Miracle-dz/UniFAR-FTUs huggingface dataset:
A one-size-fits-all artificial pancreas for people with type 1 diabetes based on physiological insight and feedback control

We propose a model-free artificial pancreas (AP) for people with type 1 diabetes. The algorithmic parameters are tuned to a virtual population of 1,000,000 individuals, and the AP repeatedly estimates the basal and bolus insulin requirements necessary for maintaining normal blood glucose levels. Therefore, the AP can be used without healthcare personnel or engineers customizing the algorithm to each user. The estimates are based on bodyweight, measurements from a continuous glucose monitor (CGM), and estimates of the meal carbohydrate contents. In a virtual clinical trial with a111,000,000 individuals (i.e., a Monte Carlo closed-loop simulation), the AP achieves a mean time in range of more than 87% and over 88% of the participants satisfy several glycemic targets.
Perceptions of the State of D&I and D&I Initiative in the ASF

Open Source Software (OSS) Foundations and projects are investing in creating Diversity and Inclusion (D&I) initiatives. However, little is known about contributors‘ perceptions about the usefulness and success of such initiatives. We aim to close this gap by investigating how contributors perceive the state of D&I in their community. In collaboration with the Apache Software Foundation (ASF), we surveyed 600+ OSS contributors and conducted 11 follow-up interviews. We used mixed methods to analyze our data-quantitative analysis of Likert-scale questions and qualitative analysis of open-ended survey question and the interviews to understand contributors‘ perceptions and critiques of the D&I initiative and how to improve it. Our results indicate that the ASF contributors felt that the state of D&I was still lacking, especially regarding gender, seniority, and English proficiency. Regarding the D&I initiative, some participants felt that the effort was unnecessary, while others agreed with the effort but critiqued its implementation. These findings show that D&I initiatives in OSS communities are a good start, but there is room for improvements. Our results can inspire the creation of new and the refinement of current initiatives. Open Source Software (OSS) is widely used in society (e.g., Linux, Chrome, and Firefox), and contributing to these projects helps individuals learn and showcase their skills, so much so that the history of contributions are increasingly being analyzed by hirers. However, the people who contribute to OSS are predominately men (about 90%). This means that women and other minorities lose out on job opportunities and OSS projects lose out on diversity of thought. OSS organizations such as the Apache Software Foundation (ASF) promote a variety of initiatives to increase diversity and inclusion (D&I) in their projects, but they are piece-meal and little is known about contributors‘ perceptions about the usefulness and success of these initiatives. Here, we surveyed and interviewed ASF contributors to understand their perceptions about the state of D&I in the ASF and the effectiveness of existing D&I initiatives. Our findings show that individuals who are in the minority face chal-lenges (e.g., stereotyping, lack of peer-network, and representation in decision making) and contributors‘ perceptions of the D&I initiative are a mixed bag, ranging from commending the current efforts to considering them to be “lip service”. These findings suggest that current D&I initiatives in OSS communities are a good start, but much needs be done in terms of creating new successful initiatives and refining current ones.
The Art of Manipulation: Threat of Multi-Step Manipulative Attacks in Security Games

This paper studies the problem of multi-step manipulative attacks in Stackelberg security games, in which a clever attacker attempts to orchestrate its attacks over multiple time steps to mislead the defender's learning of the attacker's behavior. This attack manipulation eventually influences the defender's patrol strategy towards the attacker's benefit. Previous work along this line of research only focuses on one-shot games in which the defender learns the attacker's behavior and then designs a corresponding strategy only once. Our work, on the other hand, investigates the long-term impact of the attacker's manipulation in which current attack and defense choices of players determine the future learning and patrol planning of the defender. This paper has three key contributions. First, we introduce a new multi-step manipulative attack game model that captures the impact of sequential manipulative attacks carried out by the attacker over the entire time horizon. Second, we propose a new algorithm to compute an optimal manipulative attack plan for the attacker, which tackles the challenge of multiple connected optimization components involved in the computation across multiple time steps. Finally, we present extensive experimental results on the impact of such misleading attacks, showing a significant benefit for the attacker and loss for the defender.
Semi-Supervised Learning and Data Augmentation in Wearable-based Momentary Stress Detection in the Wild

