Miracle-dz/UniFAR-FTUs huggingface dataset:
Striatal Dopamine Can Enhance Learning, Both Fast and Slow, and Also Make it Cheaper

Associations can be learned incrementally, via reinforcement learning (RL), or stored instantly in working memory (WM). While WM is fast, it is also capacity-limited and effortful. Striatal dopamine may promote RL plasticity, and WM, by facilitating updating and effort exertion. Yet, prior studies have failed to distinguish between dopamine’s effects on RL versus WM. N = 100 participants completed a paradigm isolating these systems in a double-blind study measuring dopamine synthesis with [18F]-FDOPA imaging and manipulating dopamine with methylphenidate and sulpiride. Learning is enhanced among high synthesis capacity individuals and by methylphenidate, but impaired by sulpiride. Methylphenidate also blunts effort cost learning. Computational modeling reveals that individuals with high dopamine synthesis rely more on WM, while methylphenidate boosts their RL rates. The D2 antagonist sulpiride reduces accuracy due to diminished WM involvement and faster WM decay. We conclude that dopamine enhances both slow RL, and fast WM, by promoting plasticity and reducing effort sensitivity. These results also highlight the need to control for dopamine’s effects on WM when testing its effects on RL.
The interplay between electron transport chain function and iron regulatory factors influences melanin formation in Cryptococcus neoformans

Mitochondrial functions are critical for the ability of the fungal pathogen Cryptococcus neoformans to cause disease. However, mechanistic connections between key functions such as the mitochondrial electron transport chain (ETC) and virulence factor elaboration have yet to be thoroughly characterized. Here, we observed that inhibition of ETC complex III suppressed melanin formation, a major virulence factor. This inhibition was partially blocked upon loss of Cir1 or HapX, two transcription factors that regulate iron acquisition and use. In this regard, loss of Cir1 derepresses the expression of laccase genes as a potential mechanism to restore melanin, while HapX may condition melanin formation by controlling oxidative stress. We hypothesize that ETC dysfunction alters redox homeostasis to influence melanin formation. Consistent with this idea, inhibition of growth by hydrogen peroxide was exacerbated in the presence of the melanin substrate L-DOPA. Additionally, loss of the mitochondrial chaperone Mrj1, which influences the activity of ETC complex III and reduces ROS accumulation, also partially blocked antimycin A inhibition of melanin. The phenotypic impact of mitochondrial dysfunction was consistent with RNA-Seq analyses of WT cells treated with antimycin A or L-DOPA, or cells lacking Cir1 that revealed influences on transcripts encoding mitochondrial functions (e.g., ETC components and proteins for Fe-S cluster assembly). Overall, these findings reveal mitochondria-nuclear communication via ROS and iron regulators to control virulence factor production in C. neoformans. IMPORTANCE There is a growing appreciation of the importance of mitochondrial functions and iron homeostasis in the ability of fungal pathogens to sense the vertebrate host environment and cause disease. Many mitochondrial functions such as heme and iron-sulfur cluster biosynthesis, and the electron transport chain (ETC), are dependent on iron. Connections between factors that regulate iron homeostasis and mitochondrial activities are known in model yeasts and are emerging for fungal pathogens. In this study, we identified connections between iron regulatory transcription factors (e.g., Cir1 and HapX) and the activity of complex III of the ETC that influence the formation of melanin, a key virulence factor in the pathogenic fungus Cryptococcus neoformans. This fungus causes meningoencephalitis in immunocompromised people and is a major threat to the HIV/AIDS population. Thus, understanding how mitochondrial functions influence virulence may support new therapeutic approaches to combat diseases caused by C. neoformans and other fungi.
Creating from Noise: Trace Generations Using Diffusion Model for Side-Channel Attack

