Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning With Differential Privacy

The leakage of data might have been an extreme effect on the personal level if it contains sensitive information. Common prevention methods like encryption-decryption, endpoint protection, intrusion detection system are prone to leakage. Differential privacy comes to the rescue with a proper promise of protection against leakage, as it uses a randomized response technique at the time of collection of the data which promises strong privacy with better utility. Differential privacy allows one to access the forest of data by describing their pattern of groups without disclosing any individual trees. The current adaption of differential privacy by leading tech companies and academia encourages authors to explore the topic in detail. The different aspects of differential privacy, it's application in privacy protection and leakage of information, a comparative discussion, on the current research approaches in this field, its utility in the real world as well as the trade-offs - will be discussed.
Devil’s Staircases in Continuous Systems with Modulated Forcing

The discrete circle map is the archetypical example of a driven periodic system, showing a complex resonance structure under a change of the forcing frequency known as the devil’s staircase. Adler’s equation can be seen as the direct continuous equivalent of the circle map, describing locking effects in periodic systems with continuous forcing. This type of locking produces a single fundamental resonance tongue without higher order resonances, and a devil’s staircase is not observed. We show that, with harmonically modulated forcing, nonlinear oscillations close to a Hopf bifurcation generically reproduce the devil’s staircase even in the continuous case. Experimental results on a semiconductor laser driven by a modulated optical signal show excellent agreement with our theoretical predictions. The locking appears as a modulation of the oscillation amplitude as well as the angular oscillation frequency. Our results show that by proper implementation of an external drive, additional regions of stable frequency locking can be introduced in systems which originally show only a single Adler-type resonance tongue.
RGB-D-E: Event Camera Calibration for Fast 6-DOF object Tracking

Augmented reality devices require multiple sensors to perform various tasks such as localization and tracking. Currently, popular cameras are mostly frame-based (e.g. RGB and Depth) which impose a high data bandwidth and power usage. With the necessity for low power and more responsive augmented reality systems, using solely frame-based sensors imposes limits to the various algorithms that needs high frequency data from the environement. As such, event-based sensors have become increasingly popular due to their low power, bandwidth and latency, as well as their very high frequency data acquisition capabilities. In this paper, we propose, for the first time, to use an event-based camera to increase the speed of 3D object tracking in 6 degrees of freedom. This application requires handling very high object speed to convey compelling AR experiences. To this end, we propose a new system which combines a recent RGB-D sensor (Kinect Azure) with an event camera (DAVIS346). We develop a deep learning approach, which combines an existing RGB-D network along with a novel event-based network in a cascade fashion, and demonstrate that our approach significantly improves the robustness of a state-of-the-art frame-based 6-DOF object tracker using our RGB-D-E pipeline. Our code and our RGB-D-E evaluation dataset are available at https://github.com/lvsn/rgbde-tracking.
Persona2vec: a flexible multi-role representations learning framework for graphs

Graph embedding techniques, which learn low-dimensional representations of a graph, are achieving state-of-the-art performance in many graph mining tasks. Most existing embedding algorithms assign a single vector to each node, implicitly assuming that a single representation is enough to capture all characteristics of the node. However, across many domains, it is common to observe pervasively overlapping community structure, where most nodes belong to multiple communities, playing different roles depending on the contexts. Here, we propose persona2vec, a graph embedding framework that efficiently learns multiple representations of nodes based on their structural contexts. Using link prediction-based evaluation, we show that our framework is significantly faster than the existing state-of-the-art model while achieving better performance.
Variational Variance: Simple and Reliable Predictive Variance Parameterization

