Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic Gradient Langevin Unlearning

``The right to be forgotten'' ensured by laws for user data privacy becomes increasingly important. Machine unlearning aims to efficiently remove the effect of certain data points on the trained model parameters so that it can be approximately the same as if one retrains the model from scratch. This work proposes stochastic gradient Langevin unlearning, the first unlearning framework based on noisy stochastic gradient descent (SGD) with privacy guarantees for approximate unlearning problems under convexity assumption. Our results show that mini-batch gradient updates provide a superior privacy-complexity trade-off compared to the full-batch counterpart. There are numerous algorithmic benefits of our unlearning approach, including complexity saving compared to retraining, and supporting sequential and batch unlearning. To examine the privacy-utility-complexity trade-off of our method, we conduct experiments on benchmark datasets compared against prior works. Our approach achieves a similar utility under the same privacy constraint while using $2\%$ and $10\%$ of the gradient computations compared with the state-of-the-art gradient-based approximate unlearning methods for mini-batch and full-batch settings, respectively.
Time-dependent nuclear energy-density functional theory toolkit for neutron star crust: Dynamics of a nucleus in a neutron superfluid

We present a new numerical tool designed to probe the dense layers of neutron star crusts. It is based on the Time-Dependent Hartree-Fock-Bogoliubov theory with generalized Skyrme nuclear energy density functionals, such as the Brussels-Montreal ones. We use it to study the time evolution of a nucleus accelerating through superfluid neutron medium in the inner crust of a neutron star. We extract an effective mass in the low velocity limit. We observe a threshold velocity and specify mechanisms of dissipation: phonon emission, Cooper pairs breaking, and vortex rings creation. The microscopic effects we study have impact on neutron star. Moreover, the mechanisms, we described, are general and apply also to other fermionic superfluid mixtures like liquid helium, or ultracold gases.
TractOracle: towards an anatomically-informed reward function for RL-based tractography

Reinforcement learning (RL)-based tractography is a competitive alternative to machine learning and classical tractography algorithms due to its high anatomical accuracy obtained without the need for any annotated data. However, the reward functions so far used to train RL agents do not encapsulate anatomical knowledge which causes agents to generate spurious false positives tracts. In this paper, we propose a new RL tractography system, TractOracle, which relies on a reward network trained for streamline classification. This network is used both as a reward function during training as well as a mean for stopping the tracking process early and thus reduce the number of false positive streamlines. This makes our system a unique method that evaluates and reconstructs WM streamlines at the same time. We report an improvement of true positive ratios by almost 20\% and a reduction of 3x of false positive ratios on one dataset and an increase between 2x and 7x in the number true positive streamlines on another dataset.
Determination of human vocal-tract dynamic geometry from formant trajectories using spatial and temporal Fourier analysis

This article presents a method of estimation of the vocal-tract cross-sectional area, considered as a function of time and position along the tract length. The estimation is based on the speech formant frequencies, and uses a priori information about natural tract configurations. In general lines, the method is as follows. First, the cross-sectional area is represented by a two-dimensional Fourier cosine series expansion in time and space. Then, the locally linear relationship between spatial Fourier coefficients and formant frequencies is explored to formulate an acoustical constraint in the coefficient space. Finally, the sequence of vocal-tract areas corresponding to a given sequence of formants is estimated under positional, dynamical, and acoustical constraints. The system behavior is shown first for the static case of vowels and, then, for the dynamic case of vowel-to-vowel transitions. The method can be used as a bridge between articulatory parameter models and the speech parameter space. Moreover, it is potentially useful for area driven coders and synthesizers. <<ETX>>
Tuning statistical machine translation parameters using perplexity

Statistical machine translation (SMT) involves many tasks including modeling, training, decoding, and evaluation. In this work, we present a methodology for optimizing the training process to get better translation quality using the well known GIZA ++ SMT toolkit. The methodology is based on adjusting the parameters of GIZA ++ that affect the generation of the translation model. When applying the methodology, an average improvement of 7% has been achieved in the translation quality.
Raman peak shift and broadening in crystalline nanoparticles with lattice impurities

The effect of point-like lattice impurities on nanoparticle Raman spectra (RS) is studied using both numerical and analytical methods. Particular cases of replacement atoms of various masses, vacancies, and disorder in interatomic bonds are considered. It is shown that the disorder leads not only to the broadening of optical phonon lines but also to the shift of the corresponding Raman peak. The latter can be either positive (i.e., blueshift) or negative (redshift) depending on the type of impurities. Thus there is an additional contribution to the well-known redshift that occurs due to the size-quantization (confinement) effect. Considering nanometer-sized diamond particles as a representative example, we show that the broadening and the shift are, as a rule, of the same order of magnitude. The results are discussed in the framework of the self-consistent T-matrix approach. It is argued that both effects should be considered for accurate treatment of experimental Raman spectra. Simple recipes to do so are formulated for several important cases including NV centers in nanodiamonds.
Re: Selective Inhibition of Her2-positive Breast Cancer Cells by the Hiv Protease Inhibitor Nelfinavir Notes Erratum: " Comparing Survival of a Sample to That of a Standard Population "

