retrieval (IR). However, pre-training objectives tailored for ad-hoc retrieval have not been well explored. In this paper, we propose Pre-training with Representative wOrds Prediction (PROP) for ad-hoc retrieval. PROP is inspired by the classical statistical language model for IR, specifically the query likelihood model, which assumes that the query is generated as the piece of text representative of the "ideal" document. Based on this idea, we construct the representative words prediction (ROP) task for pre-training. Given an input document, we sample a pair of word sets according to the document language model, where the set with higher likelihood is deemed as more representative of the document. We then pre-train the Transformer model to predict the pairwise preference between the two word sets, jointly with the Masked Language Model (MLM) objective. By further fine-tuning on a variety of representative downstream ad-hoc retrieval tasks, PROP achieves significant improvements over baselines without pre-training or with other pre-training methods. We also show that PROP can achieve exciting performance under both the zero- and low-resource IR settings.
Provenance Graph Kernel

Provenance is a record that describes how entities, activities, and agents have influenced a piece of data. Such provenance information is commonly represented in graphs with relevant labels on both their nodes and edges. With the growing adoption of provenance in a wide range of application domains, increasingly, users are confronted with an abundance of graph data, which may prove challenging to analyse. Graph kernels, on the other hand, have been consistently and successfully used to efficiently classify graphs. In this paper, we introduce a novel graph kernel called \emph{provenance kernel}, which is inspired by and tailored for provenance data. It decomposes a provenance graph into tree-patterns rooted at a given node and considers the labels of edges and nodes up to a certain distance from the root. We employ provenance kernels to classify provenance graphs from three application domains. Our evaluation shows that they perform well in terms of classification accuracy and yield competitive results when compared against standard graph kernel methods and the provenance network analytics method while taking significantly less time.Moreover, we illustrate how the provenance types used in provenance kernels help improve the explainability of predictive models.
Teacher-Student Consistency For Multi-Source Domain Adaptation

In Multi-Source Domain Adaptation (MSDA), models are trained on samples from multiple source domains and used for inference on a different, target, domain. Mainstream domain adaptation approaches learn a joint representation of source and target domains. Unfortunately, a joint representation may emphasize features that are useful for the source domains but hurt inference on target (negative transfer), or remove essential information about the target domain (knowledge fading). We propose Multi-source Student Teacher (MUST), a novel procedure designed to alleviate these issues. The key idea has two steps: First, we train a teacher network on source labels and infer pseudo labels on the target. Then, we train a student network using the pseudo labels and regularized the teacher to fit the student predictions. This regularization helps the teacher predictions on the target data remain consistent between epochs. Evaluations of MUST on three MSDA benchmarks: digits, text sentiment analysis, and visual-object recognition show that MUST outperforms current SoTA, sometimes by a very large margin. We further analyze the solutions and the dynamics of the optimization showing that the learned models follow the target distribution density, implicitly using it as information within the unlabeled target data.