Differentially Private Synthetic Data Generation via Lipschitz-Regularised Variational Autoencoders

Synthetic data has been hailed as the silver bullet for privacy preserving data analysis. If a record is not real, then how could it violate a person's privacy? In addition, deep-learning based generative models are employed successfully to approximate complex high-dimensional distributions from data and draw realistic samples from this learned distribution. It is often overlooked though that generative models are prone to memorising many details of individual training records and often generate synthetic data that too closely resembles the underlying sensitive training data, hence violating strong privacy regulations as, e.g., encountered in health care. Differential privacy is the well-known state-of-the-art framework for guaranteeing protection of sensitive individuals' data, allowing aggregate statistics and even machine learning models to be released publicly without compromising privacy. The training mechanisms however often add too much noise during the training process, and thus severely compromise the utility of these private models. Even worse, the tight privacy budgets do not allow for many training epochs so that model quality cannot be properly controlled in practice. In this paper we explore an alternative approach for privately generating data that makes direct use of the inherent stochasticity in generative models, e.g., variational autoencoders. The main idea is to appropriately constrain the continuity modulus of the deep models instead of adding another noise mechanism on top. For this approach, we derive mathematically rigorous privacy guarantees and illustrate its effectiveness with practical experiments

One-Shot Messaging at Any Load Through Random Sub-Channeling in OFDM

Compressive Sensing has well boosted massive random access protocols over the last decade. In this paper we apply an orthogonal FFT basis as it is used in OFDM, but subdivide its image into so-called sub-channels and let each sub-channel take only a fraction of the load. In a random fashion the subdivision is consecutively applied over a suitable number of time-slots. Within the time-slots the users will not change their sub-channel assignment and send in parallel the data. Activity detection is carried out jointly across time-slots in each of the sub-channels. For such system design we derive three rather fundamental results: i) First, we prove that the subdivision can be driven to the extent that the activity in each sub-channel is sparse by design. An effect that we call sparsity capture effect. ii) Second, we prove that effectively the system can sustain any overload situation relative to the FFT dimension, i.e. detection failure of active and non-active users can be kept below any desired threshold regardless of the number of users. The only price to pay is delay, i.e. the number of time-slots over which cross-detection is performed. We achieve this by jointly exploring the effect of measure concentration in time and frequency and careful system parameter scaling. iii) Third, we prove that parallel to activity detection active users can carry one symbol per pilot resource and time-slot so it supports so-called one-shot messaging. The key to proving these results are new concentration results for sequences of randomly sub-sampled FFTs detecting the sparse vectors "en bloc". Eventually, we show by simulations that the system is scalable resulting in a coarsely 30-fold capacity increase compared to standard OFDM

Prediction of Strong Solvatochromism in a Molecular Photocatalyst

Based on quantum chemical calculations, we predict strong solvatochromism in a light-driven molecular photocatalyst for hydrogen generation, that is we show that the electronic and optical properties of the photocatalyst strongly depend on the solvent it is dissolved in. Our calculations in particular indicate a solvent-dependent relocation of the highest occupied molecular orbital (HOMO). Ground-state density functional theory and linear response time-dependent density functional theory calculations were applied in order to investigate the influence of implicit solvents on the structural, electronic and optical properties of a molecular photocatalyst. Only at high dielectric constants of the solvent, is the HOMO located at the metal center of the photosensitizer, whereas at low dielectric constants the HOMO is centered at the metal atom of the catalytically active complex. We elucidate the electronic origins of this strong solvatochromic effect and sketch the consequences of these insights for the use of photocatalysts in different environments

Hierarchical compressed sensing

Compressed sensing is a paradigm within signal processing that provides the means for recovering structured signals from linear measurements in a highly efficient manner. Originally devised for the recovery of sparse signals, it has become clear that a similar methodology would also carry over to a wealth of other classes of structured signals. In this work, we provide an overview over the theory of compressed sensing for a particularly rich family of such signals, namely those of hierarchically structured signals. Examples of such signals are constituted by blocked vectors, with only few non-vanishing sparse blocks. We present recovery algorithms based on efficient hierarchical hard-thresholding. The algorithms are guaranteed to converge, in a stable fashion both with respect to measurement noise as well as to model mismatches, to the correct solution provided the measurement map acts isometrically restricted to the signal class. We then provide a series of results establishing the required condition for large classes of measurement ensembles. Building upon this machinery, we sketch practical applications of this framework in machine-type communications and quantum tomography.Comment: This book chapter is a report on findings within the DFG-funded priority program `Compressed Sensing in Information Processing' (CoSIP

Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments

Semantic scene understanding is essential for mobile agents acting in various environments. Although semantic segmentation already provides a lot of information, details about individual objects as well as the general scene are missing but required for many real-world applications. However, solving multiple tasks separately is expensive and cannot be accomplished in real time given limited computing and battery capabilities on a mobile platform. In this paper, we propose an efficient multi-task approach for RGB-D scene analysis~(EMSANet) that simultaneously performs semantic and instance segmentation~(panoptic segmentation), instance orientation estimation, and scene classification. We show that all tasks can be accomplished using a single neural network in real time on a mobile platform without diminishing performance - by contrast, the individual tasks are able to benefit from each other. In order to evaluate our multi-task approach, we extend the annotations of the common RGB-D indoor datasets NYUv2 and SUNRGB-D for instance segmentation and orientation estimation. To the best of our knowledge, we are the first to provide results in such a comprehensive multi-task setting for indoor scene analysis on NYUv2 and SUNRGB-D.Comment: To be published in IEEE International Joint Conference on Neural Networks (IJCNN) 202

Determining stages of cirrus evolution: a cloud classification scheme

Cirrus clouds impose high uncertainties on climate prediction, as knowledge on important processes is still incomplete. For instance it remains unclear how cloud microphysical and radiative properties change as the cirrus evolves. Recent studies classify cirrus clouds into categories including in situ, orographic, convective and liquid origin clouds and investigate their specific impact. Following this line, we present a novel scheme for the classification of cirrus clouds that addresses the need to determine specific stages of cirrus evolution. Our classification scheme is based on airborne Differential Absorption and High Spectral Resolution Lidar measurements of atmospheric water vapor, aerosol depolarization, and backscatter, together with model temperature fields and simplified parameterizations of freezing onset conditions. It identifies regions of supersaturation with respect to ice (ice-supersaturated regions, ISSRs), heterogeneous and homogeneous nucleation, depositional growth, and ice sublimation and sedimentation with high spatial resolution. Thus, all relevant stages of cirrus evolution can be classified and characterized. In a case study of a gravity lee-wave-influenced cirrus cloud, encountered during the ML-CIRRUS flight campaign, the applicability of our classification is demonstrated. Revealing the structure of cirrus clouds, this valuable tool might help to examine the influence of evolution stages on the cloud's net radiative effect and to investigate the specific variability of optical and microphysical cloud properties in upcoming research

Kinetische Studien zur OH-Bildung über die Reaktionen von HO 2 mit organischen Peroxyradikalen

