Miracle-dz/UniFAR-FTUs huggingface dataset:
What is a Randomization Test?

Abstract The meaning of randomization tests has become obscure in statistics education and practice over the last century. This article makes a fresh attempt at rectifying this core concept of statistics. A new term—“quasi-randomization test”—is introduced to define significance tests based on theoretical models and distinguish these tests from the “randomization tests” based on the physical act of randomization. The practical importance of this distinction is illustrated through a real stepped-wedge cluster-randomized trial. Building on the recent literature on randomization inference, a general framework of conditional randomization tests is developed and some practical methods to construct conditioning events are given. The proposed terminology and framework are then applied to understand several widely used (quasi-)randomization tests, including Fisher’s exact test, permutation tests for treatment effect, quasi-randomization tests for independence and conditional independence, adaptive randomization, and conformal prediction. Supplementary materials for this article are available online.
Getting more from your regression model: A free lunch?

We consider a simple approach for approximating detailed information about the conditional distribution of a real-valued response variable, given values for its covariates, using only the outputs from a standard regression model. We validate this approach by assessing its performance in the context of quantile regression; when applied to the outputs of linear, gradient boosted tree ensemble and random forest models. We find that it compares favourably to the standard approach for estimating quantile regression functions, especially for commonly selected tail probabilities, and is highly competitive with the quantile regression forest model, across a large collection of benchmark data sets.
Spoofing-Aware Speaker Verification with Unsupervised Domain Adaptation

In this paper, we initiate the concern of enhancing the spoofing robustness of the automatic speaker verification (ASV) system, without the primary presence of a separate countermeasure module. We start from the standard ASV framework of the ASVspoof 2019 baseline and approach the problem from the back-end classifier based on probabilistic linear discriminant analysis. We employ three unsupervised domain adaptation techniques to optimize the back-end using the audio data in the training partition of the ASVspoof 2019 dataset. We demonstrate notable improvements on both logical and physical access scenarios, especially on the latter where the system is attacked by replayed audios, with a maximum of 36.1% and 5.3% relative improvement on bonafide and spoofed cases, respectively. We perform additional studies such as per-attack breakdown analysis, data composition, and integration with a countermeasure system at score-level with Gaussian back-end.
Adaptive and Cascaded Compressive Sensing

Scene-dependent adaptive compressive sensing (CS) has been a long pursuing goal which has huge potential in significantly improving the performance of CS. However, without accessing to the ground truth image, how to design the scene-dependent adaptive strategy is still an open-problem and the improvement in sampling efficiency is still quite limited. In this paper, a restricted isometry property (RIP) condition based error clamping is proposed, which could directly predict the reconstruction error, i.e. the difference between the currently-stage reconstructed image and the ground truth image, and adaptively allocate samples to different regions at the successive sampling stage. Furthermore, we propose a cascaded feature fusion reconstruction network that could efficiently utilize the information derived from different adaptive sampling stages. The effectiveness of the proposed adaptive and cascaded CS method is demonstrated with extensive quantitative and qualitative results, compared with the state-of-the-art CS algorithms.
CoGS: Controllable Generation and Search from Sketch and Style

We present CoGS, a novel method for the style-conditioned, sketch-driven synthesis of images. CoGS enables exploration of diverse appearance possibilities for a given sketched object, enabling decoupled control over the structure and the appearance of the output. Coarse-grained control over object structure and appearance are enabled via an input sketch and an exemplar"style"conditioning image to a transformer-based sketch and style encoder to generate a discrete codebook representation. We map the codebook representation into a metric space, enabling fine-grained control over selection and interpolation between multiple synthesis options before generating the image via a vector quantized GAN (VQGAN) decoder. Our framework thereby unifies search and synthesis tasks, in that a sketch and style pair may be used to run an initial synthesis which may be refined via combination with similar results in a search corpus to produce an image more closely matching the user's intent. We show that our model, trained on the 125 object classes of our newly created Pseudosketches dataset, is capable of producing a diverse gamut of semantic content and appearance styles.
On the Generalization Mystery in Deep Learning

