Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-Rank Softmax Can Have Unargmaxable Classes in Theory but Rarely in Practice

Classifiers in natural language processing (NLP) often have a large number of output classes. For example, neural language models (LMs) and machine translation (MT) models both predict tokens from a vocabulary of thousands. The Softmax output layer of these models typically receives as input a dense feature representation, which has much lower dimensionality than the output. In theory, the result is some words may be impossible to be predicted via argmax, irrespective of input features, and empirically, there is evidence this happens in small language models (Demeter et al., 2020). In this paper we ask whether it can happen in practical large language models and translation models. To do so, we develop algorithms to detect such unargmaxable tokens in public models. We find that 13 out of 150 models do indeed have such tokens; however, they are very infrequent and unlikely to impact model quality. We release our algorithms and code to the public.
Bi-Sampling Approach to Classify Music Mood leveraging Raga-Rasa Association in Indian Classical Music

The impact of Music on the mood or emotion of the listener is a well-researched area in human psychology and behavioral science. In Indian classical music, ragas are the melodic structure that defines the various styles and forms of the music. Each raga has been found to evoke a specific emotion in the listener. With the advent of advanced capabilities of audio signal processing and the application of machine learning, the demand for intelligent music classifiers and recommenders has received increased attention, especially in the “Music as a service” cloud applications This paper explores a novel framework to leverage the ragarasa association in Indian classical Music to build an intelligent classifier and its application in music recommendation system based on user’s current mood and the mood they aspire to be in Keywords—Music Recommender, Indian Classical Music, Raga, Audio Signal Processing, Music Classification Algorithm
Transverse Vector Decomposition Method for Analytical Inversion of Exoplanet Transit Spectra

We develop a new method for analytical inversion of binned exoplanet transit spectra and for retrieval of planet parameters. The method has a geometrical interpretation and treats each observed spectrum as a single vector r→ in the multidimensional spectral space of observed bin values. We decompose the observed r→ into two orthogonal components: a wavelength-independent component r→∥ corresponding to the spectral mean across all observed bins, and a transverse component r→⊥ that is wavelength dependent and contains the relevant information about the atmospheric chemistry. The method allows us to extract, without any prior assumptions or additional information, the relative mass (or volume) mixing ratios of the absorbers in the atmosphere, the scale height to stellar radius ratio, H/R S , and the atmospheric temperature. The method is illustrated and validated with several examples of increasing complexity.
Taking an Emotional Look at Video Paragraph Captioning

Translating visual data into natural language is essential for machines to understand the world and interact with humans. In this work, a comprehensive study is conducted on video paragraph captioning, with the goal to generate paragraph-level descriptions for a given video. However, current researches mainly focus on detecting objective facts, ignoring the needs to establish the logical associations between sentences and to discover more accurate emotions related to video contents. Such a problem impairs fluent and abundant expressions of predicted captions, which are far below human language tandards. To solve this problem, we propose to construct a large-scale emotion and logic driven multilingual dataset for this task. This dataset is named EMVPC (standing for"Emotional Video Paragraph Captioning") and contains 53 widely-used emotions in daily life, 376 common scenes corresponding to these emotions, 10,291 high-quality videos and 20,582 elaborated paragraph captions with English and Chinese versions. Relevant emotion categories, scene labels, emotion word labels and logic word labels are also provided in this new dataset. The proposed EMVPC dataset intends to provide full-fledged video paragraph captioning in terms of rich emotions, coherent logic and elaborate expressions, which can also benefit other tasks in vision-language fields. Furthermore, a comprehensive study is conducted through experiments on existing benchmark video paragraph captioning datasets and the proposed EMVPC. The stateof-the-art schemes from different visual captioning tasks are compared in terms of 15 popular metrics, and their detailed objective as well as subjective results are summarized. Finally, remaining problems and future directions of video paragraph captioning are also discussed. The unique perspective of this work is expected to boost further development in video paragraph captioning research.
Pressure Ulcer Categorisation using Deep Learning: A Clinical Trial to Evaluate Model Performance