In side-channel analysis (SCA), the success of an attack is largely dependent on the dataset sizes and the number of instances in each class. The generation of synthetic traces can help to improve attacks like profiling attacks. However, manually creating synthetic traces from actual traces is arduous. Therefore, automating this process of creating artificial traces is much needed. Recently, diffusion models have gained much recognition after beating another generative model known as Generative Adversarial Networks (GANs) in creating realistic images. We explore the usage of diffusion models in the domain of SCA. We proposed frameworks for a known mask setting and unknown mask setting in which the diffusion models could be applied. Under a known mask setting, we show that the traces generated under the proposed framework preserved the original leakage. Next, we demonstrated that the artificially created profiling data in the unknown mask setting can reduce the required attack traces for a profiling attack. This suggests that the artificially created profiling data from the trained diffusion model contains useful leakages to be exploited.
Constrained Pseudorandom Functions for Inner-Product Predicates from Weaker Assumptions

In this paper, we build a framework for constructing Constrained Pseudorandom Functions (CPRFs) with inner-product constraint predicates, using ideas from subtractive secret sharing and related-key-attack security. Our framework can be instantiated using a random oracle or any suitable Related-Key-Attack (RKA) secure pseudorandom function. We provide three instantiations of our framework: 1. an adaptively-secure construction in the random oracle model; 2. a selectively-secure construction under the DDH assumption; and 3. a selectively-secure construction under the assumption that one-way functions exist. All three instantiations are constraint-hiding and support inner-product predicates, leading to the first constructions of such expressive CPRFs under each corresponding assumption. More-over, while the OWF-based construction is primarily of theoretical interest, the random oracle and DDH-based constructions are concretely efficient, which we show via an implementation.
Visualizing students flows to monitor persistence

Founded in 2013 , OpenClassrooms is a French online learning company that offers both paid courses and free MOOCs on a wide range of topics, including computer science and education. In 2021 , in partnership with the EDA research unit, OpenClassrooms shared a database to solve the problem of how to increase persistence in their paid courses, which consist of a series of MOOCs and human mentoring. Our statistical analysis aims to identify reasons for dropouts that are due to the course design rather than demographic predictors or external factors. We aim to identify at-risk students, i.e. those who are on the verge of dropping out at a specific moment. To achieve this, we use learning analytics to characterize student behavior. We conducted data analysis on a sample of data related to the “Web Designers” and “Instructional Design” courses. By visualizing the student flow and constructing speed and acceleration predictors, we can identify which parts of the course need to be calibrated and when particular attention should be paid to these at-risk students.
Constructing Committing and Leakage-Resilient Authenticated Encryption

The main goal of this work is to construct authenticated encryption (AE) hat is both committing and leakage-resilient. As a first approach for this we consider generic composition as a well-known method for constructing AE schemes. While the leakage resilience of generic composition schemes has already been analyzed by Barwell et al. (Asiacrypt’17), for committing security this is not the case. We fill this gap by providing a separate analysis of the generic composition paradigms with respect to committing security, giving both positive and negative results: By means of a concrete attack, we show that Encrypt-then-MAC is not committing. Furthermore, we prove that Encrypt-and-MAC is committing, given that the underlying schemes satisfy security notions we introduce for this purpose. We later prove these new notions achievable by providing schemes that satisfy them. MAC-then-Encrypt turns out to be more difficult due to the fact that the tag is not outputted alongside the ciphertext as it is done for the other two composition methods. Nevertheless, we give a detailed heuristic analysis of MAC-then-Encrypt with respect to committing security, leaving a definite result as an open task for future work. Our results, in combination with the fact that only Encrypt-then-MAC yields leakage-resilient AE schemes, show that one cannot obtain AE schemes that are both committing and leakage-resilient via generic composition. As a second approach for constructing committing and leakage-resilient AE, we develop a generic transformation that turns an arbitrary AE scheme into one that fulfills both properties. The transformation relies on a keyed function that is both binding, i.e., it is hard to find key-input pairs that result in the same output, and leakage-resilient pseudorandom.
Stage Detection of Mild Cognitive Impairment: Region-dependent Graph Representation Learning on Brain Morphable Meshes

