Miracle-dz/UniFAR-FTUs huggingface dataset:
An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games

Guessing games are a prototypical instance of the “learning by interacting” paradigm. This work investigates how well an artificial agent can benefit from playing guessing games when later asked to perform on novel NLP downstream tasks such as Visual Question Answering (VQA). We propose two ways to exploit playing guessing games: 1) a supervised learning scenario in which the agent learns to mimic successful guessing games and 2) a novel way for an agent to play by itself, called Self-play via Iterated Experience Learning (SPIEL). We evaluate the ability of both procedures to generalise: an in-domain evaluation shows an increased accuracy (+7.79) compared with competitors on the evaluation suite CompGuessWhat? ; a transfer evaluation shows improved performance for VQA on the TDIUC dataset in terms of harmonic average accuracy (+5.31) thanks to more fine-grained object representations learned via SPIEL.
Distributed Control of Multi-Robot Systems in the Presence of Deception and Denial of Service Attacks

This research proposes a distributed switching control to secure multi-robot systems in the presence of cyberattacks. Two major types of cyberattack are considered: deception attack and denial of service (DoS) attack, which compromise the integrity and availability of resources, respectively. First, a residual-based attack detection scheme is introduced to identify the type of attacks. Then, a switching control is designed to neutralize the effect of the identified attacks, satisfying the performance guarantees required for state consensus among robots. For the type of a deception attack, coordination-free consensus protocols are designed to tune the weights of each robot in a way that uncompromised robots gain more weight than compromised robots. For the type of a DoS attack, leader-follower protocols that reconfigure the communication topology are utilized to transform the compromised robots into sub-robots following the leaders. The performance of the proposed approach is evaluated on the Robotarium multi-robot testbed. A full demonstration with extensive cases is available at https://youtu.be/eSj0XS2pdxI.
Exponential Savings in Agnostic Active Learning Through Abstention

We show that in pool-based active classification without assumptions on the underlying distribution, if the learner is given the power to abstain from some predictions by paying the price marginally smaller than the average loss 1/2 of a random guess, exponential savings in the number of label requests are possible whenever they are possible in the corresponding realizable problem. We extend this result to provide a necessary and sufficient condition for exponential savings in pool-based active classification under the model misspecification.
Warning Propagation on random graphs

Warning Propagation is a combinatorial message passing algorithm that unifies and generalises a wide variety of recursive combinatorial procedures. Special cases include the Unit Clause Propagation and Pure Literal algorithms for satisfiability as well as the peeling process for identifying the $k$-core of a random graph. Here we analyse Warning Propagation in full generality on the binomial random graph. We prove that under a mild stability assumption Warning Propagation converges rapidly. In effect, the analysis of the fixed point of the message passing process on a random graph reduces to analysing the process on a Galton-Watson tree. This result corroborates and generalises a heuristic first put forward by Pittel, Spencer and Wormald in their seminal $k$-core paper (JCTB 1996).
A Categorical Semantics for Hierarchical Petri Nets

We show how a particular variety of hierarchical nets, where the firing of a transition in the parent net must correspond to an execution in some child net, can be modelled utilizing a functorial semantics from a free category -- representing the parent net -- to the category of sets and spans between them. This semantics can be internalized via Grothendieck construction, resulting in the category of executions of a Petri net representing the semantics of the overall hierarchical net. We conclude the paper by giving an engineering-oriented overview of how our model of hierarchical nets can be implemented in a transaction-based smart contract environment.
Fast Rates for Structured Prediction