Pressure ulcers are a challenge for patients and healthcare professionals. In the UK, 700,000 people are affected by pressure ulcers each year. Treating them costs the National Health Service {\pounds}3.8 million every day. Their etiology is complex and multifactorial. However, evidence has shown a strong link between old age, disease-related sedentary lifestyles and unhealthy eating habits. Pressure ulcers are caused by direct skin contact with a bed or chair without frequent position changes. Urinary and faecal incontinence, diabetes, and injuries that restrict body position and nutrition are also known risk factors. Guidelines and treatments exist but their implementation and success vary across different healthcare settings. This is primarily because healthcare practitioners have a) minimal experience in dealing with pressure ulcers, and b) a general lack of understanding of pressure ulcer treatments. Poorly managed, pressure ulcers lead to severe pain, poor quality of life, and significant healthcare costs. In this paper, we report the findings of a clinical trial conducted by Mersey Care NHS Foundation Trust that evaluated the performance of a faster region-based convolutional neural network and mobile platform that categorised and documented pressure ulcers. The neural network classifies category I, II, III, and IV pressure ulcers, deep tissue injuries, and unstageable pressure ulcers. Photographs of pressure ulcers taken by district nurses are transmitted over 4/5G communications to an inferencing server for classification. Classified images are stored and reviewed to assess the model's predictions and relevance as a tool for clinical decision making and standardised reporting. The results from the study generated a mean average Precision=0.6796, Recall=0.6997, F1-Score=0.6786 with 45 false positives using an @. 75 confidence score threshold.
Self-consistent theory of current injection into d and d + is superconductors

We present results for the steady-state nonlinear response of a dx2−y2 superconducting film connected to normal-metal reservoirs under voltage bias, allowing for a subdominant s-wave component appearing near the interfaces. Our investigation is based on a current-conserving theory that self-consistently includes the non-equilibrium distribution functions, charge imbalance, and the voltage-dependencies of order parameters and scalar impurity self-energies. For a pure d-wave superconductor with [110] orientation of the interfaces to the contacts, the conductance contains a zero-bias peak reflecting the large density of zero-energy interface Andreev bound states. Including a subdominant s-wave pairing channel, it is in equilibrium energetically favorable for an s-wave order parameter component Δs to appear near the interfaces in the time-reversal symmetry breaking combination d + is. The Andreev states then shift to finite energies in the density of states. Under voltage bias, we find that the non-equilibrium distribution in the contact area causes a rapid suppression of the s-wave component to zero as the voltage eV→Δs . The resulting spectral rearrangements and voltage-dependent scattering amplitudes lead to a pronounced non-thermally broadened split of the zero-bias conductance peak that is not seen in a non-selfconsistent Landauer–Büttiker scattering approach.
Instance-Dependent Regret Analysis of Kernelized Bandits

We study the kernelized bandit problem, that involves designing an adaptive strategy for querying a noisy zeroth-order-oracle to efficiently learn about the optimizer of an unknown function $f$ with a norm bounded by $M<\infty$ in a Reproducing Kernel Hilbert Space~(RKHS) associated with a positive definite kernel $K$. Prior results, working in a \emph{minimax framework}, have characterized the worst-case~(over all functions in the problem class) limits on regret achievable by \emph{any} algorithm, and have constructed algorithms with matching~(modulo polylogarithmic factors) worst-case performance for the \matern family of kernels. These results suffer from two drawbacks. First, the minimax lower bound gives no information about the limits of regret achievable by the commonly used algorithms on specific problem instances. Second, due to their worst-case nature, the existing upper bound analysis fails to adapt to easier problem instances within the function class. Our work takes steps to address both these issues. First, we derive \emph{instance-dependent} regret lower bounds for algorithms with uniformly~(over the function class) vanishing normalized cumulative regret. Our result, valid for all the practically relevant kernelized bandits algorithms, such as, GP-UCB, GP-TS and SupKernelUCB, identifies a fundamental complexity measure associated with every problem instance. We then address the second issue, by proposing a new minimax near-optimal algorithm which also adapts to easier problem instances.
Bit-Metric Decoding Rate in Multi-User MIMO Systems: Theory

LA is one of the most important aspects of wireless communications where the MCS used by the transmitter is adapted to the channel conditions in order to meet a certain target error-rate. In a SU-SISO system with out-of-cell interference, LA is performed by computing the post-equalization SINR at the receiver. The same technique can be employed in MU-MIMO receivers that use linear detectors. Another important use of post-equalization SINR is for PHY abstraction, where several PHY blocks like the channel encoder, the detector, and the channel decoder are replaced by an abstraction model in order to speed up system-level simulations. However, for MU-MIMO systems with non-linear receivers, there is no known equivalent of post-equalization SINR which makes both LA and PHY abstraction extremely challenging. This important issue is addressed in this two-part paper. In this part, a metric called the BMDR of a detector, which is the proposed equivalent of post-equalization SINR, is presented. Since BMDR does not have a closed form expression that would enable its instantaneous calculation, a machine-learning approach to predict it is presented along with extensive simulation results.
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation

