Miracle-dz/UniFAR-FTUs huggingface dataset:
Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?

For small privacy parameter $\epsilon$, $\epsilon$-differential privacy (DP) provides a strong worst-case guarantee that no membership inference attack (MIA) can succeed at determining whether a person's data was used to train a machine learning model. The guarantee of DP is worst-case because: a) it holds even if the attacker already knows the records of all but one person in the data set; and b) it holds uniformly over all data sets. In practical applications, such a worst-case guarantee may be overkill: practical attackers may lack exact knowledge of (nearly all of) the private data, and our data set might be easier to defend, in some sense, than the worst-case data set. Such considerations have motivated the industrial deployment of DP models with large privacy parameter (e.g. $\epsilon \geq 7$), and it has been observed empirically that DP with large $\epsilon$ can successfully defend against state-of-the-art MIAs. Existing DP theory cannot explain these empirical findings: e.g., the theoretical privacy guarantees of $\epsilon \geq 7$ are essentially vacuous. In this paper, we aim to close this gap between theory and practice and understand why a large DP parameter can prevent practical MIAs. To tackle this problem, we propose a new privacy notion called practical membership privacy (PMP). PMP models a practical attacker's uncertainty about the contents of the private data. The PMP parameter has a natural interpretation in terms of the success rate of a practical MIA on a given data set. We quantitatively analyze the PMP parameter of two fundamental DP mechanisms: the exponential mechanism and Gaussian mechanism. Our analysis reveals that a large DP parameter often translates into a much smaller PMP parameter, which guarantees strong privacy against practical MIAs. Using our findings, we offer principled guidance for practitioners in choosing the DP parameter.
Zak-OTFS and LDPC Codes

Orthogonal Time Frequency Space (OTFS) is a framework for communications and active sensing that processes signals in the delay-Doppler (DD) domain. It is informed by 6G propagation environments, where Doppler spreads measured in kHz make it more and more difficult to estimate channels, and the standard model-dependent approach to wireless communication is starting to break down. We consider Zak-OTFS where inverse Zak transform converts information symbols mounted on DD domain pulses to the time domain for transmission. Zak-OTFS modulation is parameterized by a delay period $\tau_{p}$ and a Doppler period $\nu_{p}$, where the product $\tau_{p}\nu_{p}=1$. When the channel spread is less than the delay period, and the Doppler spread is less than the Doppler period, the Zak-OTFS input-output relation can be predicted from the response to a single pilot symbol. The highly reliable channel estimates concentrate around the pilot location, and we configure low-density parity-check (LDPC) codes that take advantage of this prior information about reliability. It is advantageous to allocate information symbols to more reliable bins in the DD domain. We report simulation results for a Veh-A channel model where it is not possible to resolve all the paths, showing that LDPC coding extends the range of Doppler spreads for which reliable model-free communication is possible. We show that LDPC coding reduces sensitivity to the choice of transmit filter, making bandwidth expansion less necessary. Finally, we compare BER performance of Zak-OTFS to that of a multicarrier approximation (MC-OTFS), showing LDPC coding amplifies the gains previously reported for uncoded transmission.
Misinformation Regulation in the Presence of Competition between Social Media Platforms (Extended Version)

Social media platforms have diverse content moderation policies, with many prominent actors hesitant to impose strict regulations. A key reason for this reluctance could be the competitive advantage that comes with lax regulation. A popular platform that starts enforcing content moderation rules may fear that it could lose users to less-regulated alternative platforms. Moreover, if users continue harmful activities on other platforms, regulation ends up being futile. This article examines the competitive aspect of content moderation by considering the motivations of all involved players (platformer, news source, and social media users), identifying the regulation policies sustained in equilibrium, and evaluating the information quality available on each platform. Applied to simple yet relevant social networks such as stochastic block models, our model reveals the conditions for a popular platform to enforce strict regulation without losing users. Effectiveness of regulation depends on the diffusive property of news posts, friend interaction qualities in social media, the sizes and cohesiveness of communities, and how much sympathizers appreciate surprising news from influencers.
Resurgence in Lorentzian quantum cosmology: no-boundary saddles and resummation of quantum gravity corrections around tunneling saddles