Mild cognitive impairment (MCI), as a transitional state between normal cognition and Alzheimer’s disease (AD), is crucial for taking preventive interventions in order to slow down AD progression. Given the high relevance of brain atrophy and the neurodegeneration process of AD, we propose a novel mesh-based pooling module, RegionPool , to investigate the morphological changes in brain shape regionally. We then present a geometric deep learning framework with the RegionPool and graph attention convolutions, to perform binary classification on MCI subtypes (EMCI/LMCI). Our model does not require feature engineering and relies only on the relevant geometric information of T1-weighted magnetic resonance imaging (MRI) signals. Our evaluation reveals the state-of-the-art classification capabilities of our network and shows that current empirically derived MCI subtypes cannot identify heterogeneous patterns of cortical atrophy at the MCI stage. The class activation maps (CAMs) generated from the correct predictions provide additional visual evidence for our model’s decisions and are consistent with the atrophy patterns reported by the relevant literature.
Cretaceous tectonostratigraphy of the Faroe–Shetland region

This study presents an appraisal of the Cretaceous tectonostratigraphical development of the Faroe–Shetland region. It combines details of the rock record with seismic stratigraphical information, and the resulting stratigraphic framework provides constraints on the timing and nature of sedimentary basin development in the Faroe–Shetland region, with implications for the Late Mesozoic development of the NE Atlantic Rift Zone. The division of the Cretaceous succession into two megasequences (K1 and K2) provides a first-order analysis of basin development. The components of the K1 megasequence represent the rift initiation and early syn-rift phases that span the late Berriasian–Turonian, whereas the K2 megasequence represents the rift climax incorporating basin enlargement and increased subsidence during the Coniacian–Maastrichtian. A higher resolution (second- to third-order) analysis of the component depositional packages highlights a sedimentary succession that is punctuated by episodes of uplift, erosion and contractional deformation. This pattern of coeval extension and compression is consistent with intra-plate strike-slip tectonic activity linked to the development of the NE Atlantic Rift Zone, and modulated by plate boundary processes, including Atlantic spreading and Alpine orogenic activity. There is no evidence for a substantive through-going marine connection in the Faroe–Shetland region until the Late Cretaceous.
Use of ESI-FTICR-MS to Characterize Dissolved Organic Matter in Use of ESI-FTICR-MS to Characterize Dissolved Organic Matter in Headwater Streams Draining Forest-Dominated and Pasture-Headwater Streams Draining Forest-Dominated and Pasture-Dominated Watersheds Dominated Watersheds

Electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI-FTICR-MS) has proven to be a powerful technique revealing complexity and diversity of nat-ural DOM molecules, but its application to DOM analysis in grazing-impacted agricultural systems remains scarce. In the present study, we presented a case study of using ESI-FTICR-MS in analyzing DOM from four headwater streams draining forest-or pasture-domi-nated watersheds in Virginia, USA. In all samples, most formulas were CHO compounds (71.8 – 87.9%), with other molecular series (CHOS, CHON, CHONS, and CHOP (N, S)) accounting for only minor fractions. All samples were dominated by molecules falling in the lignin-like region (H/C = 0.7 – 1.5, O/C = 0.1 – 0.67), suggesting the predominance of allochthonous, terrestrial plant-derived DOM. Relative to the two pasture streams, DOM formulas in the two forest streams were more similar, based on Jaccard similarity coefficients and nonmetric multidimensional scaling calculated from Bray-Curtis distance. Formulas from the pasture streams were characterized by lower proportions of aromatic formulas and lower unsaturation, suggesting
Bovine and Ovine Teeth as a Substitute for the Human Teeth: An Experimental Study

Statement of the Problem: Although various kinds of research have been conducted to compare the physical and chemical properties of dentin and enamel in animal and human samples, proving the ability of animal dentin material as a good substitute for human specimens is always a challenge for experimental studies. Purpose: The aim of the present study is to investigate whether the changes in the dentin microhardness of animal samples are similar to those of human samples or not. Materials and Method: In this in vitro study, sixty single-rooted human, bovine, and ovine teeth (n=20 in each group) were decoronated at CEJ. The remaining roots were embedded in acrylic resin and a cross-section cut was made in the middle of the samples in order to achieve dentin disks. All of the 120 samples were randomly assigned to three control (n=20 for each group) and three experimental groups (n=20 for each group). In the experimental groups, calcium hydroxide with a creamy consistency was prepared and the disks were embedded in dishes containing calcium hydroxide. Control groups were embedded in physiological saline. The samples were incubated for seven days at the 37 o C and Vickers micro-hardness test was performed immediately. The average of three yielded values was considered as the final value of microhardness. Data were analyzed using two-way ANOVA, one-way ANOVA, and Tukey’s post hoc tests. Results: In the control group, the human samples showed the highest microhardness value, while the bovine teeth had the lowest microhardness value ( p < 0.001). In the calcium hydroxide group, the human samples showed the highest microhardness value in comparison to bovine and ovine to teeth. However, no significant difference was observed between the bovine and ovine samples in microhardness value. Conclusion: Based on our research, substituting bovine and ovine samples with human samples in experimental studies is not recommended. Nevertheless, more studies are needed in this regard.
Detection of Vancomycin Resistant Genes in Intrinsically Antibiotic Resistant Bacteria from the Gut Microbiota of Indonesian Individuals