An often overlooked sleight of hand performed with variational autoencoders (VAEs), which has proliferated the literature, is to misrepresent the posterior predictive (decoder) distribution's expectation as a sample from that distribution. Jointly modeling the mean and variance for a normal predictive distribution can result in fragile optimization where the ultimately learned parameters can be ineffective at generating realistic samples. The two most common principled methods to avoid this problem are to either fix the variance or use the single-parameter Bernoulli distribution--both have drawbacks, however. Unfortunately, the problem of jointly optimizing mean and variance networks affects not only unsupervised modeling of continuous data (a taxonomy for many VAE applications) but also regression tasks. To date, only a handful of papers have attempted to resolve these difficulties. In this article, we propose an alternative and attractively simple solution: treat predictive variance variationally. Our approach synergizes with existing VAE-specific theoretical results and, being probabilistically principled, provides access to Empirical Bayes and other such techniques that utilize the observed data to construct well-informed priors. We extend the VAMP prior, which assumes a uniform mixture, by inferring mixture proportions and assignments. This extension amplifies our ability to accurately capture heteroscedastic variance. Notably, our methods experimentally outperform existing techniques on supervised and unsupervised modeling of continuous data.
Effects of Fluctuating Moisture and Temperature Regimes on Sporulation of Beauveria bassiana on Cadavers of Rhodnius prolixus

The effects of both moisture and temperature on the sporulation of an isolate of Beauveria bassiana , highly virulent to the Chagas' disease vector, Rhodnius prolixus , were studied in the laboratory under fluctuating regimes. Quantitative assays for conidial production from funguskilled cadavers from 10-day-old tests showed that recycling was highly dependent on the duration of exposure to high relative humidity (RH). On first-instar larval cadavers, sporulation reached more than 10 5 conidia/nymph under most regimes with a favourable phase combining high humidity (97% RH) and temperatures of 20 or 25 C for at least 12 h/day. Sporulation on third- and fifth-instar nymphs required a longer period (16 h/day or more) of favourable conditions (97% RH and 25 C) per day. The intensity of the conidial production from Rhodnius cadavers was enhanced when diurnal temperatures were high (28 and 35 C). From a vector control standpoint, daily high humidity requirements appear to be a crucial constraint. The recycling.
Computing Homotopy Types of Directed Flag Complexes.

Combinatorially and stochastically defined simplicial complexes often have the homotopy type of a wedge of spheres. A prominent conjecture of Kahle quantifies this precisely for the case of random flag complexes. We explore whether such properties might extend to graphs arising from nature. We consider the brain network (as reconstructed by Varshney & al.) of the Caenorhabditis elegans nematode, an important model organism in biology. Using an iterative computational procedure based on elementary methods of algebraic topology, namely homology, simplicial collapses and coning operations, we show that its directed flag complex is homotopy equivalent to a wedge of spheres, completely determining, for the first time, the homotopy type of a flag complex corresponding to a brain network. We also consider the corresponding flag tournaplex and show that torsion can be found in the homology of its local directionality filtration. As a toy example, directed flag complexes of tournaments from McKay's collection are classified up to homotopy. Moore spaces other than spheres occur in this classification. As a tool, we prove that the fundamental group of the directed flag complex of any tournament is free by considering its cell structure.
Simple and effective localized attribute representations for zero-shot learning

Zero-shot learning (ZSL) aims to discriminate images from unseen classes by exploiting relations to seen classes via their semantic descriptions. Some recent papers have shown the importance of localized features together with fine-tuning the feature extractor to obtain discriminative and transferable features. However, these methods require complex attention or part detection modules to perform explicit localization in the visual space. In contrast, in this paper we propose localizing representations in the semantic/attribute space, with a simple but effective pipeline where localization is implicit. Focusing on attribute representations, we show that our method obtains state-of-the-art performance on CUB and SUN datasets, and also achieves competitive results on AWA2 dataset, outperforming generally more complex methods with explicit localization in the visual space. Our method can be implemented easily, which can be used as a new baseline for zero shot-learning. In addition, our localized representations are highly interpretable as attribute-specific heatmaps.
Multi-agent reinforcement learning in a realistic limit order book market simulation