Shim et al. (1) provided interesting data in their recent article in the Journal. It is of interest that other recent data also suggest that nelfinavir exerts antineoplastic effects in a number of other systemic malignancies besides breast carcinomas. For instance, similar effects are seen in non–small cell lung carcinomas. Nelfinavir causes caspase-dependent apoptosis as well as caspase-independent apoptosis within the pulmonary malignancies. Nelfinavir administration leads to augmentation of C/EBP homol-ogous protein levels (2). Simultaneous accentuation of activating transcription factor 3 levels is also seen (3). As a result, there is a marked increase in intratumoral apoptosis. Nelfinavir also attenuates the activation of Akt. In fact, recent studies in humans suggest that nelfinavir when used in conjunction with chemoradiotherapy demonstrates an overall response of 100% in stage IIIA/IIIB non–small cell lung car-cinomas (4). The maximum tolerated dose in this study was 2500 mg administered in two divided doses. (2) In fact, nelfinavir synergistically augments the cytotoxicity of other chemotherapeutic agents such as bortezomib in pulmonary malignancies (5). Similarly, nelfinavir attenuates tumor growth in ovarian carcinomas. It mediates this role by increasing the expression of BiP (GRP78). It also causes augmentation of eIF2α phosphorylation (6). Simultaneous downregulation of cyclin D3 is also seen. These metabolic changes result in accentuated induction of the unfolded protein response (2). As a result, intratumoral apop-tosis is markedly augmented within ovarian tumors (6). Nelfinavir administration also increases the expression of DR5 (7). As a result, it augments the proapoptotic effects of tumor necrosis factor–related apoptosis-including ligand receptor antibody. It is clearly evident from the preceding examples that nelfinavir has significant antineoplastic properties. Further studies in humans are needed to fully evaluate its anticarcinogenic role.bition of hER2-positive breast cancer cells by the hIV protease inhibitor nelfinavir. Nelfinavir, a lead hIV protease inhibitor, is a broad-spectrum, anticancer agent that induces endoplasmic reticulum stress, autophagy, and apoptosis in vitro and in vivo. A. Bortezomib targets the caspase-like protea-some activity in cervical cancer cells, triggering apoptosis that can be enhanced by nelfinavir. A phase I trial of the hIV protease inhibitor nelfina-vir with concurrent chemoradiotherapy for unresectable stage IIIA/IIIB non-small cell lung cancer: a report of toxicities and clinical response. Synergistic effects of nelfinavir and bortezomib on proteotoxic death of NSClC and multiple myeloma cells. induces the unfolded protein response in ovar-ian cancer cells, resulting in ER vacuolization, cell cycle retardation and apoptosis. The paper discusses designing a study to …
S+t-SNE -- Bringing dimensionality reduction to data streams

We present S+t-SNE, an adaptation of the t-SNE algorithm designed to handle infinite data streams. The core idea behind S+t-SNE is to update the t-SNE embedding incrementally as new data arrives, ensuring scalability and adaptability to handle streaming scenarios. By selecting the most important points at each step, the algorithm ensures scalability while keeping informative visualisations. Employing a blind method for drift management adjusts the embedding space, facilitating continuous visualisation of evolving data dynamics. Our experimental evaluations demonstrate the effectiveness and efficiency of S+t-SNE. The results highlight its ability to capture patterns in a streaming scenario. We hope our approach offers researchers and practitioners a real-time tool for understanding and interpreting high-dimensional data.
On the Impact of Random Node Sampling on Adaptive Diffusion Networks

In this paper, we analyze the effects of random sampling on adaptive diffusion networks. These networks consist in a collection of nodes that can measure and process data, and that can communicate with each other to pursue a common goal of estimating an unknown system. In particular, we consider in our theoretical analysis the diffusion least-mean-squares algorithm in a scenario in which the nodes are randomly sampled. Hence, each node may or may not adapt its local estimate at a certain iteration. Our model shows that, if the nodes cooperate, a reduction in the sampling probability leads to a slight decrease in the steady-state Network Mean-Square Deviation (NMSD), assuming that the environment is stationary and that all other parameters of the algorithm are kept fixed. Furthermore, under certain circumstances, this can also ensure the stability of the algorithm in situations in which it would otherwise be unstable. Although counter-intuitive, our findings are backed by simulation results, which match the theoretical curves well.
CPT and Lorentz symmetry tests with hydrogen using a novel in-beam hyperfine spectroscopy method applicable to antihydrogen experiments