Background: Antibiotic resistance is a global public health concern that has been exacerbated by the overuse and misuse of antibiotics, leading to the emergence of resistant bacteria. The gut microbiota, often influenced by antibiotic usage, plays a crucial role in overall health. Therefore, this study aimed to investigate the prevalence of antibiotic resistant genes in the gut microbiota of Indonesian coastal and highland populations, as well as to identify vancomycin-resistant bacteria and their resistant genes. Methods: Stool samples were collected from 22 individuals residing in Pacet, Mojokerto, and Kenjeran, Surabaya Indonesia in 2022. The read count of antibiotic resistant genes was analyzed in the collected samples, and the bacterium concentration was counted by plating on the antibiotic-containing agar plate. Vancomycin-resistant strains were further isolated, and the presence of vancomycin-resistant genes was detected using a multiplex polymerase chain reaction (PCR). Results: The antibiotic resistant genes for tetracycline, aminoglycosides, macrolides, beta-lactams, and vancomycin were found in high frequency in all stool samples (100%) of the gut microbiota. Meanwhile, those meant for chloramphenicol and sulfonamides were found in 86% and 16% of the samples, respectively. Notably, vancomycin-resistant genes were found in 16 intrinsically resistant Gram-negative bacterial strains. Among the detected vancomycin-resistant genes, vanG was the most prevalent (27.3%), while vanA was the least prevalent (4.5%). Conclusion: The presence of multiple vancomycin resistance genes in intrinsically resistant Gram-negative bacterial strains demonstrated the importance of the gut microbiota as a reservoir and hub for the horizontal transfer of antibiotic resistant genes.
Hypsometric integral for the identification of neotectonic and lithology differences in low tectonically active area (Utica-Mateur region, north-eastern Tunisia)

Abstract The present work tests the use of hypsometric integral (HI) in identifying neotectonic and lithology signals in the Utica-Mateur basin (north-eastern Tunisia), a region recognised by low deformation rates. We computed HI values using three digital elevation models (DEMs) of 10, 30 and 90 m pixel resolution. We extracted maximum, minimum and mean elevations from each DEM. The obtained results reveal that the HI spatial distributions did not demonstrate clear spatial patterns and did not correlate with geological substrates. We also find that the distribution of HI is independent of the DEM resolution, but are influenced by the size of the moving window. By applying the hot spot analysis (-statistics) on extracted HI data, we find two main clusters with high (hot spots) and low (cold spots) HI values. These hot spots correspond mainly to active faults and coincide with shallow earthquake clusters in the study area.
Reconstruction of Loch Lomond Stadial (Younger Dryas) glaciers on Ben More Coigach, north‐west Scotland, and implications for reconstructing palaeoclimate using small ice masses

