Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward a Sensitivity-based Implicit Measure of Patients' Important Goal Pursuits

When individuals arrive to receive help from mental health providers, they do not always have well-specified and well-established goals. It is the mental health providers' responsibility to work collaboratively with patients to clarify their goals – in the therapy sessions as well as life in general -through clinical interviews, diagnostic assessments, and thorough observations. However, recognizing individuals' important life goals is not always straightforward. Here we introduce a novel method that gauges a patient’s important goal pursuits from their relative sensitivity to goal-related words. Past research has shown that a person’s active goal pursuits cause them to be more sensitive to the presence of goal-related stimuli in the environment – being able to consciously report those stimuli when others cannot see them. By presenting words related to a variety of different life goal pursuits very quickly (50 msec or less), the patient would be expected to notice and be aware of words related to their strongest motivations but not the other goal-related words. These may or may not be among the goals they have identified in therapy sessions, and the ones not previously identified can be fertile grounds for further discussion and exploration in subsequent therapy sessions. Results from eight patient volunteers are described and discussed in terms of the potential utility of this supplemental personal therapy aid.
Outer commutator words are uniformly concise

We prove that outer commutator words are uniformly concise, that is, if an outer commutator word ω takes m different values in a group G, then the order of the verbal subgroup ω(G) is bounded by a function depending only on m, and not on ω or G. This is obtained as a consequence of a structure theorem for the subgroup ω(G), which is valid if G is soluble, and without assuming that ω takes finitely many values in G. More precisely, there is an abelian series of ω(G), such that every section of the series can be generated by values of ω all of whose powers are also values of ω in that section. For the proof of this latter result, we introduce a new representation of outer commutator words by means of binary trees, and we use the structure of the trees to set up an appropriate induction.
Toward a realistic model of speech processing in the brain with self-supervised learning

Several deep neural networks have recently been shown to generate activations similar to those of the brain in response to the same input. These algorithms, however, remain largely implausible: they require (1) extraordinarily large amounts of data, (2) unobtainable supervised labels, (3) textual rather than raw sensory input, and / or (4) implausibly large memory (e.g. thousands of contextual words). These elements highlight the need to identify algorithms that, under these limitations, would suffice to account for both behavioral and brain responses. Focusing on the issue of speech processing, we here hypothesize that self-supervised algorithms trained on the raw waveform constitute a promising candidate. Specifically, we compare a recent self-supervised architecture, Wav2Vec 2.0, to the brain activity of 412 English, French, and Mandarin individuals recorded with functional Magnetic Resonance Imaging (fMRI), while they listened to ~1h of audio books. Our results are four-fold. First, we show that this algorithm learns brain-like representations with as little as 600 hours of unlabelled speech -- a quantity comparable to what infants can be exposed to during language acquisition. Second, its functional hierarchy aligns with the cortical hierarchy of speech processing. Third, different training regimes reveal a functional specialization akin to the cortex: Wav2Vec 2.0 learns sound-generic, speech-specific and language-specific representations similar to those of the prefrontal and temporal cortices. Fourth, we confirm the similarity of this specialization with the behavior of 386 additional participants. These elements, resulting from the largest neuroimaging benchmark to date, show how self-supervised learning can account for a rich organization of speech processing in the brain, and thus delineate a path to identify the laws of language acquisition which shape the human brain.
Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination

Graph contrastive learning (GCL) alleviates the heavy reliance on label information for graph representation learning (GRL) via self-supervised learning schemes. The core idea is to learn by maximising mutual information for similar instances, which requires similarity computation between two node instances. However, GCL is inefficient in both time and memory consumption. In addition, GCL normally requires a large number of training epochs to be well-trained on large-scale datasets. Inspired by an observation of a technical defect (i.e., inappropriate usage of Sigmoid function) commonly used in two representative GCL works, DGI and MVGRL, we revisit GCL and introduce a new learning paradigm for self-supervised graph representation learning, namely, Group Discrimination (GD), and propose a novel GD-based method called Graph Group Discrimination (GGD). Instead of similarity computation, GGD directly discriminates two groups of node samples with a very simple binary cross-entropy loss. In addition, GGD requires much fewer training epochs to obtain competitive performance compared with GCL methods on large-scale datasets. These two advantages endow GGD with very efficient property. Extensive experiments show that GGD outperforms state-of-the-art self-supervised methods on eight datasets. In particular, GGD can be trained in 0.18 seconds (6.44 seconds including data preprocessing) on ogbn-arxiv, which is orders of magnitude (10,000+) faster than GCL baselines while consuming much less memory. Trained with 9 hours on ogbn-papers100M with billion edges, GGD outperforms its GCL counterparts in both accuracy and efficiency.
Information-field-based global Bayesian inference of the jet transport coefficient