Optimal order execution is widely studied by industry practitioners and academic researchers because it determines the profitability of investment decisions and high-level trading strategies, particularly those involving large volumes of orders. However, complex and unknown market dynamics pose significant challenges for the development and validation of optimal execution strategies. In this paper, we propose a model-free approach by training Reinforcement Learning (RL) agents in a realistic market simulation environment with multiple agents. First, we configure a multi-agent historical order book simulation environment for execution tasks built on an Agent-Based Interactive Discrete Event Simulation (ABIDES) [6]. Second, we formulate the problem of optimal execution in an RL setting where an intelligent agent can make order execution and placement decisions based on market microstructure trading signals in High Frequency Trading (HFT). Third, we develop and train an RL execution agent using the Double Deep Q-Learning (DDQL) algorithm in the ABIDES environment. In some scenarios, our RL agent converges towards a Time-Weighted Average Price (TWAP) strategy. Finally, we evaluate the simulation with our RL agent by comparing it with a market replay simulation using real market Limit Order Book (LOB) data.
MultiResolution Attention Extractor for Small Object Detection

Small objects are difficult to detect because of their low resolution and small size. The existing small object detection methods mainly focus on data preprocessing or narrowing the differences between large and small objects. Inspired by human vision "attention" mechanism, we exploit two feature extraction methods to mine the most useful information of small objects. Both methods are based on multiresolution feature extraction. We initially design and explore the soft attention method, but we find that its convergence speed is slow. Then we present the second method, an attention-based feature interaction method, called a MultiResolution Attention Extractor (MRAE), showing significant improvement as a generic feature extractor in small object detection. After each building block in the vanilla feature extractor, we append a small network to generate attention weights followed by a weighted-sum operation to get the final attention maps. Our attention-based feature extractor is 2.0 times the AP of the "hard" attention counterpart (plain architecture) on the COCO small object detection benchmark, proving that MRAE can capture useful location and contextual information through adaptive learning.
Effects of forest fragmentation on the physiological stress response of black howler monkeys

Habitat loss and fragmentation can create severe environmental problems for forest species, and may ultimately lead to local population extinction. One mechanism that might play a role in this process is the physiological stress response of individuals, because animals that experience chronic stress might show reduced rates of reproduction and survival. In this study, we investigated whether black howler monkeys Alouatta pigra inhabiting forest fragments experienced greater physiological stress than those in continuous forest. Faecal cortisol metabolite levels were compared between monkeys inhabiting fragmented and continuous forests in Southern Mexico. The sex of the animals was taken into consideration, while activity patterns and aggressive events were also recorded to examine differences in behaviour and sociality. A chemiluminescent enzyme immunoassay was utilized to determine faecal cortisol metabolite levels. Our results showed that cortisol metabolite levels were higher in howler monkeys from forest fragments, and that they vary through time. Patterns of locomotion also differed between forest types, as monkeys from fragments travelled at higher frequencies, while monkeys from continuous forests performed more of their movements within tree crowns. There was no difference in the frequency of feeding, resting and social interactions between fragmented and continuous forest monkeys. Higher faecal cortisol metabolite levels in fragmented areas are likely the result of suboptimal environments and could have long‐term adverse effects on monkey health, fertility and survival. We propose that faecal cortisol metabolite measurements can be a useful conservation tool for monitoring populations of long‐lived species subsisting under suboptimal conditions.
Quantum Logspace Algorithm for Powering Matrices with Bounded Norm