Es wurde untersucht, wie sich das Substitutionsmuster organischer Peroxyradikale (RO2) auf die Ratenkonstante k1 und die Verzweigungsverhältnisse α, β und γ der Reaktionen von RO2 mit HO2 auswirkt. Die Effekte der Deuterierung von HO2 wurden ebenfalls studiert. Für zwei RO2 wurde zusätzlich das UV-Absorptionsspektrum bestimmt.rnrn αrnRO2 + HO2 → RO + OH + O2 R1arnrn βrn → RO2H + O2 R1brnrn γrn → ROH + O3 R1crnrnIn dieser Arbeit wurde ein neues Experiment aufgebaut. Für die direkte und zeitaufgelöste Messung der OH-Konzentration wurde das Verfahren der Laser-induzierten Fluoreszenz angewendet. Die Radikalerzeugung erfolgte mittels gepulster Laserphotolyse, wodurch unerwünschte Nebenreaktionen weitgehend unterdrückt werden konnten. Mittels transienter Absorptionsspektroskopie konnten die Menge der photolytisch erzeugten Radikale bestimmt und die Ozonbildung über R1c quantifiziert werden. Für die Auswertung wurden kinetische Modelle numerisch an die Messdaten angepasst. Um die experimentellen Unsicherheiten abzuschätzen, wurde ein Monte-Carlo-Ansatz gewählt.rnrnk1 und α reagieren sehr empfindlich auf Veränderungen des RO2-Substitutionsmusters. Während sich eine OH-Bildung für das unsubstituierte C2H5O2 (EtP) mit α EtP ≤ 5 % nicht nachweisen lässt, stellt R1a bei den α-Oxo-substituierten H3CC(O)O2 (AcP) und HOCH2C(O)O2 (HAP) mit α AcP = (63 ± 11) % bzw. α HAP = (69 ± 12) % den Hauptkanal dar. Wie die mit α HEP = (10 ± 4) % geringfügige OH-Bildung bei HOC2H4O2 (HEP) zeigt, nimmt die OH-Gruppe in β-Stellung weniger Einfluss auf den Wert von α als die Oxogruppe in α-Stellung. Bei der Erzeugung α-Oxo-substituierter RO2 kann ebenfalls OH entstehen (R+O2→RO2/OH). Die Druckabhängigkeit dieser OH-Quelle wurde mit einem innovativen Ansatz bestimmt. Mit γ AcP = (15+5-6) % bzw. γ HAP = (10+2-3) % lässt sich für die Reaktionen der α-Oxo-substituierten RO2 eine erhebliche Ozonbildung nachweisen. Durch die Einführung der α-Oxogruppe steigt k1 jeweils um 1,3 • 10-11 cm3s-1 an, der Effekt der β-Hydroxygruppe ist halb so groß (k1 AcP = (2,0 ± 0,4) • 10-11 cm3s-1, k1 HAP = (2,6 ± 0,4) • 10-11 cm3s-1). Das Verzweigungsverhältnis α steigt weiter, wenn das HO2 deuteriert wird (α AcP,iso = (80 ± 14) %, k1 AcP,iso = (2,1 ± 0,4) • 10-11 cm3s-1). Vergleiche mit älteren Studien zeigen, dass die OH-Bildung über R1a bislang deutlich unterschätzt worden ist. Die möglichen Ursachen für die Unterschiede zwischen den Studien werden ebenso diskutiert wie die Hintergründe der beobachteten Substituenteneffekte.The influence of the substitution pattern of organic peroxy radicals (RO2) on the rate coefficients k1 and branching ratios α, β and γ of the reactions of RO2 with HO2 was investigated. The effect of deuteration of HO2 was also studied. In addition the UV-absorption spectra of two RO2 were recorded.rnrn αrnRO2 + HO2 → RO + OH + O2 R1arnrn βrn → RO2H + O2 R1brnrn γrn → ROH + O3 R1crnrnIn this work a new experimental set-up was designed. The laser-induced fluorescence method was applied to measure the OH-concentration directly in a time-resolved manner. Radicals were generated by pulsed laser photolysis in order to avoid unwanted side reactions. The initial concentration of photochemically generated reactant peroxy radicals and ozone formation via R1c were both quantified by transient absorption spectroscopy. The data were evaluated by numerical simulation using kinetic models of the measured concentration profiles. A Monte-Carlo approach was used to estimate the experimental uncertainties of the results.rnrnk1 and α react very sensitively to variations of the RO2 substitution pattern. Whilst no evidence for OH-formation from the non-substituted C2H5O2 (EtP) can be found (α EtP ≤ 5%), R1a could be proven to be the main reaction channel of α-oxo-substituted H3CC(O)O2 (AcP) and HOCH2C(O)O2 (HAP) with α AcP = (63 ± 11) % and α HAP = (69 ± 12) %, respectively. The weak OH-formation with α HEP = (10 ± 4) % by HOC2H4O2 (HEP) shows that an OH-group in β-position displays less influence on the α-value than an oxo-group in α-position. OH can also be produced during the formation of α-oxo-substituted RO2 (R+O2→RO2/OH), and the pressure dependence of this OH-source was derived using an innovative approach. With γ AcP = (15+5-6) % and γ HAP = (10+2-3) %, respectively, significant ozone formation from the reactions of the α-oxo-substituted RO2 was found. The introduction of the α-oxo-group causes an increase in k1 of 1,3 • 10-11 cm3s-1, the effect of the β -hydroxy-group is half as big (k1 AcP = (2,0 ± 0,4) • 10-11 cm3s-1, k1 HAP = (2,6 ± 0,4) • 10-11 cm3s-1). The branching ratio α increases further if HO2 is deuterated (α AcP,iso = (80 ± 14) %, k1 AcP,iso = (2,1 ± 0,4) • 10-11 cm3s-1). Comparison with former studies shows that OH formation via R1a has been underestimated significantly to date. Possible reasons for these discrepancies and explanations for the observed substitution effects are discussed

Hierarchical isometry properties of hierarchical measurements

Compressed sensing studies linear recovery problems under structure assumptions. We introduce a new class of measurement operators, coined hierarchical measurement operators, and prove results guaranteeing the efficient, stable and robust recovery of hierarchically structured signals from such measurements. We derive bounds on their hierarchical restricted isometry properties based on the restricted isometry constants of their constituent matrices, generalizing and extending prior work on Kronecker-product measurements. As an exemplary application, we apply the theory to two communication scenarios. The fast and scalable HiHTP algorithm is shown to be suitable for solving these types of problems and its performance is evaluated numerically in terms of sparse signal recovery and block detection capability