Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining neurophysiology and computational modeling through VGG19

During the last years neurophysiological ﬁndings and computational modeling have beneﬁted from one another: new discoveries about the brain’s mechanisms inspired some powerful models and new advances about these same models allowed to gather more insights on how neural circuits might work. In particular, the sense of vision has been largely studied in both domains. Crucial regions for object recognition in the brain have been identiﬁed and deep convolutional neural networks have achieved amazing performance in classifying images in computer vision tasks. Current computational models of visual processing focus on the bottom-up cascade of sequential operations from the retina onto primary visual cortex and higher-level processing. However, feed-back in the brain is abundant and its role is crucial, especially for goal-directed behaviors. Here, we investigated the extent to which deep convolutional neural networks can account for the variance of intracranial neural data recorded in subjects while presented with images. Moreover, we sought to understand how task goals modulate the responses along the ventral visual pathway through the development of computational models that incorporate top-down feed-back signaling carrying task-speciﬁc information. We show here that feature maps extracted from VGG19, a feed-forward deep convolutional neural network, are capable of predicting neural data with satisfying results in two diﬀerent human data-sets. Additionally, we suggest a simple yet eﬀective way to incorporate top-down modulation propagating through subsequent layers in VGG19. The top-down modulation is proven eﬀective both for target-modulation and for task-modulation, the latter being here characterized in a new unpublished data-set. The versatility of our approach shows the potential of this implementation method to be applied to a wider range of top-down signals, hence making artiﬁcial neural networks more similar to biological neural circuits.
COVID-19 vaccine perceptions: An observational study on Reddit

Objectives: As COVID-19 vaccinations accelerate in many countries, narratives skeptical of vaccination have also spread through social media. Open online forums like Reddit provide an opportunity to quantitatively examine COVID-19 vaccine perceptions over time. We examine COVID-19 misinformation on Reddit following vaccine scientific announcements. Methods: We collected all posts on Reddit from January 1 2020 - December 14 2020 (n=266,840) that contained both COVID-19 and vaccine-related keywords. We used topic modeling to understand changes in word prevalence within topics after the release of vaccine trial data. Social network analysis was also conducted to determine the relationship between Reddit communities (subreddits) that shared COVID-19 vaccine posts, and the movement of posts between subreddits. Results: There was an association between a Pfizer press release reporting 90% efficacy and increased discussion on vaccine misinformation. We observed an association between Johnson and Johnson temporarily halting its vaccine trials and reduced misinformation. We found that information skeptical of vaccination was first posted in a subreddit (r/Coronavirus) which favored accurate information and then reposted in subreddits associated with antivaccine beliefs and conspiracy theories (e.g. conspiracy, LockdownSkepticism). Conclusions: Our findings can inform the development of interventions where individuals determine the accuracy of vaccine information, and communications campaigns to improve COVID-19 vaccine perceptions. Such efforts can increase individual- and population-level awareness of accurate and scientifically sound information regarding vaccines and thereby improve attitudes about vaccines. Further research is needed to understand how social media can contribute to COVID-19 vaccination services.
Survey of Behaviour Attitudes Towards Preventive Measures Following COVID-19 Vaccination

Following the widespread vaccination program for COVID-19 carried out in Israel, a survey was conducted to preliminarily assess behavior changes in the vaccinated population, prior to the expected upcoming policy change as to mask wearing and social distancing regulation in Israel. 200 people answered at least one question pertaining to preventive behaviour. Among the respondents, 21.1% reported a decrease in mask wearing compared to 47.3% who reported a decrease in social distancing. There was no difference in these measures between the sexes. However, people under the age of 50 were more likely to decrease mask wearing (28.1%) and decrease social distancing (56.1%), as compared with people over the age of 50 (17.2% and 41.8%, respectively). Among health care workers, there was a minimal decrease in mask wearing (1/23 people) compared to a more widespread decrease in social distancing (10/23). These data suggest that preventive attitudes change following COVID-19 vaccination, with less adherence to social distancing as compared to mask wearing, and should be taken into account when planning public policy in the future.
Challenges faced by pre-service chemistry teachers teaching in a laboratory and their solution proposals