Discrete supervised learning problems such as classification are often tackled by introducing a continuous surrogate problem akin to regression. Bounding the original error, between estimate and solution, by the surrogate error endows discrete problems with convergence rates already shown for continuous instances. Yet, current approaches do not leverage the fact that discrete problems are essentially predicting a discrete output when continuous problems are predicting a continuous value. In this paper, we tackle this issue for general structured prediction problems, opening the way to"super fast"rates, that is, convergence rates for the excess risk faster than $n^{-1}$, where $n$ is the number of observations, with even exponential rates with the strongest assumptions. We first illustrate it for predictors based on nearest neighbors, generalizing rates known for binary classification to any discrete problem within the framework of structured prediction. We then consider kernel ridge regression where we improve known rates in $n^{-1/4}$ to arbitrarily fast rates, depending on a parameter characterizing the hardness of the problem, thus allowing, under smoothness assumptions, to bypass the curse of dimensionality.
Moving Pearl Vortices in Thin-Film Superconductors

The magnetic field hz of a moving Pearl vortex in a superconducting thin-film in (x,y) plane is studied with the help of the time-dependent London equation. It is found that for a vortex at the origin moving in +x direction, hz(x,y) is suppressed in front of the vortex, x>0, and enhanced behind (x<0). The distribution asymmetry is proportional to the velocity and to the conductivity of normal quasiparticles. The vortex self-energy and the interaction of two moving vortices are evaluated.
Cortical Features for Defense Against Adversarial Audio Attacks

We propose using a computational model of the auditory cortex as a defense against adversarial attacks on audio. We apply several white-box iterative optimization-based adversarial attacks to an implementation of Amazon Alexa's HW network, and a modified version of this network with an integrated cortical representation, and show that the cortical features help defend against universal adversarial examples. At the same level of distortion, the adversarial noises found for the cortical network are always less effective for universal audio attacks. We make our code publicly available at https://github.com/ilyakava/py3fst.
Uncertainty propagation and sensitivity analysis: results from the Ocular Mathematical Virtual Simulator

We propose an uncertainty propagation study and a sensitivity analysis with the Ocular Mathematical Virtual Simulator, a computational and mathematical model that predicts the hemodynamics and biomechanics within the human eye. In this contribution, we focus on the effect of intraocular pressure, retrolaminar tissue pressure and systemic blood pressure on the ocular posterior tissue vasculature. The combination of a physically-based model with experiments-based stochastic input allows us to gain a better understanding of the physiological system, accounting both for the driving mechanisms and the data variability.
Temporal evolution of small-scale internetwork magnetic fields in the solar photosphere

Context. While the longitudinal field that dominates in photospheric network regions has been studied extensively, small-scale transverse fields have recently been found to be ubiquitous in the quiet internetwork photosphere and this merits further study. Furthermore, few observations have been able to capture how this field evolves. Aims. We aim to statistically characterize the magnetic vector in a quiet Sun internetwork region and observe the temporal evolution of specific small-scale magnetic features. Methods. We present two high spatio-temporal resolution observations that reveal the dynamics of two disk-centre internetwork regions taken by the new GREGOR Infrared Spectrograph Integral Field Unit with the highly magnetically sensitive photospheric Fe I line pair at 15648.52 Å and 15652.87 Å. We record the full Stokes vector and apply inversions with the Stokes inversions based on response functions code to retrieve the parameters characterizing the atmosphere. We consider two inversion schemes: scheme 1 (S1), where a magnetic atmosphere is embedded in a field free medium, and scheme 2 (S2), with two magnetic models and a fixed 30% stray light component. Results. The magnetic properties produced from S1 inversions returned a median magnetic field strength of 200 and 240 G for the two datasets, respectively. We consider the median transverse (horizontal) component, among pixels with Stokes Q or U, and the median unsigned longitudinal (vertical) component, among pixels with Stokes V, above a noise threshold. We determined the former to be 263 G and 267 G, and the latter to be 131 G and 145 G, for the two datasets, respectively. Finally, we present three regions of interest, tracking the dynamics of small-scale magnetic features. We apply S1 and S2 inversions to specific profiles of interest and find that the latter produces better approximations when there is evidence of mixed polarities. We find patches of linear polarization with magnetic flux density of the order of 130−150 G and find that linear polarization appears preferentially at granule-intergranular lane boundaries. The weak magnetic field appears to be organized in terms of complex ‘loop-like’ structures, with transverse fields often flanked by opposite polarity longitudinal fields.
Classifications based on response times for detecting early-stage Alzheimer's disease