A significant inventory of evidence exists for Loch Lomond Stadial (LLS; ≈Younger Dryas) glaciation in Scotland, with recent work focused on satellite icefields. However, studies of more marginal settings are important for assessing the influence of topoclimatic factors on glacier functioning and, crucially, the impact of these on glacier‐derived palaeoclimatic reconstructions. We present systematic assessments of snowblow and avalanching contributions, informed by modern analogues, and test these using the first detailed palaeoglaciological reconstructions of three corrie glaciers on Ben More Coigach, north‐west Scotland. Based on morphostratigraphic principles, and lithostratigraphic evidence from the region, these have been attributed to the LLS, with the reconstructions yielding an average equilibrium‐line altitude (ELA) of 328 ± 16 m. A glacier‐derived sea‐level equivalent precipitation value of 1903 ± 178 mm a−1 is inferred for the LLS, suggesting wetter conditions than currently and contradicting assertions of a more arid LLS climate. Comparison with published palaeoprecipitation estimates indicates Ben More Coigach does not conform to expected regional precipitation gradients. We argue that these discrepancies reflect topographically enhanced snow accumulation, which lowered the ELA from the ‘true’ climatic ELA. This highlights the importance of assessing the influence of topoclimatic factors when applying small glaciers in palaeoclimatic reconstructions.
Depth-dependent study of time-reversal symmetry-breaking in the kagome superconductor $A$V$_{3}$Sb$_{5}$

The breaking of time-reversal symmetry (TRS) in the normal state of kagome superconductors $A$V$_{3}$Sb$_{5}$ stands out as a significant feature. Yet the extent to which this effect can be tuned remains uncertain, a crucial aspect to grasp in light of the varying details of TRS breaking observed through different techniques. Here, we employ the unique low-energy muon spin rotation technique combined with local field numerical analysis to study the TRS breaking response as a function of depth from the surface in single crystals of RbV$_{3}$Sb$_{5}$ with charge order and Cs(V$_{0.86}$Ta$_{0.14}$)$_{3}$Sb$_{5}$ without charge order. In the bulk (i.e.,>33 nm from the surface) of RbV$_{3}$Sb$_{5}$, we have detected a notable increase in the internal magnetic field width experienced by the muon ensemble. This increase occurs only within the charge ordered state. Intriguingly, the muon spin relaxation rate is significantly enhanced near the surface (i.e.,<33 nm from the surface) of RbV$_{3}$Sb$_{5}$, and this effect commences at temperatures significantly higher than the onset of charge order. Conversely, in Cs(V$_{0.86}$Ta$_{0.14}$)$_{3}$Sb$_{5}$, we do not observe a similar enhancement in the internal field width, neither in the bulk nor near the surface. These observations indicate a strong connection between charge order and TRS breaking on one hand, and on the other hand, suggest that TRS breaking can occur prior to long-range charge order. This research offers compelling evidence for depth-dependent magnetism in $A$V$_{3}$Sb$_{5}$ superconductors in the presence of charge order. Such findings are likely to elucidate the intricate microscopic mechanisms that underpin the TRS breaking phenomena in these materials.
Satellite Radiation Retrieval Validation in Spain: Impact of Ozone, Clouds and Aerosols

In this study, erythemal UV (UVER) and total shortwave (SW) radiation retrievals from the Ozone Monitor- ing Instrument (OMI) and the Solar Energy Mining (SOLEMI) are compared against the ground-based measure- ments under different conditions of ozone amount, aerosol load and cloud properties. The validation was carried out in 6 dif- ferent Spanish cities: A Coruna, Leon, Madrid, Murcia, Valla- dolid and Zaragoza. In general, solar radiation is overesti- mated by satellite data. The values of the mean bias error ranged between 2% and 16%. The best agreement is obtained for cloudless conditions, but there are discrepancies that can be attributed to the retrieval algorithms. The presence of clouds produces the highest difference between satellite re- trievals and ground-based measurements for UVER and SW ranges. Thick clouds can produce relative differences over 40%. Total ozone column (TOC) and aerosol load values pre- sent clear influence on UVER estimations under cloudless conditions. Changes of ozone column produced falls about 50% in the root mean square error (RMSE) for some stations; and situations under high turbidity can achieve RMSE values around 20%.
Adaptively Learning Memory Incorporating PSO

Selection of perefect parameters for low-pass filters can sometimes be an expensive problem with no analytical solution or differentiability of cost function. In this paper, we introduce a new PSO-inspired algorithm, that incorporates the positive experiences of the swarm to learn the geometry of the search space,thus obtaining the ability to consistently reach global optimum and is especially suitable for nonsmooth semiconvex functions optimization. We compare it to a set of other algorithms on test functions of choice to prove it's suitability to a certain range of problems, and then apply it to the problem of finding perfect parameters for exponential smoothing algorithm.