Abstract This study examines the challenges faced by pre-service chemistry teachers teaching in a laboratory and their proposed solutions to these challenges. The study was conducted in a 13-week-long methodology course. Written statements from participants and observations from researchers were used as data sources. From these data, it was found that the participants faced challenges related to content knowledge and connections, laboratory and experiments, students, planning and implementing instruction and general teacher qualifications. In terms of solutions, various ideas were proposed, such as improved development of content knowledge, pre-lesson preparation and the acquisition of professional experience. The challenges expressed by participants differed from those observed by the researchers and increased during the length of the course. In addition, their proposed solutions were not sufficient, coherent or explicit.
Moringa Oleifera: a natural coagulant

Moringa Oleifera, a natural polyelectrolyte, can be used as primary coagulant. As coagulant it is non-toxic and biodegradable, also environment-friendly unlike alum. Comparative studies suggest that Moringa Oleifera can be used for water treatment in shelled and non-shelled forms. The optimum dosage of shelled Moringa Oleifera seeds was almost the same, while in case of non-shelled seed, dosage was greater for low initial turbidity waters.
Long ‐ memory traces in PM 10 time series in Athens , Greece : investigation through DFA and R / S analysis

This paper investigates the existence of chaos in concentration dynamics of particulate matter with an aerodynamic diameter less than or equal to 10 μm ( PM 10 ) in the greater Athens area (GAA), Greece. It reports findings on three 16-year PM 10 time series recorded by three different air pollution monitoring stations located in GAA and examines if critical fractal epochs with long memory exist. Detrended Fluctuation Analysis (DFA) and Rescaled Range (R/S) Analysis were used via sliding windows of approximately 1-month duration. In all PM 10 time series, several segments were found with critical fractal behaviour and hidden long-memory patterns. All these segments exhibited Hurst exponents above 0.75 and DFA exponents above 1.75. Twelve PM 10 segments with fractality and long memory were commonly identified by both techniques. In one case, long memory was identified concurrently across all three air pollution monitoring stations and in another case, across two stations. The importance of the agreement between two different and independent chaos-analysis techniques is discussed in association with the proper selection of threshold values. This is the second time to address chaos in PM 10 data series in GAA, and the first time to combine two widely accepted techniques, DFA and R/S analysis.
Learning to Synthesize Data for Semantic Parsing

Synthesizing data for semantic parsing has gained increasing attention recently. However, most methods require handcrafted (high-precision) rules in their generative process, hindering the exploration of diverse unseen data. In this work, we propose a generative model which features a (non-neural) PCFG that models the composition of programs (e.g., SQL), and a BART-based translation model that maps a program to an utterance. Due to the simplicity of PCFG and pre-trained BART, our generative model can be efficiently learned from existing data at hand. Moreover, explicitly modeling compositions using PCFG leads to better exploration of unseen programs, thus generate more diverse data. We evaluate our method in both in-domain and out-of-domain settings of text-to-SQL parsing on the standard benchmarks of GeoQuery and Spider, respectively. Our empirical results show that the synthesized data generated from our model can substantially help a semantic parser achieve better compositional and domain generalization.
Spin dynamics of the director state in frustrated hyperkagome systems

We present an experimental study of the magnetic structure and dynamics of two frustrated hyperkagome compounds, Gd3Ga5O12 and Gd3Al5O12. It has previously been shown that Gd3Ga5O12 exhibits long-range correlations of multipolar directors, that are formed from antiferromagnetic spins on loops of ten ions. Using neutron diffraction and Reverse Monte Carlo simulations we prove the existence of similar magnetic correlations in Gd3Al5O12, showing the ubiquity of these complex structures in frustrated hyperkagome materials. Using inelastic neutron scattering we shed further light on the director state and the associated low lying magnetic excitations. In addition we have measured quasielastic dynamics that show evidence of spin diffusion. Finally, we present AC susceptibility measurements on both Gd3Ga5O12 and Gd3Al5O12, revealing a large difference in the low frequency dynamics between the two otherwise similar compounds.
A Maximum Principle Approach to a Deterministic Mean Field Game of Control with Absorption

