Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyperspherical Classification with Dynamic Label-to-Prototype Assignment

Aiming to enhance the utilization of metric space by the parametric softmax classifier, recent studies suggest replacing it with a non-parametric alternative. Although a non-parametric classifier may provide better metric space utilization, it introduces the challenge of capturing inter-class relationships. A shared characteristic among prior non-parametric classifiers is the static assignment of labels to prototypes during the training, ie, each prototype consistently represents a class throughout the training course. Orthogonal to previous works, we present a simple yet effective method to optimize the category assigned to each prototype (label-to-prototype assignment) during the training. To this aim, we formalize the problem as a two-step optimization objective over network parameters and label-to-prototype assignment mapping. We solve this optimization using a sequential combination of gradient descent and Bipartide matching. We demonstrate the benefits of the proposed approach by conducting experiments on balanced and long-tail classification problems using different backbone network architectures. In particular, our method outperforms its competitors by 1.22\% accuracy on CIFAR-100, and 2.15\% on ImageNet-200 using a metric space dimension half of the size of its competitors. Code: https://github.com/msed-Ebrahimi/DL2PA_CVPR24
SpikeGraphormer: A High-Performance Graph Transformer with Spiking Graph Attention

Recently, Graph Transformers have emerged as a promising solution to alleviate the inherent limitations of Graph Neural Networks (GNNs) and enhance graph representation performance. Unfortunately, Graph Transformers are computationally expensive due to the quadratic complexity inherent in self-attention when applied over large-scale graphs, especially for node tasks. In contrast, spiking neural networks (SNNs), with event-driven and binary spikes properties, can perform energy-efficient computation. In this work, we propose a novel insight into integrating SNNs with Graph Transformers and design a Spiking Graph Attention (SGA) module. The matrix multiplication is replaced by sparse addition and mask operations. The linear complexity enables all-pair node interactions on large-scale graphs with limited GPU memory. To our knowledge, our work is the first attempt to introduce SNNs into Graph Transformers. Furthermore, we design SpikeGraphormer, a Dual-branch architecture, combining a sparse GNN branch with our SGA-driven Graph Transformer branch, which can simultaneously perform all-pair node interactions and capture local neighborhoods. SpikeGraphormer consistently outperforms existing state-of-the-art approaches across various datasets and makes substantial improvements in training time, inference time, and GPU memory cost (10 ~ 20x lower than vanilla self-attention). It also performs well in cross-domain applications (image and text classification). We release our code at https://github.com/PHD-lanyu/SpikeGraphormer.
Analysis of the gene start and gene end signals of human respiratory syncytial virus: quasi-templated initiation at position 1 of the encoded mRNA

The gene start (GS) and gene end (GE) transcription signals of human respiratory syncytial virus (RSV) strain A2 were analyzed in helper-dependent monocistronic and dicistronic minireplicons which were complemented by a standard RSV strain. The GS signal, which is the start site for mRNA synthesis, is highly conserved for the first nine genes: 3'-CCCCGUUUA(U/C) (negative sense). This conserved version of the signal was analyzed by "saturation" mutagenesis, in which all 10 positions, as well as one downstream and one upstream position, were changed one at a time into each of the other three nucleotides. Most of the positions appear to contribute to the signal: positions 1, 3, 6, 7, and, in particular, 9 were the most sensitive, whereas position 5 was relatively insensitive. The effect of nucleotide substitution in the first position of the signal was examined further by cDNA cloning and sequence analysis of the residual mRNA which was produced. For the two mutants examined (1C to U, and 1C to A), the site of initiation was unchanged. However, the mRNAs were dimorphic with regard to the assignment of the 5'-terminal nucleotide: two-thirds contained the predicted mutant substitution, and one-third contained the parental assignment. Intracellular minigenome contained only the mutant assignment, indicating that the heterogeneity was at the level of transcription by the RSV polymerase. This suggests that the templated mutant assignment at position 1 can sometimes be overridden by an innate preference for the parental assignment, a phenomenon which we dubbed quasi-templated initiation. The GS signal of the L gene, encoding the 10th RSV mRNA, contains three differences (3'-CCCUGUUUUA) compared to the conserved version. It was shown to be equal in efficiency to the conserved version. This was unexpected, since the saturation mutagenesis described above indicated that U in place of A at position 9 should be highly inhibitory. Instead, the A at position 10 of the L GS signal was found to be critical for activity, indicating that an essential A residue indeed was present in both versions of the GS signal but that its spacing differed. The GE signal, which directs termination and polyadenylation, has more sequence diversity in nature than does the GS signal. The naturally occurring GE signals of strain A2 were compared by their individual incorporation into a dicistronic minigenome. They were similar in the ability to produce translatable mRNA except in the cases of NS1 and NS2, which were approximately 60% as efficient.
Towards a Unified Theory of State Abstraction for MDPs