The recent large-scale vision-language pre-training (VLP) of dual-stream architectures (e.g., CLIP) with a tremendous amount of image-text pair data, has shown its superiority on various multimodal alignment tasks. Despite its success, the resulting models are not capable of multimodal generative tasks due to the weak text encoder. To tackle this problem, we propose to augment the dual-stream VLP model with a textual pre-trained language model (PLM) via vision-language knowledge distillation (VLKD), enabling the capability for multimodal generation. VLKD is pretty data- and computation-efficient compared to the pre-training from scratch. Experimental results show that the resulting model has strong zero-shot performance on multimodal generation tasks, such as open-ended visual question answering and image captioning. For example, it achieves 44.5% zero-shot accuracy on the VQAv2 dataset, surpassing the previous state-of-the-art zero-shot model with 7\times fewer parameters. Furthermore, the original textual language understanding and generation ability of the PLM is maintained after VLKD, which makes our model versatile for both multimodal and unimodal tasks.
Spin‐Chain‐Star Systems: Entangling Multiple Chains of Spin Qubits

We consider spin‐chain‐star systems characterized by N‐wise many‐body interactions between the spins in each chain and the central one. We show that such systems can be exactly mapped into standard spin‐star systems through unitary transformations. Such an approach allows the solution of the dynamic problem of an XX$X X$ spin‐chain‐star model and transparently shows the emergence of quantum correlations in the system, based on the idea of entanglement between chains.
Dataset and Case Studies for Visual Near-Duplicates Detection in the Context of Social Media

The massive spread of visual content through the web and social media poses both challenges and opportunities. Tracking visually-similar content is an important task for studying and analyzing social phenomena related to the spread of such content. In this paper, we address this need by building a dataset of social media images and evaluating visual near-duplicates retrieval methods based on image retrieval and several advanced visual feature extraction methods. We evaluate the methods using a large-scale dataset of images we crawl from social media and their manipulated versions we generated, presenting promising results in terms of recall. We demonstrate the potential of this method in two case studies: one that shows the value of creating systems supporting manual content review, and another that demonstrates the usefulness of automatic large-scale data analysis.
Learning Circles for Advanced Professional Development in Evaluation

Abstract: Studies of Canadian evaluators have consistently shown them to be dissatisfied with opportunities for advanced training, suggesting a need to diversify the forms of professional development available to seasoned evaluators. This article reports on a trial implementation of an alternative learning model: learning circles for advanced professional development in evaluation. This model is grounded in approaches drawn from self-directed learning, self-improvement movements, adult and popular education, quality improvement, and professional journal clubs. Learning circles bring together experienced practitioners in structured collaborative learning cycles about topics of mutual interest. We experimented with an evaluation learning circle over several cycles, and report on what we learned about purpose, process, and outcomes for professional development. We hope that this model will be of interest to other evaluators, especially in the context of the competency maintenance requirements of the CE designation.
On step sizes rules for the continuous stochastic gradient scheme Convergence results for constant step sizes and an Armijo-type line search

The recently proposed continuous stochastic gradient descent (CSG) scheme represents an eﬃcient method for the solution of a large class of stochastic optimization problems. During the course of the iterations, CSG uses a tailored convex combination of previously drawn gradient and objective function samples to approximate the full gradient and objective function value at the current position. It was shown that the error in these approximations tends to zero during the optimization process and thus, CSG more and more behaves like a full gradient descent algorithm. In this contribution, we show that convergence results for constant step sizes known from full gradient descent schemes carry over to CSG. Furthermore, we augment CSG by an Armijo-type backtracking line search based on the gradient and objective function approximations and provide convergence results for this variant. The resulting novel methods are applicable for a large class of optimization problems. Their performance is demonstrated in comparison to other stochastic gradient schemes known from literature.
Adversarial amplitude swap towards robust image classifiers

The vulnerability of convolutional neural networks (CNNs) to image perturbations such as common corruptions and adversarial perturbations has recently been investigated from the perspective of frequency. In this study, we investigate the effect of the amplitude and phase spectra of adversarial images on the robustness of CNN classifiers. Extensive experiments revealed that the images generated by combining the amplitude spectrum of adversarial images and the phase spectrum of clean images accommodates moderate and general perturbations, and training with these images equips a CNN classifier with more general robustness, performing well under both common corruptions and adversarial perturbations. We also found that two types of overfitting (catastrophic overfitting and robust overfitting) can be circumvented by the aforementioned spectrum recombination. We believe that these results contribute to the understanding and the training of truly robust classifiers.
Finite-Key Analysis of Quantum Key Distribution with Characterized Devices Using Entropy Accumulation