We study a deterministic mean ﬁeld game on ﬁnite and inﬁnite time horizons arising in models of optimal exploitation of exhaustible resources. The main characteristic of our game is an absorption constraint on the players’ state process. As a result of the state constraint the optimal time of absorption becomes part of the equilibrium. Using Pontyagin’s maximum principle, we prove the existence and uniqueness of equilibria and solve the inﬁnite horizon models in closed form. As players may drop out of the game over time, equilibrium production rates need not be monotone nor smooth.
Count network autoregression

We consider network autoregressive models for count data with a non‐random neighborhood structure. The main methodological contribution is the development of conditions that guarantee stability and valid statistical inference for such models. We consider both cases of fixed and increasing network dimension and we show that quasi‐likelihood inference provides consistent and asymptotically normally distributed estimators. The article is complemented by simulation results and a data example.
The symbiotic recurrent nova V3890 Sgr: binary parameters and pre-outburst activity

We present and analyze optical photometry and high resolution SALT spectra of the symbiotic recurrent nova V3890 Sgr at quiescence. The orbital period, P = 747.6 days has been derived from both photometric and spectroscopic data. Our double-line spectroscopic orbits indicate that the mass ratio is q = Mg/MWD = 0.78± 0.05, and that the component masses are MWD ≈ 1.35±0.13M⊙ and Mg ≈ 1.05±0.11M⊙. The orbit inclination is ≈ 67−69 . The red giant is filling (or nearly filling) its Roche lobe, and the distance set by its Roche lobe radius, d ≈ 9 kpc, is consistent with that resulting from the giant pulsation period. The outburst magnitude of V3890 Sgr is then very similar to those of RNe in the Large Magellanic Cloud. V3890 Sgr shows remarkable photometric and spectroscopic activity between the nova eruptions with timescales similar to those observed in the symbiotic recurrent novae T CrB and RS Oph and Z And-type symbiotic systems. The active source has a double-temperature structure which we have associated with the presence of an accretion disc. The activity would be then caused by changes in the accretion rate. We also provide evidence that V3890 Sgr contains a CO WD accreting at a high, ∼ a few×10–10M⊙ yr , rate. The WD is growing in mass, and should give rise to a Type Ia supernova within < ∼ 10 yrs the expected lifetime of
Hard X-ray emission from a Compton scattering corona in large black hole mass tidal disruption events

We extend the relativistic time-dependent thin-disc TDE model to describe nonthermal (2−10 keV) X-ray emission produced by the Compton up-scattering of thermal disc photons by a compact electron corona, developing analytical and numerical models of the evolving nonthermal X-ray light curves. In the simplest cases, these X-ray light curves follow power-law profiles in time. We suggest that TDE discs act in many respects as scaled-up versions of XRB discs, and that such discs should undergo state transitions into harder accretion states. XRB state transitions typically occur when the disc luminosity becomes roughly one percent of its Eddington value. We show that if the same is true for TDE discs then this, in turn, implies that TDEs with nonthermal X-ray spectra should come preferentially from large-mass black holes. The characteristic hard-state transition mass is MHS ' 2 × 10M . Hence, subpopulations of thermal and nonthermal X-ray TDEs should come from systematically different black hole masses. We demonstrate that the known populations of thermal and nonthermal X-ray TDEs do indeed come from different distributions of black hole masses. The null-hypothesis of identical black hole mass distributions is rejected by a two-sample Anderson-Darling test with a p-value < 0.01. Finally, we present a model for the X-ray rebrightening of TDEs at late times as they transition into the hard state. These models of evolving TDE light curves are the first to join both thermal and nonthermal X-ray components in a unified scenario.
Detoxifying Language Models Risks Marginalizing Minority Voices