State abstraction (or state aggregation) has been extensively studied in the fields of artificial intelligence and operations research. Instead of working in the ground state space, the decision maker usually finds solutions in the abstract state space much faster by treating groups of states as a unit by ignoring irrelevant state information. A number of abstractions have been proposed and studied in the reinforcement-learning and planning literatures, and positive and negative results are known. We provide a unified treatment of state abstraction for Markov decision processes. We study five particular abstraction schemes, some of which have been proposed in the past in different forms, and analyze their usability for planning and learning.
Cerebral cortical effects of desflurane in sheep: comparison with isoflurane, sevoflurane and enflurane

Background: Different volatile anesthetic agents have differing propensities for inducing seizures. A measure of the predilection to develop seizures is the presence of interictal spike discharges (spikes) on the electrocorticogram (ECoG). In this study, we investigated the propensity of desflurane to induce cortical spikes and made a direct objective comparison with enflurane, isoflurane, and sevoflurane. The ECoG effects of desflurane have not been previously reported.
SensoryT5: Infusing Sensorimotor Norms into T5 for Enhanced Fine-grained Emotion Classification

In traditional research approaches, sensory perception and emotion classification have traditionally been considered separate domains. Yet, the significant influence of sensory experiences on emotional responses is undeniable. The natural language processing (NLP) community has often missed the opportunity to merge sensory knowledge with emotion classification. To address this gap, we propose SensoryT5, a neuro-cognitive approach that integrates sensory information into the T5 (Text-to-Text Transfer Transformer) model, designed specifically for fine-grained emotion classification. This methodology incorporates sensory cues into the T5's attention mechanism, enabling a harmonious balance between contextual understanding and sensory awareness. The resulting model amplifies the richness of emotional representations. In rigorous tests across various detailed emotion classification datasets, SensoryT5 showcases improved performance, surpassing both the foundational T5 model and current state-of-the-art works. Notably, SensoryT5's success signifies a pivotal change in the NLP domain, highlighting the potential influence of neuro-cognitive data in refining machine learning models' emotional sensitivity.
Assessing the global reach and value of a provider-facing healthcare app using large-scale analytics

Background The rapid global adoption of mobile health (mHealth) smartphone apps by healthcare providers presents challenges and opportunities in medicine. Challenges include ensuring the delivery of high-quality, up-to-date and optimised information. Opportunities include the ability to study global practice patterns, access to medical and surgical care and continuing medical education needs. Methods We studied users of a free anaesthesia calculator app used worldwide. We combined traditional app analytics with in-app surveys to collect user demographics and feedback. Results 31 173 subjects participated. Users were from 206 countries and represented a spectrum of healthcare provider roles. Low-income country users had greater rates of app use (p<0.001) and ascribed greater importance of the app to their practice (p<0.001). Physicians from low-income countries were more likely to adopt the app (p<0.001). The app was used primarily for paediatric patients. The app was used around the clock, peaking during times typical for first start cases. Conclusions This mHealth app is a valuable decision support tool for global healthcare providers, particularly those in more resource-limited settings and with less training. App adoption and use may provide a mechanism for measuring longitudinal changes in access to surgical care and engaging providers in resource-limited settings. In-app surveys and app analytics provide a window into healthcare provider behaviour at a breadth and level of detail previously impossible to achieve. Given the potentially immense value of crowdsourced information, healthcare providers should be encouraged to participate in these types of studies.
Intellectual capital ROI: a causal map of human capital antecedents and consequents

This report describes the results of a ground‐breaking research study that measured the antecedents and consequents of effective human capital management. The research sample consisted of 76 senior executives from 25 companies in the financial services industry. The results of the study yielded a holistic causal map that integrated constructs from the fields of intellectual capital, knowledge management, human resources, organizational behaviour, information technology and accounting. The integration of both quantitative and qualitative measures in an overall conceptual model yielded several research implications. The resulting structural equation model allows participating organizations and researchers to gauge the effectiveness of an organization’s human capital capabilities. This will allow practitioners and researchers to more efficiently allocate resources with regard to human capital management. The potential outcomes of the study are limitless, since a program of consistent re‐evaluation can lead to the establishment of causal relationships between human capital management and economic and business results.
Quantifying Arbitrage in Automated Market Makers: An Empirical Study of Ethereum ZK Rollups

Arbitrage can arise from the simultaneous purchase and sale of the same asset in different markets in order to profit from a difference in its price. This work systematically reviews arbitrage opportunities between Automated Market Makers (AMMs) on Ethereum ZK rollups, and Centralised Exchanges (CEXs). First, we propose a theoretical framework to measure such arbitrage opportunities and derive a formula for the related Maximal Arbitrage Value (MAV) that accounts for both price divergences and liquidity available in the trading venues. Then, we empirically measure the historical MAV available between SyncSwap, an AMM on zkSync Era, and Binance, and investigate how quickly misalignments in price are corrected against explicit and implicit market costs. Overall, the cumulative MAV from July to September 2023 on the USDC-ETH SyncSwap pool amounts to $104.96k (0.24% of trading volume).
Convolution and Knapsack in Higher Dimensions

