Miracle-dz/UniFAR-FTUs huggingface dataset:
ViT Unified: Joint Fingerprint Recognition and Presentation Attack Detection

A secure fingerprint recognition system must contain both a presentation attack (i.e., spoof) detection and recognition module in order to protect users against unwanted access by malicious users. Traditionally, these tasks would be carried out by two independent systems; however, recent studies have demonstrated the potential to have one unified system architecture in order to reduce the computational burdens on the system, while maintaining high accuracy. In this work, we leverage a vision transformer architecture for joint spoof detection and matching and report competitive results with state-of-the-art (SOTA) models for both a sequential system (two ViT models operating independently) and a unified architecture (a single ViT model for both tasks). ViT models are particularly well suited for this task as the ViT’s global embedding encodes features useful for recognition, whereas the individual, local embeddings are useful for spoof detection. We demonstrate the capability of our unified model to achieve an average integrated matching (IM) accuracy of 98.87% across LivDet 2013 and 2015 CrossMatch sensors. This is comparable to IM accuracy of 98.95% of our sequential dual-ViT system, but with $\sim 50\%$ of the parameters and $\sim 58\%$ of the latency.
Surfacing Biases in Large Language Models using Contrastive Input Decoding

Ensuring that large language models (LMs) are fair, robust and useful requires an understanding of how different modifications to their inputs impact the model's behaviour. In the context of open-text generation tasks, however, such an evaluation is not trivial. For example, when introducing a model with an input text and a perturbed,"contrastive"version of it, meaningful differences in the next-token predictions may not be revealed with standard decoding strategies. With this motivation in mind, we propose Contrastive Input Decoding (CID): a decoding algorithm to generate text given two inputs, where the generated text is likely given one input but unlikely given the other. In this way, the contrastive generations can highlight potentially subtle differences in how the LM output differs for the two inputs in a simple and interpretable manner. We use CID to highlight context-specific biases that are hard to detect with standard decoding strategies and quantify the effect of different input perturbations.
A study of the local dynamics of modified Patankar Dec and higher order modified Patankar-RK methods

Patankar schemes have attracted increasing interest in recent years because they preserve the positivity of the analytical solution of a production-destruction system (PDS) irrespective of the chosen time step size. Although they are now of great interest, for a long time it was not clear what stability properties such schemes have. Recently a new stability approach based on Lyapunov stability with an extension of the center manifold theorem has been proposed to study the stability properties of positivity preserving time integrators. In this work, we study the stability properties of the classical modified Patankar–Runge–Kutta schemes (MPRK) and the modified Patankar Deferred Correction (MPDeC) approaches. We prove that most of the considered MPRK schemes are stable for any time step size and compute the stability function of MPDeC. We investigate its properties numerically revealing that also most MPDeC are stable irrespective of the chosen time step size. Finally, we verify our theoretical results with numerical simulations.
Local measures of fluctuations in inhomogeneous liquids: statistical mechanics and illustrative applications

We show in detail how three one-body fluctuation profiles, namely the local compressibility, the local thermal susceptibility, and the reduced density, can be obtained from a statistical mechanical many-body description of classical particle-based systems. We present several different and equivalent routes to the definition of each fluctuation profile, facilitating their explicit numerical calculation in inhomogeneous equilibrium systems. This underlying framework is used for the derivation of further properties such as hard wall contact theorems and novel types of inhomogeneous one-body Ornstein–Zernike equations. The practical accessibility of all three fluctuation profiles is exemplified by grand canonical Monte Carlo simulations that we present for hard sphere, Gaussian core and Lennard–Jones fluids in confinement.
Rethinking k-means from manifold learning perspective

Although numerous clustering algorithms have been developed, many existing methods still leverage k-means technique to detect clusters of data points. However, the performance of k-means heavily depends on the estimation of centers of clusters, which is very difficult to achieve an optimal solution. Another major drawback is that it is sensitive to noise and outlier data. In this paper, from manifold learning perspective, we rethink k-means and present a new clustering algorithm which directly detects clusters of data without mean estimation. Specifically, we construct distance matrix between data points by Butterworth filter such that distance between any two data points in the same clusters equals to a small constant, while increasing the distance between other data pairs from different clusters. To well exploit the complementary information embedded in different views, we leverage the tensor Schatten p-norm regularization on the 3rd-order tensor which consists of indicator matrices of different views. Finally, an efficient alternating algorithm is derived to optimize our model. The constructed sequence was proved to converge to the stationary KKT point. Extensive experimental results indicate the superiority of our proposed method.
Multi log-normal density structure in cygnus-X molecular clouds: A fitting for N-PDF without power-law