Language models (LMs) must be both safe and equitable to be responsibly deployed in practice. With safety in mind, numerous detoxification techniques (e.g., Dathathri et al. 2020; Krause et al. 2020) have been proposed to mitigate toxic LM generations. In this work, we show that these detoxification techniques hurt equity: they decrease the utility of LMs on language used by marginalized groups (e.g., African-American English and minority identity mentions). In particular, we perform automatic and human evaluations of text generation quality when LMs are conditioned on inputs with different dialects and group identifiers. We find that detoxification makes LMs more brittle to distribution shift, especially on language used by marginalized groups. We identify that these failures stem from detoxification methods exploiting spurious correlations in toxicity datasets. Overall, our results highlight the tension between the controllability and distributional robustness of LMs.
Transformer-based Methods for Recognizing Ultra Fine-grained Entities (RUFES)

This paper summarizes the participation of the Laboratoire Informatique, Image et Interaction (L3i laboratory) of the University of La Rochelle in the Recognizing Ultra Fine-grained Entities (RUFES) track within the Text Analysis Conference (TAC) series of evaluation workshops. Our participation relies on two neural-based models, one based on a pre-trained and fine-tuned language model with a stack of Transformer layers for fine-grained entity extraction and one out-of-the-box model for within-document entity coreference. We observe that our approach has great potential in increasing the performance of fine-grained entity recognition. Thus, the future work envisioned is to enhance the ability of the models following additional experiments and a deeper analysis of the results.
Few-shot Intent Classification and Slot Filling with Retrieved Examples

Few-shot learning arises in important practical scenarios, such as when a natural language understanding system needs to learn new semantic labels for an emerging, resource-scarce domain. In this paper, we explore retrieval-based methods for intent classification and slot filling tasks in few-shot settings. Retrieval-based methods make predictions based on labeled examples in the retrieval index that are similar to the input, and thus can adapt to new domains simply by changing the index without having to retrain the model. However, it is non-trivial to apply such methods on tasks with a complex label space like slot filling. To this end, we propose a span-level retrieval method that learns similar contextualized representations for spans with the same label via a novel batch-softmax objective. At inference time, we use the labels of the retrieved spans to construct the final structure with the highest aggregated score. Our method outperforms previous systems in various few-shot settings on the CLINC and SNIPS benchmarks.
Restoring and Mining the Records of the Joseon Dynasty via Neural Language Modeling and Machine Translation

Understanding voluminous historical records provides clues on the past in various aspects, such as social and political issues and even natural science facts. However, it is generally difficult to fully utilize the historical records, since most of the documents are not written in a modern language and part of the contents are damaged over time. As a result, restoring the damaged or unrecognizable parts as well as translating the records into modern languages are crucial tasks. In response, we present a multi-task learning approach to restore and translate historical documents based on a self-attention mechanism, specifically utilizing two Korean historical records, ones of the most voluminous historical records in the world. Experimental results show that our approach significantly improves the accuracy of the translation task than baselines without multi-task learning. In addition, we present an in-depth exploratory analysis on our translated results via topic modeling, uncovering several significant historical events.
Cobbler Stick With Your Reads: People's Perceptions of Gendered Robots Performing Gender Stereotypical Tasks

Previous research found that robots should best be designed to fit their given task, whilst others identified gender effects in people's evaluations of robots. This study combines this knowledge to investigate stereotyping effects of robot genderedness and assigned tasks in an online experiment (n = 89) manipulating robot gender (male vs. female) and task type (analytical vs. social) in a between subject's design in terms of trust, social perception, and humanness. People deem robots more competent and have higher trust in their capacity when they perform analytical tasks compared to social tasks, independent of the robot's gender. Furthermore, we observed a trend in the data indicating that people seem to dehumanize female robots (regardless of task performed) to animals lacking higher-level mental processes, and additionally that people seem to dehumanize robots to emotionless objects only when gendered robots perform tasks contradicting the stereotypes of their gender.
Optimality Conditions under Policy-Dependent and Policy-Independent Static Reductions of Stochastic Dynamic Teams and Games-Part I: Dynamic Teams