We give a quantum logspace algorithm for powering contraction matrices, that is, matrices with spectral norm at most~1. The algorithm gets as an input an arbitrary $n\times n$ contraction matrix $A$, and a parameter $T \leq \mathrm{poly}(n)$ and outputs the entries of $A^T$, up to (arbitrary) polynomially small additive error. The algorithm applies only unitary operators, without intermediate measurements. We show various implications and applications of this result: First, we use this algorithm to show that the class of quantum logspace algorithms with only quantum memory and with intermediate measurements is equivalent to the class of quantum logspace algorithms with only quantum memory without intermediate measurements. This shows that the deferred-measurement principle, a fundamental principle of quantum computing, applies also for quantum logspace algorithms (without classical memory). More generally, we give a quantum algorithm with space $O(S + \log T)$ that takes as an input the description of a quantum algorithm with quantum space $S$ and time $T$, with intermediate measurements (without classical memory), and simulates it unitarily with polynomially small error, without intermediate measurements. Since unitary transformations are reversible (while measurements are irreversible) an interesting aspect of this result is that it shows that any quantum logspace algorithm (without classical memory) can be simulated by a reversible quantum logspace algorithm. This proves a quantum analogue of the result of Lange, McKenzie and Tapp that deterministic logspace is equal to reversible logspace [LMT00]. Finally, we use our results to show non-trivial classical simulations of quantum logspace learning algorithms.
Variability in height growth, survival and nursery carryover effect of Betula papyrifera provenances

Eleven paper birch (Betula papyrifera Marsh.) provenances from a single geographic region in northern Idaho and four geographic regions in British Columbia (BC) were studied to determine whether morphometric variability was due to species' genetics, growing environment, or their interaction. Seedlings were grown at three nurseries in BC and Idaho and planted in a randomised single-tree interlocking block design at Skimikin (50°47′) and Red Rock (53°45′) common gardens in BC. Geographic variation in birch height growth was regulated by genetics and a complex interaction with environmental factors. Growth does not follow latitudinal, longitudinal or elevational clines although provenances at the southern garden showed better height growth compared to the northern garden. Greater percentages of seedling mortality were observed at the northern garden compared to provenances planted at the southern garden. Birch height growth was significantly influenced by nursery displacement effects up to four years after establishment. The safe range of southward seed-transfer distance may be as much as 5°, and beyond this limit a detrimental effect likely occurs. Collectively, these results suggest that a provenance best suited to one environment might not be the best for another environment. However, these findings will be useful in understanding the genecology of paper birch when establishing operational seed-transfer guidelines in BC.
Dissemination of the biocontrol agent Vairimorpha necatrix by the spined soldier bug, Podisus maculiventris

The ability of the spined soldier bug, Podisus maculiventris (Say) (Heteroptera: Pentatomidae), to disseminate infective forms of two lepidopteran pathogens, Vairimorpha necatrix (Kramer) (Microspora: Microsporidia) and Lacanobia oleracea granulovirus (LoGV) was investigated. Individual female P. maculiventris that had fed on Lacanobia oleracea L. (Lepidoptera: Noctuidae) larvae, infected with V. necatrix, excreted approximately 6 × 108V. necatrix spores during the subsequent 7 days. Excreted spores were fed to L. oleracea larvae, causing 100% mortality, indicating that the spores remained viable after passing through the gut of the predator. Podisus maculiventris that had fed on V. necatrix or LoGV‐infected larvae were allowed to defecate on the foliage of tomato plants, prior to the infestation of the plants with L. oleracea or Spodoptera littoralis (Boisduval) (Lepidoptera: Noctuidae) larvae. This proved to be an effective way of infecting the pest larvae with the pathogens, particularly when five predatory bugs were used per plant. After 20 days, the number of S. littoralis and L. oleracea surviving on the plants was reduced by 75% and 61%, respectively. Female P. maculiventris maintained on V. necatrix‐infected prey showed reduced egg production and longevity, whilst those fed on LoGV‐infected prey showed only reduced egg production. The potential for P. maculiventris to disseminate insect pathogens is discussed in the context of improved biological control of lepidopteran pests.
TNFα and oxLDL Reduce Protein S-Nitrosylation in Endothelial Cells*