We studied the H2 column density probability distribution function (N-PDF) based on molecular emission lines using the Nobeyama 45-m Cygnus X CO survey data. Using the DENDROGRAM and SCIMES algorithms, we identified 124 molecular clouds in the 13CO data. From these identified molecular clouds, an N-PDF was constructed for 11 molecular clouds with an extent of more than 0.4 deg2. From the fitting of the N-PDF, we found that the N-PDF could be well-fitted with one or two log-normal distributions. These fitting results provided an alternative density structure for molecular clouds from a conventional picture. We investigated the column density, dense molecular cloud cores, and radio continuum source distributions in each cloud and found that the N-PDF shape was less correlated with the star-forming activity over a whole cloud. Furthermore, we found that the log-normal N-PDF parameters obtained from the fitting showed two impressive features. First, the log-normal distribution at the low-density part had the same mean column density (∼ 1021.5 cm−2) for almost all the molecular clouds. Second, the width of the log-normal distribution tended to decrease with an increasing mean density of the structures. These correlations suggest that the shape of the N-PDF reflects the relationship between the density and turbulent structure of the whole molecular cloud but is less affected by star-forming activities.
Visualizing Entanglement in multi-Qubit Systems

In the field of quantum information science and technology, the representation and visualization of quantum states and related processes are essential for both research and education. In this context, a focus especially lies on ensembles of few qubits. There exist many powerful representations for single-qubit and multi-qubit systems, such as the famous Bloch sphere and generalizations. Here, we utilize the dimensional circle notation as a representation of such ensembles, adapting the so-called circle notation of qubits and the idea of representing the n-particle system in an n-dimensional space. We show that the mathematical conditions for separability lead to symmetry conditions of the quantum state visualized, offering a new perspective on entanglement in few-qubit systems and therefore on various quantum algorithms. In this way, dimensional notations promise significant potential for conveying nontrivial quantum entanglement properties and processes in few-qubit systems to a broader audience, and could enhance understanding of these concepts as a bridge between intuitive quantum insight and formal mathematical descriptions.
A Critical View Of Vision-Based Long-Term Dynamics Prediction Under Environment Misalignment

Dynamics prediction, which is the problem of predicting future states of scene objects based on current and prior states, is drawing increasing attention as an instance of learning physics. To solve this problem, Region Proposal Convolutional Interaction Network (RPCIN), a vision-based model, was proposed and achieved state-of-the-art performance in long-term prediction. RPCIN only takes raw images and simple object descriptions, such as the bounding box and segmentation mask of each object, as input. However, despite its success, the model's capability can be compromised under conditions of environment misalignment. In this paper, we investigate two challenging conditions for environment misalignment: Cross-Domain and Cross-Context by proposing four datasets that are designed for these challenges: SimB-Border, SimB-Split, BlenB-Border, and BlenB-Split. The datasets cover two domains and two contexts. Using RPCIN as a probe, experiments conducted on the combinations of the proposed datasets reveal potential weaknesses of the vision-based long-term dynamics prediction model. Furthermore, we propose a promising direction to mitigate the Cross-Domain challenge and provide concrete evidence supporting such a direction, which provides dramatic alleviation of the challenge on the proposed datasets.
Learning-Augmented Online Packet Scheduling with Deadlines

The modern network aims to prioritize critical traffic over non-critical traffic and effectively manage traffic flow. This necessitates proper buffer management to prevent the loss of crucial traffic while minimizing the impact on non-critical traffic. Therefore, the algorithm's objective is to control which packets to transmit and which to discard at each step. In this study, we initiate the learning-augmented online packet scheduling with deadlines and provide a novel algorithmic framework to cope with the prediction. We show that when the prediction error is small, our algorithm improves the competitive ratio while still maintaining a bounded competitive ratio, regardless of the prediction error.
Structural rigidity and flexibility using graphs of groups

In structural rigidity, one studies frameworks of bars and joints in Euclidean space. Such a framework is an articulated structure consisting of rigid bars, joined together at joints around which the bars may rotate. In this paper, we will describe articulated motions of realisations of incidence geometries that uses the terminology of graph of groups, and describe the motions of such a framework using group theory. Our approach allows to model a variety of situations, such as parallel redrawings, scenes, polytopes, realisations of graphs on surfaces, and even unique colourability of graphs. This approach allows a concise description of various dualities in rigidity theory. We also provide a lower bound on the dimension of the infinitesimal motions of such a framework in the special case when the underlying group is a Lie group.
Disclosing Early Excited State Relaxation Events in Prototypical Linear Carbon Chains