We revisit the path-integral approach to the wave function of the universe by utilizing Lefschetz thimble analyses and resurgence theory. The traditional Euclidean path-integral of gravity has the notorious ambiguity of the direction of Wick rotation. In contrast, the Lorentzian method can be formulated concretely with the Picard-Lefschetz theory. Yet, a challenge remains: the physical parameter space lies on a Stokes line, meaning that the Lefschetz-thimble structure is still unclear. Through complex deformations, we resolve this issue by uniquely identifying the thimble structure. This leads to the tunneling wave function, as opposed to the no-boundary wave function, offering a more rigorous proof of the previous results. Further exploring the parameter space, we discover rich structures: the ambiguity of the Borel resummation of perturbative series around the tunneling saddle points is exactly cancelled by the ambiguity of the contributions from no-boundary saddle points. This indicates that resurgence works also in quantum cosmology, particularly in the minisuperspace model.
On Formally Undecidable Traits of Intelligent Machines

Building on work by Alfonseca et al. (2021), we study the conditions necessary for it to be logically possible to prove that an arbitrary artificially intelligent machine will exhibit certain behavior. To do this, we develop a formalism like -- but mathematically distinct from -- the theory of formal languages and their properties. Our formalism affords a precise means for not only talking about the traits we desire of machines (such as them being intelligent, contained, moral, and so forth), but also for detailing the conditions necessary for it to be logically possible to decide whether a given arbitrary machine possesses such a trait or not. Contrary to Alfonseca et al.'s (2021) results, we find that Rice's theorem from computability theory cannot in general be used to determine whether an arbitrary machine possesses a given trait or not. Therefore, it is not necessarily the case that deciding whether an arbitrary machine is intelligent, contained, moral, and so forth is logically impossible.
On-Demand Myoelectric Control Using Wake Gestures to Eliminate False Activations During Activities of Daily Living

While myoelectric control has recently become a focus of increased research as a possible flexible hands-free input modality, current control approaches are prone to inadvertent false activations in real-world conditions. In this work, a novel myoelectric control paradigm -- on-demand myoelectric control -- is proposed, designed, and evaluated, to reduce the number of unrelated muscle movements that are incorrectly interpreted as input gestures . By leveraging the concept of wake gestures, users were able to switch between a dedicated control mode and a sleep mode, effectively eliminating inadvertent activations during activities of daily living (ADLs). The feasibility of wake gestures was demonstrated in this work through two online ubiquitous EMG control tasks with varying difficulty levels; dismissing an alarm and controlling a robot. The proposed control scheme was able to appropriately ignore almost all non-targeted muscular inputs during ADLs (>99.9%) while maintaining sufficient sensitivity for reliable mode switching during intentional wake gesture elicitation. These results highlight the potential of wake gestures as a critical step towards enabling ubiquitous myoelectric control-based on-demand input for a wide range of applications.
Criterion collapse and loss distribution control

In this work, we consider the notion of"criterion collapse,"in which optimization of one metric implies optimality in another, with a particular focus on conditions for collapse into error probability minimizers under a wide variety of learning criteria, ranging from DRO and OCE risks (CVaR, tilted ERM) to non-monotonic criteria underlying recent ascent-descent algorithms explored in the literature (Flooding, SoftAD). We show how collapse in the context of losses with a Bernoulli distribution goes far beyond existing results for CVaR and DRO, then expand our scope to include surrogate losses, showing conditions where monotonic criteria such as tilted ERM cannot avoid collapse, whereas non-monotonic alternatives can.
Phase transitions between confinement and higgs phases in ${\cal N}=1\,\, SU(N_c)$ SQCD with $1\leq N_F\leq N_c$ quark flavors