1Institute for Quantum Computing and Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada 2Department of Electrical & Computer Engineering, University of Illinois, Urbana, Illinois 61801, USA 3Department of Electrical & Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Canada 4Institute for Quantum Computing, Baidu Research, Beijing 100193, China
CLIP Models are Few-Shot Learners: Empirical Studies on VQA and Visual Entailment

CLIP has shown a remarkable zero-shot capability on a wide range of vision tasks. Previously, CLIP is only regarded as a powerful visual encoder. However, after being pre-trained by language supervision from a large amount of image-caption pairs, CLIP itself should also have acquired some few-shot abilities for vision-language tasks. In this work, we empirically show that CLIP can be a strong vision-language few-shot learner by leveraging the power of language. We first evaluate CLIP’s zero-shot performance on a typical visual question answering task and demonstrate a zero-shot cross-modality transfer capability of CLIP on the visual entailment task. Then we propose a parameter-efficient fine-tuning strategy to boost the few-shot performance on the vqa task. We achieve competitive zero/few-shot results on the visual question answering and visual entailment tasks without introducing any additional pre-training procedure.
Entropic Optimal Planning for Path-Dependent Mean Field Games

In the context of mean field games, with possible control of the diffusion coefficient, we consider a path-dependent version of the planning problem introduced by P.L. Lions: given a pair of marginal distributions $(\mu_0, \mu_1)$, find a specification of the game problem starting from the initial distribution $\mu_0$, and inducing the target distribution $\mu_1$ at the mean field game equilibrium. Our main result reduces the path-dependent planning problem into an embedding problem, that is, constructing a McKean-Vlasov dynamics with given marginals $(\mu_0,\mu_1)$. Some sufficient conditions on $(\mu_0,\mu_1)$ are provided to guarantee the existence of solutions. We also characterize, up to integrability, the minimum entropy solution of the planning problem. In particular, as uniqueness does not hold anymore in our path-dependent setting, one can naturally introduce an optimal planning problem which would be reduced to an optimal transport problem along with controlled McKean-Vlasov dynamics.
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation

We consider model-based multi-agent reinforcement learning, where the environment transition model is unknown and can only be learned via expensive interactions with the environment. We propose H-MARL (Hallucinated Multi-Agent Reinforcement Learning), a novel sample-efficient algorithm that can efficiently balance exploration, i.e., learning about the environment, and exploitation, i.e., achieve good equilibrium performance in the underlying general-sum Markov game. H-MARL builds high-probability confidence intervals around the unknown transition model and sequentially updates them based on newly observed data. Using these, it constructs an optimistic hallucinated game for the agents for which equilibrium policies are computed at each round. We consider general statistical models (e.g., Gaussian processes, deep ensembles, etc.) and policy classes (e.g., deep neural networks), and theoretically analyze our approach by bounding the agents' dynamic regret. Moreover, we provide a convergence rate to the equilibria of the underlying Markov game. We demonstrate our approach experimentally on an autonomous driving simulation benchmark. H-MARL learns successful equilibrium policies after a few interactions with the environment and can significantly improve the performance compared to non-optimistic exploration methods.
Maxway CRT: Improving the Robustness of the Model-X Inference

The model-X conditional randomization test (CRT) is a flexible and powerful testing procedure for the conditional independence hypothesis: X is independent of Y conditioning on Z. Though having many attractive properties, the model-X CRT relies on the model-X assumption that we have perfect knowledge of the distribution of X | Z. If there is an error in modeling the distribution of X | Z, this approach may lose its validity. This problem is even more severe when the adjustment covariates Z are of high dimensionality, in which situation precise modeling of X against Z can be hard. In response to this, we propose the Maxway (Model and Adjust X With the Assistance of Y) CRT, which learns the distribution of Y | Z, and uses it to calibrate the resampling distribution of X to gain robustness to the error in modeling X. We prove that the type-I error inflation of the Maxway CRT can be controlled by the learning error for the low-dimensional adjusting model plus the product of learning errors for X | Z and Y | Z, which could be interpreted as an"almost doubly robust"property. Based on this, we develop implementing algorithms of the Maxway CRT in practical scenarios including (surrogate-assisted) semi-supervised learning and transfer learning where valid information about Y | Z can be potentially provided by some auxiliary or external data. Through extensive simulation studies under different scenarios, we demonstrate that the Maxway CRT achieves significantly better type-I error control than existing model-X inference approaches while preserving similar powers. Finally, we apply our methodology to two real examples, including (1) studying obesity paradox with electronic health record (EHR) data assisted by surrogate variables; (2) inferring the side effect of statins among the ethnic minority group via transferring knowledge from the majority group.