Miracle-dz/UniFAR-FTUs huggingface dataset:
Three Variants of Differential Privacy: Lossless Conversion and Applications

We consider three different variants of differential privacy (DP), namely approximate DP, Rényi DP (RDP), and hypothesis test DP. In the first part, we develop a machinery for optimally relating approximate DP to RDP based on the joint range of two <inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula>-divergences that underlie the approximate DP and RDP. In particular, this enables us to derive the optimal approximate DP parameters of a mechanism that satisfies a given level of RDP. As an application, we apply our result to the moments accountant framework for characterizing privacy guarantees of noisy stochastic gradient descent (SGD). When compared to the state-of-the-art, our bounds may lead to about 100 more stochastic gradient descent iterations for training deep learning models for the same privacy budget. In the second part, we establish a relationship between RDP and hypothesis test DP which allows us to translate the RDP constraint into a tradeoff between type I and type II error probabilities of a certain binary hypothesis test. We then demonstrate that for noisy SGD our result leads to tighter privacy guarantees compared to the recently proposed <inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula>-DP framework for some range of parameters.
Alpha Net: Adaptation with Composition in Classifier Space

Deep learning classification models typically train poorly on classes with small numbers of examples. Motivated by the human ability to solve this task, models have been developed that transfer knowledge from classes with many examples to learn classes with few examples. Critically, the majority of these models transfer knowledge within model feature space. In this work, we demonstrate that transferring knowledge within classified space is more effective and efficient. Specifically, by linearly combining strong nearest neighbor classifiers along with a weak classifier, we are able to compose a stronger classifier. Uniquely, our model can be implemented on top of any existing classification model that includes a classifier layer. We showcase the success of our approach in the task of long-tailed recognition, whereby the classes with few examples, otherwise known as the "tail" classes, suffer the most in performance and are the most challenging classes to learn. Using classifier-level knowledge transfer, we are able to drastically improve - by a margin as high as 12.6% - the state-of-the-art performance on the "tail" categories.
Deep Learning Enables Robust and Precise Light Focusing on Treatment Needs

If light passes through the body tissues, focusing only on areas where treatment needs, such as tumors, will revolutionize many biomedical imaging and therapy technologies. So how to focus light through deep inhomogeneous tissues overcoming scattering is Holy Grail in biomedical areas. In this paper, we use deep learning to learn and accelerate the process of phase pre-compensation using wavefront shaping. We present an approach (LoftGAN, light only focuses on treatment needs) for learning the relationship between phase domain X and speckle domain Y . Our goal is not just to learn an inverse mapping F:Y->X such that we can know the corresponding X needed for imaging Y like most work, but also to make focusing that is susceptible to disturbances more robust and precise by ensuring that the phase obtained can be forward mapped back to speckle. So we introduce different constraints to enforce F(Y)=X and H(F(Y))=Y with the transmission mapping H:X->Y. Both simulation and physical experiments are performed to investigate the effects of light focusing to demonstrate the effectiveness of our method and comparative experiments prove the crucial improvement of robustness and precision. Codes are available at this https URL.
Assessing Viewer's Mental Health by Detecting Depression in YouTube Videos

Depression is one of the most prevalent mental health issues around the world, proving to be one of the leading causes of suicide and placing large economic burdens on families and society. In this paper, we develop and test the efficacy of machine learning techniques applied to the content of YouTube videos captured through their transcripts and determine if the videos are depressive or have a depressing trigger. Our model can detect depressive videos with an accuracy of 83%. We also introduce a real-life evaluation technique to validate our classification based on the comments posted on a video by calculating the CES-D scores of the comments. This work conforms greatly with the UN Sustainable Goal of ensuring Good Health and Well Being with major conformity with section UN SDG 3.4.
A delay equation model for the Atlantic Multidecadal Oscillation

A new technique to derive delay models from systems of partial differential equations, based on the Mori–Zwanzig (MZ) formalism, is used to derive a delay-difference equation model for the Atlantic Multidecadal Oscillation (AMO). The MZ formalism gives a rewriting of the original system of equations, which contains a memory term. This memory term can be related to a delay term in a resulting delay equation. Here, the technique is applied to an idealized, but spatially extended, model of the AMO. The resulting delay-difference model is of a different type than the delay differential model which has been used to describe the El Niño Southern Oscillation. In addition to this model, which can also be obtained by integration along characteristics, error terms for a smoothing approximation of the model have been derived from the MZ formalism. Our new method of deriving delay models from spatially extended models has a large potential to use delay models to study a range of climate variability phenomena.
Detection of Gait Abnormalities caused by Neurological Disorders

