Miracle-dz/UniFAR-FTUs huggingface dataset:
Demeter: A Fast and Energy-Efficient Food Profiler Using Hyperdimensional Computing in Memory

Food profiling is an essential step in any food monitoring system needed to prevent health risks and potential frauds in the food industry. Significant improvements in sequencing technologies are pushing food profiling to become the main computational bottleneck. State-of-the-art profilers are unfortunately too costly for food profiling. Our goal is to design a food profiler that solves the main limitations of existing profilers, namely (1) working on massive data structures and (2) incurring considerable data movement, for a real-time monitoring system. To this end, we propose Demeter, the first platform-independent framework for food profiling. Demeter overcomes the first limitation through the use of hyperdimensional computing (HDC) and efficiently performs the accurate few-species classification required in food profiling. We overcome the second limitation by the use of an in-memory hardware accelerator for Demeter (named Acc-Demeter) based on memristor devices. Acc-Demeter actualizes several domain-specific optimizations and exploits the inherent characteristics of memristors to improve the overall performance and energy consumption of Acc-Demeter. We compare Demeter’s accuracy with other industrial food profilers using detailed software modeling. We synthesize Acc-Demeter’s required hardware using UMC’s 65nm library by considering an accurate PCM model based on silicon-based prototypes. Our evaluations demonstrate that Acc-Demeter achieves a (1) throughput improvement of <inline-formula> <tex-math notation="LaTeX">$192\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$724\times $ </tex-math></inline-formula> and (2) memory reduction of <inline-formula> <tex-math notation="LaTeX">$36\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$33\times $ </tex-math></inline-formula> compared to Kraken2 and MetaCache (2 state-of-the-art profilers), respectively, on typical food-related databases. Demeter maintains an acceptable profiling accuracy (within 2% of existing tools) and incurs a very low area overhead.
Canonical embedded and non-embedded resolution of singularities of excellent two-dimensional schemes

We prove the existence of resolution of singularities for arbitrary (not necessarily reduced or irreducible) excellent two-dimensional schemes, via permissible blow-ups. The resolution is canonical, and functorial with respect to automorphisms or etale or Zariski localizations. We treat the embedded case as well as the non-embedded case, with or without a boundary, and we relate the diferent versions. In the non-embedded case, a boundary is a collection of locally principal closed subschemes. Our main tools are the stratifications by Hilbert-Samuel functions and the characteristic polyhedra introduced by H. Hironaka. In an appendix we show that the standard method used in characteristic zero - the theory of maximal contact - does not work for surfaces in positive characteristic (the counterexamples are hypersurfaces in affine threespace and work over any field of positive characteristic). In this new version, we treat the case of locally noetherian but not necessarily noetherian schemes in an appropriate way. Here one does not have a finite resolution sequence, but still a canonical resolution morphism by glueing. The same techniques allow to treat algebraic spaces and stacks.
Quasivelocities and Optimal Control for underactuated Mechanical Systems

This paper is concerned with the application of the theory of quasivelocities for optimal control for underactuated mechanical systems. Using this theory, we convert the original problem in a variational second‐order lagrangian system subjected to constraints. The equations of motion are geometrically derived using an adaptation of the classical Skinner and Rusk formalism.
Communication-Constrained Hypothesis Testing: Optimality, Robustness, and Reverse Data Processing Inequalities

We study hypothesis testing under communication constraints, where each sample is quantized before being revealed to a statistician. Without communication constraints, it is well known that the sample complexity of simple binary hypothesis testing is characterized by the Hellinger distance between the distributions. We show that the sample complexity of simple binary hypothesis testing under communication constraints is at most a logarithmic factor larger than in the unconstrained setting and this bound is tight. We develop a polynomial-time algorithm that achieves the aforementioned sample complexity. Our framework extends to robust hypothesis testing, where the distributions are corrupted in total variation distance. Our proofs rely on a new reverse data processing inequality and a reverse Markov inequality, which may be of independent interest. For simple <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>-ary hypothesis testing, the sample complexity in the absence of communication constraints has a logarithmic dependence on <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>. We show that communication constraints can cause an exponential blow-up, leading to <inline-formula> <tex-math notation="LaTeX">$\Omega (M)$ </tex-math></inline-formula> sample complexity even for adaptive algorithms.
Zero-Shot Voice Conditioning for Denoising Diffusion TTS Models

We present a novel way of conditioning a pretrained denoising diffusion speech model to produce speech in the voice of a novel person unseen during training. The method requires a short (~3 seconds) sample from the target person, and generation is steered at inference time, without any training steps. At the heart of the method lies a sampling process that combines the estimation of the denoising model with a low-pass version of the new speaker's sample. The objective and subjective evaluations show that our sampling method can generate a voice similar to that of the target speaker in terms of frequency, with an accuracy comparable to state-of-the-art methods, and without training.
Body schema or the body as its own best model