Physiological and behavioral data collected from wearable or mobile sensors have been used to estimate self-reported stress levels. Since the stress annotation usually relies on self-reports during the study, a limited amount of labeled data can be an obstacle in developing accurate and generalized stress predicting models. On the other hand, the sensors can continuously capture signals without annotations. This work investigates leveraging unlabeled wearable sensor data for stress detection in the wild. We first applied data augmentation techniques on the physiological and behavioral data to improve the robustness of supervised stress detection models. Using an auto-encoder with actively selected unlabeled sequences, we pre-trained the supervised model structure to leverage the information learned from unlabeled samples. Then, we developed a semi-supervised learning framework to leverage the unlabeled data sequences. We combined data augmentation techniques with consistency regularization, which enforces the consistency of prediction output based on augmented and original unlabeled data. We validated these methods using three wearable/mobile sensor datasets collected in the wild. Our results showed that combining the proposed methods improved stress classification performance by 7.7% to 13.8% on the evaluated datasets, compared to the baseline supervised learning models.
Fixing the rotating-wave approximation for strongly detuned quantum oscillators

Periodically driven oscillators are commonly described in a frame corotating with the drive and using the rotating-wave approximation (RWA). This description, however, is known to induce errors for off-resonant driving. Here, we show that the standard quantum description, using the creation and annihilation of particles with the oscillators’ natural frequency, necessarily leads to incorrect results when combined with the RWA. We demonstrate this on the simple (quantum) harmonic oscillator and present an alternative operator basis that reconciles the RWA with off-resonant driving. The approach is also applicable to more complex models, where it accounts for known discrepancies. As an example, we demonstrate the advantage of our scheme on a driven quantum Dufﬁng oscillator.
Vertical Machine Unlearning: Selectively Removing Sensitive Information From Latent Feature Space

Recently, the enactment of privacy regulations has promoted the rise of machine unlearning paradigm. Most existing studies mainly focus on removing unwanted data samples from a learnt model. Yet we argue that they remove overmuch information of data samples from latent feature space, which is far beyond the sensitive feature scope that gen-uinely needs to be unlearned. In this paper, we investigate a vertical unlearning mode, aiming at removing only sensitive information from latent feature space. First, we introduce intuitive and formal deﬁnitions for this unlearning and show its orthogonal relationship with existing horizontal un-learning. Secondly, given the fact of lacking general solutions to vertical unlearning, we introduce a ground-breaking solution based on representation detachment, where the task-related information is encouraged to retain while the sensitive information is progressively forgotten. Thirdly, observing that some computation results during representation detachment are hard to obtain in practice, we propose an approximation with an upper bound to estimate it, with rigorous theoretical analysis. We validate our method by spanning several datasets and models with prevailing performance. We en-vision this work as a necessity for future machine unlearning system and a essential component of the latest privacy-related legislation.
Decomposing past and future: Integrated information decomposition based on shared probability mass exclusions

A core feature of complex systems is that the interactions between elements in the present causally constrain their own futures, and the futures of other elements as the system evolves through time. To fully model all of these interactions (between elements, as well as ensembles of elements), it is possible to decompose the total information flowing from past to future into a set of non-overlapping temporal interactions that describe all the different modes by which information can be stored, transferred, or modified. To achieve this, I propose a novel information-theoretic measure of temporal dependency (Iτsx) based on the logic of local probability mass exclusions. This integrated information decomposition can reveal emergent and higher-order interactions within the dynamics of a system, as well as refining existing measures. To demonstrate the utility of this framework, I apply the decomposition to spontaneous spiking activity recorded from dissociated neural cultures of rat cerebral cortex to show how different modes of information processing are distributed over the system. Furthermore, being a localizable analysis, Iτsx can provide insight into the computational structure of single moments. I explore the time-resolved computational structure of neuronal avalanches and find that different types of information atoms have distinct profiles over the course of an avalanche, with the majority of non-trivial information dynamics happening before the first half of the cascade is completed. These analyses allow us to move beyond the historical focus on single measures of dependency such as information transfer or information integration, and explore a panoply of different relationships between elements (and groups of elements) in complex systems.
Generalized Label Shift Correction via Minimum Uncertainty Principle: Theory and Algorithm

As a fundamental problem in machine learning, dataset shift induces a paradigm to learn and transfer knowledge under changing environment. Previous methods assume the changes are induced by covariate, which is less practical for complex real-world data. We consider the Generalized Label Shift (GLS), which provides an interpretable insight into the learning and transfer of desirable knowledge. Current GLS methods: 1) are not well-connected with the statistical learning theory; 2) usually assume the shifting conditional distributions will be matched with an implicit transformation, but its explicit modeling is unexplored. In this paper, we propose a conditional adaptation framework to deal with these challenges. From the perspective of learning theory, we prove that the generalization error of conditional adaptation is lower than previous covariate adaptation. Following the theoretical results, we propose the minimum uncertainty principle to learn conditional invariant transformation via discrepancy optimization. Specifically, we propose the \textit{conditional metric operator} on Hilbert space to characterize the distinctness of conditional distributions. For finite observations, we prove that the empirical estimation is always well-defined and will converge to underlying truth as sample size increases. The results of extensive experiments demonstrate that the proposed model achieves competitive performance under different GLS scenarios.
Subgradient Sampling for Nonsmooth Nonconvex Minimization