The generalization mystery in deep learning is the following: Why do over-parameterized neural networks trained with gradient descent (GD) generalize well on real datasets even though they are capable of ﬁtting random datasets of comparable size? Furthermore, from among all solutions that ﬁt the training data, how does GD ﬁnd one that generalizes well (when such a well-generalizing solution exists)? We argue that the answer to both questions lies in the interaction of the gradients of diﬀerent examples during training. Intuitively, if the per-example gradients are well-aligned, that is, if they are coherent, then one may expect GD to be (algorithmically) stable, and hence generalize well. We formalize this argument with an easy to compute and interpretable metric for coherence, and show that the metric takes on very diﬀerent values on real and random datasets for several common vision networks. The theory also explains a number of other phenomena in deep learning, such as why some examples are reliably learned earlier than others, why early stopping works, and why it is possible to learn from noisy labels. Moreover, since the theory provides a causal explanation of how GD ﬁnds a well-generalizing solution when one exists, it motivates a class of simple modiﬁcations to GD that attenuate memorization and improve generalization. Generalization in deep learning is an extremely broad phenomenon, and therefore, it requires an equally general explanation. We conclude with a survey of alternative lines of attack on this problem, and argue that the proposed approach is the most viable one on this basis.
Towards Axiomatic Foundations for Conceptual Modeling: An Example

Conceptual modeling is a strongly interdisciplinary field of research. Although numerous proposals for axiomatic foundations of the main ideas of the field exist, there is still a lack of understanding main concepts such as system, process, event, data, and many more. Against the background of the tremendously gaining importance of digital phenomena, we argue that axiomatic foundations are needed for our discipline. Besides the general call, we provide a particular case study using HERAKLIT. This modeling infrastructure encompasses the architecture, statics, and dynamics of computer-integrated systems. The case study illustrates how axiomatically well-founded conceptual models may look like in practice. We argue that axiomatic foundations do not only have positive effects for theoretical research, but also for empirical research, because, for instance, assumed axioms can explicitly be tested. It is now time to spark the discussion on axiomatic foundations of our field.
Learning Distributionally Robust Models at Scale via Composite Optimization

To train machine learning models that are robust to distribution shifts in the data, distributionally robust optimization (DRO) has been proven very effective. However, the existing approaches to learning a distributionally robust model either require solving complex optimization problems such as semidefinite programming or a first-order method whose convergence scales linearly with the number of data samples -- which hinders their scalability to large datasets. In this paper, we show how different variants of DRO are simply instances of a finite-sum composite optimization for which we provide scalable methods. We also provide empirical results that demonstrate the effectiveness of our proposed algorithm with respect to the prior art in order to learn robust models from very large datasets.
Learning Compressed Embeddings for On-Device Inference

In deep learning, embeddings are widely used to represent categorical entities such as words, apps, and movies. An embedding layer maps each entity to a unique vector, causing the layer's memory requirement to be proportional to the number of entities. In the recommendation domain, a given category can have hundreds of thousands of entities, and its embedding layer can take gigabytes of memory. The scale of these networks makes them difficult to deploy in resource constrained environments. In this paper, we propose a novel approach for reducing the size of an embedding table while still mapping each entity to its own unique embedding. Rather than maintaining the full embedding table, we construct each entity's embedding"on the fly"using two separate embedding tables. The first table employs hashing to force multiple entities to share an embedding. The second table contains one trainable weight per entity, allowing the model to distinguish between entities sharing the same embedding. Since these two tables are trained jointly, the network is able to learn a unique embedding per entity, helping it maintain a discriminative capability similar to a model with an uncompressed embedding table. We call this approach MEmCom (Multi-Embedding Compression). We compare with state-of-the-art model compression techniques for multiple problem classes including classification and ranking. On four popular recommender system datasets, MEmCom had a 4% relative loss in nDCG while compressing the input embedding sizes of our recommendation models by 16x, 4x, 12x, and 40x. MEmCom outperforms the state-of-the-art techniques, which achieved 16%, 6%, 10%, and 8% relative loss in nDCG at the respective compression ratios. Additionally, MEmCom is able to compress the RankNet ranking model by 32x on a dataset with millions of users' interactions with games while incurring only a 1% relative loss in nDCG.
Whose AI Dream? In search of the aspiration in data annotation.

Data is fundamental to AI/ML models. This paper investigates the work practices concerning data annotation as performed in the industry, in India. Previous human-centred investigations have largely focused on annotators’ subjectivity, bias and efficiency. We present a wider perspective of the data annotation: following a grounded approach, we conducted three sets of interviews with 25 annotators, 10 industry experts and 12 ML/AI practitioners. Our results show that the work of annotators is dictated by the interests, priorities and values of others above their station. More than technical, we contend that data annotation is a systematic exercise of power through organizational structure and practice. We propose a set of implications for how we can cultivate and encourage better practice to balance the tension between the need for high quality data at low cost and the annotators’ aspiration for well-being, career perspective, and active participation in building the AI dream.
Local-Global Context Aware Transformer for Language-Guided Video Segmentation

