Miracle-dz/UniFAR-FTUs huggingface dataset:
TACOformer: Token-channel compounded Cross Attention for Multimodal Emotion Recognition

Recently, emotion recognition based on physiological signals has emerged as a field with intensive research. The utilization of multi-modal, multi-channel physiological signals has significantly improved the performance of emotion recognition systems, due to their complementarity. However, effectively integrating emotion-related semantic information from different modalities and capturing inter-modal dependencies remains a challenging issue. Many existing multimodal fusion methods ignore either token-to-token or channel-to-channel correlations of multichannel signals from different modalities, which limits the classification capability of the models to some extent. In this paper, we propose a comprehensive perspective of multimodal fusion that integrates channel-level and token-level cross-modal interactions. Specifically, we introduce a unified cross attention module called Token-chAnnel COmpound (TACO) Cross Attention to perform multimodal fusion, which simultaneously models channel-level and token-level dependencies between modalities. Additionally, we propose a 2D position encoding method to preserve information about the spatial distribution of EEG signal channels, then we use two transformer encoders ahead of the fusion module to capture long-term temporal dependencies from the EEG signal and the peripheral physiological signal, respectively. Subject-independent experiments on emotional dataset DEAP and Dreamer demonstrate that the proposed model achieves state-of-the-art performance.
"Filling the Blanks'': Identifying Micro-activities that Compose Complex Human Activities of Daily Living

Complex activities of daily living (ADLs) often consist of multiple micro-activities. When performed sequentially, these micro-activities help the user accomplish the broad macro-activity. Naturally, a deeper understanding of these micro-activities can help develop more sophisticated human activity recognition (HAR) models and add explainability to their inferred conclusions. Previous research has attempted to achieve this by utilizing fine-grained annotated data that provided the required supervision and rules for associating the micro-activities to identify the macro-activity. However, this ``bottom-up'' approach is unrealistic as getting such high-quality, fine-grained annotated sensor datasets is challenging, costly, and time-consuming. Understanding this, in this paper, we develop AmicroN, which adapts a ``top-down'' approach by exploiting coarse-grained annotated data to expand the macro-activities into their constituent micro-activities without any external supervision. In the backend, AmicroN uses \textit{unsupervised} change-point detection to search for the micro-activity boundaries across a complex ADL. Then, it applies a \textit{generalized zero-shot} approach to characterize it. We evaluate AmicroN on two real-life publicly available datasets and observe that AmicroN can identify the micro-activities with micro F\textsubscript{1}-score $>0.75$ for both datasets. Additionally, we also perform an initial proof-of-concept on leveraging the state-of-the-art (SOTA) large language models (LLMs) with attribute embeddings predicted by AmicroN to enhance further the explainability surrounding the detection of micro-activities.
Robustness of Segment Anything Model (SAM) for Autonomous Driving in Adverse Weather Conditions

Segment Anything Model (SAM) has gained considerable interest in recent times for its remarkable performance and has emerged as a foundational model in computer vision. It has been integrated in diverse downstream tasks, showcasing its strong zero-shot transfer capabilities. Given its impressive performance, there is a strong desire to apply SAM in autonomous driving to improve the performance of vision tasks, particularly in challenging scenarios such as driving under adverse weather conditions. However, its robustness under adverse weather conditions remains uncertain. In this work, we investigate the application of SAM in autonomous driving and specifically explore its robustness under adverse weather conditions. Overall, this work aims to enhance understanding of SAM's robustness in challenging scenarios before integrating it into autonomous driving vision tasks, providing valuable insights for future applications.
Lesion Detection on Leaves using Class Activation Maps

Lesion detection on plant leaves is a critical task in plant pathology and agricultural research. Identifying lesions enables assessing the severity of plant diseases and making informed decisions regarding disease control measures and treatment strategies. To detect lesions, there are studies that propose well-known object detectors. However, training object detectors to detect small objects such as lesions can be problematic. In this study, we propose a method for lesion detection on plant leaves utilizing class activation maps generated by a ResNet-18 classifier. In the test set, we achieved a 0.45 success rate in predicting the locations of lesions in leaves. Our study presents a novel approach for lesion detection on plant leaves by utilizing CAMs generated by a ResNet classifier while eliminating the need for a lesion annotation process.
System-Level Natural Language Feedback

Natural language (NL) feedback offers rich insights into user experience. While existing studies focus on an instance-level approach, where feedback is used to refine specific examples, we introduce a framework for system-level use of NL feedback. We show how to use feedback to formalize system-level design decisions in a human-in-the-loop-process – in order to produce better models. In particular this is done through: (i) metric design for tasks; and (ii) language model prompt design for refining model responses. We conduct two case studies of this approach for improving search query and dialog response generation, demonstrating the effectiveness of system-level feedback. We show the combination of system-level and instance-level feedback brings further gains, and that human written instance-level feedback results in more grounded refinements than GPT-3.5 written ones, underlying the importance of human feedback for building systems.
Coupling metric-affine gravity to the standard model and dark matter fermions