Risk minimization for nonsmooth nonconvex problems naturally leads to firstorder sampling or, by an abuse of terminology, to stochastic subgradient descent. We establish the convergence of this method in the path-differentiable case, and describe more precise results under additional geometric assumptions. We recover and improve results from Ermoliev-Norkin by using a different approach: conservative calculus and the ODE method. In the definable case, we show that first-order subgradient sampling avoids artificial critical point with probability one and applies moreover to a large range of risk minimization problems in deep learning, based on the backpropagation oracle. As byproducts of our approach, we obtain several results on integration of independent interest, such as an interchange result for conservative derivatives and integrals, or the definability of set-valued parameterized integrals.
Hubble Space Telescope Detection of the Nucleus of Comet C/2014 UN271 (Bernardinelli–Bernstein)

We present a high-resolution observation of the distant comet C/2014 UN271 (Bernardinelli–Bernstein) using the Hubble Space Telescope on 2022 January 8. The signal of the nucleus was successfully isolated by means of a nucleus extraction technique, with an apparent V-band magnitude measured to be 21.65 ± 0.11, corresponding to an absolute magnitude of 8.63 ± 0.11. The product of the visual geometric albedo with the effective radius squared is pVRn2=157±16 km2. If the ALMA observation by Lellouch et al. refers to a bare nucleus, we derived a visual geometric albedo of 0.033 ± 0.009. If dust contamination of the ALMA signal is present at the maximum allowed level (24%), we found an albedo of 0.044 ± 0.012 for the nucleus having an effective diameter of 119 ± 15 km. In either case, we confirm that C/2014 UN271 is the largest long-period comet ever detected. Judging from the measured surface brightness profile of the coma, whose logarithmic gradient varies azimuthally between ∼1.0 and 1.7 as a consequence of solar radiation pressure, the mass production is consistent with steady-state production but not with impulsive ejection, as would be produced by an outburst. Using aperture photometry, we estimated an enormous (albeit uncertain) mass-loss rate of ∼103 kg s−1 at a heliocentric distance of ∼20 au.
TaSPM: Targeted Sequential Pattern Mining

Sequential pattern mining (SPM) is an important technique in the field of pattern mining, which has many applications in reality. Although many efficient SPM algorithms have been proposed, there are few studies that can focus on targeted tasks. Targeted querying of the concerned sequential patterns can not only reduce the number of patterns generated, but also increase the efficiency of users in performing related analysis. The current algorithms available for targeted sequence querying are based on specific scenarios and can not be extended to other applications. In this paper, we formulate the problem of targeted sequential pattern mining and propose a generic algorithm, namely TaSPM. What is more, to improve the efficiency of TaSPM on large-scale datasets and multiple-item-based sequence datasets, we propose several pruning strategies to reduce meaningless operations in the mining process. Totally four pruning strategies are designed in TaSPM, and hence TaSPM can terminate unnecessary pattern extensions quickly and achieve better performance. Finally, we conducted extensive experiments on different datasets to compare the baseline SPM algorithm with TaSPM. Experiments show that the novel targeted mining algorithm TaSPM can achieve faster running time and less memory consumption.
A self-similar morphology detected in a composite produced by densification of co-crumpled metallic thin foils

A new kind of composite were manufactured by densification of co-crumpled aluminium and tantalum thin foils using close die compression. It was shown by optical micrography that its microstructure is highly interlocked. The morphology was analysed quantitatively in terms of the following three parameters: the area of the foil interface per unit volume, the interface tortuosity, and a characteristic of the local orientation of the foil surface. Based on these parameters, co-crumpled material studied has been compared with conventional laminates. A fractal nature of its self-similar structure was revealed.
Inkorrect: Online Handwriting Spelling Correction

We introduce Inkorrect, a data- and label-efficient approach for online handwriting (Digital Ink) spelling correction - DISC. Unlike previous work, the proposed method does not require multiple samples from the same writer, or access to character level segmentation. We show that existing automatic evaluation metrics do not fully capture and are not correlated with the human perception of the quality of the spelling correction, and propose new ones that correlate with human perception. We additionally surface an interesting phenomenon: a trade-off between the similarity and recognizability of the spell-corrected inks. We further create a family of models corresponding to different points on the Pareto frontier between those two axes. We show that Inkorrect's Pareto frontier dominates the points that correspond to prior work.
Vortex shedding frequency of a moving obstacle in a Bose–Einstein condensate