We explore the task of language-guided video segmentation (LVS). Previous algorithms mostly adopt 3D CNNs to learn video representation, struggling to capture long-term context and easily suffering from visual-linguistic misalignment. In light of this, we present <sc>Locater</sc> (<underline>lo</underline>cal-global <underline>c</underline>ontext <underline>a</underline>ware <underline>T</underline>ransform<underline>er</underline>), which augments the Transformer architecture with a finite memory so as to query the entire video with the language expression in an efficient manner. The memory is designed to involve two components – one for persistently preserving global video content, and one for dynamically gathering local temporal context and segmentation history. Based on the memorized local-global context and the particular content of each frame, <sc>Locater</sc> holistically and flexibly comprehends the expression as an adaptive query vector for each frame. The vector is used to query the corresponding frame for mask generation. The memory also allows <sc>Locater</sc> to process videos with linear time complexity and constant size memory, while Transformer-style self-attention computation scales quadratically with sequence length. To thoroughly examine the visual grounding capability of LVS models, we contribute a new LVS dataset, A2D-S<inline-formula><tex-math notation="LaTeX">$^+$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="yang-ieq1-3262578.gif"/></alternatives></inline-formula>, which is built upon A2D-S dataset but poses increased challenges in disambiguating among similar objects. Experiments on three LVS datasets and our A2D-S<inline-formula><tex-math notation="LaTeX">$^+$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="yang-ieq2-3262578.gif"/></alternatives></inline-formula> show that <sc>Locater</sc> outperforms previous state-of-the-arts. Further, we won the 1<italic>st</italic> place in the Referring Video Object Segmentation Track of the 3<italic>rd</italic> Large-scale Video Object Segmentation Challenge, where <sc>Locater</sc> served as the foundation for the winning solution.
Validation of a Wave Heated 3D MHD Coronal-wind Model using Polarized Brightness and EUV Observations

The physical properties responsible for the formation and evolution of the corona and heliosphere are still not completely understood. 3D MHD global modeling is a powerful tool to investigate all the possible candidate processes. To fully understand the role of each of them, we need a validation process where the output from the simulations is quantitatively compared to the observational data. In this work, we present the results from our validation process applied to the wave turbulence driven 3D MHD corona-wind model WindPredict-AW. At this stage of the model development, we focus the work to the coronal regime in quiescent condition. We analyze three simulation results, which differ by the boundary values. We use the 3D distributions of density and temperature, output from the simulations at the time of around the first Parker Solar Probe perihelion (during minimum of the solar activity), to synthesize both extreme ultraviolet (EUV) and white-light-polarized (WL pB) images to reproduce the observed solar corona. For these tests, we selected AIA 193 Å, 211 Å, and 171 Å EUV emissions, MLSO K-Cor, and LASCO C2 pB images obtained on 2018 November 6 and 7. We then make quantitative comparisons of the disk and off limb corona. We show that our model is able to produce synthetic images comparable to those of the observed corona.
Boost Test-Time Performance with Closed-Loop Inference

Conventional deep models predict a test sample with a single forward propagation, which, however, may not be sufficient for predicting hard-classified samples. On the contrary, we human beings may need to carefully check the sample many times before making a final decision. During the recheck process, one may refine/adjust the prediction by referring to related samples. Motivated by this, we propose to predict those hard-classified test samples in a looped manner to boost the model performance. However, this idea may pose a critical challenge: how to construct looped inference, so that the original erroneous predictions on these hard test samples can be corrected with little additional effort. To address this, we propose a general Closed-Loop Inference (CLI) method. Specifically, we first devise a filtering criterion to identify those hard-classified test samples that need additional inference loops. For each hard sample, we construct an additional auxiliary learning task based on its original top-$K$ predictions to calibrate the model, and then use the calibrated model to obtain the final prediction. Promising results on ImageNet (in-distribution test samples) and ImageNet-C (out-of-distribution test samples) demonstrate the effectiveness of CLI in improving the performance of any pre-trained model.
Optimizing a superconducting radio-frequency gun using deep reinforcement learning

Superconducting photoelectron injectors are a promising technique for generating high brilliant pulsed electron beams with high repetition rates and low emittances. Experiments such as ultra-fast electron diffraction, experiments at the Terahertz scale, and energy recovery linac applications require such properties. However, optimization of the beam properties is challenging due to the high amount of possible machine parameter combinations. In this article, we show the successful automated optimization of beam properties utilizing an already existing simulation model. To reduce the amount of required computation time, we replace the costly simulation by a faster approximation with a neural network. For optimization, we propose a reinforcement learning approach leveraging the simple computation of the derivative of the approximation. We prove that our approach outperforms common optimization methods for the required function evaluations given a defined minimum accuracy.
Event Coreference Resolution for Contentious Politics Events