One-dimensional (1D) linear nanostructures comprising sp-hybridized carbon atoms, as derivatives of the prototypical allotrope known as carbyne, are predicted to possess outstanding mechanical, thermal, and electronic properties. Despite recent advances in their synthesis, their chemical and physical properties are still poorly understood. Here, we investigate the photophysics of a prototypical polyyne (i.e., 1D chain with alternating single and triple carbon bonds) as the simplest model of finite carbon wire and as a prototype of sp-carbon-based chains. We perform transient absorption experiments with high temporal resolution (<30 fs) on monodispersed hydrogen-capped hexayne H—(C≡C)6—H synthesized by laser ablation in liquid. With the support of computational studies based on ground state density functional theory (DFT) and excited state time-dependent (TD)-DFT calculations, we provide a comprehensive description of the excited state relaxation processes at early times following photoexcitation. We show that the internal conversion from a bright high-energy singlet excited state to a low-lying singlet dark state is ultrafast and takes place with a 200 fs time constant, followed by thermalization on the picosecond time scale and decay of the low-energy singlet state with hundreds of picoseconds time constant. We also show that the time scale of these processes does not depend on the end groups capping the sp-carbon chain. The understanding of the primary photoinduced events in polyynes is of key importance both for fundamental knowledge and for potential optoelectronic and light-harvesting applications of low-dimensional nanostructured carbon-based materials.
Model Life Extension for Continuous Process: Non-Invasive Correction of Model-Plant Mismatch with Regularization

In continuous process plants controlled by model predictive control, model-plant mismatch (MPM), due to the aging of processes, causes degradation of control performance. We propose a concept called Model Life Extension (MLE) and its implementation to mitigate this degradation in a non-invasive manner. The purpose of MLE is to continually update (re-identify) process models by using routine operating data on the assumption that the timescale of aging is much larger than the interval of excitation of reference signals. We implemented MLE by estimating MPM via $\mathcal{L}_{1}$ regularized regression and by finding an optimal regularization parameter via cross-validation and showed through numerical experiments that an optimal parameter can exist and be found by cross-validation for a pilot-scale distillation column. We then constructed the updated model based on the found parameter to demonstrate the possibility of correcting static-gain mismatch and transport-delay mismatch without injecting excitation signals to process inputs.
Complex Mapping between Neural Response Frequency and Linguistic Units in Natural Speech

Abstract When listening to connected speech, the human brain can extract multiple levels of linguistic units, such as syllables, words, and sentences. It has been hypothesized that the time scale of cortical activity encoding each linguistic unit is commensurate with the time scale of that linguistic unit in speech. Evidence for the hypothesis originally comes from studies using the frequency-tagging paradigm that presents each linguistic unit at a constant rate, and more recently extends to studies on natural speech. For natural speech, it is sometimes assumed that neural encoding of different levels of linguistic units is captured by the neural response tracking speech envelope in different frequency bands (e.g., around 1 Hz for phrases, around 2 Hz for words, and around 4 Hz for syllables). Here, we analyze the coherence between speech envelope and idealized responses, each of which tracks a single level of linguistic unit. Four units, that is, phones, syllables, words, and sentences, are separately considered. We show that the idealized phone-, syllable-, and word-tracking responses all correlate with the speech envelope both around 3–6 Hz and below ∼1 Hz. Further analyses reveal that the 1-Hz correlation mainly originates from the pauses in connected speech. The results here suggest that a simple frequency-domain decomposition of envelope-tracking activity cannot separate the neural responses to different linguistic units in natural speech.
Energy-efficient quantum non-demolition measurement with a spin-photon interface

Spin-photon interfaces (SPIs) are key devices of quantum technologies, aimed at coherently transferring quantum information between spin qubits and propagating pulses of polarized light. We study the potential of a SPI for quantum non demolition (QND) measurements of a spin state. After being initialized and scattered by the SPI, the state of a light pulse depends on the spin state. It thus plays the role of a pointer state, information being encoded in the light's temporal and polarization degrees of freedom. Building on the fully Hamiltonian resolution of the spin-light dynamics, we show that quantum superpositions of zero and single photon states outperform coherent pulses of light, producing pointer states which are more distinguishable with the same photon budget. The energetic advantage provided by quantum pulses over coherent ones is maintained when information on the spin state is extracted at the classical level by performing projective measurements on the light pulses. The proposed schemes are robust against imperfections in state of the art semi-conducting devices.
When Giant Language Brains Just Aren't Enough! Domain Pizzazz with Knowledge Sparkle Dust