We present a Rabi-type measurement of two ground-state hydrogen hyperfine transitions performed in two opposite external magnetic field directions. This puts first constraints at the level of 2.3 10^-21 GeV on a set of coefficients of the Standard Model Extension, which were not measured by previous experiments. Moreover, we introduce a novel method, applicable to antihydrogen hyperfine spectroscopy in a beam, that determines the zero-field hyperfine transition frequency from the two transitions measured at the same magnetic field. Our value, nu_0 = 1.420 405 751 63(63) GHz, is in agreement with literature at a relative precision of 0.44 ppb. This is the highest precision achieved on hydrogen in a beam, improving over previous results by a factor of 6.
Deploying and monitoring hadoop MapReduce analytics on single-chip cloud computer

Modern data analytics applications exhibit scale-out characteristics, requiring large amount of computational power. Recent research has shown that modern manycore architectures forms a promising platform solution for this emerging type of workloads. In this paper, we present a framework for the deployment, monitoring and automated exploration of Hadoop MapReduce clusters implementing data analytics applications onto the Intel SCC manycore platform. We provide an in-depth analysis on the performance and energy characteristics of Hadoop MapReduce workloads on the Intel SCC, i.e. on a real-silicon manycore which highly differentiates from typical server and accelerator architectures. Through extensive experimentation, we show that there is a trade-off between the number of worker nodes and the per-node available I/O bandwidth and that intelligently scaling the frequency of data-nodes yields in energy savings with minimal impact on performance.
The Recording of Emotional Speech-JST / CREST database research -

This paper reports on a recent development in Japan, funded by the Science and Technology Agency, for the creation and analysis of a very large database, including emotional speech, for the purpose of speech technology research. In order to capture spontaneous samples of naturally-occurring emotion without recourse to acting, a very large amount of conversational speech and daily spoken interaction will be recorded and labeled. The algorithms developed for automating the labeling of this speech will form the foundation for user-interactive speech processing technology that is sensitive to speaking style and speaker-state. This paper addresses the issue of collecting such a large amount of natural speech data.
Universal entropy transport far from equilibrium across the BCS-BEC crossover

The transport properties of strongly interacting fermionic systems can reveal exotic states of matter, but experiments and theory have predominantly focused on bulk systems in the hydrodynamic limit describable with linear response coefficients such as electrical and thermal conductivity. In a ballistic channel connecting two superfluid reservoirs, recent experiments revealed a far-from-equilibrium regime beyond linear hydrodynamics where particle and entropy currents respond nonlinearly to biases of chemical potential and temperature, and their ratio is robust to the channel geometry. However, the origin of this robustness and its relation to the strong interparticle interactions remain unknown. Here, we study the coupled transport of particles and entropy tuning the interaction across the Bardeen-Cooper-Schrieffer to Bose-Einstein condensate (BCS-BEC) crossover, the reservoir degeneracy across the superfluid phase transition, as well as the local potentials and confinement of the channel. Surprisingly, the entropy advectively transported per particle depends only on the interactions and reservoir degeneracy and not on the details of the channel, suggesting that this property has its origin in the universal equilibrium properties of the reservoirs. In contrast, the magnitudes of the advective and diffusive entropy currents vary significantly with the channel details. The advective current increases monotonically towards the BEC side, which can be largely explained by the estimated superfluid gap in the channel. The Wiedemann-Franz law that links the advective and diffusive currents in Fermi liquids is most egregiously violated at unitarity, suggesting a change in the nature of the excitations responsible for entropy diffusion near unitarity. These observations pose fundamental questions regarding transport phenomena in strongly interacting Fermi systems far from equilibrium.
Have we made any progress? Catalogues of the future revisited

Purpose – Library online public access catalogues (OPACs) are considered to be unattractive in comparison with popular internet sites. In 2000, the authors presented some suggestions on how library catalogues should change. Have librarians actually made their OPACs more user‐friendly by adopting techniques and technologies already present in other information resources? This paper aims to address these issues.Design/methodology/approach – The characteristics of four OPACs, one online bookstore and two internet search engines are analyzed. The paper reviews some of the changes and directions suggested by researchers and adds some of authors own. All this is in the hope that library catalogues will survive “Google attack. ”Findings – Changes are identified in the information services studied over a seven‐year period. Least development is found in library catalogues. Suggestions are made for library catalogues of the future.Research limitations/implications – A library catalogue, a web search engine and an in.
Chain-of-Action: Faithful and Multimodal Question Answering through Large Language Models