In this paper, we leverage gait to potentially detect some of the important neurological disorders, namely Parkinson’s disease, Diplegia, Hemiplegia, and Huntington’s Chorea. Persons with these neurological disorders often have a very abnormal gait, which motivates us to target gait for their potential detection. Some of the abnormalities involve the circumduction of legs, forward-bending, involuntary movements, etc. To detect such abnormalities in gait, we develop gait features from the key-points of the human pose, namely shoulders, elbows, hips, knees, ankles, etc. To evaluate the effectiveness of our gait features in detecting the abnormalities related to these diseases, we build a synthetic video dataset of persons mimicking the gait of persons with such disorders, considering the difficulty in finding a sufficient number of people with these disorders. We name it NeuroSynGait video dataset. Experiments demonstrated that our gait features were indeed successful in detecting these abnormalities.
Multi-Task Learning for Interpretable Weakly Labelled Sound Event Detection

Weakly Labelled learning has garnered lot of attention in recent years due to its potential to scale Sound Event Detection (SED) and is formulated as Multiple Instance Learning (MIL) problem. This paper proposes a Multi-Task Learning (MTL) framework for learning from Weakly Labelled Audio data which encompasses the traditional MIL setup. To show the utility of proposed framework, we use the input TimeFrequency representation (T-F) reconstruction as the auxiliary task. We show that the chosen auxiliary task de-noises internal T-F representation and improves SED performance under noisy recordings. Our second contribution is introducing two step Attention Pooling mechanism. By having 2-steps in attention mechanism, the network retains better T-F level information without compromising SED performance. The visualisation of first step and second step attention weights helps in localising the audio-event in T-F domain. For evaluating the proposed framework, we remix the DCASE 2019 task 1 acoustic scene data with DCASE 2018 Task 2 sounds event data under 0, 10 and 20 db SNR resulting in a multi-class Weakly labelled SED problem. The proposed total framework outperforms existing benchmark models over all SNRs, specifically 22.3 %, 12.8 %, 5.9 % improvement over benchmark model on 0, 10 and 20 dB SNR respectively. We carry out ablation study to determine the contribution of each auxiliary task and 2-step Attention Pooling to the SED performance improvement. The code is publicly released
Target competition for resources under diffusive search-and-capture

Random search for one or more targets in a bounded domain occurs widely in nature, with examples ranging from animal foraging to the transport of vesicles within cells. Most theoretical studies take a searcher-centric viewpoint, focusing on the first passage time (FTP) problem to find a target. This single search-and-capture event then triggers a downstream process or provides the searcher with some resource such as food. In this paper we take a target-centric viewpoint, by analyzing the accumulation of resources in a set of targets due to multiple rounds of search-and-capture events; whenever a searcher finds a target it delivers a resource packet to the target, after which it escapes and returns to its initial position (resetting after capture). The searcher is then resupplied with cargo and a new search process is initiated after a random delay. Assuming that the accumulation of resources is counterbalanced by degradation, one can derive general expressions for the moments of the resource distribution. We develop the theory by considering diffusive search in two distinct geometric configurations. First, we determine the mean and Fano factor of the distribution of resources between pair of concentric spherical targets, and explore how these depend on the initial location of the searcher, the dimension $d$, the degradation rate and the distribution of delays. Second, we use asymptotic analysis to determine the mean number of resources in each of $N$ small interior targets within a three-dimensional bounded domain. In particular, we show that the mean number of resources in a target is proportional to its effective ``shape capacitance. '' Finally, we extend the analysis to the case of diffusive search with stochastic resetting before capture.
On the Sample Complexity of Reinforcement Learning with Policy Space Generalization

We study the optimal sample complexity in large-scale Reinforcement Learning (RL) problems with policy space generalization, i.e. the agent has a prior knowledge that the optimal policy lies in a known policy space. Existing results show that without a generalization model, the sample complexity of an RL algorithm will inevitably depend on the cardinalities of state space and action space, which are intractably large in many practical problems. To avoid such undesirable dependence on the state and action space sizes, this paper proposes a new notion of eluder dimension for the policy space, which characterizes the intrinsic complexity of policy learning in an arbitrary Markov Decision Process (MDP). Using a simulator oracle, we prove a near-optimal sample complexity upper bound that only depends linearly on the eluder dimension. We further prove a similar regret bound in deterministic systems without the simulator.
Principal Ellipsoid Analysis (PEA): Efficient non-linear dimension reduction & clustering