Rodney Brooks (1991) put forth the idea that during an agent’s interaction with its environment, representations of the world often stand in the way. Instead, using the world as its own best model, i.e. interacting with it directly without making models, often leads to better and more natural behavior. The same perspective can be applied to representations of the agent’s body. I analyze different examples from biology—octopus and humans in particular—and compare them with robots and their body models. At one end of the spectrum, the octopus, a highly intelligent animal, largely relies on the mechanical properties of its arms and peripheral nervous system. No central representations or maps of its body were found in its central nervous system. Primate brains do contain areas dedicated to processing body-related information and different body maps were found. Yet, these representations are still largely implicit and distributed and some functionality is also ofﬂoaded to the periphery. Robots, on the other hand, rely almost ex-clusively on their body models when planning and ex-ecuting behaviors. I analyze the pros and cons of these different approaches and propose what may be the best solution for robots of the future.
Beamforming Design and Performance Evaluation for Reconfigurable Intelligent Surface Assisted Wireless Communication Systems With Non-Ideal Hardware

Reconfigurable intelligent surface (RIS) can effectively control the wavefront of the impinging signals and has emerged as a cost-effective promising solution to improve the spectrum and energy efficiency of wireless systems. Most existing researches on RIS assume that the hardware operations are perfect. However, both physical transceiver and RIS suffer from inevitable hardware impairments in practice, which can lead to severe system performance degradation and increase the complexity of beamforming optimization. Consequently, the existing researches on RIS, including channel estimation, beamforming optimization, spectrum and energy efficiency analysis, etc., cannot directly apply to the case of hardware impairments. In this paper, by taking hardware impairments into consideration, we conduct the joint transmit and reflect beamforming optimization, and reevaluate the system performance. First, we characterize the closed-form estimators of direct and cascaded channels in both single-user and multi-user cases, and analyze the impact of hardware impairments on channel estimation accuracy. Then, the optimal transmit beamforming solution is derived, and a gradient descent method-based algorithm is also proposed to optimize the reflect beamforming. Moreover, we analyze the three types of asymptotic channel capacities with respect to the transmit power, the antenna number, and the reflecting element number. Finally, in terms of the system energy consumption, we analyze the power scaling law and the energy efficiency. Our experimental results also reveal an encouraging phenomenon that the RIS-assisted wireless system with massive reflecting elements can achieve both high spectrum and energy efficiency without the need for massive antennas and without allocating too many resources to optimize the reflect beamforming.
On the separation profile of infinite graphs

Initial steps in the study of inner expansion properties of infinite Cayley graphs and other infinite graphs, such as hyperbolic ones, are taken, in a flavor similar to the well-known Lipton-Tarjan square root separation result for planar graphs. Connections to relaxed versions of quasi-isometries are explored, such as regular and semiregular maps.
Chemical biology to dissect molecular mechanisms underlying plant circadian clocks.

Circadian clocks regulate the diel rhythmic physiological activities of plants, enabling them to anticipate and adapt to day-night and seasonal changes. Genetic and biochemical approaches suggested that transcription-translation feedback loops (TTFL) are crucial for Arabidopsis clock function. Recently, the study of chemical chronobiology has emerged as a discipline within the circadian clock field, with important and complementary discoveries from both plant and animal research. In this review, we introduce recent advances in chemical biology using small molecules to perturb plant circadian clock function via TTFL components. Studies using small molecule clock modulators have been instrumental for revealing the role of post-translational modification in the clock, or the metabolite-dependent clock input pathway, as well as for controlling clock-dependent flowering time.
Alemtuzumab-induced immune phenotype and repertoire changes: implications for secondary autoimmunity