We propose a dataset for event coreference resolution, which is based on random samples drawn from multiple sources, languages, and countries. Early scholarship on event information collection has not quantified the contribution of event coreference resolution. We prepared and analyzed a representative multilingual corpus and measured the performance and contribution of the state-of-the-art event coreference resolution approaches. We found that almost half of the event mentions in documents co-occur with other event mentions and this makes it inevitable to obtain erroneous or partial event information. We showed that event coreference resolution could help improving this situation. Our contribution sheds light on a challenge that has been overlooked or hard to study to date. Future event information collection studies can be designed based on the results we present in this report. The repository for this study is on https://github.com/emerging-welfare/ECR4-Contentious-Politics.
Enumeration of symmetric arc diagrams

We give recurrence relations for the enumeration of symmetric elements within four classes of arc diagrams corresponding to certain involutions and set partitions whose blocks contain no consecutive integers. These arc diagrams are motivated by the study of RNA secondary structures. For example, classic RNA secondary structures correspond to 3412-avoiding involutions with no adjacent transpositions, and structures with base triples may be represented as partitions with crossings. Our results rely on combinatorial arguments. In particular, we use Motzkin paths to describe noncrossing arc diagrams that have no arc connecting two adjacent nodes, and we give an explicit bijection to ternary words whose length coincides with the sum of their digits. We also discuss the asymptotic behavior of some of the sequences considered here in order to quantify the extremely low probability of finding symmetric structures with a large number of nodes.
Short-Term Load Forecasting Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

: Electric load forecasting is a real-life problem in industry. This report presents the development of an Adaptive Neuro Fuzzy Inference System (ANFIS) based short-term load forecasting model which forecast the electric load. This report presents prediction of electric load by considering various information like time, temperature and historical load data. Historical load data is taken from MPEB Jabalpur and weather data is taken from the website www.worldweatheronline. com. ANFIS is a class of adaptive networks that are functionally equivalent to fuzzy inference systems. The prime inherent advantage associated with the soft computing techniques of not requiring a mathematical model has been a motivating factor for consideration in our present work. Motivated by this advantageous feature of soft computing based load forecasting, the present work focuses on building a model for an ill defined real world system based on its available record of input-output data using ANFIS. From the analysis carried out on the ANFIS-based model; Mean absolute percentage error (MAPE) for a particular Tuesday was found to be 5.705 %. Results and forecasting performance obtained reveal the effectiveness of the proposed approach and shows that it is possible to build a high accuracy model with less historical data using a combination of neural network and fuzzy logic which can be used in real time.
Learning to Control DC Motor for Micromobility in Real Time With Reinforcement Learning

Autonomous micromobility has been attracting the attention of researchers and practitioners in recent years. A key component of many micro-transport vehicles is the DC motor, a complex dynamical system that is continuous and non-linear. Learning to quickly control the DC motor in the presence of disturbances and uncertainties is desired for various applications that require robustness and stability. Techniques to accomplish this task usually rely on a mathematical system model, which is often insufficient to anticipate the effects of time-varying and interrelated sources of non-linearities. While some model-free approaches have been successful at the task, they rely on massive interactions with the system and are trained in specialized hardware in order to fit a highly parameterized controller. In this work, we learn to steer a DC motor via sample-efficient reinforcement learning. Using data collected from hardware interactions in the real world, we additionally build a simulator to experiment with a wide range of parameters and learning strategies. With the best parameters found, we learn an effective control policy in one minute and 53 seconds on a simulation and in 10 minutes and 35 seconds on a physical system.
Dynamics of purely kinetic k -essence in presence of a perfect ﬂuid

We investigate the dynamics of purely kinetic k -essence scalar ﬁeld in three diﬀerent scenarios. We begin with the case of a purely kinetic k -essence theory and try to formulate a late time accelerating universe model. Then we introduce a perfect ﬂuid in presence of the purely kinetic k -essence ﬁeld and calculate how the features of the previous model are modiﬁed. We discuss two cases primarily, in the ﬁrst case the ﬂuid and the scalar ﬁeld does not exchange energy and momentum and in the later case the scalar ﬁeld and the ﬂuid interact non-minimally and exchange energy and momentum. Using dynamical analysis technique, we examine the stability of these systems in the background of a spatially ﬂat Friedman-Lemaˆıtre-Robertson-Walker (FLRW) universe for a particular chosen functional form of k -essence kinetic energy term. From the evolution of the eﬀective equation of state parameter of the ﬁeld-ﬂuid system we conclude that a non-minimally coupled ﬁeld-ﬂuid scenario can produce physically interesting results. We also ﬁnd that the k -essence ﬁeld can eﬀectively hide in a radiation background.