Even with the rise in popularity of over-parameterized models, simple dimensionality reduction and clustering methods, such as PCA and k-means, are still routinely used in an amazing variety of settings. A primary reason is the combination of simplicity, interpretability and computational efficiency. The focus of this article is on improving upon PCA and k-means, by allowing non-linear relations in the data and more flexible cluster shapes, without sacrificing the key advantages. The key contribution is a new framework for Principal Elliptical Analysis (PEA), defining a simple and computationally efficient alternative to PCA that fits the best elliptical approximation through the data. We provide theoretical guarantees on the proposed PEA algorithm using Vapnik-Chervonenkis (VC) theory to show strong consistency and uniform concentration bounds. Toy experiments illustrate the performance of PEA, and the ability to adapt to non-linear structure and complex cluster shapes. In a rich variety of real data clustering applications, PEA is shown to do as well as k-means for simple datasets, while dramatically improving performance in more complex settings.
Time-Supervised Primary Object Segmentation

We describe an unsupervised method to detect and segment portions of live scenes that, at some point in time, are seen moving as a coherent whole, which we refer to as primary objects. Our method first segments motions by minimizing the mutual information between partitions of the image domain, which bootstraps a static object detection model that takes a single image as input. The two models are mutually reinforced within a feedback loop, enabling extrapolation to previously unseen classes of objects. Our method requires video for training, but can be used on either static images or videos at inference time. As the volume of our training sets grows, more and more objects are seen moving, thus turning our method into unsupervised (or time-supervised) training to segment primary objects. The resulting system outperforms the state-of-the-art in both video object segmentation and salient object detection benchmarks, even when compared to methods that use explicit manual annotation.
Multi-label Learning with Missing Values using Combined Facial Action Unit Datasets

Facial action units allow an objective, standardized description of facial micro movements which can be used to describe emotions in human faces. Annotating data for action units is an expensive and time-consuming task, which leads to a scarce data situation. By combining multiple datasets from different studies, the amount of training data for a machine learning algorithm can be increased in order to create robust models for automated, multi-label action unit detection. However, every study annotates different action units, leading to a tremendous amount of missing labels in a combined database. In this work, we examine this challenge and present our approach to create a combined database and an algorithm capable of learning under the presence of missing labels without inferring their values. Our approach shows competitive performance compared to recent competitions in action unit detection.
A Two-Stage Optimal Bidding Algorithm for Incentive-based Aggregation of Electric Vehicles in Workplace Parking Lots

This paper proposes an incentive-based aggregator for electric vehicles (EVs) in workplace parking lots to participate in the energy and regulation markets. With the implementation of seasonal Autoregressive Integrated Moving Average (ARIMA) model to predict the market information, a Day-Ahead (DA) planning model coordinated with the EV's responses to the incentive is formulated to determine the days to activate the aggregation program and the optimal incentives that are broadcasted to the EV owners in the first stage. Given the determined incentives and EV's responses, an real-time (RT) optimal bidding algorithm complying with EVs' energy demand in the second stage is designed to maximize the aggregator's profits in the markets. The proposed models are tested using the data collected from PJM's energy and regulation markets. The results show the incentive-based aggregator can benefit both the EV owners and the aggregator from the credits obtained from the markets. Meanwhile, the results also indicate the proposed optimal bidding algorithm is capable of handling the uncertainty of regulation signals and following the signals with high precision.
Advancing weakly supervised cross-domain alignment with optimal transport

Cross-domain alignment between image objects and text sequences is key to many visual-language tasks, and it poses a fundamental challenge to both computer vision and natural language processing. This paper investigates a novel approach for the identification and optimization of fine-grained semantic similarities between image and text entities, under a weakly-supervised setup, improving performance over state-of-the-art solutions. Our method builds upon recent advances in optimal transport (OT) to resolve the cross-domain matching problem in a principled manner. Formulated as a drop-in regularizer, the proposed OT solution can be efficiently computed and used in combination with other existing approaches. We present empirical evidence to demonstrate the effectiveness of our approach, showing how it enables simpler model architectures to outperform or be comparable with more sophisticated designs on a range of vision-language tasks.
Comparative efficacy of different exercise interventions in chronic non-specific low back pain: protocol of a systematic review and network meta-analysis