Introduction: This paper mainly describes a way to detect with high accuracy patients with early-stage Alzheimer's disease (ES-AD) versus healthy control (HC) subjects, from datasets built with handwriting and drawing task records. Method: The proposed approach uses subject's response times. An optimal subset of tasks is first selected with a"Support Vector Machine"(SVM) associated with a grid search. Mixtures of Gaussian distributions defined in the space of task durations are then used to reproduce and explain the results of the SVM. Finally, a surprisingly simple and efficient ad hoc classification algorithm is deduced from the Gaussian mixtures. Results: The solution presented in this paper makes two or even four times fewer errors than the best results of the state of the art concerning the classification HC/ES-AD from handwriting and drawing tasks. Discussion: The best SVM learning model reaches a high accuracy for this classification but its learning capacity is too large to ensure a low overfitting risk regarding the small size of the dataset. The proposed ad hoc classification algorithm only requires to optimize three real-parameters. It should therefore benefit from a good generalization ability.
A Linear Frequency Principle Model to Understand the Absence of Overfitting in Neural Networks

Why heavily parameterized neural networks (NNs) do not overfit the data is an important long standing open question. We propose a phenomenological model of the NN training to explain this non-overfitting puzzle. Our linear frequency principle (LFP) model accounts for a key dynamical feature of NNs: they learn low frequencies first, irrespective of microscopic details. Theory based on our LFP model shows that low frequency dominance of target functions is the key condition for the non-overfitting of NNs and is verified by experiments. Furthermore, through an ideal two-layer NN, we unravel how detailed microscopic NN training dynamics statistically gives rise to an LFP model with quantitative prediction power.
Challenges in Automated Debiasing for Toxic Language Detection

Biased associations have been a challenge in the development of classifiers for detecting toxic language, hindering both fairness and accuracy. As potential solutions, we investigate recently introduced debiasing methods for text classification datasets and models, as applied to toxic language detection. Our focus is on lexical (e.g., swear words, slurs, identity mentions) and dialectal markers (specifically African American English). Our comprehensive experiments establish that existing methods are limited in their ability to prevent biased behavior in current toxicity detectors. We then propose an automatic, dialect-aware data correction method, as a proof-of-concept. Despite the use of synthetic labels, this method reduces dialectal associations with toxicity. Overall, our findings show that debiasing a model trained on biased toxic language data is not as effective as simply relabeling the data to remove existing biases.
How many phases nucleate in the bidimensional Potts model?

We study the kinetics of the two-dimensional q > 4-state Potts model after a shallow quench to a temperature slightly below the critical one and above the pseudo spinodal. We use numerical methods and we focus on intermediate values of q, 4 < q ⩽ 100. We show that, initially, the system evolves as if it were quenched to the critical temperature: the configurations exhibit correlations that are indistinguishable from the ones in equilibrium at T c(q) over longer and longer length scales as time elapses. The further decay from the metastable state occurs by nucleation of an average number k out of the q possible phases. For a given quench temperature, k is a logarithmically increasing function of the system size, bounded by q. This unusual finite size dependence is a consequence of a scaling property underlying the nucleation phenomenon for these parameters.
Exact Recovery of Clusters in Finite Metric Spaces Using Oracle Queries