Considered is the standard 4-dimensional ${\cal N}=1\,\,SU(N_c)$ SQCD with $1\leq N_F\leq N_c$ quark flavors with masses $m_{Q,i}$. {\bf The gauge invariant order parameter $\rho$ is introduced in \cite{ch1} distinguishing confinement (with $\rho=0$) and higgs (with $\rho\neq 0$) phases}. Using a number of independent arguments for different variants of transition between the confinement and higgs regimes in these theories, it is shown that {\bf transitions between these regimes are not crossovers but the phase transitions}. In \cite{FS} the very special $SU(N_c)$ QCD theory with $N_F=N_c$ defective scalar"quarks"was considered by E.Fradkin and S.H. Shenker. The conclusion of \cite{FS} was that {\bf the transition between the confinement and higgs regimes is the analytic crossover}. And although the theory considered in \cite{FS} was very specific, the experience shows that up to now there is a widely spread opinion that this conclusion has general applicability. This model \cite{FS} is criticized in \cite{ch1},\cite{ch2} as incompatible with and very different from the standard non-SUSY $SU(N_c)\,\,N_F=N_c$ QCD theory with standard scalar quarks with all their physical degrees of freedom. It is emphasized that this model \cite{FS} is really {\bf the Stuckelberg $SU(N_c)$ YM-theory with no dynamical electric quarks and massive all $N_c^2-1$ electric gluons with fixed by hands nonzero masses}. There is no genuine confinement in this theory, it stays permanently in the completely higgsed (i.e. condensed) by hands phase only. And this is a reason for a crossover in this theory. While in the theory with standard scalar quarks there is the phase transition between the confinement (at $0<|v|\ll \Lambda_{QCD}$) and higgs (at $|v|\gg\Lambda_{QCD}$) regimes.
Hydrological variability on vegetation seasonality, productivity and composition in tropical ecosystems of Africa

As the world’s second largest and most populous continent behind Asia, the African continent harbors vast area of tropical ecosystems spanning great gradients from savannas to rainforests, which provide essential ecosystem services at regional and global scales. In the context of global climate change, many of these drought-sensitive ecosystems are expected to be modified or endangered. This dissertation aims to advance fundamental understandings on response and vulnerability of African tropical ecosystems to various possible hydroclimate changes at continental scales by addressing two major questions: (1) What climatic variables do control vegetation seasonality and phenology in tropical ecosystems and how (Chapter 2-5)? (2) How do intra-seasonal and inter-annual hydrological variabilities influence function and structure of tropical ecosystems (Chapter 6-8)? Chapter 2 provides an advanced algorithm to derive vegetation phenological and trajectory information over Africa. Using the derived decadal-long phenological record, I explore the hydrological controls on the phenology of African savannas and woodlands in Chapter 3 by examining phase correspondences and inter-annual variations between rainy season and growing season, revealing the possible role of groundwater in African woodlands. Chapter 3 also provides the non-linear impacts of rainy season length on ecosystem structures (e.g. tree fraction). Chapter 4 reveals a possible seasonal decoupling between canopy functioning and canopy structure from optical and microwave remote sensing respectively in African tropical evergreen forests. Chapter 5 discovers a diverse seasonal pattern of canopy photosynthesis in tropical evergreen forests at global scale, and a threshold of annual total rainfall (~2000mm/year) governs the supply-demand relationship between seasonal subsurface water storage and dry season water deficit, which determines these global patterns. Chapter 6 studies the inter-annual sensitivity of different biomes to precipitation variability using multi-sensor data, and generates an
Accumulation of cross-channel non-linear interference in dispersion-managed and disaggregated optical network segment