Introduction Chronic non-specific low back pain is a major public health problem. Evidence supports the effectiveness of exercise as an intervention. Due to a paucity of direct comparisons of different exercise categories, medical guidelines were unable to make specific recommendations regarding the type of exercise working best in improving chronic low back pain. This network meta-analysis (NMA) of randomised controlled trials aims to investigate the comparative efficacy of different exercise interventions in patients with chronic non-specific low back pain. Methods and analysis MEDLINE, Scopus, Cochrane Central Register of Controlled Trials, Physiotherapy Evidence Database, SPORTDiscus, Clinicaltrials.gov and the WHO International Clinical Trials Registry Platform search portal were searched on November 2019 and without language restrictions. The search will be updated after data analysis. Studies on adults with non-specific low back pain of at least 12 weeks duration comparing exercise to either no specific intervention (ie, no treatment, wait-list or usual care at the treating physician’s discretion) and/or functionally inert interventions (ie, sham or attention control interventions) will be eligible. Pain intensity and back-specific disability are defined as primary outcomes. Secondary outcomes will include health-related physical and mental quality of life, work disability, frequency of analgesic use and adverse events. All outcomes will be analysed short-term, intermediate-term and long-term. Data will be extracted independently by two review authors. Risk of bias will be assessed using the recommendations by the Cochrane Back and Neck Group and be based on an adaptation of the Cochrane Risk of Bias tool. Ethics and dissemination This NMA will be reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses_NMA checklist. The results will be presented in peer-reviewed journals, implemented in existing national and international guidelines and will be presented to health care providers and decision makers. The planned completion date of the study is 1 July 2021. PROSPERO registration number CRD42020151472.
Two Surveys Per Spring Are Enough to Obtain Robust Population Trends of Common and Widespread Birds in Yearly Monitoring Programmes

Summary. Extensive bird monitoring programmes are fundamental for estimating inter-annual population trends using data provided by thousands of observers through standardised fieldwork. Gordo (2018) has proposed that abundance data recorded by common bird monitoring schemes (e.g. SACRE programme) should be used cautiously due to its potential inaccuracy, because two surveys per spring are not enough to record the actual maximum number of individual birds at a sampling location. We carried out numerical simulations and analysed the interspecific pattern of statistical significance of the published population trends of the Spanish common birds census, the SACRE programme (1998-2011), in order to test how the number of repetitions of censuses per year affects the power of tests: (i.e. the probability of detecting significant trends that are in fact true), and the probability of obtaining low false discovery rates: i.e. identifying significant changes that are actually false, when estimating yearly population changes. We agree with Gordo (2018) that two surveys of the same sampling stations per year are unable to detect the maximum number of birds throughout a breeding season. Nevertheless, the goal of monitoring programmes is not to obtain the maximum number of birds at each sampling unit over a long time span but to measure reliable population trends. Our results demonstrate that the average number of birds recorded in two surveys per season provides a highly reliable indication of population trends for abundant and widely distributed bird species, the focal taxa in common birds monitoring schemes, especially of long-term average trends > ±2.5% change annually. The actual population trends for very rare species, such as those with data from fewer than 50 UTM squares and < 5 individual birds per census and UTM cell, are hard to detect unless they show yearly percentage population changes greater than ±5%. —Carrascal, L.M. & del Moral, J.C. (2021). Two surveys per spring are enough to obtain robust population trends of common and widespread birds in yearly monitoring programmes. Ardeola, 68: 33-51.
Flocking of Foraging Yellow-Billed Choughs Pyrrhocorax Graculus Reflects the Availability of Grasshoppers and the Extent of Human Influence in High Elevation Ecosystems

Summary. Mountain ecosystems are subject to many pressures, including changes in land use, rising temperatures and increasing recreational activities. These factors may disrupt food webs, threatening the survival of organisms and, ultimately, ecosystem functioning. However, few studies focus on the trends of different groups joined by trophic links in alpine ecosystems. The Yellow-billed (Alpine) Chough, Pyrrhocorax graculus, a gregarious bird that moves in flocks, is an ideal target species within this framework since it has a grasshopper-based diet during the summer and may attend sites that are heavily developed for recreational activities. We tested therefore whether flock size co-varied with grasshopper abundance along an elevational gradient in two alpine areas subject to different levels of human disturbance. Chough flock size and grasshopper abundance were measured along elevational transects. The median flock size was analysed in relation to grasshopper abundance and biomass, we also conducted negative binomial GLMs to account for effects of period (i.e. from June to mid-August), site and elevation. In both study areas, chough flocks were larger at the elevation where grasshoppers were more abundant. This may indicate the capacity of the species to follow large-scale fluctuations in their key insect prey. On the other hand, the relationship between flock size and grasshopper biomass was positive at a natural site and negative at a tourist site. This suggests that where there is a high level of human influence, other factors, such as the availability of human-provided food, may disturb the natural relationship between choughs and their key prey. Overall, our results indicate that flocking by foraging Yellow-billed Choughs reflects both grasshopper abundance and the degree of anthropogenic influence. —Vallino, C. , Caprio, E. , Genco, F. , Chamberlain, D. , Palestrini, C. , Roggero, A. , Bocca, M. & Rolando, A. (2021). Flocking of foraging Yellow-billed Choughs Pyrrhocorax graculus reflects the availability of grasshoppers and the extent of human influence in high elevation ecosystems. Ardeola, 68: 53-70.