In the Knapsack problem, one is given the task of packing a knapsack of a given size with items in order to gain a packing with a high profit value. In recent years, a connection to the $(\max,+)$-convolution problem has been established, where knapsack solutions can be combined by building the convolution of two sequences. This observation has been used to give conditional lower bounds but also parameterized algorithms. In this paper we want to carry these results into higher dimension. We consider Knapsack where items are characterized by multiple properties - given through a vector - and a knapsack that has a capacity vector. The packing must now not exceed any of the given capacity constraints. In order to show a similar sub-quadratic lower bound we introduce a multi-dimensional version of convolution as well. Instead of combining sequences, we will generalize this problem and combine higher dimensional matrices. We will establish a few variants of these problems and prove that they are all equivalent in terms of algorithms that allow for a running time sub-quadratic in the number of entries of the matrix. We further develop a parameterized algorithm to solve higher dimensional Knapsack. The techniques we apply are inspired by an algorithm introduced by Axiotis and Tzamos. In general, we manage not only to extend their result to higher dimension. We will show that even for higher dimensional Knapsack, we can reduce the problem to convolution on one-dimensional sequences, leading to an $\mathcal{O}(d(n + D \cdot \max\{\Pi_{i=1}^d{t_i}, t_{\max}\log t_{\max}\} ))$ algorithm, where $D$ is the number of different weight vectors, $t$ the capacity vector and $d$ is the dimension of the problem. Finally we also modify this algorithm to handle items with negative weights to cross the bridge from solving not only Knapsack but also Integer Linear Programs (ILPs) in general.
Acceleration of control flow on CGRA using advanced predicated execution

Coarse-grained reconfigurable array is a very attractive architecture from the viewpoint of performance and flexibility. However, because the performance improvement is achieved by exploiting parallelism, the architecture is typically poor at handling control flow, which is sequential in nature. There have been many attempts to overcome this problem by using predicated execution techniques; however, they do not support all types of control flow or suffer from performance degradation in doing so. In addition, predicated execution schemes in general require a longer execution time because both the if- and else-paths are always executed. This paper proposes advanced predicated execution techniques that can handle and accelerate all types of control flow with only 2% hardware overhead. These techniques can also be easily extended to general SIMD machines. We implemented these techniques on a coarse-grained reconfigurable array architecture and verified its functionality and effectiveness by accelerating an H.264 deblocking filter, a kernel which is both data- and control-intensive. The results show that the proposed approach achieves up to 43% improvement in execution time compared to speculation by sacrificing 76% code size, and 24% improvement in execution time compared to the previous full predication approach, with a smaller code size.
Geometeric Thermodynamics of Collapse of Gels

Stimulus-induced volumetric phase transition in gels may be potentially exploited for various bio-engineering and mechanical engineering applications. Since the discovery of the phenomenon in the 1970s, extensive experimental research has helped in understanding the phase transition and related critical phenomena. Yet, little insight is available on the evolving microstructure. In this article, we aim at unravelling certain geometric aspects of the micromechanics underlying discontinuous phase transition in polyacrylamide gels. Towards this, we use geometric thermodynamics and a Landau-Ginzburg type free energy functional involving a squared gradient, in conjunction with Flory-Huggins theory. We specifically exploit Ruppeiner's approach of Riemannian geometry-enriched thermodynamic fluctuation theory that has been previously employed to investigate phase transitions in van der Waals fluids and black holes. The framework equips us with a scalar curvature that relates to the microstructural interactions of a gel during phase transition and at critical points. This curvature also provides an insight into the universality class of phase transition and the nature of polymer-polymer interactions.
Contact-aware Human Motion Generation from Textual Descriptions

This paper addresses the problem of generating 3D interactive human motion from text. Given a textual description depicting the actions of different body parts in contact with objects, we synthesize sequences of 3D body poses that are visually natural and physically plausible. Yet, this task poses a significant challenge due to the inadequate consideration of interactions by physical contacts in both motion and textual descriptions, leading to unnatural and implausible sequences. To tackle this challenge, we create a novel dataset named RICH-CAT, representing ``Contact-Aware Texts'' constructed from the RICH dataset. RICH-CAT comprises high-quality motion, accurate human-object contact labels, and detailed textual descriptions, encompassing over 8,500 motion-text pairs across 26 indoor/outdoor actions. Leveraging RICH-CAT, we propose a novel approach named CATMO for text-driven interactive human motion synthesis that explicitly integrates human body contacts as evidence. We employ two VQ-VAE models to encode motion and body contact sequences into distinct yet complementary latent spaces and an intertwined GPT for generating human motions and contacts in a mutually conditioned manner. Additionally, we introduce a pre-trained text encoder to learn textual embeddings that better discriminate among various contact types, allowing for more precise control over synthesized motions and contacts. Our experiments demonstrate the superior performance of our approach compared to existing text-to-motion methods, producing stable, contact-aware motion sequences. Code and data will be available for research purposes.
Exact results on quantum field theories interpolating between pairs of conformal field theories