Bayesian statistical inference is a powerful tool for model-data comparisons and extractions of physical parameters that are often unknown functions of system variables. Existing Bayesian analyses often rely on explicit parametrizations of the unknown function. It can introduce long-range correlations that impose fictitious constraints on physical parameters in regions of the variable space that are not probed by the experimental data. We develop an information field (IF) approach to modeling the prior distribution of the unknown function that is free of long-range correlations. We apply the IF approach to the first global Bayesian inference of the jet transport coefficient $\hat q$ as a function of temperature ($T$) from all existing experimental data on single-inclusive hadron, di-hadron and $\gamma$-hadron spectra in heavy-ion collisions at RHIC and LHC energies. The extracted $\hat q/T^3$ exhibits a strong $T$-dependence as a result of the progressive constraining power when data from more central collisions and at higher colliding energies are incrementally included. The IF method guarantees that the extracted $T$-dependence is not biased by a specific functional form.
Rethinking Positive Sampling for Contrastive Learning with Kernel

Data augmentation is a crucial component in unsupervised contrastive learning (CL). It determines how positive samples are deﬁned and, ultimately, the quality of the representation. While efﬁcient augmentations have been found for standard vision datasets, such as ImageNet, it is still an open problem in other applications, such as medical imaging, or in datasets with easy-to-learn but irrelevant imaging features. In this work, we propose a new way to deﬁne positive samples using kernel theory along with a novel loss called decoupled uniformity . We propose to integrate prior information, learnt from generative models or given as auxiliary attributes, into contrastive learning, to make it less dependent on data augmentation. We draw a connection between contrastive learning and the conditional mean embedding theory to derive tight bounds on the downstream classiﬁcation loss. In an unsupervised setting, we empirically demonstrate that CL beneﬁts from generative models, such as VAE and GAN, to less rely on data augmentations. We validate our framework on vision datasets including CIFAR10, CIFAR100, STL10 and ImageNet100 and a brain MRI dataset. In the weakly supervised setting, we demonstrate that our formulation provides state-of-the-art results.
Optimal Activation Functions for the Random Features Regression Model

The asymptotic mean squared test error and sensitivity of the Random Features Regression model (RFR) have been recently studied. We build on this work and identify in closed-form the family of Activation Functions (AFs) that minimize a combination of the test error and sensitivity of the RFR under different notions of functional parsimony. We find scenarios under which the optimal AFs are linear, saturated linear functions, or expressible in terms of Hermite polynomials. Finally, we show how using optimal AFs impacts well-established properties of the RFR model, such as its double descent curve, and the dependency of its optimal regularization parameter on the observation noise level.
Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards

Incrementality, which is used to measure the causal effect of showing an ad to a potential customer (e.g. a user in an internet platform) versus not, is a central object for advertisers in online advertising platforms. This paper investigates the problem of how an advertiser can learn to optimize the bidding sequence in an online manner \emph{without} knowing the incrementality parameters in advance. We formulate the offline version of this problem as a specially structured episodic Markov Decision Process (MDP) and then, for its online learning counterpart, propose a novel reinforcement learning (RL) algorithm with regret at most $\widetilde{O}(H^2\sqrt{T})$, which depends on the number of rounds $H$ and number of episodes $T$, but does not depend on the number of actions (i.e., possible bids). A fundamental difference between our learning problem from standard RL problems is that the realized reward feedback from conversion incrementality is \emph{mixed} and \emph{delayed}. To handle this difficulty we propose and analyze a novel pairwise moment-matching algorithm to learn the conversion incrementality, which we believe is of independent of interest.
Research on Unbalance Fault-Tolerant Control Strategy of Modular Multilevel Photovoltaic Grid-Connected Inverter

The average switching model of modular multilevel converter (MMC) is built in this paper when the hot reserved strategy is adopted as a fault-tolerant control. When the MMC SM faults, the rest of the SMs cannot support the DC-link voltage, which results in interruption of the inverter. To tackle this issue, a novel fault-tolerant control strategy is proposed to bypass the SM under fault and re-regulate the SM capacitor voltage and carrier phase-shift angle to maintain the main components of circulating current, and reduce the Total Harmonic Distortion (THD) of grid connected current to enable the stable operation of the photovoltaic inverter. The maximum power tracking control is improved to solve the problem of long restoration time when faults occur, based on a constant voltage startup method combined with a fix-step incremental conductance method. Simulations in MATLAB/Simulink and experimental results have verified the feasibility and effectiveness of the proposed control strategy.
Minimal prime ages, words and permutation graphs Extended abstract