Static reduction of dynamic stochastic team (or decentralized stochastic control) problems has been an effective method for establishing existence and approximation results for optimal policies. In this Part I of a two-part paper, we address stochastic dynamic teams. Part II addresses stochastic dynamic games. We consider two distinct types of static reductions: (i) those that are policy-independent (as those introduced by Witsenhausen), and (ii) those that are policy-dependent (as those introduced by Ho and Chu for partially nested dynamic teams). For the first type, we show that there is a bijection between stationary (person-by-person optimal, globally optimal) policies and their policy-independent static reductions, and then we present a variational analysis for convex dynamic teams under policy-independent static reductions. For the second type, although there is a bijection between globally optimal policies of dynamic teams with partially nested information structures and their static reductions, in general there is no bijection between stationary (person-by-person optimal) policies of dynamic teams and their policy-dependent static reductions. We present, however, sufficient conditions under which such a relationship holds and a functional form of the bijection can be constructed. Furthermore, we study the connections between optimality and stationarity concepts when a control-sharing information structure is considered (via an expansion of the information structures) under policy-dependent static reductions. An implication is a convexity characterization of dynamic team problems under policy-dependent static reduction with a control-sharing information structure. Finally, we study multi-stage team problems, where we discuss the connections between optimality concepts through times and players under the introduced static reductions.
Subaru High-z Exploration of Low-luminosity Quasars (SHELLQs). XIII. Large-scale Feedback and Star Formation in a Low-luminosity Quasar at z = 7.07 on the Local Black Hole to Host Mass Relation

We present Atacama Large Millimeter/submillimeter Array [C ii] 158 μm line and underlying far-infrared (FIR) continuum emission observations (0.″70 × 0.″56 resolution) toward HSC J124353.93+010038.5 (J1243+0100) at z = 7.07, the only low-luminosity (M 1450 > −25 mag) quasar currently known at z > 7. The FIR continuum is bright (1.52 mJy) and resolved with a total luminosity of L FIR = 3.5 × 1012 L ⊙. The spatially extended component is responsible for ∼40% of the emission. The area-integrated [C ii] spectrum shows a broad wing (FWHM = 997 km s−1, L [C ii] = 1.2 × 109 L ⊙), as well as a bright core (FWHM = 235 km s−1, L [C ii] = 1.9 × 109 L ⊙). This wing is the first detection of a galactic-scale quasar-driven outflow (atomic outflow rate >447 M ⊙ yr−1) at z > 7. The estimated large mass-loading factor of the total outflow (e.g., ≳9 relative to the [C ii]-based star formation rate) suggests that this outflow will soon quench the star formation of the host. The core gas dynamics are governed by rotation, with a rotation curve suggestive of a compact bulge (∼3.3 × 1010 M ⊙), although it is not yet spatially resolved. Finally, we found that J1243+0100 has a black hole mass–to–dynamical mass (and –to–bulge mass) ratio of ∼0.4% (∼1%), consistent with the local value within the uncertainties. Our results therefore suggest that the black hole–host coevolution relation is already in place at z ∼ 7 for this object.
Practical Defences Against Model Inversion Attacks for Split Neural Networks

We describe a threat model under which a split network-based federated learning system is susceptible to a model inversion attack by a malicious computational server. We demonstrate that the attack can be successfully performed with limited knowledge of the data distribution by the attacker. We propose a simple additive noise method to defend against model inversion, finding that the method can significantly reduce attack efficacy at an acceptable accuracy trade-off on MNIST. Furthermore, we show that NoPeekNN, an existing defensive method, protects different information from exposure, suggesting that a combined defence is necessary to fully protect private user data.