I review some recent results on conformal eld theories in four dimensions and quantum eld theories interpolating between conformalxed points, supersymmetric and non-supersymmetric. The paper is structured in three parts: i)centralcharges,ii)anomalousdimensions andiii)quantum irreversibility. The family of theories, in general UV free, thatinterpolatebetweentwoconformalxedpoints, suchthattheIRlimitisreachableby(resummed) perturbation theory is calledconformal window. TheconformalwindowdoesnotcontaintrueQCD, whereothereectshavetobetakenintoaccount, butseparatestheperturbativeregionfromQCD.
Exploit High-Dimensional RIS Information to Localization: What Is the Impact of Faulty Element?

This paper proposes a novel localization algorithm using the reconfigurable intelligent surface (RIS) received signal, i.e., RIS information. Compared with BS received signal, i.e., BS information, RIS information offers higher dimension and richer feature set, thereby providing an enhanced capacity to distinguish positions of the mobile users (MUs). Additionally, we address a practical scenario where RIS contains some unknown (number and places) faulty elements that cannot receive signals. Initially, we employ transfer learning to design a two-phase transfer learning (TPTL) algorithm, designed for accurate detection of faulty elements. Then our objective is to regain the information lost from the faulty elements and reconstruct the complete high-dimensional RIS information for localization. To this end, we propose a transfer-enhanced dual-stage (TEDS) algorithm. In \emph{Stage I}, we integrate the CNN and variational autoencoder (VAE) to obtain the RIS information, which in \emph{Stage II}, is input to the transferred DenseNet 121 to estimate the location of the MU. To gain more insight, we propose an alternative algorithm named transfer-enhanced direct fingerprint (TEDF) algorithm which only requires the BS information. The comparison between TEDS and TEDF reveals the effectiveness of faulty element detection and the benefits of utilizing the high-dimensional RIS information for localization. Besides, our empirical results demonstrate that the performance of the localization algorithm is dominated by the high-dimensional RIS information and is robust to unoptimized phase shifts and signal-to-noise ratio (SNR).
An experimental evaluation of choices of SSA forecasting parameters

Six time series related to atmospheric phenomena are used as inputs for experiments offorecasting with singular spectrum analysis (SSA). Existing methods for SSA parametersselection are compared throughout their forecasting accuracy relatively to an optimal aposteriori selection and to a naive forecasting methods. The comparison shows that awidespread practice of selecting longer windows leads often to poorer predictions. It alsoconfirms that the choices of the window length and of the grouping are essential. Withthe mean error of rainfall forecasting below 1.5%, SSA appears as a viable alternative forhorizons beyond two weeks. Six séries temporelles ont servi pour des évaluations expérimentales, en fonction des paramètres choisis, d'exactitude de prévisions de phénomènes atmosphériques par la méthode d'analyse de spectre singulier (SSA). Les méthodes les plus connues de sélection automatique des ces paramètres ont été comparées avec une sélection optimale a posteriori et des méthodes de prévision naïves. On constate notamment qu'une pratique répandue d'utiliser des fenêtres plus larges conduit souvent à des prévisions de médiocre qualité. On confirme aussi que le choix du groupement est capital. Avec l'erreur moyenne observée en dessous de 1,5% de prévisions de pluviométrie pour des horizons au delà de deux semaines, la SSA apparaît comme une alternative viable à d'autres méthodes de prévision.
Participatory mapping: an additional depositional layer of the Godthåbsfjord, Greenland

ABSTRACT The knowledge held by people who have been closely and directly involved with their surrounding environments is valuable not only for capturing elements of an understanding of the past but also for obtaining an understanding of environmental variations. This study amplifies that combining traditional knowledge, historical and archaeological data is unique and can be integrated in archaeological survey methodology and interpretations of results; it can equally contribute to historical and cultural studies, studies of climate and environmental changes, and to identify knowledge gaps; and it can be a significant tool for natural science to validate date, prolong time series, strengthen monitoring and understand variations of the past, present and future. This study visualizes how these can be used as a scientific cartographic tool with multidisciplinary layers and perceptions. Furthermore, it demonstrates how participatory mapping can add new layers to depositional history by visualizing cultural and environmental changes and giving a broader and more contextual understanding of past land use and human–nature interactions as well as present and future cultural perceptions in the inner region of the Godthåbsfjord.