We investigate the problem of exact cluster recovery using oracle queries. Previous results show that clusters in Euclidean spaces that are convex and separated with a margin can be reconstructed exactly using only $O(\log n)$ same-cluster queries, where $n$ is the number of input points. In this work, we study this problem in the more challenging non-convex setting. We introduce a structural characterization of clusters, called $(\beta,\gamma)$-convexity, that can be applied to any finite set of points equipped with a metric (or even a semimetric, as the triangle inequality is not needed). Using $(\beta,\gamma)$-convexity, we can translate natural density properties of clusters (which include, for instance, clusters that are strongly non-convex in $\mathbb{R}^d$) into a graph-theoretic notion of convexity. By exploiting this convexity notion, we design a deterministic algorithm that recovers $(\beta,\gamma)$-convex clusters using $O(k^2 \log n + k^2 (6/\beta\gamma)^{dens(X)})$ same-cluster queries, where $k$ is the number of clusters and $dens(X)$ is the density dimension of the semimetric. We show that an exponential dependence on the density dimension is necessary, and we also show that, if we are allowed to make $O(k^2 + k\log n)$ additional queries to a"cluster separation"oracle, then we can recover clusters that have different and arbitrary scales, even when the scale of each cluster is unknown.
The diversity of wild edible fruit plants and traditional knowledge in West Aceh region, Indonesia

Rural communities in the West Aceh region continue to collect and consume many edible wild fruit plants as a food source. This study was an account of the traditional knowledge and use of wild edible fruit plants by local people in the West Aceh region. The study was conducted in Sungai Mas and Pante Ceureumen, West Aceh Regency, between April and June 2019. Plant specimens have been gathered from the forest, agroforestry and home garden. A total of 100 informants (50 informants at each site) were involved in the survey of Ethnobotanical data. The questionnaires used to investigate the local name of the species, the habitats, the location of the collection, the season of collection, the parts used, the categories of use and the manner of fruit consumption. A total of 44 species of edible fruit plants recorded in West Aceh region, Aceh Province, Indonesia. The Myrtaceae and Malvaceae were the most represented families. Baccaurea motleyana, Durio zibethinus, Garcinia mangostana, Lansium domesticum, Mangifera odorata, and Mangifera foetida were the top six most common wild edible fruits in this region. The local community uses wild edible fruit species for food (44 species), medicine (11 species), construction materials (9 species), furniture (9 species) and firewood. D. zibethinus, M. foetida, M. odorata, M. quadrifida, B. motleyana, L. domesticum, G. xanthochymus, and G. mangostana are also commonly traded in traditional markets.
MEK/ERK pathway to regulate the proliferation and metastasis of colon cancer

Several studies have proved the tumor-suppressive effects of miR-335 but its role in colon cancer via regulation of the Raf/MEK/ERK signalling pathway is yet unknown. As such the main motive of conducting the present study was to elucidate the role of miR-335 in colon cancer via regulation of Raf/MEK/ERK signalling pathway and to explore its therapeutic potential. The results revealed significant (P < 0.05) downregulation of miR-335 in colon cancer and its overexpression led to a significant (P < 0.05) decline in viability of the HT-29 and SW948 cells. The TUNNEL assay showed miR-335 promotes apoptosis in the HT-29 and SW948 colon cancer cells and is also associated with increase in Bax and decrease in Bcl-2 expression. The results also revealed that miR-335 overexpression enhances the sensitivity of the HT-29 and SW948 cells to the apoptotic effects of cisplatin. From the transwell assays, it was found that the migration of the HT-29 and SW948 cells was decreased by 53% and 45% and while as invasion was decreased by 49% and 42% respectively (P < 0.05). Finally, western blot analysis showed that miR-335 blocks the Raf/MEK/ERK signalling pathway in HT-29 colon cancer cells. The results of in vivo study showed that miR-335 also exhibits tumor-suppressive effects on xenografted tumors. Taken together, it is concluded that miR335 acts as tumor-suppressor in colon cancer and may exhibit therapeutic implications in its treatment.
Utilization of inland saline water for Spirulina cultivation