Large language models (LLMs) have significantly advanced the field of natural language processing, with GPT models at the forefront. While their remarkable performance spans a range of tasks, adapting LLMs for real-world business scenarios still poses challenges warranting further investigation. This paper presents an empirical analysis aimed at bridging the gap in adapting LLMs to practical use cases. To do that, we select the question answering (QA) task of insurance as a case study due to its challenge of reasoning. Based on the task we design a new model relied on LLMs which are empowered by additional knowledge extracted from insurance policy rulebooks and DBpedia. The additional knowledge helps LLMs to understand new concepts of insurance for domain adaptation. Preliminary results on two QA datasets show that knowledge enhancement significantly improves the reasoning ability of GPT-3.5 (55.80% and 57.83% in terms of accuracy). The analysis also indicates that existing public knowledge bases, e.g., DBPedia is beneficial for knowledge enhancement. Our findings reveal that the inherent complexity of business scenarios often necessitates the incorporation of domain-specific knowledge and external resources for effective problem-solving.
Methods to Fill in the Gaps Between Pads of Electrical Logging Images

Electric logging images can't fully cover the borehole walls and leave gaps between the pads in the images.The principle of IDW(Inverse Distance Weighting Interpolation)method and Filtersim analogy method in the multiple-point geostatistics are discussed.Then use the FMI data as a case example and fill in the gaps using both IDW and Filtersim method.After filling in the gaps,the images can fully cover the boreholes and makes it easy to process the images and interpret geological phenomenon(fractures,vugs,faults and so on). Finally compare the 2 methods and find that multi-point geostatistics method is more feasible,because it uses the statistics theory and considers the statistic features of the entire images in the processing windows lengths.
Adaptive plasticity and fitness costs of endangered, nonendangered, and invasive plants in response to variation in nitrogen and phosphorus availabilities

Abstract Global change drivers such as eutrophication and plant invasions will create novel environments for many plant species. Through adaptive trait plasticity plants may maintain their performance under these novel conditions and may outcompete those showing low‐adaptive trait plasticity. In a greenhouse study, we determined if plasticity in traits is adaptive or maladaptive in endangered, nonendangered, and invasive plant species in response to variation of nitrogen (N) and phosphorus (P) availability (N:P ratios 1.7, 15, and 135) and whether plastic trait responses are adaptive and/or costly for fitness (i.e., biomass). Species choice comprised 17 species from three functional groups (legumes, nonlegume forbs, and grasses), either classified as endangered, nonendangered, or invasive. After 2 months, plants were harvested and nine traits related to carbon assimilation and nutrient uptake were measured (leaf area, SLA, LDMC, SPAD, RMR, root length, SRL, root surface area, and PME activity). We found more traits responding plastically to variation in P than in N. Plasticity only created costs when P was varied. Plasticity in traits was mostly adaptively neutral toward fitness, with plasticity in three traits being similarly adaptive across all species groups: SPAD (as a measure of chlorophyll content, adaptive to N and P limitation), leaf area, and root surface area (adaptive to P limitation). We found little differences in trait plasticity between endangered, nonendangered, and invasive species. Synthesis. Along a gradient from N limitation, balanced N:P supply, and P limitation, we found that the type of fluctuating nutrient (i.e., if N or P is varied) is decisive for the adaptive value of a trait. Variation in P availability (from balanced supply to P limitation) created both a stronger reduction in fitness as well as created plasticity costs in more traits than variation in N availability (from balanced supply to N limitation). However, the patterns observed in our study may change if nutrient availability is altered, either by nutrient inputs or by a shift in nutrient availabilities, for example, by decreasing N input as foreseen by European Legislation, but without simultaneously decreasing P input.
Motorized spiral enteroscopy – a prospective analysis of 82 procedures at a single tertiary center

Abstract Introduction Motorized spiral enteroscopy (MSE) is a promising novel deep enteroscopy technique. The purpose of our study was to evaluate efficiency and safety of MSE in a single tertiary endoscopy center. Methods We prospectively evaluated all consecutive patients undergoing MSE in our endoscopy unit from June 2019 through June 2022. Main outcomes were technical success rate, proportion of procedures with sufficient depth of insertion, success rate of total enteroscopy, diagnostic yield and complication rate. Results A total of 82 examinations in 62 patients (56% males, mean age 58 ± 18 years) were performed, 56 from antegrade and 26 from retrograde approach. Technical success rate was 94% (77/82) and in 89% (72/82) of cases, depth of insertion was considered sufficient. Total enteroscopy was indicated in 19 patients and it was achieved in 16 of them (84%), either by antegrade in 4 or by combined approach in 12 cases. Diagnostic yield was 81%. Lesion of the small bowel was found in 43 of patients. Mean insertion time was 40 for antegrade and 44 min for retrograde procedures, respectively. Complications occurred in 3% (2/62) of patients. One patient suffered from mild acute pancreatitis after total enteroscopy and there was one intussusception of the sigmoid during endoscope withdrawal resolved by parallel colonoscope insertion. Conclusion In our series of 82 procedures in 62 patients examined by MSE during a three-year period, we show high technical success rate (94%), diagnostic yield (81%) and low complication rate (3%).