Nitric oxide (NO) plays an important role in the regulation of the functional integrity of the endothelium. The intracellular reaction of NO with reactive cysteine groups leads to the formation of S-nitrosothiols. To investigate the regulation of S-nitrosothiols in endothelial cells, we first analyzed the composition of the S-nitrosylated molecules in endothelial cells. Gel filtration revealed that more than 95% of the detected S-nitrosothiols had a molecular mass of more than 5000 Da. Moreover, inhibition of de novosynthesis of glutathione using N-butyl-sulfoximine did not diminish the overall cellular S-NO content suggesting that S-nitrosylated glutathione quantitatively plays only a minor role in endothelial cells. Having demonstrated that most of the S-nitrosothiols are proteins, we determined the regulation of the S-nitrosylation by pro-inflammatory and pro-atherogenic factors, such as TNFα and mildly oxidized low density lipoprotein (oxLDL). TNFα and oxLDL induced denitrosylation of various proteins as assessed by Saville-Griess assay, by immunostaining with an anti-S-nitrosocysteine antibody, and by a Western blot approach. Furthermore, the caspase-3 p17 subunit, which has previously been shown to be S-nitrosylated and thereby inhibited, was denitrosylated by TNFα treatment suggesting thatS-nitrosylation and denitrosylation are important regulatory mechanisms in endothelial cells contributing to the integrity of the endothelial cell monolayer.
Distribution-Free Consistent Independence Tests via Center-Outward Ranks and Signs

Abstract This article investigates the problem of testing independence of two random vectors of general dimensions. For this, we give for the first time a distribution-free consistent test. Our approach combines distance covariance with the center-outward ranks and signs developed by Marc Hallin and collaborators. In technical terms, the proposed test is consistent and distribution-free in the family of multivariate distributions with nonvanishing (Lebesgue) probability densities. Exploiting the (degenerate) U-statistic structure of the distance covariance and the combinatorial nature of Hallin’s center-outward ranks and signs, we are able to derive the limiting null distribution of our test statistic. The resulting asymptotic approximation is accurate already for moderate sample sizes and makes the test implementable without requiring permutation. The limiting distribution is derived via a more general result that gives a new type of combinatorial noncentral limit theorem for double- and multiple-indexed permutation statistics. Supplementary materials for this article are available online.
Uncertainties in the relationship between atmospheric nitrogen deposition and forest carbon sequestration

In a recent study, Magnani et al. report how atmospheric nitrogen deposition drives stand‐lifetime net ecosystem productivity (NEPav) for midlatitude forests, with an extremely high C to N response (725 kg C kg−1 wet‐deposited N for their European sites). We present here a re‐analysis of these data, which suggests a much smaller C : N response for total N inputs. Accounting for dry, as well as wet N deposition reduces the C : N response to 177 : 1. However, if covariance with intersite climatological differences is accounted for, the actual C : N response in this dataset may be <70 : 1. We then use a model analysis of 22 European forest stands to simulate the findings of Magnani et al. Multisite regression of simulated NEPav vs. total N deposition reproduces a high C : N response (149 : 1). However, once the effects of intersite climatological differences are accounted for, the value is again found to be much smaller, pointing to a real C : N response of about 50–75 : 1.
Analysis of node2vec random walks on networks

Random walks have been proven to be useful for constructing various algorithms to gain information on networks. Algorithm node2vec employs biased random walks to realize embeddings of nodes into low-dimensional spaces, which can then be used for tasks such as multi-label classification and link prediction. The performance of the node2vec algorithm in these applications is considered to depend on properties of random walks that the algorithm uses. In the present study, we theoretically and numerically analyse random walks used by the node2vec. Those random walks are second-order Markov chains. We exploit the mapping of its transition rule to a transition probability matrix among directed edges to analyse the stationary probability, relaxation times in terms of the spectral gap of the transition probability matrix, and coalescence time. In particular, we show that node2vec random walk accelerates diffusion when walkers are designed to avoid both backtracking and visiting a neighbour of the previously visited node but do not avoid them completely.