General Relativity (GR) exists in different formulations, which are equivalent in pure gravity. Once matter is included, however, observable predictions generically depend on the version of GR. In order to quantify the resulting ambiguity, we employ metric-affine gravity, which encompasses as special cases the metric, Palatini, Einstein-Cartan and Weyl formulations. We first discuss the interaction of fermions with torsion and non-metricity, also commenting on projective symmetry. With a view towards the Standard Model, we then construct a generic model of (complex) scalar, fermionic and gauge fields coupled to GR and derive an equivalent metric theory, which features numerous new interaction terms. As a first observable consequence, we point out that a gravitational mechanism for producing dark matter in the form of singlet fermions can be used to distinguish between metric gravity and other formulations of GR.
HamLib: A Library of Hamiltonians for Benchmarking Quantum Algorithms and Hardware

For a considerable time, large datasets containing problem instances have proven valuable for analyzing computer hardware, software, and algorithms. One notable example of the value of large datasets is ImageNet [1], a vast repository of images that has been instrumental in testing numerous deep learning packages. Similarly, in the domain of computational chemistry and materials science, the availability of extensive datasets such as the Protein Data Bank [2], the Materials Project [3], and QM9 [4] has greatly facilitated the evaluation of new algorithms and software approaches, while also promoting standardization within the field. These well-defined datasets and problem instances, in turn, serve as the foundation for creating benchmarking suites like MLPerf [5] and LINPACK [6], [7]. These suites enable fair and rigorous comparisons of different methodologies and solutions, fostering continuous advancements in various areas of computer science and beyond.
Estimation robuste pour des distributions à queue lourde

Nous nous interessons a estimer la moyenne d'une variable aleatoire de loi a queue lourde. Nous adoptons une approche plus robuste que la moyenne empirique classique communement utilisee. L'objectif est de developper des inegalites de concentration de type sous-gaussien sur l'erreur d'estimation. En d'autres termes, nous cherchons a garantir une forte concentration sous une hypothese plus faible que la bornitude : une variance finie. Deux estimateurs de la moyenne pour une loi a support reel sont invoques et leurs resultats de concentration sont rappeles. Plusieurs adaptations en dimension superieure sont envisagees. L'utilisation appropriee de ces estimateurs nous permet d'introduire une nouvelle technique de minimisation du risque empirique pour des variables aleatoires a queue lourde. Quelques applications de cette technique sont developpees. Nous appuyons ces resultats sur des simulations sur des jeux de donnees simulees. Dans un troisieme temps, nous etudions un probleme d'estimation multivarie dans le cadre des U-statistiques ou les estimateurs precedents offrent, la aussi, une generalisation naturelle d'estimateurs presents dans la litterature.
Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity

In federated learning, data heterogeneity is a critical challenge. A straightforward solution is to shuffle the clients' data to homogenize the distribution. However, this may violate data access rights, and how and when shuffling can accelerate the convergence of a federated optimization algorithm is not theoretically well understood. In this paper, we establish a precise and quantifiable correspondence between data heterogeneity and parameters in the convergence rate when a fraction of data is shuffled across clients. We prove that shuffling can quadratically reduce the gradient dissimilarity with respect to the shuffling percentage, accelerating convergence. Inspired by the theory, we propose a practical approach that addresses the data access rights issue by shuffling locally generated synthetic data. The experimental results show that shuffling synthetic data improves the performance of multiple existing federated learning algorithms by a large margin.
Optimal Power Flow for Integrated Primary-Secondary Distribution Networks with Service Transformers

Secondary distribution networks (SDNets) play an increasingly important role in smart grids due to a high proliferation of distributed energy resources (DERs) in SDNets. However, most existing optimal power flow (OPF) problems do not take into account SDNets with service transformers. Handling the nonlinear and nonconvex SDNet power flow constraints is still an outstanding problem. To meet this gap, we first utilize the second-order cone programming relaxation and linearization to make service transformer constraints convex, respectively. Then, the linearized triplex service line power flow model, including its compact matrix-vector form, is further developed to compose the SDNet OPF model with our proposed service transformer model. This proposed SDNet OPF model can be easily embedded into existing primary distribution network (PDNet) OPF models, resulting in a holistic power system decision-making solution for integrated primary-secondary distribution networks. A case study is presented for an integrated primary-secondary distribution network that demonstrates the practical effectiveness of this model.
Torsion Graph Neural Networks