This paper is a contribution to the study of hereditary classes of finite graphs. We classify these classes according to the number of prime structures they contain. We consider such classes that are \emph{minimal prime}: classes that contain infinitely many primes but every proper hereditary subclass contains only finitely many primes. We give a complete characterization of such classes. In fact, each one of these classes is a well quasi ordered age and there are uncountably many of them. Eleven of these ages remain well quasi ordered when labels in a well quasi ordering are added. Among the remaining ones, countably many remain well quasi ordered when one label is added. Except for six examples, members of these ages we characterize are permutation graphs. In fact, every age which is not among the eleven ones is the age of a graph associated to a uniformly recurrent $0$-$1$ word on the integers. A characterization of minimal prime classes of posets and bichains is also provided.
Mean Field Approximations via Log-Concavity

We propose a new approach to deriving quantitative mean field approximations for any probability measure $P$ on $\mathbb {R}^{n}$ with density proportional to $e^{f(x)}$, for $f$ strongly concave. We bound the mean field approximation for the log partition function $\log \int e^{f(x)}dx$ in terms of $\sum _{i \neq j}\mathbb {E}_{Q^{*}}|\partial _{ij}f|^{2}$, for a semi-explicit probability measure $Q^{*}$ characterized as the unique mean field optimizer, or equivalently as the minimizer of the relative entropy $H(\cdot \,|\,P)$ over product measures. This notably does not involve metric-entropy or gradient-complexity concepts which are common in prior work on nonlinear large deviations. Three implications are discussed, in the contexts of continuous Gibbs measures on large graphs, high-dimensional Bayesian linear regression, and the construction of decentralized near-optimizers in high-dimensional stochastic control problems. Our arguments are based primarily on functional inequalities and the notion of displacement convexity from optimal transport.
Improving Fairness in Large-Scale Object Recognition by CrowdSourced Demographic Information

There has been increasing awareness of ethical issues in machine learning, and fairness has become an important research topic. Most fairness efforts in computer vision have been focused on human sensing applications and preventing discrimination by people's physical attributes such as race, skin color or age by increasing visual representation for particular demographic groups. We argue that ML fairness efforts should extend to object recognition as well. Buildings, artwork, food and clothing are examples of the objects that define human culture. Representing these objects fairly in machine learning datasets will lead to models that are less biased towards a particular culture and more inclusive of different traditions and values. There exist many research datasets for object recognition, but they have not carefully considered which classes should be included, or how much training data should be collected per class. To address this, we propose a simple and general approach, based on crowdsourcing the demographic composition of the contributors: we define fair relevance scores, estimate them, and assign them to each class. We showcase its application to the landmark recognition domain, presenting a detailed analysis and the final fairer landmark rankings. We present analysis which leads to a much fairer coverage of the world compared to existing datasets. The evaluation dataset was used for the 2021 Google Landmark Challenges, which was the first of a kind with an emphasis on fairness in generic object recognition.
A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge

The Visual Question Answering (VQA) task aspires to provide a meaningful testbed for the development of AI models that can jointly reason over visual and natural language inputs. Despite a proliferation of VQA datasets, this goal is hindered by a set of common limitations. These include a reliance on relatively simplistic questions that are repetitive in both concepts and linguistic structure, little world knowledge needed outside of the paired image, and limited reasoning required to arrive at the correct answer. We introduce A-OKVQA, a crowdsourced dataset composed of a diverse set of about 25K questions requiring a broad base of commonsense and world knowledge to answer. In contrast to the existing knowledge-based VQA datasets, the questions generally cannot be answered by simply querying a knowledge base, and instead require some form of commonsense reasoning about the scene depicted in the image. We demonstrate the potential of this new dataset through a detailed analysis of its contents and baseline performance measurements over a variety of state-of-the-art vision-language models. Project page: http://a-okvqa.allenai. org/
What Are Expected Queries in End-to-End Object Detection?

End-to-end object detection is rapidly progressed after the emergence of DETR. DETRs use a set of sparse queries that replace the dense candidate boxes in most traditional detectors. In comparison, the sparse queries cannot guarantee a high recall as dense priors. However, making queries dense is not trivial in current frameworks. It not only suffers from heavy computational cost but also difficult optimization. As both sparse and dense queries are imperfect, then \emph{what are expected queries in end-to-end object detection}? This paper shows that the expected queries should be Dense Distinct Queries (DDQ). Concretely, we introduce dense priors back to the framework to generate dense queries. A duplicate query removal pre-process is applied to these queries so that they are distinguishable from each other. The dense distinct queries are then iteratively processed to obtain final sparse outputs. We show that DDQ is stronger, more robust, and converges faster. It obtains 44.5 AP on the MS COCO detection dataset with only 12 epochs. DDQ is also robust as it outperforms previous methods on both object detection and instance segmentation tasks on various datasets. DDQ blends advantages from traditional dense priors and recent end-to-end detectors. We hope it can serve as a new baseline and inspires researchers to revisit the complementarity between traditional methods and end-to-end detectors. The source code is publicly available at \url{https://github.com/jshilong/DDQ}.
Itinerant spin polaron and metallic ferromagnetism in semiconductor moiré superlattices