We experimentally investigate the periodic vortex shedding dynamics in a highly oblate Bose–Einstein condensate using a moving penetrable Gaussian obstacle. The shedding frequency f v is measured as a function of the obstacle velocity v and characterized by a linear relationship of f v = a(v − v c) with v c being the critical velocity. The proportionality constant a is linearly decreased with a decrease in the obstacle strength, whereas v c approaches the speed of sound. When the obstacle size increases, both a and v c are decreased. We discuss a possible association of a with the Strouhal number in the context of universal shedding dynamics of a superfluid. The critical vortex shedding is further investigated for an oscillating obstacle and found to be consistent with the measured f v. When the obstacle’s maximum velocity exceeds v c but its oscillation amplitude is not large enough to create a vortex dipole, we observe that vortices are generated in the low-density boundary region of the trapped condensate, which is attributed to the phonon emission from the oscillating obstacle.
A Note on the Hardness of Problems from Cryptographic Group Actions

Given a cryptographic group action, we show that the Group Action Inverse Problem (GAIP) and other related problems cannot be NP-hard unless the Polynomial Hierarchy collapses. We show this via random self-reductions and the design of interactive proofs. Since cryptographic group actions are the building block of many security protocols, this result serves both as an upper bound on the worst-case complexity of some cryptographic assumptions and as proof that the hardness in the worst and in the average case coincide. We also point out the link with Graph Isomorphism and other related NP intermediate problems.
The evolution of scientific literature as metastable knowledge states

The problem of identifying common concepts in the sciences and deciding when new ideas have emerged is an open one. Metascience researchers have sought to formalize principles underlying stages in the life cycle of scientific research, understand how knowledge is transferred between scientists and stakeholders, and explain how new ideas are generated and take hold. Here, we model the state of scientific knowledge immediately preceding new directions of research as a metastable state and the creation of new concepts as combinatorial innovation. Through a novel approach combining natural language clustering and citation graph analysis, we predict the evolution of ideas over time and thus connect a single scientific article to past and future concepts in a way that goes beyond traditional citation and reference connections.
A Simple but Effective Pluggable Entity Lookup Table for Pre-trained Language Models

Pre-trained language models (PLMs) cannot well recall rich factual knowledge of entities exhibited in large-scale corpora, especially those rare entities. In this paper, we propose to build a simple but effective Pluggable Entity Lookup Table (PELT) on demand by aggregating the entity’s output representations of multiple occurrences in the corpora. PELT can be compatibly plugged as inputs to infuse supplemental entity knowledge into PLMs. Compared to previous knowledge-enhanced PLMs, PELT only requires 0.2%-5% pre-computation with capability of acquiring knowledge from out-of-domain corpora for domain adaptation scenario. The experiments on knowledge-related tasks demonstrate that our method, PELT, can flexibly and effectively transfer entity knowledge from related corpora into PLMs with different architectures. Our code and models are publicly available at https://github.com/thunlp/PELT
Optical Inspection Method for Surface Defects of Micro-components

In order to realize the rapid precise detection for surface defects of micro-components,the optical detection technology was studied.Taking a typical micro ring component as object and considering its surface features and defect characteristics,an optical detection system was established on the basis of machine vision and pattern recognition.Through studies of the surface flaw texture in the component,an algorithm using weighted information entropy of image gradient variance was proposed to suppress the random surface texture,extract the flaws and detect the flaws in the micro-component.Experiment results show that the algorithm can effectively detect the flaws,and the recognition rate of the defect is about 96.5%. Also,the experiments verify its correctness,rapidity and preciseness.
Adversarial Contrastive Self-Supervised Learning

Recently, learning from vast unlabeled data, especially self-supervised learning, has been emerging and attracted widespread attention. Self-supervised learning followed by the supervised fine-tuning on a few labeled examples can significantly improve label efficiency and outperform standard supervised training using fully annotated data. In this work, we present a novel self-supervised deep learning paradigm based on online hard negative pair mining. Specifically, we design a student-teacher network to generate multi-view of the data for self-supervised learning and integrate hard negative pair mining into the training. Then we derive a new triplet-like loss considering both positive sample pairs and mined hard negative sample pairs. Extensive experiments demonstrate the effectiveness of the proposed method and its components on ILSVRC-2012.