Geometric deep learning (GDL) models have demonstrated a great potential for the analysis of non-Euclidian data. They are developed to incorporate the geometric and topological information of non-Euclidian data into the end-to-end deep learning architectures. Motivated by the recent success of discrete Ricci curvature in graph neural network (GNNs), we propose TorGNN, an analytic Torsion enhanced Graph Neural Network model. The essential idea is to characterize graph local structures with an analytic torsion based weight formula. Mathematically, analytic torsion is a topological invariant that can distinguish spaces which are homotopy equivalent but not homeomorphic. In our TorGNN, for each edge, a corresponding local simplicial complex is identified, then the analytic torsion (for this local simplicial complex) is calculated, and further used as a weight (for this edge) in message-passing process. Our TorGNN model is validated on link prediction tasks from sixteen different types of networks and node classification tasks from three types of networks. It has been found that our TorGNN can achieve superior performance on both tasks, and outperform various state-of-the-art models. This demonstrates that analytic torsion is a highly efficient topological invariant in the characterization of graph structures and can significantly boost the performance of GNNs.
Relationships between the Phase Retrieval Problem and Permutation Invariant Embeddings

This paper discusses the connection between the phase retrieval problem and permutation invariant embeddings. We show that the real phase retrieval problem for ℝd/O(1) is equivalent to Euclidean embeddings of the quotient space ℝ2×d/S2 performed by the sorting encoder introduced in an earlier work. In addition, this relationship provides us with inversion algorithms of the orbits induced by the group of permutation matrices.
On minimum t-claw deletion in split graphs

For $t\geq 3$, $K_{1, t}$ is called $t$-claw. In minimum $t$-claw deletion problem (\texttt{Min-$t$-Claw-Del}), given a graph $G=(V, E)$, it is required to find a vertex set $S$ of minimum size such that $G[V\setminus S]$ is $t$-claw free. In a split graph, the vertex set is partitioned into two sets such that one forms a clique and the other forms an independent set. Every $t$-claw in a split graph has a center vertex in the clique partition. This observation motivates us to consider the minimum one-sided bipartite $t$-claw deletion problem (\texttt{Min-$t$-OSBCD}). Given a bipartite graph $G=(A \cup B, E)$, in \texttt{Min-$t$-OSBCD} it is asked to find a vertex set $S$ of minimum size such that $G[V \setminus S]$ has no $t$-claw with the center vertex in $A$. A primal-dual algorithm approximates \texttt{Min-$t$-OSBCD} within a factor of $t$. We prove that it is $\UGC$-hard to approximate with a factor better than $t$. We also prove it is approximable within a factor of 2 for dense bipartite graphs. By using these results on \texttt{Min-$t$-OSBCD}, we prove that \texttt{Min-$t$-Claw-Del} is $\UGC$-hard to approximate within a factor better than $t$, for split graphs. We also consider their complementary maximization problems and prove that they are $\APX$-complete.
NeurJSCC Enabled Semantic Communications: Paradigms, Applications, and Potentials

Recent advances in deep learning have led to increased interest in solving high-efficiency end-to-end transmission problems using methods that employ the nonlinear property of neural networks. These techniques, we call neural joint source-channel coding (NeurJSCC), extract latent semantic features of the source signal across space and time, and design corresponding variable-length NeurJSCC approaches to transmit latent features over wireless communication channels. Rapid progress has led to numerous research papers, but a consolidation of the discovered knowledge has not yet emerged. In this article, we gather diverse ideas to categorize the expansive aspects on NeurJSCC as two paradigms, i.e., explicit and implicit NeurJSCC. We first focus on those two paradigms of NeurJSCC by identifying their common and different components in building end-to-end communication systems. We then focus on typical applications of NeurJSCC to various communication tasks. Our article highlights the improved quality, flexibility, and capability brought by NeurJSCC, and we also point out future directions.
Learning Descriptive Image Captioning via Semipermeable Maximum Likelihood Estimation

Image captioning aims to describe visual content in natural language. As 'a picture is worth a thousand words', there could be various correct descriptions for an image. However, with maximum likelihood estimation as the training objective, the captioning model is penalized whenever its prediction mismatches with the label. For instance, when the model predicts a word expressing richer semantics than the label, it will be penalized and optimized to prefer more concise expressions, referred to as conciseness optimization. In contrast, predictions that are more concise than labels lead to richness optimization. Such conflicting optimization directions could eventually result in the model generating general descriptions. In this work, we introduce Semipermeable MaxImum Likelihood Estimation (SMILE), which allows richness optimization while blocking conciseness optimization, thus encouraging the model to generate longer captions with more details. Extensive experiments on two mainstream image captioning datasets MSCOCO and Flickr30K demonstrate that SMILE significantly enhances the descriptiveness of generated captions. We further provide in-depth investigations to facilitate a better understanding of how SMILE works.
Abdominal Aortic Aneurysm with Essential Thrombocythemia: A Case Report Describing Perioperative Management in Open Surgery