We present a Chain-of-Action (CoA) framework for multimodal and retrieval-augmented Question-Answering (QA). Compared to the literature, CoA overcomes two major challenges of current QA applications: (i) unfaithful hallucination that is inconsistent with real-time or domain facts and (ii) weak reasoning performance over compositional information. Our key contribution is a novel reasoning-retrieval mechanism that decomposes a complex question into a reasoning chain via systematic prompting and pre-designed actions. Methodologically, we propose three types of domain-adaptable `Plug-and-Play' actions for retrieving real-time information from heterogeneous sources. We also propose a multi-reference faith score (MRFS) to verify and resolve conflicts in the answers. Empirically, we exploit both public benchmarks and a Web3 case study to demonstrate the capability of CoA over other methods.
Towards Inclusive Video Commenting: Introducing Signmaku for the Deaf and Hard-of-Hearing

Previous research underscored the potential of danmaku--a text-based commenting feature on videos--in engaging hearing audiences. Yet, for many Deaf and hard-of-hearing (DHH) individuals, American Sign Language (ASL) takes precedence over English. To improve inclusivity, we introduce"Signmaku,"a new commenting mechanism that uses ASL, serving as a sign language counterpart to danmaku. Through a need-finding study (N=12) and a within-subject experiment (N=20), we evaluated three design styles: real human faces, cartoon-like figures, and robotic representations. The results showed that cartoon-like signmaku not only entertained but also encouraged participants to create and share ASL comments, with fewer privacy concerns compared to the other designs. Conversely, the robotic representations faced challenges in accurately depicting hand movements and facial expressions, resulting in higher cognitive demands on users. Signmaku featuring real human faces elicited the lowest cognitive load and was the most comprehensible among all three types. Our findings offered novel design implications for leveraging generative AI to create signmaku comments, enriching co-learning experiences for DHH individuals.
SciCapenter: Supporting Caption Composition for Scientific Figures with Machine-Generated Captions and Ratings

Crafting effective captions for figures is important. Readers heavily depend on these captions to grasp the figure's message. However, despite a well-developed set of AI technologies for figures and captions, these have rarely been tested for usefulness in aiding caption writing. This paper introduces SciCapenter, an interactive system that puts together cutting-edge AI technologies for scientific figure captions to aid caption composition. SciCapenter generates a variety of captions for each figure in a scholarly article, providing scores and a comprehensive checklist to assess caption quality across multiple critical aspects, such as helpfulness, OCR mention, key takeaways, and visual properties reference. Users can directly edit captions in SciCapenter, resubmit for revised evaluations, and iteratively refine them. A user study with Ph.D. students indicates that SciCapenter significantly lowers the cognitive load of caption writing. Participants' feedback further offers valuable design insights for future systems aiming to enhance caption writing.
Exact Results for the Giant Graviton four-point Correlator

We study the four-point correlator $\langle \mathcal{O}_2 \mathcal{O}_2 \mathcal{D} \mathcal{D} \rangle$ in $\mathcal{N}=4$ super Yang-Mills theory (SYM) with $SU(N)$ gauge group, where $\mathcal{O}_2$ represents the superconformal primary operator with dimension two, while $\mathcal{D}$ denotes a determinant operator of dimension $N$, which is holographically dual to a giant graviton D3-brane extending along $S^5$. We analyse the integrated correlator associated with this observable, obtained after integrating out the spacetime dependence over a supersymmetric invariant measure. Similarly to other classes of integrated correlators in $\mathcal{N}=4$ SYM, this integrated correlator can be computed through supersymmetric localisation on the four-sphere. Employing matrix-model recursive techniques, we demonstrate that the integrated correlator can be reformulated as an infinite sum of protected three-point functions with known coefficients. This insight allows us to circumvent the complexity associated with the dimension-$N$ determinant operator, significantly streamlining the large-$N$ expansion of the integrated correlator. In the planar limit and beyond, we derive exact results for the integrated correlator valid for all values of the 't Hooft coupling, and investigate the resurgent properties of their strong coupling expansion. Additionally, in the large-$N$ expansion with fixed (complexified) Yang-Mills coupling, we deduce the $SL(2, \mathbb{Z})$ completion of these results in terms of the non-holomorphic Eisenstein series. The proposed modular functions are confirmed by explicit instanton calculations from the matrix model, and agree with expectations from the holographic dual picture of known results in type IIB string theory.
Task-Oriented Paraphrase Analytics

Since paraphrasing is an ill-defined task, the term"paraphrasing"covers text transformation tasks with different characteristics. Consequently, existing paraphrasing studies have applied quite different (explicit and implicit) criteria as to when a pair of texts is to be considered a paraphrase, all of which amount to postulating a certain level of semantic or lexical similarity. In this paper, we conduct a literature review and propose a taxonomy to organize the 25~identified paraphrasing (sub-)tasks. Using classifiers trained to identify the tasks that a given paraphrasing instance fits, we find that the distributions of task-specific instances in the known paraphrase corpora vary substantially. This means that the use of these corpora, without the respective paraphrase conditions being clearly defined (which is the normal case), must lead to incomparable and misleading results.