An attempt was made to assess the suitability of the inland saline water of Rohtak (Haryana) for mass cultivation of Spirulina platensis , a salt loving cyanobacterium. Cultivation of S. platensis was performed in indoor and outdoor culture units. The investigation revealed that the yield of biomass in de-calcified inland saline water was comparable to the yield obtained in synthetic chemical-based prescribed growth medium. Further, the quality of biomass in terms of protein, chlorophyll a , carotenoids and phycocyanin contents was also comparable to the prescribed medium. The downstream processing of the biomass through a three-step process resulted in an appreciable quantity of a highly valuable pigment, phycocyanin (purity ratio: A 620/ A 280 = 3.13–3.39). The overall observations of the study suggest that inland saline water can be used for cost-effective production of Spirulina biomass and value-added chemicals. The removal of calcium and salts from inland saline water by S. platensis cultivation also offers an added advantage for the reuse of the spent medium for agricultural and aquacultural purposes.
FERTILIZING ABILITY OF HO COCK SEMEN BY DIFFERENT INSEMINATION DOSES AND DILUTON

New castle disease (ND) is one of devastating poultry disease. The causative agent of ND is avian paramyxovirus (APMV) type 1, virulent strain. Although there are many ND vaccines available in the market, ND case still be found as a common case in poultry industry. Many factors can be influence the disease outbreak, one of them is virus strain circulated in the field. If the virus could not be neutralized by vaccination induced antibody then the outbreaks will be occured. Objective of this study was to determine variation of gene encoding fusion (F) protein especially the amino acid motif at F-gene cleavage site. The amino acid motif at F-gene cleavage site play important role in the pathogenicity of virus. Additionally, this study also would like to determine the genotype of the ND virus currently circulate in the field. Polymerase chain reaction and sequencing technique were applied to get the F-gene sequences based on the samples collected from chicken farms in the province of Bali during the year 2014, then they were analyzed and phylogenetic tree was constructed by MEGA 7. The amino acids motif at the F-gene cleavage sites for three isolates of D5, B1 and G1 are R-R-Q-K-R-F, which is categorized as typical characteristic of virulent virus strain. Meanwhile, two isolates i.e T1 and K1 got from layer chicken had the amino acid sequence of G-R-Q-G-R-L, which is grouped into avirulent virus strains. It was concluded that APMV-1 isolates found in ND cases in the year of 2014 were caused by virulent isolate and were grouped into VII-genotype
We’re All Teachers Now: Remote Learning During COVID-19

ABSTRACT This study examined educational experiences of families under COVID and their schooling decisions in the 2021 school year. Results from a survey of 1743 parents indicate most schools provided educational resources ranging from hardcopy packets to live online instruction. Parents were generally positive about the experience. Parents in private and charter schools reported a more positive experience than those in traditional public schools. Only a small percentage of respondents said they were going to homeschool in fall 2020, but more than a third planned to send their child to a virtual school out of concern about their child’s health.
The perceptions and attitudes of undergraduate students in Obafemi Awolowo University towards Alternative Sexual Relationships (ASRs)

Abstract The paper examines the perceptions and attitudes of undergraduate students in Obafemi Awolowo University (OAU) towards alternative sexual relationships (ASRs). In most parts of Africa, ASRs are largely frowned against, as most countries on the continent have illegalised the practice. While literature abounds generally on the acceptance or otherwise of ASRs, studies x-raying this subject matter from the perspective of an enthusiastic and explorative youth population among undergraduate students in Nigeria are scanty. The paper adopts descriptive research design and mixed (quantitative and qualitative) method. Structured questionnaire and semi-structured interviews were used to collect data. Data were analysed using inferential and descriptive statistical tools of the Statistical Package for Social Sciences (SPSS). Findings show that majority of undergraduate students in OAU (70.4%) hold negative perception of ASRs; and 55.2% of the students hold negative attitude towards ASRs. On gender basis, while more male students (56%) show negative attitude towards ASRs, a lower percentage (54.37%) of female students shows negative attitude towards the practice. The paper also finds that background characteristics (age, gender, religion and level of study) of students do not influence their perceptions and attitudes towards ASRs. The paper concludes that OAU students condemn and do not condone ASRs.