Abstract Alemtuzumab is a monoclonal antibody that causes rapid depletion of CD52-expressing immune cells. It has proven to be highly efficacious in active relapsing–remitting multiple sclerosis; however, the high risk of secondary autoimmune disorders has greatly complicated its use. Thus, deeper insight into the pathophysiology of secondary autoimmunity and potential biomarkers is urgently needed. The most critical time points in the decision-making process for alemtuzumab therapy are before or at Month 12, where the ability to identify secondary autoimmunity risk would be instrumental. Therefore, we investigated components of blood and CSF of up to 106 multiple sclerosis patients before and after alemtuzumab treatment focusing on those critical time points. Consistent with previous reports, deep flow cytometric immune-cell profiling (n = 30) demonstrated major effects on adaptive rather than innate immunity, which favoured regulatory immune cell subsets within the repopulation. The longitudinally studied CSF compartment (n = 18) mainly mirrored the immunological effects observed in the periphery. Alemtuzumab-induced changes including increased numbers of naïve CD4+ T cells and B cells as well as a clonal renewal of CD4+ T- and B-cell repertoires were partly reminiscent of haematopoietic stem cell transplantation; in contrast, thymopoiesis was reduced and clonal renewal of T-cell repertoires after alemtuzumab was incomplete. Stratification for secondary autoimmunity did not show clear immununological cellular or proteomic traits or signatures associated with secondary autoimmunity. However, a restricted T-cell repertoire with hyperexpanded T-cell clones at baseline, which persisted and demonstrated further expansion at Month 12 by homeostatic proliferation, identified patients developing secondary autoimmune disorders (n = 7 without secondary autoimmunity versus n = 5 with secondary autoimmunity). Those processes were followed by an expansion of memory B-cell clones irrespective of persistence, which we detected shortly after the diagnosis of secondary autoimmune disease. In conclusion, our data demonstrate that (i) peripheral immunological alterations following alemtuzumab are mirrored by longitudinal changes in the CSF; (ii) incomplete T-cell repertoire renewal and reduced thymopoiesis contribute to a proautoimmune state after alemtuzumab; (iii) proteomics and surface immunological phenotyping do not identify patients at risk for secondary autoimmune disorders; (iv) homeostatic proliferation with disparate dynamics of clonal T- and B-cell expansions are associated with secondary autoimmunity; and (v) hyperexpanded T-cell clones at baseline and Month 12 may be used as a biomarker for the risk of alemtuzumab-induced autoimmunity.
Novel Concepts for Differential -Equation-Based Electromagnetic Field Simulations

This thesis presents novel concepts for electromagnetic field simulations via partial differential equation (PDE) solvers. A vital aspect for any successful general implementation of a PDE solver is the use of an efficient absorbing boundary condition (ABC). The perfectly matched layer (PML) is a recently introduced ABC in Cartesian coordinates which provides reflection errors orders of magnitude smaller than previously employed ABCs. In this work, a new interpretation of the PML as an analytic continuation of the coordinate space is used to extend the PML to other coordinate systems. Modified equations replace the original Maxwell's equations, mapping propagating solutions into exponentially decaying solutions. Alternative (Maxwellian) formulations are also put forth, where the PML is represented as an artificial media with complex constitutive tensors, and the form of Maxwell's equations is retained. The causality and dynamic stability of the PML is characterized through a spectral analysis. In addition, a rationale is presented to extend the PML to complex media, e.g., dispersive and/or (bi-)anisotropic. For the Maxwellian formulation, the general expressions for the PML tensors matched to any interior dispersive and/or (bi-)anisotropic linear media are obtained. A finite-difference time-domain (FDTD) algorithm in Cartesian coordinates which combines the PML ABC with piecewise-linear recursive convolution (PLRC) is proposed and implemented, allowing the simulation of electromagnetic fields in inhomogeneous and dispersive media with conductive loss. Two PML-PLRC-FDTD algorithms in cylindrical coordinates are also proposed and implemented. The first is developed through a split-field PML formulation, and the second through a Maxwellian (unsplit) PML formulation. A comparison is made between numerical properties of these two algorithms. The PML concept is then studied within the language of differential forms to unify the various PML formulations. Finally, the language of differential forms is also utilized to provide a coordinate-free description and analyze consistency properties of the electromagnetic theory on lattice, for PDE solvers such as the finite-difference, finite-volume or finite-element methods. iii ACKNOWLEDGMENTS First and foremost, I would like to thank my advisor, Professor Weng Cho Chew, for the support, encouragement and guidance he has provided me over the past three years. It has been a great privilege to work with him. Schutt-Aine for their helpful discussions and suggestions. During the years of my graduate study, my colleagues, past and present, at the University of Illinois, have been very helpful in many aspects. There are too many to name them all. On the technical side, I would like …
End-to-End Learning to Grasp via Sampling From Object Point Clouds

The ability to grasp objects is an essential skill that enables many robotic manipulation tasks. Recent works have studied point cloud-based methods for object grasping by starting from simulated datasets and have shown promising performance in real-world scenarios. Nevertheless, many of them still rely on ad-hoc geometric heuristics to generate grasp candidates, which fail to generalize to objects with significantly different shapes with respect to those observed during training. Several approaches exploit complex multi-stage learning strategies and local neighborhood feature extraction while ignoring semantic global information. Furthermore, they are inefficient in terms of number of training samples and time required for inference. In this letter, we propose an end-to-end learning solution to generate 6-DOF parallel-jaw grasps starting from the 3D partial view of the object. Our Learning to Grasp (L2G) method gathers information from the input point cloud through a new procedure that combines a differentiable sampling strategy to identify the visible contact points, with a feature encoder that leverages local and global cues. Overall, L2G is guided by a multi-task objective that generates a diverse set of grasps by optimizing contact point sampling, grasp regression, and grasp classification. With a thorough experimental analysis, we show the effectiveness of L2G as well as its robustness and generalization abilities.
Superconducting properties of pseudobinary telluride Chevrel Phase Mo 4 Re 2 Te 8