Itinerant spin polaron and metallic ferromagnetism are theoretically predicted in the Mott insulator in semiconductor moir\'e superlattices doped below and above half filling of the narrow moir\'e band, respectively. The existence of spin polaron can be directly identified from the kink in the dependence of the charge gap on the magnetic field.
Hybrid Models for Mixed Variables in Bayesian Optimization

We systematically describe the problem of simultaneous surrogate modeling of mixed variables (i.e., continuous, integer and categorical variables) in the Bayesian optimization (BO) context. We provide a unified hybrid model using both Monte Carlo tree search (MCTS) and Gaussian processes (GP) that encompasses and generalizes multiple state-of-the-art mixed BO surrogates. Based on the architecture, we propose applying a new dynamic model selection criterion among novel candidate families of covariance kernels, including non-stationary kernels and associated families. Different benchmark problems are studied and presented to support the superiority of our model, along with results highlighting the effectiveness of our method compared to most state-of-the-art mixed-variable methods in BO.
Cool early Albian climates; new data from Argentina

Previous studies on the stable isotope geochemistry of dimitobelid belemnites from Antarctica and Australia indicated cool climatic conditions during the early Albian in the shelf seas around the Gondwanan margin. In this paper new analyses from diagenetically unaltered specimens of Dimitobelus cf. stimulus from early Albian deposits of the Rio Mayer Formation of Lago San Martin, southern Argentina are presented. Oxygen isotope values for diagenetically least altered samples give values ranging between d 18 O C 0.07 and C 0.96 vPDB, which equate to a mean palaeotemperature of 9.5 ( C (assuming d w of (cid:1) 1.2 & SMOW). These data are consistent with a model of cool early Albian shelf seas in the Southern Hemisphere.
Anomalies of E8 gauge theory on String manifolds

In this note we revisit the subject of anomaly cancelation in string theory and M-theory on manifolds with String structure and give three observations. First, that on String manifolds there is no E8 x E8 global anomaly in heterotic string theory. Second, that the description of the anomaly in the phase of the M-theory partition function of Diaconescu-Moore-Witten extends from the Spin case to the String case. Third, that the cubic refinement law of Diaconescu-Freed-Moore for the phase of the M-theory partition function extends to String manifolds. The analysis relies on extending from invariants which depend on the Spin structure to invariants which instead depend on the String structure. Along the way, the one-loop term is refined via the Witten genus.
Electrostatic Layer Jamming Variable Stiffness for Soft Robotics

A novel layer jamming variable stiffness technique for soft robotics is proposed in this paper, which we call electrostatic layer jamming (ELJ). The basic principle of the ELJ is using electrostatic attraction to squeeze material layers to generate friction and then engage jamming. Based on this technique, several specimens used in two common application scenarios including variable tensile stiffness and variable bending stiffness are fabricated, and their stiffness adjustment characteristics are investigated experimentally. Surprisingly, the test data are much larger than the theoretical prediction, which we think is because of the formation of local low air pressure regions between the contact surfaces. Also, the experimental results show that the ELJ technique possesses a large capability of stiffness changing and is space saving. The potential values of the ELJ have been shown by performing with a soft linear actuator for three representative practical applications in the soft robotic field. Finally, the existing problems and advantages of the ELJ technique are discussed, and we believe that this technique will inspire new ways and new opportunities for the soft robotic community.
Roles of the NF- k B Pathway in Lymphocyte Development and Function

This article focuses on the functions of NF- k B that vitally impact lymphocytes and thus adaptive immunity. NF- k B has long been known to be essential for many of the responses of mature lymphocytes to invading pathogens. In addition, NF- k B has important functions in shaping the immune system so it is able to generate adaptive responses to pathogens. In both contexts, NF- k B executes critical cell-autonomous functions within lymphocytes as well as within supportive cells, such as antigen-presenting cells or epithelial cells. It is these aspects of NF- k B’s physiologic impact that we address in this article. pre-BCR pre-B cells late pro-B) drives development into small (late) pre-B cells, in turn to immature B cells (ﬁrst to a full Self-antigen (AG)-reactive immature B cells their receptors by further light chain gene rearrangements or they are (negative selection). immature and mature” immature B cells (T2-like) to the they the and T2 stages to B cell MZB cells in transitional-staged MZBs early transitional receptors to survival, but also to regulate cell