We report perioperative management and open surgery to treat a case of infrarenal abdominal aortic aneurysm with essential thrombocythemia (ET), a chronic myeloproliferative disorder associated with arterial or venous thrombosis, idiopathic bleeding, and heparin-resistant diathesis. Following careful preoperative management, including assessment of heparin resistance, open surgery was successfully performed to treat the aortic aneurysm of our patient. This report shows that optimal preparation for surgery is important to safely perform abdominal aortic aneurysm repair and prevent perioperative thrombosis and bleeding in patients with abdominal aortic aneurysm with ET.
Algorithm for Generating N-dimensional Hilbert Curve

The Hilbert curve is a way of mapping the multidimensional space into the one-dimensional space.Such mappings are of interest in a number of application domains including image processing and the indexing of multidimensional data.However,little has been discussed on its high dimensional algorithms due to the complexity.In this paper,a novel algorithm is presented for generating an N-dimensional Hilbert curve,which analyzes a Hilbert curve from bottom to top,based on a static evolvement rule table.The experimental results show that our method is easier to implement and faster in computation than other methods.
Multiple environmental factors, but not nutrient addition, directly affect wet grassland soil microbial community structure: a mesocosm study

Abstract Nutrient addition may change soil microbial community structure, but soil microbes must simultaneously contend with other, interacting factors. We studied the effect of soil type (peat, mineral), water level (low, high), and nutrient addition (unfertilized, fertilized) on wet grassland soil microbial community structure in both vegetated and un-vegetated soils after five years of treatment application in a mesocosm, using Illumina sequencing of the bacterial V4 region of the small ribosomal sub-units. Soil type, water level, and plant presence significantly affected the soil microbial structure, both singly and interactively. Nutrient addition did not directly impact microbiome structure, but acted indirectly by increasing plant biomass. The abundance of possible plant growth promoting bacteria and heterotrophic bacteria indicates the importance of bacteria that promote plant growth. Based on our results, a drier and warmer future would result in nutrient-richer conditions and changes to microbial community structure and total microbial biomass and/or abundances, with wet grasslands likely switching from areas acting as C sinks to C sources.
Antibiotic resistance and biofilm‐forming ability of α‐toxin‐positive Clostridium septicum isolates worsen patient prognosis

A total of, 78 Clostridium septicum (CLSE) isolates were screened for genes encoding: α‐toxin, flagellin, and resistance to vancomycin (VANg). The isolates were also tested for their ability to form biofilm and their antibiotic susceptibility. All isolates were positive for α‐toxin and flagellin genes. However, only 19 isolates (24.3%) showed prevalence for VANg. We observed the strongest capacity to form a biofilm (100%) in isolates from patients with oncologic or septic and febrile diagnoses. This percentage was also very high in patients with colitis and gastrointestinal hemorrhage (72.7%). No less than 43 isolates showed antibiotic resistance, and 21 were multidrug‐resistant (MDR). Interestingly, our studies showed a correlation between antibiotic resistance and biofilm formation. A statistically significant difference was observed between biofilm‐forming MDR isolates and those with low/no biofilm‐forming ability. However, the most impressive observation was the correlation with mortality rate. While the overall mortality rate for CLSE infections was 16.7% (13/78), the mortality rate for patients infected with MDR isolates forming biofilm moderately or strongly reached 38.1% (8/21). This number increased even further when only infections with the biofilm‐forming VANg‐positive isolates were considered (61.5%; 8/13). Therefore, the ability of a VANg‐positive CLSE isolate to form a biofilm has been suggested as a biomarker of poor prognosis.
Social Motivation in Schizophrenia: What's Effort Got to Do With It?

BACKGROUND AND HYPOTHESIS Social motivation, defined as the fundamental human desire to seek out, engage in, and maintain interpersonal bonds, has become a growing area of research in schizophrenia. The major focus has been on understanding the impact of social reward-related processes. An obvious but rarely acknowledged fact is that social interactions, much like other goal-directed acts, require the exertion of effort. In this Review Article, we argue that social motivation in schizophrenia can be conceptualized through the lens of an established framework: effort-based decision-making (EBDM). STUDY DESIGN We conducted a literature review on social reward processing in schizophrenia, then extended these findings by applying concepts and insights from the literature on EBDM to the study of social motivation. STUDY RESULTS Within the EBDM framework, decisions about whether or not to pursue social interactions are bound by cost/benefit calculations. That is, people do not pursue social behaviors when the estimated "cost" of the required effort outweighs the anticipated "benefit" or reward. We propose that people with schizophrenia are less likely to engage in social interaction compared with healthy samples because they: (1) underestimate the benefits of relationships (based on expectations of reward/punishment), (2) overestimate the effort costs associated with social interaction, and/or (3) fail to integrate cost-benefit information in an optimal manner. CONCLUSIONS EBDM is an especially promising framework of social motivation that goes beyond the current focus on social reward processing to include a focus on effort.