We evaluate the generation of the cross-channel interference (XCI) for coherent transmission through a variety of dispersion-managed segments in a disaggregated optical network framework, using split-step Fourier method (SSFM) simulations and an implementation of the Gaussian noise (GN) model. We observe that the small inline residual dispersion remaining after each span affects the accumulation of the XCI, causing GN model predictions to no longer be conservative. We find an asymptotic upper bound to this additional accumulation, providing a worst-case prediction, and observe that this depends upon the residual dispersion within the link. This upper bound scales similarly to the self-channel interference (SCI) accumulation, and is well characterized by the parameters of the underlying fiber spans and the transmitted signals.
Parameterized Vertex Integrity Revisited

Vertex integrity is a graph parameter that measures the connectivity of a graph. Informally, its meaning is that a graph has small vertex integrity if it has a small separator whose removal disconnects the graph into connected components which are themselves also small. Graphs with low vertex integrity are extremely structured; this renders many hard problems tractable and has recently attracted interest in this notion from the parameterized complexity community. In this paper we revisit the NP-complete problem of computing the vertex integrity of a given graph from the point of view of structural parameterizations. We present a number of new results, which also answer some recently posed open questions from the literature. Specifically: We show that unweighted vertex integrity is W[1]-hard parameterized by treedepth; we show that the problem remains W[1]-hard if we parameterize by feedback edge set size (via a reduction from a Bin Packing variant which may be of independent interest); and complementing this we show that the problem is FPT by max-leaf number. Furthermore, for weighted vertex integrity, we show that the problem admits a single-exponential FPT algorithm parameterized by vertex cover or by modular width, the latter result improving upon a previous algorithm which required weights to be polynomially bounded.
Spectral function for 4 He using the Chebyshev expansion in coupled-cluster theory

We compute spectral function for 4 He by combining coupled-cluster theory with an expansion of integral transforms into Chebyshev polynomials. Our method allows to estimate the uncertainty of spectral reconstruction. The properties of the Chebyshev polynomials make the procedure numerically stable and considerably lower in memory usage than the typically employed Lanczos algorithm. We benchmark our predictions with other calculations in the literature and with electron scattering data in the quasi-elastic peak. The spectral function formalism allows one to extend ab-initio lepton-nucleus cross sections into the relativistic regime. This makes it a promising tool for modeling this process at higher energy transfers. The results we present open the door for studies of heavier nuclei, important for the neutrino oscillation programs.
Canalization reduces the nonlinearity of regulation in biological networks

Biological networks such as gene regulatory networks possess desirable properties. They are more robust and controllable than random networks. This motivates the search for structural and dynamical features that evolution has incorporated in biological networks. A recent meta-analysis of published, expert-curated Boolean biological network models has revealed several such features, often referred to as design principles. Among others, the biological networks are enriched for certain recurring network motifs, the dynamic update rules are more redundant, more biased and more canalizing than expected, and the dynamics of biological networks are better approximable by linear and lower-order approximations than those of comparable random networks. Since most of these features are interrelated, it is paramount to disentangle cause and effect, that is, to understand which features evolution actively selects for, and thus truly constitute evolutionary design principles. Here, we show that approximability is strongly dependent on the dynamical robustness of a network, and that increased canalization in biological networks can almost completely explain their recently postulated high approximability.
Data Engineering for Scaling Language Models to 128K Context

We study the continual pretraining recipe for scaling language models' context lengths to 128K, with a focus on data engineering. We hypothesize that long context modeling, in particular \textit{the ability to utilize information at arbitrary input locations}, is a capability that is mostly already acquired through large-scale pretraining, and that this capability can be readily extended to contexts substantially longer than seen during training~(e.g., 4K to 128K) through lightweight continual pretraining on appropriate data mixture. We investigate the \textit{quantity} and \textit{quality} of the data for continual pretraining: (1) for quantity, we show that 500 million to 5 billion tokens are enough to enable the model to retrieve information anywhere within the 128K context; (2) for quality, our results equally emphasize \textit{domain balance} and \textit{length upsampling}. Concretely, we find that naively upsampling longer data on certain domains like books, a common practice of existing work, gives suboptimal performance, and that a balanced domain mixture is important. We demonstrate that continual pretraining of the full model on 1B-5B tokens of such data is an effective and affordable strategy for scaling the context length of language models to 128K. Our recipe outperforms strong open-source long-context models and closes the gap to frontier models like GPT-4 128K.
Gauge-Independent Metric Reconstruction of Perturbations of Vacuum Spherically-Symmetric Spacetimes