Unconventional superconductivity in the Chevrel phase oﬀers a wide structural aspect to understand the superconducting ground state. A detailed investigation on the superconducting properties of Re based pseudobinary telluride Chevrel phase Mo 4 Re 2 Te 8 is reported. It crystallizes in a trigonal structure with the space group R ¯3 H having superconducting transition temperature at T C = 3.26(3) K. Speciﬁc heat measurements suggests a fully gapped superconducting state; however, the proximity of upper critical ﬁeld value from the Pauli limiting ﬁeld can be attributed to unconventional nature.
When Personalization Harms Performance: Reconsidering the Use of Group Attributes in Prediction

Machine learning models are often personalized with categorical attributes that are protected, sensitive, self-reported, or costly to acquire. In this work, we show models that are personalized with group attributes can reduce performance at a group level. We propose formal conditions to ensure the"fair use"of group attributes in prediction tasks by training one additional model -- i.e., collective preference guarantees to ensure that each group who provides personal data will receive a tailored gain in performance in return. We present sufficient conditions to ensure fair use in empirical risk minimization and characterize failure modes that lead to fair use violations due to standard practices in model development and deployment. We present a comprehensive empirical study of fair use in clinical prediction tasks. Our results demonstrate the prevalence of fair use violations in practice and illustrate simple interventions to mitigate their harm.
On the number of walks on a regular Cayley tree

We provide a new derivation of the well-known generating function counting the number of walks on a regular tree that start and end at the same vertex, and more generally, a generating function for the number of walks that end at a vertex a distance i from the start vertex. These formulas seem to be very old, and go back, in an equivalent form, at least to Harry Kesten's work on symmetric random walks on groups from 1959, and in the present form to Brendan McKay (1983).
PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding

We are now witnessing significant progress of deep learning methods in a variety of tasks (or datasets) of proteins. However, there is a lack of a standard benchmark to evaluate the performance of different methods, which hinders the progress of deep learning in this field. In this paper, we propose such a benchmark called PEER, a comprehensive and multi-task benchmark for Protein sEquence undERstanding. PEER provides a set of diverse protein understanding tasks including protein function prediction, protein localization prediction, protein structure prediction, protein-protein interaction prediction, and protein-ligand interaction prediction. We evaluate different types of sequence-based methods for each task including traditional feature engineering approaches, different sequence encoding methods as well as large-scale pre-trained protein language models. In addition, we also investigate the performance of these methods under the multi-task learning setting. Experimental results show that large-scale pre-trained protein language models achieve the best performance for most individual tasks, and jointly training multiple tasks further boosts the performance. The datasets and source codes of this benchmark are all available at https://github.com/DeepGraphLearning/PEER_Benchmark
On the question of second sound in germanium: A theoretical viewpoint

We revisit the recent work from Beardo et al. \cite{beardo2021observation} wherein the observation of second sound in germanium is claimed. We review the requirements imposed on the collision operator (or equivalently, the full scattering matrix) of the linearized phonon Boltzmann transport equation (LBTE) for the observation of driftless second sound as established by Hardy. By performing an eigendecomposition of the full scattering matrix, we show that the requirement that the smallest nonzero eigenvalue must be associated with an odd eigenvector is not satisfied. Furthermore, direct solutions to the LBTE for a frequency modulated heat source do not reveal the presence of second sound. Finally, numerical solutions to the BTE under the relaxation time approximation (RTA) in the 1D frequency-domain thermoreflectance (1D-FDTR) experimental geometry demonstrate that phase lag alone is not a suitable experimental observable for inferring second sound. We conclude by discussing the need for a second sound `smoking gun'.
Enabling Heterogeneous, Multicore SoC Research with RISC-V and ESP

Heterogeneous, multicore SoC architectures are a critical component of today's computing landscape. However, supporting both increasing heterogeneity and multicore execution are significant design challenges. Meanwhile, the growing RISC-V and open-source hardware (OSH) movements have resulted in an increased number of open-source RISC-V processor implementations; however, there are fewer open source SoC design platforms that integrate these processor cores. We present modifications to ESP, an open-source SoC design platform, to enable multicore execution with the RISC-V CVA6 processor. Our implementation is modular and based on standardized interfaces. These properties simplify the integration of new cores. Our modifications enable RISC-V-based SoCs designed with ESP for FPGA to boot Linux SMP and execute multithreaded applications. Coupled with ESP's emphasis on accelerator-centric architectures, our contributions enable the seamless design of a wide range of heterogeneous, multicore SoCs.