Perturbation theory of vacuum spherically-symmetric spacetimes (including the cosmological constant) has greatly contributed to the understanding of black holes, relativistic compact stars and even inhomogeneous cosmological models. The perturbative equations can be decoupled in terms of (gauge-invariant) master functions satisfying $1+1$ wave equations. In this work, building on previous work on the structure of the space of master functions and equations, we study the reconstruction of the metric perturbations in terms of the master functions. To that end, we consider the general situation in which the perturbations are driven by an arbitrary energy-momentum tensor. Then, we perform the metric reconstruction in a completely general perturbative gauge. In doing so, we investigate the role of Darboux transformations and Darboux covariance, responsible for the isospectrality between odd and even parity in the absence of matter sources and also of the physical equivalence between the descriptions based on all the possible master equations. We also show that the metric reconstruction can be carried out in terms of any of the possible master functions and that the expressions admit an explicitly covariant form.
Preserving Data Privacy for ML-driven Applications in Open Radio Access Networks

Deep learning offers a promising solution to improve spectrum access techniques by utilizing data-driven approaches to manage and share limited spectrum resources for emerging applications. For several of these applications, the sensitive wireless data (such as spectrograms) are stored in a shared database or multistakeholder cloud environment and are therefore prone to privacy leaks. This paper aims to address such privacy concerns by examining the representative case study of shared database scenarios in 5G Open Radio Access Network (O-RAN) networks where we have a shared database within the near-real-time (near-RT) RAN intelligent controller. We focus on securing the data that can be used by machine learning (ML) models for spectrum sharing and interference mitigation applications without compromising the model and network performances. The underlying idea is to leverage a (i) Shuffling-based learnable encryption technique to encrypt the data, following which, (ii) employ a custom Vision transformer (ViT) as the trained ML model that is capable of performing accurate inferences on such encrypted data. The paper offers a thorough analysis and comparisons with analogous convolutional neural networks (CNN) as well as deeper architectures (such as ResNet-50) as baselines. Our experiments showcase that the proposed approach significantly outperforms the baseline CNN with an improvement of 24.5% and 23.9% for the percent accuracy and F1-Score respectively when operated on encrypted data. Though deeper ResNet-50 architecture is obtained as a slightly more accurate model, with an increase of 4.4%, the proposed approach boasts a reduction of parameters by 99.32%, and thus, offers a much-improved prediction time by nearly 60%.
Intertwined Magnetism and Superconductivity in Isolated Correlated Flat Bands

Multi-orbital electronic models hosting a non-trivial band-topology in the regime of strong electronic interactions are an ideal playground for exploring a host of complex phenomenology. We consider here a sign-problem-free and time-reversal symmetric model with isolated topological (chern) bands involving both spin and valley degrees of freedom in the presence of a class of repulsive electronic interactions. Using a combination of numerically exact quantum Monte Carlo computations and analytical field-theoretic considerations we analyze the phase-diagram as a function of the flat-band filling, temperature and relative interaction strength. The low-energy physics is described in terms of a set of intertwined orders -- a spin-valley hall (SVH) insulator and a spin-singlet superconductor (SC). Our low-temperature phase diagram can be understood in terms of an effective SO(4) pseudo-spin non-linear sigma model. Our work paves the way for building more refined and minimal models of realistic materials, including moir\'e systems, to study the universal aspects of competing insulating phases and superconductivity in the presence of non-trivial band-topology.